Evidence Report/Technology Assessment

Number 188 

Impact of Consumer Health Informatics Applications 

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

http://www.ahrq.gov

Contract No. HHSA 290-2007-120061-I 

Task Order No. 5 

Prepared by: 

The Johns Hopkins University Evidence-based Practice Center 

Investigators 

M. Christopher Gibbons, M.D., M.P.H. 

Renee F. Wilson, M.S. 

Lipika Samal, M.D. 

Christoph U. Lehmann, M.D. 

Kay Dickersin, M.A., Ph.D. 

Harold P. Lehmann, M.D., Ph.D. 

Hanan Aboumatar, M.D. 

Joseph Finkelstein, M.D., Ph.D. 

Erica Shelton, M.D. 

Ritu Sharma, B.S. 

Eric B. Bass, M.D., M.P.H. 

AHRQ Publication No. 09(10)-E019 

October 2009 
This document is in the public domain and may be used and reprinted without permission except 

those copyrighted materials noted for which further reproduction is prohibited without the 

specific permission of copyright holders. 

Suggested Citation: 

Gibbons MC, Wilson RF, Samal L, Lehmann CU, Dickersin K, Lehmann HP, Aboumatar H, 

Finkelstein J, Shelton E, Sharma R, Bass EB. Impact of Consumer Health Informatics 

Applications. Evidence Report/Technology Assessment No. 188. (Prepared by Johns Hopkins 

University Evidence-based Practice Center under contract No. HHSA 290-2007-10061-I). 

AHRQ Publication No. 09(10)-E019. Rockville, MD. Agency for Healthcare Research and 

Quality. October 2009. 

No investigators have any affiliations or financial involvement (e.g., employment, 

consultancies, honoraria, stock options, expert testimony, grants or patents received or 

pending, or royalties) that conflict with material presented in this report. 

ii
Preface 

 The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-Based 

Practice Centers (EPCs), sponsors the development of evidence reports and technology 

assessments to assist public- and private-sector organizations in their efforts to improve the 

quality of health care in the United States. The EPCs systematically review the relevant scientific 

literature on topics assigned to them by AHRQ and conduct additional analyses when 

appropriate prior to developing their reports and assessments. 

 To bring the broadest range of experts into the development of evidence reports and health 

technology assessments, AHRQ encourages the EPCs to form partnerships and enter into 

collaborations with other medical and research organizations. The EPCs work with these partner 

organizations to ensure that the evidence reports and technology assessments they produce will 

become building blocks for health care quality improvement projects throughout the Nation. The 

reports undergo peer review prior to their release. 

 AHRQ expects that the EPC evidence reports and technology assessments will inform 

individual health plans, providers, and purchasers as well as the health care system as a whole by 

providing important information to help improve health care quality. 

 We welcome comments on this evidence report. They may be sent by mail to the Task Order 

Officer named below at: Agency for Healthcare Research and Quality, 540 Gaither Road, 

Rockville, MD 20850, or by e-mail to epc@ahrq.gov. 

Carolyn M. Clancy, M.D. 

Director 

Agency for Healthcare Research and Quality 

Jean Slutsky, P.A., M.S.P.H. 

Director, Center for Outcomes and Evidence 

Agency for Healthcare Research and Quality 

Beth A. Collins Sharp, R.N., Ph.D. 

Director, EPC Program 

Agency for Healthcare Research and Quality 

Teresa Zayas-Cabán, Ph.D. 

Task Order Officer, Center for Primary Care, 

Prevention, and Clinical Partnerships 

Agency for Healthcare Research and Quality 

iii
iv

Acknowledgments 

The EPC thanks Dr. Hosne Begum, Dr. Olaide Odelola, Dr. Christine Chang, Beth Barnett, Todd 

Noletto, and Rebecca Stainman for their assistance with the final assembly and formatting of this 

report, and Dr. Teresa Zayas-Cabán for her valuable insight throughout the project. 
Structured Abstract 

Objective. The objective of the report is to review the evidence on the impact of consumer health 

informatics (CHI) applications on health outcomes, to identify the knowledge gaps and to make 

recommendations for future research. 

Data sources. We searched MEDLINE®, EMBASE®, The Cochrane Library, ScopusTM, and 

CINAHL® databases, references in eligible articles and the table of contents of selected journals; 

and query of experts. 

Methods. Paired reviewers reviewed citations to identify randomized controlled trials (RCTs) of 

the impact of CHI applications, and all studies that addressed barriers to use of CHI applications. 

All studies were independently assessed for quality. All data was abstracted, graded, and reviewed 

by 2 different reviewers. 

Results. One hundred forty-six eligible articles were identified including 121 RCTs. Studies were 

very heterogeous and of variable quality. 

 Four of five asthma care studies found significant positive impact of a CHI application on at 

least one healthcare process measure. 

 In terms of the impact of CHI on intermediate health outcomes, significant positive impact was 

demonstrated in at least one intermediate health outcome of; all three identified breast cancer 

studies, 89 percent of 32 diet, exercise, physical activity, not obesity studies, all 7 alcohol abuse 

studies, 58 percent of 19 smoking cessation studies, 40 percent of 12 obesity studies, all 7 diabetes 

studies, 88 percent of 8 mental health studies, 25 percent of 4 asthma/COPD studies, and one of two 

menopause/HRT utilization studies. Thirteen additional single studies were identified and each 

found evidence of significant impact of a CHI application on one or more intermediate outcomes. 

 Eight studies evaluated the effect of CHI on the doctor patient relationship. Five of these studies 

demonstrated significant positive impact of CHI on at least one aspect of the doctor patient 

relationship. 

 In terms of the impact of CHI on clinical outcomes, significant positive impact was 

demonstrated in at least one clinical outcome of; one of three breast cancer studies, four of five diet, 

exercise, or physical activity studies, all seven mental health studies, all three identified diabetes 

studies. No studies included in this review found any evidence of consumer harm attributable to a 

CHI application. 

 Evidence was insufficient to determine the economic impact of CHI applications. 

Conclusions: Despite study heterogeneity, quality variability, and some data paucity, available 

literature suggests that select CHI applications may effectively engage consumers, enhance 

traditional clinical interventions, and improve both intermediate and clinical health outcomes. 

v 
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Contents 

 

Executive Summary.........................................................................................................................1 

Evidence Report...........................................................................................................................11 

Chapter 1. Introduction .................................................................................................................13 

Consumer Health Informatics..................................................................................................13

Purpose of Evidence Report.....................................................................................................14 

Chapter 2. Methods........................................................................................................................15 

Recruitment of Technical Experts and Peer Reviewers...........................................................15 

Key Questions..........................................................................................................................15 

Conceptual Framework............................................................................................................16 

Literature Search Methods.......................................................................................................16 

Sources...............................................................................................................................18 

Search terms and strategies................................................................................................18 

Organization and tracking of the literature search.............................................................18 

Title Review.............................................................................................................................18 

Abstract Review.......................................................................................................................19 

Article Review .........................................................................................................................19 

Data Abstraction ......................................................................................................................19 

Quality Assessment..................................................................................................................20 

Data Synthesis..........................................................................................................................20 

Data Entry and Quality Control ...............................................................................................20

Grading of the Evidence ..........................................................................................................21 

Peer Review .............................................................................................................................21 

Chapter 3. Results ..........................................................................................................................23 

Results of the Literature Search...............................................................................................23 

Description of Types of Studies Retrieved ..............................................................................25 

Key Question 1a: What evidence exists that consumer health informatics applications 

 impact health care process outcomes? ...............................................................................25 

Summary of the findings....................................................................................................25 

Strengths and limitations of the evidence ..........................................................................25 

General study characteristics .............................................................................................25 

Outcomes ...........................................................................................................................26 

Key Question 1b: What evidence exists that consumer health informatics applications 

 impact intermediate outcomes?..........................................................................................28 

 Breast Cancer........................................................................................................................28 

Summary of the findings....................................................................................................28 

Strengths and limitations of the evidence ..........................................................................28 

General study characteristics .............................................................................................30 

Outcomes ...........................................................................................................................30 

 Diet, Exercise, Physical Activity, not Obesity......................................................................30 

Summary of the findings....................................................................................................30 
viii

Strengths and limitations of the evidence ..........................................................................36 

General study characteristics .............................................................................................36 

Outcomes ...........................................................................................................................37 

 Alcohol Abuse and Smoking Cessation................................................................................41 

Summary of the findings....................................................................................................41 

Strengths and limitations of the evidence ..........................................................................41 

General study characteristics .............................................................................................41 

Outcomes ...........................................................................................................................44 

 Obesity ..................................................................................................................................48 

Summary of the findings....................................................................................................48 

Strengths and limitations of the evidence ..........................................................................49 

General study characteristics .............................................................................................49 

Outcomes ...........................................................................................................................52 

 Diabetes.................................................................................................................................54 

Summary of the findings....................................................................................................54 

Strengths and limitations of the evidence ..........................................................................54 

General study characteristics .............................................................................................54 

Outcomes ...........................................................................................................................56 

 Mental Health........................................................................................................................57 

Summary of the findings....................................................................................................57 

Strengths and limitations of the evidence ..........................................................................59 

General study characteristics .............................................................................................59 

Outcomes ...........................................................................................................................60 

 Asthma and Chronic Obstructive Pulmonary Disease..........................................................61 

Summary of the findings....................................................................................................61 

Strengths and limitations of the evidence ..........................................................................61 

General study characteristics .............................................................................................62 

Outcomes ...........................................................................................................................62 

 Miscellaneous Intermediate Outcomes.................................................................................65 

Summary of the findings....................................................................................................65 

Strengths and limitations of the evidence ..........................................................................65 

General study characteristics .............................................................................................65 

Outcomes ...........................................................................................................................66 

Key Question 1c: What evidence exists that consumer health informatics applications 

 impact relationship-centered outcomes?............................................................................68 

Summary of the findings....................................................................................................68 

Strengths and limitations of the evidence ..........................................................................69 

General study characteristics .............................................................................................70 

Outcomes ...........................................................................................................................70 

Key Question 1d: What evidence exists that consumer health informatics applications 

 impact clinical outcomes?..................................................................................................72 

Breast Cancer........................................................................................................................72 

Summary of the findings....................................................................................................72 

Strengths and limitations of the evidence ..........................................................................73 

General study characteristics .............................................................................................73 

Outcomes ...........................................................................................................................73 
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Diabetes Mellitus ..................................................................................................................74 

Summary of the findings....................................................................................................74 

Strengths and limitations of the evidence ..........................................................................74 

Outcomes ...........................................................................................................................75 

Diet, Exercise, Physical Activity, no Obesity.......................................................................76 

Summary of the findings....................................................................................................76 

Strengths and limitations of the evidence ..........................................................................76 

Outcomes ...........................................................................................................................76 

Mental Health........................................................................................................................79 

Summary of the findings....................................................................................................79 

Strengths and limitations of the evidence ..........................................................................79 

Outcomes ...........................................................................................................................81 

Miscellaneous Outcomes ......................................................................................................83 

Summary of the findings....................................................................................................83 

Strengths and limitations of the evidence ..........................................................................83 

Outcomes ...........................................................................................................................83 

Key Question 1e: What evidence exists that consumer health informatics applications 

 impact economic outcomes?..............................................................................................87 

Summary of the findings....................................................................................................87 

Strengths and limitations of the evidence ..........................................................................87 

General study characteristics .............................................................................................87 

Outcomes ...........................................................................................................................88 

Key Question 2: What are the barriers that clinicians, developers, and consumers and 

 their families or caregivers encounter that limit implementation of consumer health 

informatics applications? ...................................................................................................88 

Disease/Problem Domain...................................................................................................88 

Methodology......................................................................................................................89 

Barriers...............................................................................................................................90 

Key Question 3. What knowledge or evidence deficits exist regarding needed information 

 to support estimates of cost, benefit, and net value with regard to consumer health 

informatics applications? ...................................................................................................92 

Patient-related questions ....................................................................................................92

CHI utilization-related factors ...........................................................................................93 

Technology/hardware/software/platform-related issues....................................................93 

Health-related factors.........................................................................................................94 

Key Question 4: What critical information regarding the impact of consumer health 

informatics applications is needed in order to give consumers, their families, clinicians, 

and developers a clear understanding of the value proposition particular to them?..........94 

Clinician and provider value proposition information needs.............................................95 

Patient, family, and caregiver value proposition information needs .................................95 

Research in Progress................................................................................................................95 

Chapter 4. Discussion ....................................................................................................................97 

Summary of Key Findings.......................................................................................................97 

Limitations ...............................................................................................................................98 

Future Research Needs ............................................................................................................98 
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Implications............................................................................................................................100 

References and Included Studies .................................................................................................103 

Figures 

Figure 1. Conceptual model addressing Key Questions 1 and 2: Impact of CHI on health and 

process outcomes, and barriers to use of CHI....................................................................17 

Figure 2. Summary of the literature search (number of articles).............................................24 

Summary Tables 

Table 1. Summary of studies of CHI applications impacting health care process 

 outcomes (N=5) .................................................................................................................26 

Table 2. Grade of the body of evidence addressing CHI impact on health care processes in 

asthma ................................................................................................................................27 

Table 3. Results of studies of CHI applications impacting intermediate outcomes in breast 

 cancer (N=3) ......................................................................................................................29 

Table 4. Grade of the body of evidence addressing CHI impact on intermediate outcomes 

 in breast cancer ..................................................................................................................29 

Table 5. Results of studies on CHI applications impacting intermediate outcomes in diet, 

exercise, or physical activity, not obesity (N=32). ............................................................31 

Table 6. Grade of the body of evidence addressing CHI impacts on intermediate outcomes 

 in diet, exercise, nutrition intervention (not obesity).........................................................36 

Table 7. Results of studies on CHI applications impacting intermediate outcomes in 

 alcohol abuse and smoking (N=26) ...................................................................................42 

Table 8. Grade of the body of evidence addressing CHI impact on intermediate outcomes 

 in alcohol abuse and smoking............................................................................................44 

Table 9. Results of studies on CHI applications impacting intermediate outcomes related 

 to obesity (N=11) ...............................................................................................................50 

Table 10. Grade of the body of evidence addressing CHI impact on intermediate outcomes 

 in obesity............................................................................................................................52 

Table 11. Results of studies on CHI applications impacting intermediate outcomes in 

 diabetes (N=7)....................................................................................................................55 

Table 12. Grade of the body of evidence addressing CHI impact on intermediate outcomes 

 in diabetes ..........................................................................................................................56 

Table 13. Results of studies on CHI applications impacting intermediate outcomes of 

 mental health (N=8). ..........................................................................................................58 

Table 14. Grade of the body of evidence addressing CHI impact on intermediate outcomes 

 in mental health..................................................................................................................59 

Table 15. Results of studies on CHI applications impacting intermediate outcomes in 

 asthma and COPD (N=4)...................................................................................................63 

Table 16. Grade of the body of evidence addressing CHI impact on intermediate outcomes 

 in asthma/COPD ................................................................................................................64 

Table 17. Studies of CHI applications impacting relationship-centered outcomes in women 

 with breast cancer (N=4)....................................................................................................69

Table 18. Grade of the body of evidence addressing CHI impact on relationship-centered 

outcomes in breast cancer ..................................................................................................70 
xi

Table 19: Results of studies on CHI applications impacting relationship-centered outcomes 

 in breast cancer (N=4)........................................................................................................71 

Table 20: Results of studies on CHI applications impacting clinical outcomes in breast 

 cancer (N=3) ......................................................................................................................72 

Table 21. Grade of the body of evidence addressing CHI impact on clinical outcomes in 

individuals with breast cancer............................................................................................74 

Table 22. Results of studies on CHI applications impacting clinical outcomes in diabetes 

 mellitus (N=3).....................................................................................................................75 

Table 23. Grade of the body of evidence addressing CHI impact on clinical outcomes in 

individuals with diabetes mellitus......................................................................................75 

Table 23. Results of studies on CHI applications impacting clinical outcomes in diet, 

 exercise, physical activity, not obesity (N=5)....................................................................77 

Table 24. Grade of the body of evidence addressing CHI impact on clinical outcomes 

 related to diet, exercise, or physical activity, not obesity .................................................78 

Table 25. Results of studies on CHI applications impacting clinical outcomes in mental 

 health (N=7).......................................................................................................................80 

Table 26. Grade of the body of evidence addressing CHI impact on clinical outcomes in 

 mental health......................................................................................................................81 

Table 27. Studies of CHI applications impacting miscellaneous clinical outcomes (N=10)...84 

Table 28. Studies of CHI applications impacting economic outcomes (N=3) ........................87 

Table 29. The distribution of methodologies for identifying barriers to the use of 

 consumer health informatics by disease /problem domain................................................90 

Table 30. The distribution of methodology by barrier type.....................................................90 

Table 31. The distribution of barrier levels by disease/problem domain ................................91 

Appendixes 

Appendix A: List of Acronyms 

Appendix B: Technical Experts and Peer Reviewers 

Appendix C: Detailed Search Strategies 

Appendix D: Grey Literature Detailed Search Strategies 

Appendix E: Screen and Data Abstraction Forms 

Appendix F: Excluded Articles 

Appendix G: Evidence Tables 

Appendixes and Evidence Tables for this report are provided electronically at 

http://www.ahrq.gov/clinic/tp/chiapptp.htm.
1

Executive Summary

Many people are excited about the potential to improve the health of the public by using 

health information technology (health IT) and eHealth solutions that are tailored to consumers. 

Despite growing interest in this field referred to as consumer health informatics (CHI), the value 

of CHI applications has not been rigorously reviewed. The objectives of this report were to 

review the literature on the evidence of the influence of currently developed CHI applications on 

health and health care process outcomes, to identify the gaps in the CHI literature, and to make 

recommendations for future CHI research. For the purposes of this review, CHI is defined as any 

electronic tool, technology, or electronic application that is designed to interact directly with 

consumers, with or without the presence of a health care professional that provides or uses 

individualized (personal) information and provides the consumer with individualized assistance, 

to help the patient better manage their health or health care. 

The specific Key Questions were: 

1. What evidence exists that CHI applications impact: 

a. Health care process outcomes (e.g., receiving appropriate treatment) among users? 

b. Intermediate health outcomes (e.g., self-management, health knowledge, and health 

behaviors) among users? 

c. Relationship-centered outcomes (e.g., shared decisionmaking or clinician-patient 

communication) among users? 

d. Clinical outcomes (including quality of life) among users? 

e. Economic outcomes (e.g., cost and access to care) among users? 

2. What are the barriers that clinicians, developers, consumers, and their families or 

caregivers encounter that limit utilization or implementation of CHI applications? 

3. What knowledge or evidence exists to support estimates of cost, benefit, and net value 

with regard to CHI applications? 

4. What critical information regarding the impact of CHI applications is needed to give 

consumers, their families, clinicians, and developers a clear understanding of the value 

proposition particular to them? 

The best evidence available to answer Key Question 1 is found in randomized controlled 

trials (RCTs). However, RCTs are not the best study design for addressing Key Question 2, so 

for this question we included articles on any study that was designed to look at barriers to use of 

CHI, including but not limited to the RCTs that addressed Key Question 1. Key Question 3 

addressed knowledge and evidence deficits regarding needed information to support the 

estimation of costs, benefits, and value regarding CHI applications. Key Question 4 addresses 

critical information regarding the effect of CHI applications needed to give consumers, their 

families, clinicians, and developers a clear understanding of the value of CHI applications. 

To identify articles that addressed Key Question 1, we searched computerized literature 

databases using terms relevant to our definition of CHI applications, combined with terms 

relevant to our definition of “consumer,” combined with terms identifying RCTs as the study 

design of interest. To search for articles that were relevant to Key Question 2, we used terms 

relevant to our definition of CHI applications, combined with terms relevant to barriers; the 

search was not limited by study design. Our comprehensive search included electronic searching 
2 

of MEDLINE®, EMBASE®, The Cochrane Library, ScopusTM, and CINAHL® databases. We 

also looked for eligible studies by reviewing the references in pertinent reviews, by querying our 

experts, and by searching grey literature sources such as conference proceedings. 

Studies were eligible for inclusion in the review if they applied to Key Question 1 or 2 and 

did not have one of the following reasons for exclusion: no health informatics application, health 

informatics application does not apply to the consumer, health informatics applications is for 

general information only (e.g., general Web site) and is not tailored to individual consumers, 

study of a “point of care” device (defined as requiring a clinician to use or obtain and is part of 

the regular provision of care), or no original data. 

We assessed the eligible studies on the basis of the quality of their reporting of relevant data. 

For the RCTs, we used the study quality scoring system developed by Jadad et al. For the other 

studies, we used a form to identify key elements that should be reported when reporting results. 

The quality assessments were done independently by paired reviewers. 

We then created a set of detailed evidence tables containing information extracted from the 

eligible studies. We stratified the tables according to the applicable Key Question and 

subquestion (for Key Question 1). We did not quantitatively pool the data for any of the 

outcomes because of the marked heterogeneity of target conditions of interest and the wide 

variety of outcomes studied. 

Data were abstracted by one investigator and entered into online data abstraction forms using 

SRS (Mobius Analytics, Inc., Ottawa, Ontario, CA) Second reviewers were generally more 

experienced members of the research team, and one of their main priorities was to check the 

quality and consistency of the first reviewers’ answers. 

At the completion of our review, we graded the quantity, quality, and consistency of the best 

available evidence for each type of outcome in each clinical area, using an evidence grading 

scheme recommended by the GRADE Working Group and modified for use by the Evidencebased Practice Centers (EPC) Program. For each outcome of interest, two investigators 

independently assigned a grade, and then the entire team discussed their recommendations and 

reached a consensus. 

Throughout the project, the core team sought feedback from external experts with expertise 

in systematic reviews, CHI, consumer advocacy, decision aids, and ethics. A draft of the report 

was sent to the external experts. The EPC team addressed the comments of the external experts 

before submitting the final version of the evidence report. 

Results 

Our literature search identified 146 articles that were eligible for inclusion in this report: 121 

for Key Question 1 and 31 for Key Question 2; 6 articles were eligible for both Key Question 1 

and Key Question 2. All of the Key Question 1 eligible studies were RCTs. The 31 articles 

addressing barriers to use of CHI applications fell under a variety of study designs and data 

collection types. Data on barriers was collected mostly in non-validated surveys and qualitative 

studies from trial data. 

In terms of types of applications studied, 55 percent of studies evaluated interactive Website–based applications or Web-based tailored educational Web sites. Another 15 percent of 

studies evaluated computer-generated tailored feedback applications. Interactive computer 

programs and personal monitoring devices were evaluated in approximately 8 percent of studies 

each. Finally, health risk assessments, decision aids, cell phones, laptops, CD ROMs, personal 
3 

digital assistants (PDA/smartphones), short message system texting (SMS/text), discussion/chat 

groups and computer-assisted imagery were evaluated in less than 5 percent of studies each. In 

terms of participant age groups, 77 percent (76/99) of studies reporting age of participants 

targeted adult CHI users. Approximately 12 percent of studies targeted adolescents/teens, 3 

percent of studies targeted seniors and another 3 percent of studies targeted children. Five 

percent of studies targeted participants from overlapping age groups. In terms of intervention 

delivery setting or location, 58 percent of studies reporting delivery location evaluated CHI 

applications that were used in the home or residence. A minority of evaluations were completed 

in schools (15 percent), clinical settings (17 percent), communities (3 percent), online (5 percent) 

or kiosks (2 percent). Finally, of studies reporting the race of the participants 92 percent (49/53) 

of the studies employed populations that were greater than 50 percent white/Caucasian. There 

was only one study with greater than 50 percent African-American participants and no studies 

with a majority of participants who were Hispanic, American Indian/Alaska Native, or 

Asian/Pacific Islander. 

Key Question 1: What is the evidence of impact of CHI applications 

on health outcomes? 

First, we sought to understand the impact of CHI applications on health care process 

outcomes (Key Question 1a). There were only five studies that met the inclusion-exclusion 

criteria and thus were available to shed light on this question. Five of these studies focused on 

asthma and one additional study focused on contraceptive medication utilization. All of the 

asthma studies showed a significant positive effect of the CHI application on at least one health 

care process measure. The oral contraceptive medication use application failed to reduce 

contraceptive discontinuation. No study found any evidence of harm. 

This review identified 108 studies that addressed the influence of CHI applications on 

intermediate health outcomes (Key Question 1b). These 108 studies evaluated the effects of CHI 

applications on intermediate outcomes in the context of nine categories of diseases or health 

conditions. Intermediate outcomes were evaluated related to breast cancer in three studies, diet, 

exercise, physical activity, not obesity in 32 studies, alcohol abuse in seven studies, smoking 

cessation in 19 studies, and obesity in 11 studies, diabetes mellitus (or diabetes with associated 

conditions) in seven studies, mental health in eight studies, asthma/chronic obstructive 

pulmonary disease (COPD) in four studies, and miscellaneous health conditions in another 15 

studies. 

With regard to breast cancer, evaluated intermediate outcomes included social support, 

information competence, level of conflict, and satisfaction. All three studies reported significant 

positive effect on at least one intermediate health outcome. No study found any evidence of 

harm. 

In terms of diet, exercise, physical activity, not obesity, evaluated intermediate outcomes 

included self-management, knowledge, program adherence, and change in health behaviors. 

Eighty-nine percent of these studies demonstrated significant positive effect on at least one 

intermediate health outcome related to diet, exercise, and physical activity. No study found any 

evidence of harm. 

Evaluated intermediate outcomes related to alcohol abuse included self-management, 

knowledge attainment, and change in health behaviors. All studies found significant positive 
4 

effect on at least one intermediate outcome related to alcohol abuse. No study found any 

evidence of harm. 

With regard to smoking cessation, intermediate outcomes assed in these smoking cessation 

CHI trials included self-management, knowledge attainment, and change in health behaviors. 

Fifty-seven percent of these studies demonstrated a positive effect on at least one intermediate 

outcome related to smoking cessation. No study found any evidence of harm. 

Evaluated intermediate outcomes of interest related to obesity included weight loss behaviors 

and body composition. Only 36 percent of studies demonstrated positive effect on intermediate 

outcomes related to obesity. No study found any evidence of harm. 

Seven studies were identified to evaluate the influence of CHI on intermediate outcomes 

related to diabetes mellitus. Intermediate outcomes of interest included perceived self- efficacy, 

satisfaction, and readiness to change, perceived competence, exercise minutes per day, and selfreported global health. All seven studies found evidence of effect of CHI applications on one or 

more intermediate outcomes related to diabetes mellitus. No study found any evidence of harm. 

Eight studies were identified to evaluate the effect of CHI applications on intermediate 

outcomes related to mental health issues. Intermediate outcomes of interest included work and 

social adjustment, perceived stress, self-rated self-management, sleep quality, mental energy, and 

concentration. Seven of the eight studies found evidence of positive effect of CHI applications 

on at one or more intermediate outcomes related to mental health. No study found any evidence 

of harm. 

Four studies were identified to evaluate the effect of CHI applications on intermediate 

outcomes related to asthma/COPD. Intermediate outcomes of interest included adherence, 

knowledge, change in behavior, dyspnea knowledge, and self-efficacy. Only one of the four 

studies demonstrated a significant effect on any intermediate outcome related to asthma/COPD. 

No study found any evidence of harm. 

Two studies were identified to evaluate the effect of CHI applications on intermediate 

outcomes related to menopause or hormone replacement therapy (HRT). Only one study found 

evidence of significant effect on an intermediate outcome related to menopause/HRT utilization. 

Finally, an additional 15 studies were identified to evaluate the influence of intermediate 

health outcomes in other clinical areas. These intermediate outcomes were in health areas related 

to arthritis, back pain, behavioral risk factor control, contraception, cardiovascular disease, 

cancer, caregiver decisionmaking, fall prevention, health behavior change, headache, HIV/AIDS, 

and adolescent risk behaviors. Each of these studies found evidence of significant effect of the 

CHI application on intermediate outcomes related to the health condition under study. No study 

found evidence of harm. 

Another subquestion of this key question this review sought to answer was regarding the 

effect of CHI applications on relationship centered outcomes (Key Question 1c). Eight studies 

were identified that met the inclusion-exclusion criteria. Relationship centered outcomes of 

interest included social support, quality of life, decisionmaking skill, social support, positive 

interaction with the provider, and satisfaction with care. These relationship centered outcomes 

were evaluated in the context of HIV/AIDS, cancer, osteoarthritis, and pregnancy. Just over 60 

percent (5/8) of studies demonstrated significant effect of CHI on at least one aspect of 

relationship centered care. No study found any evidence of harm. 

Twenty-eight studies addressed the question about the impact of CHI applications on clinical 

outcomes (Key Question 1d). Clinical outcomes evaluated in the identified studies included 

disease-specific outcomes in the context of cancer (three studies), diabetes mellitus (three 
5 

studies), mental health (seven studies), diet, exercise, or physical activity (five studies), and 

Alzheimer’s disease, arthritis, asthma, back pain, aphasia, COPD, HIV/AIDS, headache, obesity, 

and pain (one study each). Over 80 percent of studies found significant influence of CHI 

applications on at least one clinical outcome. Three studies evaluated the effect of CHI 

applications on breast cancer clinical outcomes, but only one found any evidence of significant 

CHI impact. Of the five studies that evaluated the effect of CHI applications on clinical 

outcomes related to diet, exercise or physical activity, four studies found a significant positive 

effect on one or more clinical outcomes. Among the seven studies that evaluated the effect of 

CHI applications on mental health clinical outcomes, all seven found evidence of significant 

effect of CHI on one or more clinical outcomes. Three studies evaluated the effect of CHI 

applications on diabetes mellitus clinical outcomes. All three studies found evidence of 

significant effect of CHI on at least one clinical outcome. The remaining nine studies evaluated a 

CHI application in different health areas including Alzheimer’s disease, arthritis, asthma, back 

pain, aphasia, COPD, headache, HIV/AIDS, and general pain. With the exception of the general 

pain study, the eight remaining studies all found evidence of significant effect of CHI on one or 

more clinical outcomes. None of these 27 studies found any evidence of harm attributable to a 

CHI application. 

The fifth subquestion of this key question was about the evidence of impact of CHI 

applications on economic outcomes (Key Question 1e). Three studies addressed this question. 

Economic outcomes evaluated in these studies included cost of program delivery, cost of 

computer information system with manual data extraction versus cost of the computer system 

with use of the electronic patient record, materials costs, total costs, and incremental costeffectiveness. These outcomes were evaluated in the context of asthma, cancer, and obesity. 

Each of these studies used different economic metrics and methodologies. One study failed to 

provide any cost estimates for the control group. One study was done in an adult population, 

another in a pediatric population, and the third study did not provide any details regarding the 

age of study participants. Given the very small number of studies and the significant limitations 

and heterogeneity of these studies, no conclusions regarding the economic impact of CHI 

applications can be made. 

Key Question 2: What are the barriers that clinicians, developers, 

consumers, and their families or caregivers encounter that limit 

utilization or implementation of CHI applications? 

Thirty-one studies addressed the barriers to CHI applications. Studies focused on a wide 

variety of clinical conditions including cancer, HIV/AIDS (and sexually transmitted disease), 

mental health, physical activity/diet/obesity, smoking cessation, prostate cancer, and 

hypertension. The methodology used to identify barriers included validated and nonvalidated 

surveys, and qualitative and empirical research. Because CHI applications involve the 

participation of consumers, their caregivers, clinicians, and often developers, barriers can apply 

to any of the participants and the type and impact of the barrier may vary significantly between 

providers, developers, patients, and their caregivers. Thus, this analysis of the barriers included 

barriers that impede participation of any of the above groups. 

In terms of systems-level barriers, six studies addressed Internet access at home or in the 

community and six found this to be a barrier. One study identified hardware requirements and 
6 

another study identified mobile device shape/design/configuration as a systems-level barrier. 

Another five studies cited incompatibility with current health care as a barrier. 

Identified individual-level barriers included clinic staff who feared increased workloads, lack 

of built-in social support, forgotten passwords, automated data entry inability to allow for back 

entry of old data, lack of adequate user customization, and substantial financial investment. 

Nineteen studies queried application usability or user-friendliness and all 19 found evidence of 

this barrier. Eleven studies explored patient knowledge, literacy, and skills to use the CHI 

application. All found these deficits to be barriers while one study found no evidence that 

literacy or knowledge deficits were a barrier. Six studies considered the possibility that users 

would find the application too time-consuming and five of these studies cited the evidence in the 

results section, while the one additional study cited too many emails to participants as a barrier. 

Utilization fees were also identified as a barrier. Five studies sought information about privacy 

concerns and four reported concerns over privacy as a barrier. These studies also found concerns 

over the control of information or lack of trust to be barriers. Only two studies queried for 

potential cultural barriers and one study found evidence of this. The expectations of consumers 

including acceptability, usefulness, credibility, expectations, and goals were found to be barriers 

in eight studies. Cost was mentioned as a barrier in only one study and only one study found 

evidence that physical or cognitive impairment resulted in barriers to the use of CHI 

applications. Finally, anxiety over the use of computers, complaints about lack of personal 

contact with clinicians and the belief that health IT would not be an improvement to current care 

were mentioned in two studies as barriers. 

Key Question 3: What knowledge or evidence deficits exist regarding 

needed information to support estimates of cost, benefit, and net 

value with regard to consumer health informatics applications? 

The literature was at a very early stage of development. Many questions have only been 

evaluated by one study. Thus, confirmatory studies have generally not been done. In addition, no 

high quality studies have been conducted regarding several important questions. Broadly, these 

questions can be grouped into at least one of four categories: patient-related questions; CHI 

utilization factors; technology-related issues (i.e., hardware, software, and platform related 

issues, and health-related questions). 

Patient-related questions. The literature is relatively silent on the question of whether or not 

significant differences in patient preferences, knowledge, attitudes, beliefs, needs, utilization and 

potential benefits exists across gender, age and race/ethnicity. The same could be said for 

potential gender and race or ethnicity-based differences. Beyond these demographic differences, 

the field of CHI is developing within the context of a global emergence of technology based 

realities including Web 2.0/Web 3.0 and ubiquitous computing which are enabling an 

unprecedented level of user determined interactivity and functionality. The degree to which this 

functionality could be harnessed for the health benefit of consumers is unknown. The targeted 

uses of CHI applications must increasingly be focused on more than just the index patient. The 

role of sociocultural and community factors will likely exert significant effect on access, 

usability, desirability and benefit of CHI applications. Issues related to trust, security, 

confidentiality need to be further explored. Because the bulk of the currently available research 

has been conducted on the 18-to 65-year-old adult population, more work needs to be done 

among the populations that may have the most potential for using CHI applications. Seniors may 
7 

stand to benefit from those applications that reduce social isolation and independence. 

Adolescents are some of the most intense technology users. Their natural affinity for technology 

may prove advantageous to CHI applications that could be developed in the future. Finally, most 

of the currently CHI research is being conducted among predominately white/Caucasian 

populations. Early evidence suggests that differential utilization patterns and preferences exist by 

race. Such differences could potentially lead to differential efficacy of emerging CHI 

applications. This could have the unintended consequence of enhancing rather than reducing 

some racial and ethnic disparities in health care. Age and race/ethnicity subgroup differences 

need to be netter understood and those differences incorporated into the development of 

emerging applications to ensure efficacy among all population subgroups. 

CHI utilization factors. Despite a rapid increase in access to broadband services among all 

population groups, age groups and geographic regions of the country, differential access to 

broadband internet access may have significant implications in terms of health benefits that may 

be derived from these tools and applications. While many in the younger generations become 

very technically savvy at an early age, many Americans still have limited health literacy. These 

CHI utilization factors suggest the need for a more robust evaluation of the epidemiology of 

broadband access and technology literacy in the United States. 

Technology-related issues. The majority of CHI applications are designed for use on 

personal computers as Web-based applications. Many more potential platforms exist that have 

not been evaluated. In addition, emerging evidence is suggesting that the CHI applications and 

functionality that consumers want and need are not always what health care practitioners think 

they need. As a result, important sociocultural and human computer interface design elements 

may not get incorporated adequately into emerging CHI applications and therefore lead to CHI 

applications with limited efficacy. 

Health-related questions. Finally, most CHI applications that have been evaluated tend to 

focus on one or more domains of chronic disease management. Insufficient attention has been 

given to the role of CHI applications in addressing acute health problems. The role of CHI 

applications in primary, secondary, and tertiary prevention also needs to be more adequately 

explored. Sociocultural factors are increasingly important determinants of health care outcomes. 

The potential influence on social factors including social isolation and social support and perhaps 

even broader social determinants of health need to be evaluated and may prove useful in helping 

consumers address specific health concerns in the home and community-based setting. 

 

Key Question 4: What critical information regarding the impact of 

consumer health informatics applications is needed in order to give 

consumers, their families, clinicians, and developers a clear 

understanding of the value proposition particular to them? 

Several critical information needs must be addressed to enable a clear understanding of the 

value proposition of CHI applications. It is likely that the knowledge gaps needed to establish a 

value proposition, while overlapping, are not identical across all potential stakeholders. Because 

providers are often most concerned about clinical outcomes and costs, it seems reasonable that 

questions of the impact of CHI applications on provider or health care processes, costs, and 

outcomes as addressed in this report will need to be more definitively characterized. In addition, 

the potential liability a provider might incur from a patient using a CHI application will also need 

to be addressed.
8 

Patients often cite convenience and anonymity as the primary reasons the Internet has 

become such a major source of health information. It is likely that the more these elements can 

be incorporated into emerging CHI applications, the more likely they will be considered of value 

by consumers. Other related factors such as usability, portability, and patient-centered 

functionality are likely important characteristics of CHI applications that may help drive 

utilization. Those technologies that exist and enable consumers to accomplish tasks (empower) 

without further complicating individuals’ lives may ultimately prove to be the most widely 

valued CHI applications. By expanding the number of platforms available to consumers, CHI 

applications may become more appealing to a broader consumer base and thus prove valuable to 

those consumers who could most benefit, but may not otherwise use a more traditional CHI 

application. 

Discussion

Overall, despite the significant heterogeneity and limited nature of the literature, the 

following themes were suggested by the studies included in this review. First, there may be a role 

for CHI applications to reach consumers at a low cost and obviate the need for some activities 

currently performed by humans. In addition, the data suggest that CHI applications may also be 

used to enhance the efficacy of interventions currently delivered by humans. Several studies 

compared the use of a CHI application and traditional therapy against traditional therapy alone. 

Many found that the group receiving traditional therapy with a CHI application had more benefit 

than traditional therapy alone. Thirdly, the studies evaluated in this review tended to support the 

finding that at least three critical elements are most often found in those CHI applications found 

to exert a significant effect on health outcomes. These three factors are (1) individual tailoring, 

(2) personalization, and (3) behavioral feedback. Personalization involves designing the 

intervention to be delivered in a way that makes it specific for a given individual. Tailoring 

refers to building an intervention in part on specific knowledge of actual characteristics of the 

individual receiving the intervention. Finally, behavioral feedback refers to providing consumers 

with messages regarding their progression through the intervention. Interestingly, it is not clear 

from this literature that CHI-derived behavioral feedback is any better than feedback originating 

from human practitioners or others. Rather, it appears that the feedback must happen with an 

appropriate periodicity, in a format that is appealing and acceptable to the consumer, not just the 

provider. 

Finally, despite the paucity of studies in many areas of this emerging field and because of the 

methodological limitations found in many of the studies, the body of the available scientific 

evidence suggests that CHI applications may hold significant future promise for improving 

outcomes across a wide variety of diseases and health issues. In terms of health care processes 

and relationship centered outcomes, the literature is positive but very limited. Most of the 

currently available research has evaluated the impact of CHI applications on intermediate health 

outcomes. Due in part to the number of studies conducted to date, the evaluation of both shortterm and longer-term outcomes, the utilization of significant sample sizes, appropriate statistics, 

the near uniformity of dependent variables across studies, and cogent articulation of the theoretic 

bases of the CHI content and methodology in most studies, the literature appears strongest for 

CHI applications targeting intermediate outcomes related to smoking cessation. In terms of 

clinical outcomes, the weight of the evidence appears strongest for the use of CHI applications 
9 

on mental health outcomes. Evidence-based conclusions regarding economic outcomes can not 

be made at this time. 

Despite the positive nature of some of the available evidence, significant research 

opportunities and knowledge gaps exist in terms of understanding the role of CHI applications 

targeting children, adolescents, the elderly, and specifically nontraditional (family members, 

friends, allied health workers) patient caregivers. The role of Web 2.0, social networking, and 

health gaming technology in CHI has not been adequately evaluated. Much more work needs to 

be done to understand consumer desires and needs versus provider perceptions of patient desires 

and needs in terms of emerging CHI applications and tools. Similarly, much more work is 

needed to explicate the effect of CHI applications on health outcomes among racial and ethnic 

minority populations, low-literate populations, and the potential effect of these applications on 

health care disparities. 

Finally, CHI research would be greatly enhanced with standardization and widespread 

utilization of a transdisciplinary CHI nomenclature and a CHI evaluation registry to facilitate 

uniform reporting and synthesis of results across emerging CHI applications, interventions, and 

evaluations. 
Evidence Report 
12
Chapter 1. Introduction 

Consumer Health Informatics 

 Interest is emerging concerning the potential of technology and eHealth solutions that are 

tailored to consumers. This emerging field has been referred to as consumer health informatics 

(CHI) (see Appendix A1 for a list of acronyms). It has been defined by Eysenbach as a branch of 

medical informatics that “analyzes consumers’ needs for information, studies and implements 

methods of making information accessible to consumers, and models and integrates consumers’ 

preferences into medical information systems.”1

 In 2001, Houston et al2 conducted a survey of 

members of the American Medical Informatics Association (AMIA) to generate a consensus 

definition of CHI. Respondents indicated that CHI incorporated a broad range of topics, the most 

common being patient decision support and patient access to their own health information. 

Despite this growing interest, the value of CHI has not been rigorously reviewed. We will review 

the evidence regarding the proposed questions, focusing on several kinds of outcomes. 

 For the purpose of this review, we define CHI applications as any electronic tool, technology, 

or system that is: 1) primarily designed to interact with health information users or consumers 

(anyone who seeks or uses health care information for nonprofessional work) and 2) interacts 

directly with the consumer who provides personal health information to the CHI system and 

receives personalized health information from the tool application or system; and 3) is one in 

which the data, information, recommendations or other benefits provided to the consumer, may 

be used with a healthcare professional, but is not dependent on a healthcare professional. As 

such, for the purposes of this review, we have excluded point of care devices (e.g., glucometer, 

remote monitoring devices), prescribed clinical devices that are part of the provision of clinical 

care, general information websites, message boards, and applications that are designed for use in 

a work environment. 

 This definition has the following advantages: 

1) It keeps the focus of the review on how CHI applications meet the needs of consumers 

rather than the needs of clinicians; 

2) It helps avoid a categorical disease-oriented evaluation of every clinical technological 

development for every disease which is not necessarily focused on the needs of 

consumers; 

3) It helps to keep the focus of the review on studies that demonstrate impact, value or 

efficacy from the perspective of consumers; 

4) It facilitates categorization of CHI applications in ways that may be more meaningful for 

patients. 

 Potential categories of CHI tools/technologies/applications include but may not be limited to: 

a. Applications and technologies that facilitate knowing/tracking/understanding clinical 

parameters (disease management); 

b. Applications and technologies that facilitate knowing/tracking/understanding observations 

of daily living (ODL’s); 

c. Applications and technologies that facilitate calendaring (lifestyle management assistance); 

d. Applications and technologies that facilitate prevention and health promotion; 

 

1 Appendixes and evidence tables cited in this report are available at: http://www.ahrq.gov/clinic/tp/chiapptp.htm. 

13
14

e. Applications and technologies that facilitate self-care; and 

f. Applications and technologies that facilitate assisted care and caregiving. 

Purpose of Evidence Report 

 The objective of the report is to review the literature on the evidence of the impact of 

currently developed CHI applications on health and health care process outcomes, to identify the 

gaps in the literature, and to recommend future research endeavors to better assess these 

information technology (IT) applications. The specific Key Questions were: 

1. What evidence exists that CHI applications impact: 

a. Health care process outcomes (e.g., receiving appropriate treatment) among users? 

b. Intermediate health outcomes (e.g., self management, health knowledge, and health 

behaviors) among users? 

c. Relationship-centered outcomes (e.g., shared decision making or clinician-patient 

communication) among users? 

d. Clinical outcomes (including quality of life) among users? 

e. Economic outcomes (e.g., cost and access to care) among users? 

2. What are the barriers that clinicians, developers and consumers and their families or 

caregivers encounter that limit utilization or implementation of CHI applications? 

3. What knowledge or evidence exists to support estimates of cost, benefit, and net value 

with regard to CHI applications? 

4. What critical information regarding the impact of CHI applications is needed in order to 

give consumers, their families, clinicians, and developers a clear understanding of the 

value proposition particular to them? 

 We will discuss gaps in research, including specific areas that should be addressed. We also 

will suggest possible public and private organizational types to perform the research and/or 

analysis. 
15 

Chapter 2. Methods 

 The objective of the report is to review and synthesize the available evidence regarding the 

impact of currently developed CHI applications on health and health care process outcomes. This 

report will also identify barriers to the use of CHI applications. This review will help to identify 

the gaps in published information on costs, benefits, and net value of these applications in 

existing research on CHI applications. Additionally, we will use this report to identify what 

critical information is needed for consumers, their families, clinicians, and developers to clearly 

understand the value of CHI applications. 

Recruitment of Technical Experts and Peer Reviewers 

 We assembled a core team of experts from Johns Hopkins University (JHU) who have strong 

expertise in health information technology IT, including: clinical IT and health sciences IT; 

clinical trials; systematic literature reviews; epidemiological studies; and general medicine. We 

recruited two advisors who have done extensive research in the areas of open access, health 

policy, eHeath, and CHI. We recruited seven external technical experts, referred to as a 

“Technical Expert Panel” (TEP), from diverse professional backgrounds including consumer 

advocates, a methods expert for another Evidence-based Practice Center (EPC), and academic 

experts in ethics, decision aids, CHI, and CHI user acceptance. An additional group of two peer 

reviewers was identified to provide comments on the report. Peer reviewers differed from the 

TEP members in that they were not involved during the project development phase of the project 

(See Appendix B1, List of Internal Advisors, Technical Experts, and Peer Reviewers). 

Key Questions 

 The core team worked with the external advisors, technical experts, and representatives of 

the Agency for Healthcare Research and Quality (AHRQ) to refine a set of key questions 

originally proposed by AHRQ for this project. These Key Questions are presented in the “The 

Purpose of This Evidence Report” section of Chapter 1 (Introduction). Before searching for the 

relevant literature, we clarified the definitions of these Key Questions and the types of evidence 

that we would include in our review. 

Key Question 1 addresses the impact CHI applications have on health and health care process 

outcomes. Based on conversations with AHRQ, the external advisors and the TEP, there was 

agreement that the best evidence available to answer this question would be found in randomized 

controlled trials (RCTs). 

Key Question 2 addresses the barriers that users of a CHI application might encounter. Based 

on conversations with AHRQ, the external advisors, and the TEP, we agreed that RCTs were not 

the best study design to identify and evaluate barriers. We decided to include articles on any 

study design whose specified purpose was to look at barriers to use of CHI. All RCTs evaluated 

for Key Question 1 were reviewed to determine whether barriers were assessed as well. 

Key Question 3 addresses knowledge and evidence deficits regarding needed information to 

support estimation of costs, benefits, and value regarding CHI applications. Key Question 4 

addresses the identification of critical information regarding the impact of CHI applications to 

 

1

 Appendixes and evidence tables cited in this report are available at: http://www.ahrq.gov/clinic/tp/chiapptp.htm.
16 

give consumers, their families, clinicians, and developers a clear understanding of the value of 

CHI applications. There was agreement amongst the core team, external advisors, AHRQ, and 

the TEP that the answers to these two questions (regarding knowledge deficits and missing 

information) would emerge from our review of the evidence on Key Questions 1 and 2. 

Conceptual Framework 

Experts from medical informatics, public health, health services research, behavioral 

sciences, human factors, and primary care were consulted to assist the EPC in the development 

of a conceptual framework to address the key questions (above). During the process, we 

evaluated several different types of conceptual models. We ultimately developed a model that 

incorporates barriers to CHI use as well as health outcomes, health care process measures, 

intermediate outcomes, relationship-centered outcomes, and economic outcomes. The barriers as 

well as the health care process measures were incorporated based on the key questions presented 

to us. Our purpose was to focus the model to direct our review of the relevant literature and to 

assist reviewers in understanding which articles applied to our strict criteria for inclusion. 

Knowing that CHI applications are being employed across the spectrum of health and illness, 

we aimed to encompass activities that are not traditionally considered preventive health but are 

emerging as potentially important to patient health concerns such as observations of daily living 

(a personal log of activities such as sleep, diet, exercise, mood, etc.). The final framework 

encompassed selected concepts of CHI applications (Figure 1). 

Literature Search Methods 

 Searching the literature involved identifying reference sources, formulating a search strategy 

for each source, and executing and documenting each search. For the searching of electronic 

databases, we used medical subject heading (MeSH) terms. To identify articles that that were 

potentially relevant to Key Question 1, we searched for terms relevant to our definition of CHI 

applications (see Chapter 1, Introduction), combined with terms relevant to our definition of 

“consumer” (see Chapter 1, Introduction), combined with terms identifying RCTs as the study 

design of interest. To identify articles that that were potentially relevant to Key Question 2, we 

searched for terms relevant to our definition of CHI applications (see Chapter 1, Introduction), 

combined with terms relevant to barriers; the search was not limited by study design. We used a 

systematic approach to searching the literature to minimize the risk of bias in selecting articles 

for inclusion in the review. 

 We also looked for eligible studies by reviewing the references in pertinent reviews, by 

querying our experts, and by taking advantage of knowledge shared at core team meetings
17 

Figure 1. Conceptual model addressing Key Questions 1 and 2: Impact of CHI on health and health care process oto use of CHI.
18 

Sources 

Our comprehensive search included electronic searching of peer reviewed literature 

databases and grey literature databases as well as hand searching. On December 22, 2008, we ran 

searches of the MEDLINE®, EMBASE®, The Cochrane Library, Scopus, and Cumulative Index 

to Nursing and Allied Health Literature (CINAHL) databases. This search was updated after the 

submission of the draft report to ensure we included the most current relevant articles; this search 

was extended to June 1, 2009. A supplemental search targeting grey literature sources was 

conducted on January 7, 2009; it was also extended to June 1, 2009. Sources searched were: 

Health Services Research Projects in Progress, The Institute of Electrical and Electronics 

Engineers (IEEE) Conference Proceedings, Institution of Engineering and Technology (IET) 

Conference Proceeding, Proceedings of the American Society for Information Science and 

Technology (Wiley InterScience), World Health Organization (WHO) –International Clinical 

Trials Registry Platform, American Public Health Association (APHA) 2000-2008, OpenSIGLE 

–System for Information on Grey Literature in Europe, and The New York Academy of 

Medicine – Grey Literature. 

Search Terms and Strategies 

 Search strategies specific to each database were designed to enable the team to focus the 

available resources on articles that were most likely to be relevant to the Key Questions. We 

developed a core strategy for MEDLINE®, accessed via PubMed, on the basis of an analysis of 

the medical subject heading (MeSH) terms and text words of key articles identified a priori. The 

PubMed strategy formed the basis for the strategies developed for the other electronic databases 

(see Appendix C, Detailed Search Strategies; and Appendix D, Grey Literature Search 

Strategies). 

Organization and Tracking of the Literature Search 

 The results of the searches were downloaded into ProCite® version 5.0.3 (ISI ResearchSoft, 

Carlsbad, CA). Duplicate articles retrieved from the multiple databases were removed prior to 

initiating the review. From ProCite, the articles were uploaded to SRS 4.0 (TrialStat© 2003-

2007). SRS is a secure, Web-based collaboration and management system designed to speed the 

review process and introduce better process control and scientific rigor. In February of 2009, the 

SRS system was transferred to new owners, Mobius Analytics (Ottawa, Canada). Functionality 

of the system was unchanged. We used this database to store full articles in portable document 

format (PDF) and to track the search results at the title review, abstract review, article 

inclusion/exclusion, and data abstraction levels.

Title Review 

The study team scanned all the titles retrieved. Two independent reviewers conducted title 

scans in a parallel fashion. For a title to be eliminated at this level, both reviewers had to indicate 

that it was ineligible. If the first reviewer marked a title as eligible, it was promoted to the next 
19 

elimination level, or if the two reviewers did not agree on the eligibility of an article, it was 

automatically promoted to the next level (see Appendix E, Title Review Form). 

The title review phase was designed to capture as many studies as possible that reported on 

either the impact of CHI applications on process or clinical outcomes, or on barriers to consumer 

use of CHI applications. All titles that were thought to address the above criteria were promoted 

to the abstract review phase. 

Abstract Review 

 The abstract review phase was designed to identify articles that applied to Key Questions 1 

and/or 2. An abstract was excluded at this level if it did not apply to one of these Key Questions 

or for any of the following reasons: no health informatics application; health informatics 

application does not apply to the consumer; health informatics application is for general 

information only (e.g., general website, message board, survey, etc.) AND is not tailored to the 

individual consumer; study of a "point of care" device (requires a clinician to use or obtain and is 

part of the regular provision of care, such as a device or telemedicine used at the point of care); 

no original data (letter to the editor, comment, systematic review); not an RCT (this is only an 

exclusion for KQ1, any article that may apply to KQ2 should not be excluded based on study 

design);or non-English language (Appendix E, Abstract Review Form). 

 Abstracts were promoted to the article review level if both reviewers agreed that the abstract 

could apply to one or more of the Key Questions and did not meet any of the exclusion criteria. 

Differences of opinion were resolved by discussion between the two reviewers. 

Article Review 

 Full articles selected for review during the abstract review phase underwent another 

independent review by paired investigators to determine whether they should be included in the 

full data abstraction. At this phase of review, investigators determined which of the Key 

Question(s) and sub-question(s) each article addressed (see Appendix E, Article 

Inclusion/Exclusion Form). If articles were deemed to have applicable information, they were 

included in the data abstraction. Differences of opinion regarding article eligibility were resolved 

through consensus adjudication. 

 

Data Abstraction 

Once an article was included at this level, reviewers were given a final option to exclude the 

article if it was found to be inapplicable once the data abstraction was underway. This process 

was used to eliminate articles that did not contribute to the evidence under review (see Appendix 

E, General Data Abstraction Form). If an article was excluded at this level by the data abstractor, 

it was moved from this level to the previous level (article review) and tagged with the 

appropriate reason for exclusion. 

 We used a sequential review process to abstract data from the final pool of articles. In this 

process, the primary reviewer completed all the relevant data abstraction forms. The second 

reviewer checked the first reviewer’s data abstraction forms for completeness and accuracy. 

Reviewer pairs were formed to include personnel with both clinical and methodological 
20 

expertise. The reviews were not blinded in terms of the articles’ authors, institutions, or journal.3

Differences of opinion that could not be resolved between the reviewers were resolved through 

consensus adjudication. 

 For all articles, reviewers extracted information on general study characteristics: study 

design, location, disease of interest, inclusion and exclusion criteria, description of the 

consumers under study, and description of the CHI application (see Appendix E, General Form). 

Specific participant (consumer) characteristics were abstracted: information on intervention 

arms, age, race, gender, education, socioeconomic status, and other related data on the 

application under study. 

 Outcomes data were abstracted from the articles that were applicable to Key Question 1 

regarding a CHI application’s impact on a health or health care process outcome (see Appendix 

E, KQ1 CHI (categorical) variables, and KQ1 CHI (continuous) variables). Articles addressing 

Key Question 2 on barriers to CHI were abstracted to capture data on the condition of interest, 

the CHI application, data collection/study design, and barriers identified (see Appendix E, KQ2 

CHI barriers).

Quality Assessment 

 We assessed the included studies on the basis of the quality of their reporting of relevant 

data. For the RCTs, we used the scoring system developed by Jadad et al.4 The 5 questions 

(according to the Jadad criteria) used to assess the quality of RCTs were: 1) Was the study 

described as randomized (this includes the use of words such as “randomly,” “random,” and 

“randomization”)? 2) Was the method used to generate the sequence of randomization described, 

and was it appropriate? 3) Was the study described as double-blind? 4) Was the method of 

double-blinding described, and was it appropriate? 5) Was there a description of withdrawals and 

dropouts? 

 

Data Synthesis 

 We created a set of detailed evidence tables containing information extracted from the 

eligible studies. We stratified the tables according to the applicable Key Question, and subquestion (for Key Question 1). In addition, tables were further stratified to pool together the 

common target conditions of interest. Once evidence tables were created, we rechecked selected 

data elements against the original articles. If there was a discrepancy between the data abstracted 

and the data appearing in the article, this discrepancy was brought to the attention of the 

investigator in charge of the specific data set, and the data were corrected in the final evidence 

tables. We did not quantitatively pool the data for any of the outcomes because of the marked 

heterogeneity of the interventions, target conditions, and outcomes studied. 

Data Entry and Quality Control 

 Data were abstracted by one investigator and entered into the online data abstraction forms 

(see Appendix E, Forms). Second reviewers were generally more experienced members of the 

research team, and one of their main priorities was to check the quality and consistency of the 

first reviewers’ answers. 
21 

Grading of the Evidence 

 At the completion of our review, we graded the quantity, quality, and consistency of the best 

available evidence, addressing Key Questions 1 and 2 adapting an evidence grading scheme 

recommended by the GRADE Working Group5 and modified in Chapter 11 of the EPC Manual 

currently under development.6 We separately considered the evidence from studies addressing 

the 5 identified outcomes of Key Question 1: health care process outcomes, intermediate 

outcomes, relationship-centered outcomes, clinical outcomes, and economic outcomes. Each of 

these main categories was stratified into subcategories by target disease or conditions, and if a 

particular outcome was evaluated by at least two RCTs, we graded the evidence. If an outcome 

was evaluated by only one RCT, we did not grade the body of evidence, but rather narratively 

described the information available. The body of evidence addressing Key Question 2 included a 

variety of different study designs. Most of the articles under review in this category were not 

RCTs and were assessed differently. 

 We assessed the quality and consistency of the best available evidence, including an 

assessment of the risk of bias in relevant studies (using individual study quality scores), whether 

the study data directly addressed the Key Questions, and the precision and strength of the 

findings of individual studies. We classified evidence bodies pertaining to each Key Question 

into four basic categories: (1) “high” grade (high confidence that the evidence reflected the true 

effect; further research is very unlikely to change our confidence in the estimate of the effect); 

(2) “moderate” grade (moderate confidence that the evidence reflected the true effect; further 

research may change our confidence in the estimate of effect and may change the estimate); (3) 

“low” grade (low confidence that the evidence reflected the true effect; further research is likely 

to change the confidence in the estimate of effect and is likely to change the estimate); and (4) 

“insufficient” (evidence was either unavailable or did not permit the estimation of an effect). 

Peer Review 

 Throughout the project, the core team sought feedback from the internal advisors and 

technical experts. A draft of the report was sent to the technical experts and peer reviewers as 

well as to representatives of AHRQ. In response to the comments from the technical experts and 

peer reviewers, we revised the evidence report and prepared a summary of the comments and 

their disposition for submission to AHRQ. 
23 

Chapter 3. Results

Results of the Literature Search 

 The literature search process identified 24,794 citations that were deemed potentially relevant 

to Key Questions 1 and/or 2 (see Figure 2) and 6673 additional articles were identified through 

hand searching, as described in Chapter 2. We identified no additional eligible articles in the 

grey literature. We excluded 8943 duplicate citations from the electronic search results. Most 

duplicates came from concurrently searching MEDLINE®, The Cochrane Library, EMBASE®, 

CINAHL, and SCOPUS. The search strategy used in all search engines was modeled on that 

which we used in MEDLINE®, with similar search terms (see Appendix C1). Additionally, the 

EMBASE® search engine allows the user to search the MEDLINE® database as well as 

EMBASE®, a strategy that often yields many duplicates between the two search sites. Our EPC 

employs this strategy to improve the sensitivity of the search. 

 In the title review process, we excluded 19,377 citations that clearly did not apply to the Key 

Questions. In the abstract review process, we excluded 2642 citations that did not meet one or 

more of the eligibility criteria (see Chapter 2 for details). At the article review phase, we 

excluded an additional 340 articles that did not meet one or more of the eligibility criteria (for a 

detailed list see Appendix F, list of excluded articles). Two more articles were removed from the 

pool of articles identified through the electronic databases at this stage due to difficulty in 

retrieving the article (Figure 2). Details on the grey literature search are available in Appendix D. 

The Johns Hopkins University Welch Library works with other libraries to ensure that University 

faculty and employees have access to nearly all published articles. Periodically, an article cannot 

be located through any of the cooperating libraries, and the EPC team goes directly to the authors 

to obtain the article — this was not possible for these two articles. Ultimately we were left with 

162 articles that were eligible for inclusion in this report: One hundred thirty-seven for Key 

Question 1 and 31 for Key Question 2; six articles were eligible for both Key Question 1 and 

Key Question 2.

 

1 Appendixes and evidence tables cited in this report are available at: http://www.ahrq.gov
24 

Electronic Databases 

MEDLINE® (14561)

Cochrane (3716)

EMBASE® (1421)

CINAHL (1462)

SCOPUS (5577)

Retrieved 

33410

Title Review 

22524

Duplicates 

10886

Abstract Review 

3147

Excluded 

19377

Article Review 

505

Excluded 

2642

Included Articles 

162 

KQ1: 137 

KQ2: 31

6 articles apply to KQ1 

and KQ2 

Excluded 

341 

unretrievable 

2

Reasons for Exclusion at Article Review Level*

No health informatics application: 98 

Health informatics application does not apply to the 

consumer:57 

Health informatics application is for general information 

only: 82 

Study of a point of care device: 66 

No original data: 50 

Not a RCT, and not a study addressing barriers: 38 

Other: 85 

Non-English language: 0

Reasons for Exclusion at Abstract Review Level*

No health informatics application: 843 

Health informatics application does not apply to the 

consumer: 723 

Health informatics application is for general information 

only :453 

Study of a point of care device: 617 

No original data: 673 

Not a RCT, and not a study addressing barriers: 168 

Other: 269 

Non-English language: 0

Hand Searching 

6673

* Total exceeds the # in the exclusion box because reviewers were allowed to mark more than 1 reason for exclusion 

Figure 2. Summary of literature search (number of articles)
25 

Description of the Types of Studies Retrieved 

One hundred thirty-seven studies applied to Key Question 1. The EPC team along with the 

TEP and AHRQ agreed that the best evidence available to measure outcomes of the impact of 

CHI applications on consumers would be found in randomized controlled trials (RCTs). 

Therefore, all of the Key Question 1 eligible studies were RCTs. The above group agreed that all 

study designs should be included when searching for and including articles investigating barriers 

to the use of CHI applications. The 31 articles addressing barriers to use of CHI applications fell 

under a variety of study designs and data collection types. Data on barriers was collected most 

commonly in non-validated surveys (24) or qualitative studies (7). 

Key Question 1a: What evidence exists that consumer health 

informatics applications impact health care process 

outcomes? 

Summary of the Findings 

Very few studies evaluated the impact of CHI applications on health care processes (Table 

1). Measures included monitoring and therapeutic adherence, and health care utilization. The 

quality of these trials was variable, ranging from moderate to very low, as measured by the 

Jadad4 criteria for RCT quality (Appendix F, Evidence Table 1). Postintervention followup 

duration varied from 12 weeks up to 1 year. The study results suggested a positive effect of CHI 

applications on monitoring and therapeutic adherence, and health care utilization. 

Strengths and Limitations of the Evidence 

Five studies assessed the impact of CHI applications on health care process outcomes in 

asthma, and another on the process outcome of contraceptive medication use. The asthma studies 

enrolled from 527 to 2288 patients. The sample size in the contraception study was 949 

(Appendix G, Evidence Tables 2-4).9

 The overall strength of the body of the evidence from the 

asthma studies was graded as moderate (Table 2) based on a modified version of the GRADE 

criteria”5 and Chapter 11 of the EPC Manual6

General Study Characteristics 

The asthma studies involved children as young as 17 years of age8,10-12 while the 

contraception study participants were young women ( 20 yrs or younger). 9 One of the asthma 

studies involved a majority of female participants,10 the others had a majority of male 

participants.8,11,12 All of the asthma studies reported on race, one on caregiver education. The 

contraception study was conducted at two separate family planning clinic sites resulting in a 

highly diverse participant background in terms of race and socioeconomic status (Appendix G, 

Evidence Tables 2 and 3). 
26 

Table 1. Summary of studies of CHI applications impacting health care process outcomes (N=5). 

Target 

condition 

N Author, year Interventions Primary outcomes measured Effect of CHI 

application* 

Bartholomew, 

200012 Watch, 

Discover, 

Think and Act 

(An 

Interactive 

multimedia 

application on 

CD-ROM) 

Enhancement of self-management skills 0 

Guendelman, 

200211 Health 

Buddy(person

al and 

interactive 

communicatio

n device) 

Health and quality of life and process 

evaluation 

+ 

Monitoring adherence + 

Therapeutic adherence - 

Adherence to daily diary entry + 

Therapeutic adherence: dry powder inhaler 

(DPI) or metered dose inhaler (MDI) 

plus spacer technique score

+ 

Jan, 200710 Asthma 

education and 

an interactive 

asthma 

monitoring 

system 

Peak flow meter technique score - 

Days of quick relief medicine + 

Urgent physician visit +

Asthma 4 

Krishna,20038

 Internetenabled 

asthma 

education 

program 

Emergency room visit + 

Oral contra- Oral contraceptive efficacy Chicago 0 

ceptive use 

1 Chewning, 

19999 Computerized 

decision aid 

Oral contraceptive efficacy Madison 0 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

DPI=dry powder inhaler; MDI=metered dose inhaler 

Outcomes

Asthma. When evaluating therapeutic and monitoring adherence among children with 

asthma, Jan et al10 found that the children using the Blue Angel for Asthma Kids application, an 

Internet based interactive asthma program , monitored their peak expiratory flows and adhered to 

an asthma diary significantly more than those receiving standard asthma education including 

written diary and instructions for self management at 12 weeks ( p < 0.05) . Similarly their 

therapeutic adherence to inhaled corticosteroid treatment was significantly higher (63 percent 

among intervention vs. 42 percent among control group). In this intervention, participants 

received a self management plan from the Blue Angel program after entering their symptoms and 

peak flow measurement on a daily basis into the computer (Appendix G, Evidence Table 4). 

 Krishna et al8 showed a positive impact2 of an interactive computer program that delivers 

tailored educational messages in the form of brief vignettes for asthma education on health care 

utilization rates. This intervention was delivered in the clinic’s waiting area and required no 

 

2

 “positive impact”: the appropriate increase or decrease if a specific outcome that leads to a benefit to the consumer. 
27 

Table 2. Grade of the body of evidence addressing CHI impact on health care processes in 

asthma. 

1 Protection against risk of bias (relates to study design, study quality, reporting bias) High 

2 Number of studies 4 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

-1 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

0 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Moderate 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2).

change in clinic flow or staffing levels. In this study, all participants in the intervention and 

control group also received standard education based on the National Asthma Education and 

prevention program. Participants in the intervention arm had significantly fewer emergency room 

visits (1.93 vs. 0.62 per year, p<0.01,) and a significantly lower daily dose of inhaled 

corticosteroids (434 vs. 754 μg, p < 0.01) possibly due to improved avoidance of asthma triggers. 

No statistically significant difference was found for the number of hospitalizations. Increased 

knowledge levels about asthma in both the control and intervention arms positively correlated 

with fewer urgent visits to physicians and reduced use of quick relief medications (correlation 

coefficient r = 0.37 and 0.30, respectively) 

Guendelman et al11 studied the impact of the Health Buddy (an interactive communication 

device) compared to an asthma diary on health related quality of life and health processes. This 

study demonstrated that the intervention group was significantly more likely to have no 

limitation of activity (p=0.03), significantly less likely to report peak flow readings in the yellow 

or red zone (p=.01) or to make urgent calls to the hospital (p=.05). 

Finally Bartholomew et al12 evaluated an interactive multimedia computer game designed to 

enhance self-management skills and thereby improve asthma outcomes. The study demonstrated 

that the intervention group had fewer hospitalizations, better symptom scores, increased 

functional status, greater knowledge of asthma management, and better child self-management 

behavior as compared to controls at baseline. (Appendix G, Evidence Table 4). 

Oral contraceptive use. In this study involving two family planning clinics, increased 

knowledge about oral contraceptive methods as a result of using a decision support aid did not 

reduce discontinuation rates for oral contraceptives among female adolescents (Appendix G, 

Evidence Table 4). Although not a primary outcome in this study, it is interesting to note that the 

reasons for discontinuation of oral contraceptives, however, were mainly medication side effects 

and changes in sexual relationships altering perceived need for using contraceptives.9
28 

Key Question 1b: What evidence exists that consumer health 

informatics applications impact intermediate outcomes? 

Breast Cancer 

Summary of the Findings 

Three studies examined the impact of CHI in the context of breast cancer (Table 3), 13-15 and 

one of these was a study of multiple cancers that included breast cancer.15 Outcomes examined 

were similar in two of the studies, which were from the same research group and involved the 

same CHI intervention (Comprehensive Health Enhancement Support System [CHESS]). These 

studies examined quality of life, as well as the woman’s perception of social support, unmet 

information needs, information competence, and involvement in her own health care.13,14 One 

additional study addressed satisfaction with the information, computer versus provider 

consultation preference, and anxiety and depression.15 

Over the longer term, CHESS participants reported better social support and information 

competence than the comparison groups.13,14 In the study comparing personalized computer 

information with two comparison groups -- general computer information and information 

booklets – patients given access to personalized information on the computer a few days after 

they were given information about their cancer were more satisfied than patients in the other two 

groups. 

Strengths and Limitations of the Evidence 

Only three studies examined intermediate outcomes in patients with breast cancer, and one 

included a spectrum of different types of cancer, with breast cancer patients representing about 

half of all patients. The test interventions and outcomes examined were identical or nearly so in 

two of the trials, with outcome measures designed to be short term. Sample sizes in each of 

these studies were modest. All three studies were randomized, with only one 15 providing the 

details of how the randomization sequence was generated (a random numbers table), and only 

one 14 providing details on how allocation was concealed (sealed envelopes). Intermediate 

outcomes were all self-reported, and masking of the patients was not possible. Dropouts and 

withdrawals over the study period were over 10 percent in the 2001 CHESS study, 13 and slightly 

less in the 1999 CHESS study, 14 and nearly 20 percent in the study by Jones and colleagues. 15 

An intention-to-treat analysis was only performed in the 2008 CHESS study.14 Overall, these 

studies were given a low study quality score according to the Jadad criteria4

 (See Appendix G, 

Evidence Table 1). The overall strength of this body of evidence was graded as low (Table 4) 

based on a modified version of the GRADE criteria”5

 and Chapter 11 of the EPC Manual6
29 

Table 3. Results of studies of CHI applications impacting intermediate outcomes in breast cancer 

(N=3). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

application* 

Satisfaction Score >2, n(%) a few days 

after information given 

Jones, +

199915 ComputerPersonal 

Information via 

computer 

Computer - 

General 

information 

about cancer 

Prefer computer to 10 minute 

consultation with professional (at 3 

months of follow up)†

‐ 

Social Support +

Information competence 0

Unmet information needs 0

Participation, behavioral involvement +

Participation, level of comfort +

Gustafson, 

200113 CHESS 

Confidence in doctors 0

Breast 

cancer

3

Gustafson, 

200814 CHESS Social support +

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 A 10 minute professional consultation was preferred to the intervention, however, the group randomized to the internet group 

was more likely to prefer using it. 

CHESS = Comprehensive Health Enhancement Support System 

Table 4. Grade of the body of evidence addressing CHI impact on intermediate outcomes in breast 

cancer.

1 Protection against risk of bias (relates to study design, study quality, reporting bias) Moderate 

2 Number of studies 3 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 
30 

General Study Characteristics

The studies identified were evaluations of the impact of CHI applications on intermediate 

outcomes tested among adult populations with cancer. One study included patients younger than 

6113 (mean age about 44 years old), and the other two studies did not report patient ages. One 

study16 reported on the percent of “Caucasian” study participants – about 75 percent 

Outcomes 

In the 2001 CHESS study,13 patients allocated to CHESS reported statistically significantly 

greater social and information support, participation in health care, and confidence in the doctor, but 

not greater quality of life than patients with Internet access alone, at 2 months of followup. The 

positive effect of CHESS remained for social support at 5 months while no evidence of a beneficial 

effect of CHESS was observed at 5 months for information support, participation in health care, 

confidence in the doctor, or quality of life (Appendix G, evidence Table 7). 

In the 2008 CHESS study,14 patients allocated to CHESS reported greater social support during 

the 5-month intervention period than did those offered books and audiotapes or those in the Internet 

access group. At 9 months, about 4 months after the intervention period ended, the CHESS group 

reported greater quality of life, social support, and health and information competence compared with 

the control group offered books and audiotapes, but not compared with the group given Internet 

access (Appendix G, evidence Table 7). 

Jones et al15found that at the time the intervention was offered, more patients in the Internet 

groups (both personal and general information), found information more easily than those offered 

booklets, and those given booklets felt more overwhelmed by the information. However, respondents 

allocated to the computer groups more often found the information available too limited, compared 

to those assigned to the booklets. At 3 months of followup, all three groups overwhelmingly 

preferred a 10 minute professional consultation to use of the computer, although those assigned to the 

computer were more likely to prefer the computer (29 percent of those receiving personal 

information on the computer vs. 20 percent general information vs. 10 percent booklet information). 

At 3 months of followup, significantly more patients assigned to the general computer information 

group reported anxiety and depression (Appendix G, Evidence Table 7). 

Diet, Exercise, Physical Activity, not Obesity 

Summary of the Findings 

 Thirty-two studies evaluated the impact of CHI applications on a variety of intermediate 

health outcomes related to diet, exercise, or physical activity, not obesity, including selfmanagement, knowledge attainment (program adherence), and change in health behaviors (Table 

5). The quality of these trials was highly variable with Jadad4

 study quality scores ranging from 

very low to moderately high (although only one of the 32 articles was scored as moderately high) 

(Appendix G, Evidence Table 1). Included in the 32 studies were two studies that evaluated the 

impact of CHI applications on outcomes related to eating disorders, one of which focused 

specifically on overweight and binge eating, 
31 

Table 5. Results of studies on CHI applications impacting intermediate outcomes in diet, exercise, 

or physical activity, not obesity (N=32). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Body Weight + 

BMI + 

Adachi, 

2007‡17 Computer 

tailored 

program with 

6-month 

weight and 

targeted 

behavior’s selfmonitoring, 

Computer 

tailored 

program only 

Percent weight loss + 

Fat (% calories) Composites Scores 0 

Fiber (g/1,000kcals) 0 

Fruit and vegetables 

(servings/1000kcals) 

0 

Self Efficacy/ Low-Fat Meals 0 

Self-Efficacy/ Low-Fat Snacks 0 

Self-Efficacy/Fruit, Vegetables, Fiber 0 

Outcome Expectations/Appetite 

Satisfaction 

0 

Outcome Expectations/Budgetary 

Outcomes 

0 

Anderson, 

200118 Computer 

kiosk nutrition 

intervention 

Outcome Expectations/Health 

Outcomes 

0 

Fat (points per day) + 

Vegetables (servings per day) 0 

Fruit (servings per day) 0 

Brug, 199619 Tailored 

feedback 

Positive attitude to increasing 

vegetables and fruits 

+ 

Fat (fat points per day) + 

Fruit (servings per day) + 

Brug, 199820 Computertailored fat, 

fruit, and 

vegetable 

intake 

intervention 

Vegetables (servings per day) 0 

Fat score 0 

Servings of vegetables + 

Servings of fruit 0 

Intention to reduce fat intake 0 

Brug, 199921 Computerized 

feedback on 

fat, fruit, and 

vegetable 

intake Intention to increase vegetable intake 0 

Campbell, 

199422 Tailored 

nutrition 

intervention 

Fat (g/day) + 

Knowledge score of low fat foods 0 

Self-efficacy 0 

Fat score 0 

Stage of change- Precontemplation + 

Stage of change- Contemplation 0 

Stage of change- Preparation 0 

Diet, exercise, 

physical 

activity, not 

obesity 

32 

Campbell, 

199923 Tailored 

multimedia 

intervention 

Stage of change- Action/maintenance 0 
32 

Table 5. Results of studies on CHI applications impacting intermediate outcomes in diet, exercise, 

or physical activity, not obesity (N=32) (continued). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Total Low-fat 

knowledge score 

+ 

Total Infant feeding 

knowledge score 

+ 

Campbell, 

200424 Computer 

based 

interactive 

nutrition 

education 

Total self-efficacy score 0 

Fat intake (g/day, day 21) 0 

Fruit intake (pieces week) 0 

Soft drinks (glasses day) 0 

Water (glasses/day, day 21) 0 

Pre- and post-test intake levels for fat 

intake in girls 

+ 

Haerens, 

200525 CD-ROM 

based nutrition 

support 

Pre- and post-test intake levels (mean ^ 

SD) for % energy from fat in girls 

+ 

Haerens, 

200726 Computer 

tailored 

intervention 

Dietary fat intake 0 

Cycling for transportation 0 

Walking for transportation 0 

Walking in leisure time 0 

Haerens, 

200927 Computertailored 

exercise 

intervention Total moderate to vigorous activity 0 

Change in perception of exercise + 

Change in ratings of expectation; 

satisfaction with motivation to exercise 

+ 

Hurling, 

200628 Internet-based 

exercise 

motivation 

The mean change in ratings of the 

statement ‘‘I am very satisfied with my 

current level of motivation to do 

exercise’ 

0 

Hurling, MET min/week 0 

200729 Had access to 

Internet and 

mobile phone 

Change in weekly hours spent sitting 

(MET min/week leisure time) 

+ 

BMI + 

BMIzScore + 

Binge eating (OBEs and SBEs) + 

Binge eating (OOEs) 0 

Weight and shape concerns + 

Dietary fat intake 0 

Jones, 

200830§

Student Bodies 

2 -BED 

Depressed mood 0 

Total physical activity + 

Diet, exercise, 

physical 

activity, not 

obesity 

(continued) 

King, 200631 Interactive CDROM for health 

risk appraisal 

Moderate physical activity + 
33 

Table 5. Results of studies on CHI applications impacting intermediate outcomes in diet, exercise, 

or physical activity, not obesity (N=32) (continued). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Kristal 2000 Fat-related diet habit + 32 Computergenerated 

personalized 

letter for fruit 

and vegetable 

intake 

Fruit and vegetables (servings/day) + 

Lewis, 2008 median number of logins + 33 Internet-based 

physical 

activity 

program 

5-itemWebsite Quality 

Questionnaire 

+ 

EDI- Bulimia + 

EDI-Body Dissatisfaction + 

Low, 200634 Student bodies 

with a 

moderated 

discussion 

group 

Un-moderated 

discussion 

group 

Program alone 

Weight and Shape Concerns + 

Physical activity per week 0 

Improvement in functional capacity 

(estimated volume 02 at 85% of 

predicted maximum heart rate)(ml/kg 

per minute) 

0 

Marcus, 

200735 Tailored 

Internet 

Standard 

Internet 

150 minutes of physical activity per 

week 

0 

Evaluation of Health 0 

Evaluation of Fruit Advice (pleasant) 

(Likert Scale) 

+ 

Acceptability (Was fruit advice targeted 

to you?) 

+ 

Acceptability (Did you enjoy it?) + 

Quality of Intervention (relevant) 0 

Quality of Intervention (credible) + 

Mangunkusumo, 

200736

Internet Group 

Quality of Intervention (useful) + 

Minutes moderate physical activity + 

Minutes, walking + 

Napolitano, 

200337 Internet 

intervention 

Stage of change, progression + 

Intention to eat less fat + 

Self-rated fat intake compared to others + 

Self-rated fruit intake + 

Self rated fat intake + 

Self rated fruit intake compared to 

others 

+ 

Self-rated vegetable intake 0 

Diet, exercise, 

physical 

activity, not 

obesity 

(continued) 

Oenema, 

200138 Web based 

tailored 

nutrition 

education 

Self-rated vegetable intake compared to 

others 

0 
34 

Table 5. Results of studies on CHI applications impacting intermediate outcomes in diet, exercise, 

or physical activity, not obesity (N=32) (continued). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Richardson, Total Steps 0 

200739 Group 

receiving 

tailored 

feedback on 

lifestyle goals 

Bout Steps 0 

Likeability of learning materials 

(hypothesis 1) [authors identify 3 

subscales -- attention, 

understanding, intention] 

Silk, 2008 + 40 Web site 

Video game 

Nutrition literacy scores (hypothesis 2) 

[authors identify 6 subscales: 

MyPyramid, Food groups, Food 

servings, Serving size, Food safety, 

Food cost] 

+ 

Smeets, Fat consumption (gm) + 

200741 Intervention 

group, 

receiving one 

tailored letter 

Fruit consumption (pieces/day) + 

Increase in total physical activity + 

Increase in moderate to vigorous 

physical activity 

+ 

Spittaels, 

200742 On-line tailored 

Physical 

activity 

advice+ stage 

based 

reinforcement 

emails 

On-line tailored 

physical 

activity advice 

Increase in physical activity in leisure 

time 

+ 

Spittaels, 

200743 Website with 

computer 

tailored 

feedback on 

physical 

activity 

Total moderate to vigorous physical 

activity scores 

0 

Dietary intake (kcal/day) + 

Fat intake (% day) + 

Diet, exercise, 

physical 

activity, not 

obesity 

(continued) 

Tate, 2006†44 Tailored 

ComputerAutomated 

Feedback 

Human Email 

Counseling 

Physical activity (kcal/week) + 
35 

Table 5. Results of studies on CHI applications impacting intermediate outcomes in diet, exercise, 

or physical activity, not obesity (N=32).(continued). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Vandelanotte, Increase physical activity + 

200545 Sequential 

interactive 

computer 

tailored 

intervention 

Simultaneous 

interactive 

computer 

tailored 

intervention 

Decrease fat intake + 

Perceived support 0 

Social network 0 

BMI ( kg/m2) 0 

Systolic blood pressure 0 

Diastolic blood pressure 0 

Verheijden, 

200446 Web-Based 

Targeted 

nutrition 

counseling and 

social support 

Total cholesterol 0 

Body Shape Measure + 

EDI-drive for thinness + 

EDI-Bulimia 0 

EDE-Q Weight Concerns 0 

EDE-Q Shape Concern 0 

Saturated Fat (g/day) + 

Winzelberg, 

200047** Internetdelivered 

computerassisted health 

education 

program 

Vegetable/Fruit (servings/day) + 

Dietary Intake 0 

Exercise (Blocks walked daily) 0 

Exercise (min walked continuously) 0 

Weight (lb) + 

Diet, exercise, 

physical 

activity, not 

obesity 

(continued) 

Wylie-Rosett, 

200148 Computer tailored 

lifestyle 

modification 

BMI + 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 There were significant effects of human email counseling and computer-automated counseling on decrease in fat intake when 

compared to control; however, no treatment difference between the human email counseling and computer-automated counseling 

were demonstrated. 

‡

 Long-term effects of a 1-month behavioral weight control program assisted by computer tailored advice with weight and 

targeted behavior self-monitoring were more effective when compared to the behavioral weight control program assisted by 

computer tailored advice alone, an untailored self-help booklet with self-monitoring of weight and walking, and a self-help 

booklet alone. 

§

 study focuses on binge eating and overweight 

║

z score: “A z-score is the deviation of the value for an individual from the mean value of the reference population divided by the 

standard deviation for the reference population. Because z-scores have a direct relationship with percentiles, a conversion can

occur in either direction using a standard normal distribution table. Therefore, for every z-score there is a corresponding 

percentile and vice versa.”49

**Study focused on eating disorders. 

BMI=body mass index; EDE-Q = Eating Disorder Examination—Questionnaire; EDI = Eating Disorder Inventory; g/day = 

grams per day; gm = gram; g/1,000 = grams per 1,000; kcal = kilocalorie; kg/m2 = kilogram per meter squared; lb = pound; 

ml/kg = milliliters per kilogram ; min/wk = minutes per week; OBE= objective binge episode; OOE= objective overeating 

episode; SBE= subjective binge episode; SD = standard deviation 
36 

Strengths and Limitations of the Evidence 

 Twenty-nine studies are available to evaluate CHI impact on intermediate health outcomes 

within the context of diet, exercise, or physical activity, not obesity. Additionally two studies 

were available to evaluate impact within the contexts of eating disorders and one study was 

available to evaluate the impact in the context of overweight and binge eating. Limitations 

included the occasional imprecision of study results due to wide-ranging confidence intervals. 

Many, though not all of these studies relied on very small sample sizes. (Appendix G, Evidence 

Tables 8-10). The overall strength of the body of this evidence (Table 6) on the impact of CHI 

applications on diet, exercise, or physical activity, not obesity was graded as moderate based on 

a modified version of the GRADE criteria5

 and Chapter 11 of the EPC Manual6 All of the 

studies were included in this grading of the evidence because they all had at least one outcome 

relevant to the effects on diet, exercise, or physical activity, not obesity. 

Table 6. Grade of the body of evidence addressing CHI impacts on intermediate outcomes in diet, 

exercise, nutrition intervention (not obesity). 

1 Protection against risk of bias (relates to study design, study quality, reporting bias) High 

2 Number of studies 32 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Moderate 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

General Study Characteristics 

 The studies on the impact of CHI applications on intermediate health outcomes were 

generally conducted among adult, non-elderly populations. Five studies however were 

conducted specifically among adolescent populations 25,26,27, 25,27,30,36 (Appendix G, Evidence 

Tables 8 and 9). 

 Many studies were conducted among female participants. When reported, the race/ethnicity 

of respondents was generally such that the majority of subjects identified as Caucasian, with 

smaller percentages of Asian, Native American, African American or Black, or other groups 

reported. Educational level varied, with higher rates of higher education within studies conducted 

among young adults in the workplace or on college campuses. Patient post-intervention 

evaluation ranged from as little as immediately post-test to as long as twelve months. Upon 
37 

review, the body of scientific evidence from these studies indicated that most CHI applications 

evaluated to date had effects on intermediate health outcomes (Appendix G, Evidence Tables 8 

and 9). 

Outcomes 

 Diet, exercise, or physical activity, not obesity. Haerens et al25 evaluated the effects of a 

middle-school healthy eating promotion intervention combining environmental changes and 

computer-tailored feedback, with and without an explicit parent involvement component. This 

study demonstrated that in girls, fat intake and percentage of energy from fat decreased 

significantly more in the intervention group with parental support, compared with the 

intervention alone group (p = 0.05) and the control group (p=0.001). No impacts were found in 

boys or in girls for fruit, soft drinks, and water consumption. 

 In another study by Haerens et al27 evaluated the differences in effects of a computer tailored 

physical activity advice as compared to providing generic information among adolescents. After 

4 weeks, most physical activity scores increased in both groups. No differences between groups 

were found. After 3 months, the generic intervention was more effective at increasing “walking 

in leisure time” among students not complying with recommendations. For all other physical 

activity scores, no differences between groups were found.

In a third study Haerens et al 26 investigated a computer-tailored dietary fat intake 

intervention for adolescents as compared to control and found no intervention effects for the total 

sample. 

 Marcus et al 35 investigated the effects of an internet-based tailored physical activity 

intervention, a standard internet physical activity intervention, and a tailored print physical 

activity intervention and found that all groups increased physical activity behavior similarly and 

no significant treatment effects were detected between groups. 

 When evaluating behavior change regarding changes in weekly hours spent sitting, Hurling 

et al29 found that an Internet and mobile phone technology delivering an automated physical 

activity program was associated with greater perceived control and intention/expectation to 

exercise when compared to a control group than those who received no support (p<0.001) 

(Appendix G, Evidence Table 10). 

 Regarding a decrease in fat consumption and increase in fruit consumption, Smeets et al41

found that a computer tailored intervention was associated with these behaviors at 3 months 

(p<0.05 and p<0.01, respectively). While this intervention did not enhance the health behaviors, 

it did reduce the decline in these behaviors over the followup period (Appendix G, Evidence 

Table 10). 

 Spittaels et al42 found that an increase in total physical activity, increase in moderate to 

vigorous physical activity, increase in physical activity during leisure time, and decrease in body 

fat were behaviors more strongly associated with use of an online-tailored physical activity 

advice program with stage-based reinforcement emails when compared to online-tailored 

physical activity advice without reinforcement emails or on-line non-tailored standard physical 

activity advice (p<0.001, p<0.05, p<0.001, and p<0.05, respectively) (Appendix G, Evidence 

Table 10). 

 Tate et al 44 investigated the effects of human e-mail counseling, computer-automated 

tailored counseling, and no counseling in an internet weight loss program. Significant effects of 

human email counseling and computer-automated counseling on decrease in fat intake when 
38 

compared to control were demonstrated at 3 and 6 months (p<0.04 and p<0.004, respectively); 

however, no treatment difference between the human email counseling and computer-automated 

counseling were demonstrated. (Appendix G, Evidence Table 10) 

 Mangunkusumo et al36 found that Internet-administered adolescent health promotion in a 

preventive-care setting was more effective when compared to a control of usual practice with 

paper and pencil for some outcomes but not for others. Subjects found the Internet-tailored fruit 

advice more pleasant, easy to use, personally targeted, and enjoyable but less credible when 

compared to generic preprinted advice (p<0.01) (Appendix G, Evidence Table 10). 

 Adachi et al17 found that the long-term effects of a 1-month behavioral weight control 

program assisted by computer tailored advice with weight and targeted behavior self-monitoring 

was more effective when compared to the behavioral weight control program assisted by 

computer tailored advice alone, an untailored self-help booklet with self-monitoring of weight 

and walking, and a self-help booklet alone. While dietary habits and physical activity were 

improved in all subjects, the mean weight loss associated with these improvements was greatest 

in the behavioral weight control program assisted by computer tailored advice with weight and 

targeted behavior self-monitoring (p<0.05) (Appendix G, Evidence Table 10). 

 Vandelanotte et al45 found that sequential and simultaneous interactive computer-tailored 

interventions were more effective when compared to a control group for producing higher 

physical activity scores and lower fat intake scores (p<0.001) (Appendix G, Evidence Table 10). 

 Verheijden et al 46 investigated Web-based targeted nutrition counseling and social support 

for patients at increased cardiovascular risk in general practice as compared to control treatment 

of usual care and found no significant treatment differences in outcomes (Appendix G, Evidence 

Table 10). 

 In another study, Oenema et al38 found that a Web-based tailored nutrition education 

intervention had greater effect on self-rated fruit intake compared to others as well as intention to 

eat less fat when compared to a control group at post-test (p<0.01, p<0.05, and p<0.01, 

respectively) (Appendix G, Evidence Table 10). 

 Napolitano et al37 found that an Internet-based physical activity intervention was more 

strongly associated with progression in stage of motivational readiness for physical activity when 

compared with a control group at one month (p<0.05) and at three months (p<0.01). 

Additionally, the Internet-based physical activity intervention was also more strongly associated 

with increases in walking minutes when compared with a control group at one month (p<0.001) 

and at three months (p<0.05) (Appendix G, Evidence Table 10). 

 Caroline et al39 evaluated the effect of technology enhanced pedometers and interactive, 

tailored, Web based, feedback on physical activity among sedentary adults with Type II 

Diabetes. Individuals in all groups increased their physical activity from baseline, however no 

significant between group differences were achieved. 

 In a study conducted with patients attending family practice clinics in North Carolina 

Campbell et al22 tested the effect of individually computer-tailored messages designed to 

decrease fat intake and increase fruit and vegetable intake. At 4 month followup, the data 

indicated that the tailored intervention produced significant decreases in total fat and saturated 

fat scores compared with those of the control group p<0.05). Fruit and vegetable consumption 

did not increase in any study group. 

 Kristal et al32 evaluated a tailored, multiple-component self-help intervention designed to 

promote lower fat and higher fruit and vegetable consumption .The intervention consisted of a 

computer-generated personalized letter and behavioral feedback, a motivational phone call, a 
39 

self-help manual and newsletters and was compared to a no material control. The intervention 

significantly reduced fat intake (p<0.001) and significantly increased fruit and vegetable intake 

(p<0.001) as compared to controls. 

 Hurling et al28 evaluated an Internet-based exercise motivation and action support system 

(Test system), relative to a group receiving no intervention (Reference) and another receiving a 

less interactive version of the same system (Control). Seven months after the intervention, 

participants who used the test system reported greater levels of increase in exercise motivation 

than the control or reference groups (p < 0.05). 

 Brug et al21 evaluated the impact of two computer-tailored nutrition education interventions 

and tailored psychosocial feedback compared to computer tailored nutrition education alone, 

regarding reducing their fat consumption and increasing consumption of fruit and vegetables. 

No significant differences in consumption of fat, fruit, and vegetables were found. 

In another study by Brug et al20 the impact of individualized computer-generated nutrition 

information and additional effects of iterative feedback on changes in intake of fat, fruits, and 

vegetables was evaluated. The experimental group received computer-generated, tailored dietary 

feedback letters. Half of the experimental group received additional iterative tailored feedback. 

Controls received a single general nutrition information letter. The results indicated that 

Computer-tailored feedback had a significantly greater impact on fat reduction (p<0.01) and 

increased fruit (p<0.01) and vegetable intake (p<0.01) than did general information. Iterative 

computer-tailored feedback had an additional impact on fat intake (p=0.02). 

 Anderson et al 18 studied the impact of a self administered computer tailored nutrition 

intervention. The application was located in kiosks and involved local grocery store shoppers. 

The results indicate that while an immediate post test suggested that individuals in the 

intervention group were more likely to attain dietary fat (p<0.001), fiber (p<0.001), fruits and 

vegetable consumption goals (p<0.05), they were only more likely to achieve dietary fat 

(p<0.05) and fiber (p<0.01) goals at follow up. 

 Campbell et al23 evaluated a tailored multimedia program designed to improve dietary 

behavior among low income women. The computer-based intervention consisted of a tailored 

soap opera and interactive ‘infomercials’ that provided individualized feedback about dietary fat 

intake, knowledge and strategies for lowering fat based on stage of change. Results from this 

study indicate that the intervention group participants had improved significantly in knowledge 

(P < 0.001), stage of change (P < 0.05) and certain eating behaviors (P < 0.05) compared to the 

control group. 

In another study Campbell et al24 evaluated a tailored nutrition education CDROM program 

for participants in the Special Supplemental Nutrition Program for Women, Infants, and Children 

(WIC). Results from this study indicate that intervention group members increased self-efficacy 

(p<0.01) and scored significantly higher (p<0.05) on both low-fat and infant feeding knowledge 

compared with controls. No differential effect was observed for dietary intake variables. 

 Lewis et al33 evaluated the impact of instantaneous Web-based tailored feedback vs. general 

Websites currently available to the public among sedentary adults. The results indicated that 

individuals in the intervention group logged onto their Website significantly more times than the 

general Website controls (median 50 vs. 38; pb.05). Among participants in the intervention, the 

self-monitoring feature (i.e., logging) followed by goal setting were rated as the most useful 

Website components. 

King et al31 evaluated the impact of a computer-assisted, tailored self-management physical 

activity intervention compared with health risk appraisal with feedback on sedentary adults with 
40 

Type II Diabetes. At 2-month post intervention follow-up, the intervention significantly 

improved all physical activity (p<0.01) and moderate physical activity (metabolic equivalents > 

3.0, p<0.01) relative to controls. 

Spittaels et al43 evaluated a Website-delivered physical activity intervention, that provides 

participants with computer-tailored feedback, to ascertain the impact of the intervention on 

physical activity in the general population. Potential participants were allocated to one of three 

study groups. Participants in group 1 and 2 received the tailored physical activity advice on their 

computer screen immediately following their baseline assessment with the option to visit other 

Website sections. Participants in group 1 also received non-tailored e-mails inviting them to visit 

a specific Website section by following a hyperlink. Group 3 was a delayed treatment control 

group. Participants in both intervention groups reported a significant increase in transportation 

(movement, walking or running) (p<0.05), leisure time physical activity levels (p<0.05), and 

decrease in time spent sitting (p<0.05) at 6-month follow-up compared with the control group. 

Wilie-Rosett et al48 evaluated the impact on weight loss of kiosk-based computer-tailored 

behavioral feedback versus the computer feedback plus in-person consultation versus a print 

workbook control. The results indicate that all groups had a significant decrease in energy and fat 

intake and increased physical activity (p<0.01). The greater the intensity of the intervention, the 

greater the increase or decrease. 

When evaluating likeability of learning materials and nutrition literacy attainment, Silk et al40

found that an interactive Web site modality was associated with higher scores among participants 

when compared with a computer game and an information pamphlet at 2 weeks (p<0.05) 

(Appendix G, Evidence Table 10). 

 When evaluating reduction of fat intake and positive attitudes regarding this behavior, Brug 

et al19 found that a computer-tailored nutrition intervention with tailored feedback letters was 

more strongly associated with these outcomes when compared to a control group receiving 

general nutrition information at three weeks (p<0.01) (Appendix G, Evidence Table 10). 

Eating Disorder. When evaluating drive for thinness and body shape concerns, Winzelberg 

et al47 found that the Internet-delivered computer–assisted health education program Student 

Bodies was associated with a decrease in these behaviors when compared to a control group at 

three months (p<0.05 and p<0.01, respectively) (Appendix G, Evidence Table 10). 

 Low et al34 found that decreases in self-reported bulimia, body dissatisfaction concerns, and 

weight and shape concerns were more strongly associated with the use of a computer-based 

interactive eating disorder prevention program (Student Bodies) with an unmoderated discussion 

group when compared to the Student Bodies program with a moderated discussion group, the 

Student Bodies program alone, or a control group (p<0.05) (Appendix G, Evidence Table 10). 

Overweight and binge eating. When evaluating binge eating behaviors and concern with 

weight and shape, Jones et al30 found that the Internet-facilitated intervention Student Bodies2-

Binge Eating Disorder (SB2-BED) was associated with a decrease in these behaviors when 

compared to a wait-list control at 16 weeks (p<0.05) (Appendix G, Evidence Table 10). 
41 

Alcohol Abuse and Smoking Cessation 

Summary of the Findings 

Twenty-six studies evaluated the impact of CHI applications on a variety of intermediate 

health outcomes related to the use of alcohol and tobacco (Table 7). Outcomes of interest include 

self-management, knowledge attainment (program adherence), and change in health behaviors. 

The quality of these 26 trials was good. All were RCTs with sample sizes ranging from 83 to 288 

respondents for the alcohol abuse studies and ranging from 139 to 3971 respondents for the 

tobacco use studies. Post-intervention evaluation ranged from as little as 30 days to as long as 24 

months. Upon review, the body of scientific evidence from these studies indicates that most CHI 

applications evaluated to date had statistically significant effects on intermediate health 

outcomes. 

Strengths and Limitations of the Evidence 

 Twenty-six studies were available to evaluate CHI impact on intermediate health outcomes 

related to use of alcohol and tobacco. Seven studies were available to evaluate CHI impact 

within the context of alcohol abuse and 19 studies were available to evaluate this impact within 

the context of tobacco use. The sample sizes yielded appropriate power in these studies, with 

sample sizes ranging from 83 to 288 respondents for the alcohol abuse studies and ranging from 

139 to 3971 respondents for the tobacco use studies. The overall strength of the body of this 

evidence (Table 8) for the effects on intermediate outcomes was graded as high for smoking 

cessation and high for alcohol abuse based on a modified version of the GRADE criteria5

 and 

Chapter 11 of the EPC Manual6. It is important to note that many of the intermediate outcome 

measures were patient-reported. 

General Study Characteristics 

 The studies on the impact of CHI applications on intermediate health outcomes related to use 

of alcohol and tobacco were generally conducted among adult, non-elderly populations. Most of 

the respondents in these studies were under 40 years of age, although the mean age range of 

participants across studies was 18-70 years of age. Five studies 50-54 specifically targeted either 

adolescents or young adults (age range 11-26 years). Information regarding gender suggested 

that female participants represented a little over half of the study population. When reported, the 

race/ethnicity of respondents was generally Caucasian, with smaller percentages of Asian, Native 

American, African-American or Black, or other groups participating. Educational level and 

marital status was variable across studies. (Appendix G, Evidence Tables 11, 12, 14, 15). 
42 

Table 7. Results of studies on CHI applications impacting intermediate outcomes in alcohol abuse 

and smoking (N=26). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Mean drinks per typical week + 

Mean AUDIT test score + 

Cunningham, 

2005‡55 Internet plus 

self help book 

Mean # of alcohol consequences + 

Average drinks per day + 

Drinks per drinking day 0 

Hester, 

200556 DCU/Immediate 

treatment group 

Average peak BAC + 

Kypri, 199951 Computerized 

Assessment 

and Behavioral 

Intervention 

Drinking Frequency + 

Lieberman, Number of modules complete + 

200657 Multimedia 

Perceived helpfulness of the modules 0 

Effect size in perceived norms 0 

Effect size in reduction in alcohol 

consumption 

+ 

Neighbors, 

200452 Computerized 

normative 

feedback 

Effect size in reduction in alcohol 

consumption 

+ 

Riper, 200858 Intervention 

condition DL 

Weekly alcohol consumption (second 

outcomes) 

+ 

Alcohol abuse 7 

Riper, 200859 Web-based selfhelp 

intervention 

without therapist 

guidance 

Mean alcohol consumption at 6 months 

and 12 months follwup 

+ 

An, 200853 Real U‡

intervention 

Percent abstinent for 30 days + 

Brendryen, 

2008 60 Happy ending 

program—

internet 

delivered 

smoking 

cessation 

Repeated Points of Abstinence (1 + 3 + 

6 + 12 months) 

+ 

Curry, 1995 7-day abstinence at 21 months 0 61 Computergenerated 

tailored 

feedback 

Abstinent at 3, 12 and 21 months 0 

Affective attitude + 

Cognitive attitude + 

Dijkstra, 2005 

62 Computer 

tailored letters 

Quitting attempts + 

Number of cigarettes smoked per day 

0 

Hang, 2009 63 Personalize 

smoking 

cessation via 

SMS 

24 hour quit attempt 

0 

Japuntich, 

200664 CHESS SCRP Abstinent 0 

Pattens, 

200654 Internet based 

intervention 

Smoking abstinence 0 

Point Prevalence Abstinence, 

Precontemplation stage 

0 

Point Prevalence Abstinence, 

Contemplation stage 

0 

Smoking 19 

Prochaska, 

1993 65 Interactive 

computer 

support 

Point Prevalence Abstinence, 

Preparation stage 

0 
43 

Table 7. Results of studies on CHI applications impacting intermediate outcomes in alcohol abuse 

and smoking (N=26) (continued). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Smoking initiation rates at 18 months 

(nonsmokers at baseline) 

Prokhorov, 0 

2008 66 CD-ROM 

smoking 

cessation Smoking cessation rates at 18 months 

(smokers at BL) 

0 

Tobacco abstinence (complete case) + 

Tobacco abstinence (intent-to-treat) + 

Smokeless tobacco use abstinence 

(complete case) 

+ 

Severson, 

200867

Interactive, 

tailored Webbased 

intervention 

Smokeless tobacco use abstinence 

(intent-to-treat) 

+ 

Schiffman, 

200068 Computer 

tailored smoking 

cessation 

materials 

Abstinence rates + 

Average probability of progression 

(precontemplation and contemplation) 

Schumann, 0 

2006 69 Computer 

generated 

tailored letters Average probability of regression 

(precontemplation and contemplation) 

0 

Schumann, Point-prevalence abstinence 0 

200870

Computertailored smoking 

cessation 

intervention 

Prolonged abstinence 0 

Depth of efficacy expectation of 

smoking cessation intervention 

0 

Depth of outcome expectation of 

smoking cessation intervention 

0 

Depth of success stories of smoking 

cessation intervention 

+ 

Personalization of message source + 

Strecher, 

200871 High depth 

efficacy 

expectation 

Low depth 

efficacy 

expectation 

Timing of message exposure 0 

Tobacco related illness + 

Non-smoking children in household + 

Strecher, 

200672 Web-based 

Committed 

Quitters Stop 

Smoking Plan 

(CQ Plan) 

Frequency of alcohol consumption + 

7-day abstinence at 12 months (intent 

to treat analysis) 

+ 

7-day abstinence at 12 months of 

subjects who were abstinent at 5 

months (intent to treat analysis) 

0 

Strecher, 

2005 73 Computergenerated 

tailored letter 

7-day abstinence at 12 months of 

subjects who were abstinent at 5 

months (per protocol analysis) 

+ 

Strecher, 28 day abstinence rate + 

200574 CQ Plan 

10 week continuous rates + 

7-day abstinence (all smokers) 0 

7-day abstinence (light smokers) + 

Smoking 

(continued) 

Strecher, 

1994 75

Study 1 

Computergenerated 

tailored letter 7-day abstinence (heavy smokers) + 
44 

Table 7. Results of studies on CHI applications impacting intermediate outcomes in alcohol abuse 

and smoking (N=26) (continued). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

 Swartz, 

200676 Received 

immediate 

access to the 

Web site 

Behavioral 

intervention for 

smoking (intent 

to treat model) 

Cessation of smoking at 90 days + 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 significance of these outcomes was not reported 

‡ Study investigates internet-based intervention with addition of self-help booklet compared to internet-based intervention alone

‡ a randomized trial testing a Web‐assisted cessation intervention for college smokers 

AUDIT = Alcohol Use Disorders Identification Test; BAC = blood alcohol concentration; BL = baseline; 

CHESS SCRP= Comprehensive Health Enhancement Support System for Smoking Cessation and Relapse Prevention; 

CQ Plan = committed quitters plan; DCU = Drinker’s Check-up; DL = drinking less 

Table 8. Grade of the body of evidence addressing CHI impact on intermediate outcomes in 

alcohol abuse and smoking.

1 Protection against risk of bias (relates to study design, study quality, reporting 

bias) 

(Alcohol 

abuse) 

High 

(Smoking 

cessation) 

High 

2 Number of studies 7 19 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to 

which the people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

0 0 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 0 

 Overall grade of evidence‡ High High 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

Outcomes 

Alcohol abuse. Riper et al58 investigated the effects of a Web-based, multi-component, 

interactive self-help intervention for problem drinkers without therapist guidance compared to a 

control intervention consisting of receiving access to an online psychoeducational brochure on 
45 

alcohol use. Based on complete case analysis, the intervention group decreased their mean 

weekly alcohol consumption significantly more than the control group (p=0.001). In a 

subsequent secondary analysis of data from this study the authors demonstrated that at six and 12 

month follow up women and those with higher levels of education were more likely to have 

lower alcohol consumption levels, based on self report, as compared to controls.59 (Appendix G, 

Evidence Table 13).

Lieberman57 investigated program adherence to an online alcohol-use evaluation among 

study participants. After completing four standard questionnaires to evaluate problem drinking, 

an intervention consisting of a multimedia condition involving a personified guide was compared 

with a control treatment of feedback from the questionnaire results in text form. Increased levels 

of program adherence, as assessed by completion of greater numbers of modules of the online 

alcohol-use evaluation, were more strongly associated with the multimedia feedback via the 

personified guide (p<0.01) (Appendix G, Evidence Table 13). 

Cunningham et al55 investigated the effects of an Internet-based personalized feedback 

intervention compared to the same intervention with the addition of a self-help book based on 

three outcomes: mean typical number of drinks per week, mean Alcohol Use Disorders 

Identification Test (AUDIT) scores, and mean number of alcohol consequences experienced. 

Study participants who received the additional self-help book reported decreased consumption of 

alcoholic drinks per week (p<0.05), a lower AUDIT score (p<0.05), and fewer alcohol-related 

consequences (p<0.05) compared to participants who received the Internet-based intervention 

alone (Appendix G, Evidence Table 13). 

 Hester et al56 investigated the effect a computer-based brief motivational intervention, the 

Drinker’s Checkup (DCU). The intervention was randomly assigned to participants in either an 

immediate treatment group or to a 4-week Delayed Treatment group and participants were 

followed over a 12-month period. Significant effects were reported for the Immediate group 

when comparing baseline measurement to measurement at 12 months for the outcomes of 

average drinks per day and average peak blood alcohol content (BAC) (p=0.002 and p=0.001, 

respectively). For the Delayed group, significant effects were also reported when comparing 

baseline measurement to measurement at 12 months for the outcomes of average drinks per day 

and average peak BAC (p=0.008 and p=0.003, respectively). Significance was not reported for 

the outcome of drinks per drinking day for either the Immediate or Delayed Treatment groups 

(Appendix G, Evidence Table 13). 

Kypri et al51 investigated the effects 10-15 minutes of Web-based assessment and 

personalized feedback for hazardous drinking as compared with a control treatment of an 

informational leaflet only. Six outcomes were measured at 6 weeks and 6 months: frequency of 

drinking; typical occasion quantity; total consumption; frequency of very episodic heavy 

drinking; personal, social, sexual, and legal consequences of episodic heavy drinking; and 

consequences related to academic performance. Significant effects of the intervention were seen 

on outcomes of total consumption at 6 weeks (p=0.03); frequency of very episodic heavy 

drinking at 6 weeks (p=0.02); and personal, social, sexual, and legal consequences of episodic 

heavy drinking at both 6 weeks and 6 months (p=0.01 and p=0.03, respectively). No significant 

effects of the intervention on other outcomes were demonstrated (Appendix G, Evidence Table 

13). 

Neighbors et al 52 investigated the effects of a computer-delivered personalized normative 

feedback intervention in decreasing alcohol consumption among heavy-drinking college 

students. Outcomes assessed were effect size in perceived norms and the effect size in reduction 
46 

in alcohol consumption. The effect size for the intervention effect on drinking was reported to be 

significant at 3 and 6 months (p<0.01). Significance of the effect size for the intervention effect 

on perceived norms was not reported. 

Smoking cessation. When evaluating behavior change regarding smoking cessation, An et 

al. 53 found that an online college life magazine providing personalized smoking cessation 

messages and peer email support (the RealU intervention) was associated with a higher selfreported 30-day abstinence rate among college smokers when compared to a control group 

(p<0.001) . There was no difference reported between study groups for self-reported 6-month 

prolonged abstinence, however (Appendix G, Evidence Table 16). 

Strecher et al71 evaluated the effectiveness of web-based smoking cessation programs with 

experimentally manipulated depth of tailoring. The research team used the term “tailoring” to 

refer to a process consisting of 1) assessment of individual characteristics relevant to smoking 

cessation, 2) algorithms that use the assessment data to generate intervention messages relevant 

to the specific needs of the user, 3) a feedback protocol that delivers these messages to the 

participant in a clear format. The intervention was a web-based smoking cessation program plus 

nicotine patch with use of tailoring depth of the intervention based on five randomized 

components: high- versus low-depth tailored success story, outcome expectation, efficacy 

expectation messages, high- versus low-personalized source, and multiple versus single exposure 

to the intervention components. Although depth of tailoring with a web-based smoking cessation 

program plus nicotine patch was shown to influence rates of point-prevalence abstinence at 6-

month follow-up, results were most significant for high- versus low-depth success story 

(p<0.018) and high- versus low-personalization of message (p<0.039) (Appendix G, Evidence 

Table 16). 

In another study, Strecher et al72 investigated the effects of a web-based computer-tailored 

smoking cessation program (CQ Plan) as compared to an intervention of nontailored web-based 

cessation materials (CONTROL) among nicotine patch users. Significant effects for increased 

rates of ten-week continuous abstinence at 12 week follow-up were seen with the CQ Plan 

intervention when the study groups were stratified according to presence or absence of tobaccorelated illness (p<0.001 and p<0.05, respectively), presence or absence of non-smoking children 

in the household (p<0.001 and p<0.10, respectively), and frequency of alcohol consumption of 

greater than three times per week as compared to less than three times per week among 

participants (p<0.001 and p<0.10, respectively) (Appendix G, Evidence Table 16). 

A third study by Strecher et al 74 found that an intervention of web-based tailored behavioral 

smoking cessation materials was more effective than a control of web-based non-tailored 

materials. Outcomes of 28-day continuous abstinence rates at 6 weeks and 10-week continuous 

abstinence rates at 12 weeks were more strongly associated with the intervention group (p<0.008 

and p<0.0004, respectively) (Appendix G, Evidence Table 16). 

Strecher et al75 also evaluated the impact of computer tailored smoking cessation letters on 

smoking cessation behaviors among a group patients (n=51) recruited from a family practice 

clinic in North Carolina. At four month follow up smoking cessation rates differed significantly 

in the computer tailored group among patients who smoked less than 1 pack per day (p<0.05). 

No difference was seen among those who smoked more than 1 pack per day. In a similar study of 

a larger sample (n=1484) reported in the same paper again found significantly higher smoking 

cessation rates at 6 months follow up only among those who smoked less than one pack per day 

(p<0.05) (Appendix G, Evidence Table 16). 
47 

One additional study by Strecher et al73 evaluated the efficacy of adding computer tailored 

letters to an established telephone based smoking cessation intervention. At 12 month follow up, 

the intervention failed to produce any additional impact on smoking cessation rates as compared 

to quitline only controls. 

Severson et al. 67 found that an interactive, tailored web-based intervention (Enhanced 

Condition) when compared to a more linear, text-based website (Basic condition) was more 

effective for cessation of all forms of tobacco use as well as specifically for smokeless tobacco 

use at 3 and 6 months (p<0.001) (Appendix G, Evidence Table 16). 

Schumann et al.70 investigated a computer-tailored transthoretical model-based smoking 

cessation intervention in a general population setting in Germany and found the intervention to 

be ineffective (Appendix G, Evidence Table 16). 

Japuntich et al.64investigated an internet-based intervention as an adjuvant treatment in a 

smoking cessation program as compared to a control group of pharmaceutical treatment and 

counseling alone and did not find significant intergroup effects (Appendix G, Evidence Table 

16). 

Patten et al.54 found an internet-based intervention when compared to a brief office 

intervention did not produce significant treatment differences for smoking abstinence rates 

among adolescent study participants (Appendix G, Evidence Table 16). 

Swartz et al 76investigated a video-based internet site presenting strategies for smoking 

cessation and motivational materials tailored to the user’s race/ethnicity, sex, and age. Rates of 

abstinence at 90-day follow-up were measured for participants using this intervention and 

compared with abstinence rates among participants using the control intervention of a 90-day 

wait period prior to accessing the internet program. Greater abstinence rates were associated 

with the intervention group as compared to the control group, using both complete case analysis 

(p<0.002) as well as intent-to-treat analysis (p<0.015). (Appendix G, Evidence Table 16). 

Shiffman et al.68 investigated the effects of computer-tailored materials offered to purchasers 

of nicotine polacrilex gum in the Committed Quitters Program (CQP) compared to the use of a 

brief untailored user’s guide and audiotape in the starter package of the nicotine polacrilex gum . 

Outcomes of 28-day continuous abstinence rates at 6 weeks and 10-week continuous abstinence 

rates at 12 weeks were more strongly associated with the intervention group (p<0.001) 

(Appendix G, Evidence Table 16). 

Dijkstra et al.62 evaluated the efficacy of computerized smoking cessation messages that were 

either personalized, adapted or provided with personal feedback on smoking cessation rates at 

four months. Results of this investigation indicate that significantly higher rates of cessation 

were achieved in the personalization and feedback groups as compared to controls (p>0.05) 

(Appendix G, Evidence Table 16). 

Hang et al77 investigated the value of using individualized text messaging (short message 

service (SMS) for continuous individual support of smoking cessation among young adults. Post 

intervention analysis revealed no significant effect of text messaging on smoking behavior 

(Appendix G, Evidence Table 16). 

Brendryen et al60 sought to evaluate a multicomponent, one year smoking cessation 

intervention delivered via the Internet and cell phone and consisting of email contacts, Web 

pages, interactive voice response, text messaging technology and a craving telephone helpline. 

The results indicate that the intervention group achieved statistically significantly higher 

abstinence rates than control participants (20 percent versus 7 percent, odds ratio [OR] = 3.43, 95 

percent CI = 1.60-7.34, p=0.002) (Appendix G, Evidence Table 16). 
48 

Prokhorov et al66 evaluated the long term efficacy of a CD ROM based smoking initiation 

prevention program among urban inner city adolescents. The CD ROM contained embedded 

animations, video, and interactive activities and was composed of five weekly sessions in one 

semester and two ‘‘booster’’ sessions in the following semester (each 30 min in duration). At the 

beginning of each session, students were given a series of activities that were tailored to their 

stage of intention and designed to promote smoking cessation or reduced likelihood of initiation 

(for nonsmokers). At 18-month follow-up, smoking initiation rates were significantly lower in 

the intervention group compared to control (1.9 percent vs. 5.8 percent, p=0.05) (Appendix G, 

Evidence Table 16).

Schumann et al69 evaluated a CHI application that involved up to 3 individualized feedback 

letters generated by special computerized expert-system software and additional stage-tailored 

self-help booklets. This intervention failed to demonstrate any significant effect on smoking rates 

(Appendix G, Evidence Table 16). 

Prochaska et al65 compared standardized self-help manuals, individualized manuals, an 

interactive computer system plus individualized manuals or personalized counselor calls plus 

manuals. As compared to the standardized self help manual control group the interactive 

computer group had a significantly larger impact on point prevalence abstinence than all other 

groups at 6 months (p<0.05), 12 months (p<0.05) and 18 months (p<0.05). The interactive 

computer group also significantly improved prolonged abstinence rates at 18 months (p<0.05) 

(Appendix G, Evidence Table 16). 

Schneider 78 et al) tested the efficacy of an online personalized, comprehensive behavioral 

smoking cessation forum offered through a commercial computer networking business. The 

intervention was an asynchronous chat/discussion group moderated by a psychologist, a 

psychiatrist, and a lay ex-smoker. The results of this investigation indicated that the intervention 

did not significantly improve smoking cessation rates as compared to no intervention controls. 

Curry et al61 compared the efficacy three treatments on smoking cessation behavior: a selfhelp booklet alone; a self-help booklet with computer-generated personalized feedback; and a 

self-help booklet, personalized feedback, and outreach telephone counseling. Salivary cotinine 

levels were obtained to validate self reports at 12 month follow up. At three month follow up 

only the telephone counseling group achieved significantly higher 7 day cessation rates as 

compared to controls (p=0.02) (Appendix G, Evidence Table 16). 

Obesity 

Summary of the Findings 

Eleven studies evaluated the impact of CHI applications on intermediate outcomes related to 

obesity (Table 9). The studies mostly addressed middle-class consumers across the United States 

(US) and United Kingdom (UK), while one study targeted lower socioeconomic status school 

children. The interventions often employed online, Web based technical platforms. In addition, 

one study employed a pocket computer device and another used a laptop computer. No 

application had a large effect on improving weight-loss behavior, weight change, or body 

composition. The quality of the studies investigating obesity was variable with Jadad study 

quality scores 4

 ranging from moderately high (one study) to low. (Appendix G, Evidence Table 

1) 
49 

 Several studies employed Internet-based technical platforms while one study employed a 

pocket computer device and another utilized a laptop computer. Educational content used in the 

applications was custom designed by the investigators based on a range of Theoretic models: 

Precaution Adoption Process Model Theory of Planned Behavior,79,80 evidence from obesity 

research,81 and behavioral family-based treatment.82,83 Other sites listed their features: social 

support,84 ethnic-related sources,83 or self-monitoring food exercises85 (Appendix G, Evidence 

Tables 17-19). The overall strengths of the body of this evidence (Table 10) was graded as 

moderate based on a modified version of the GRADE criteria5

 and Chapter 11 of the EPC 

Manual.6

Strengths and Limitations of the Evidence 

 Eleven studies evaluated several domains of CHI impact on obesity. Enrolled study 

participants included adults (18-65) 80 middle-aged consumers81,84-87 teenagers, 83 school aged 

adolescents,88 overweight women89,90 and overweight/obese men.91 Several studies were 

restricted to consumers already obese on the basis of body mass index (BMI).80 In terms of 

race/ethnicity,81,83-86 studies enrolled American populations, European,91 British81 and Dutch80

consumers. One study targeted African-Americans83 and another targeted primarily AfricanAmericans and Hispanics88 (Appendix G, Evidence Tables 17-19). 

General Study Characteristics 

Across studies, the average age of enrolled consumers was early 40s. Williamson et al83

however, recruited teens with an average age of 13 years (SD 1.4), and Frenn88 recruited middleschool seventh graders aged 12-14. Five studies80,88-90 targeted either predominately female or 

only female consumers. One study targeted only males. 91 In terms of educational levels, the 

Kroeze et al80 study included participants who had the following distribution of educational 

attainment (tertiary 42 percent, higher secondary 37 percent, the remainder below that). A study 

by Hunter et al enrolled mostly Caucasian participants while Frenn’s study enrolled 

approximately 30 percent African-American and 30 percent Hispanics (Appendix G, Evidence 

Tables 17 and 18). 
50 

Table 9. Results of studies on CHI applications impacting intermediate outcomes related to 

obesity (N=11). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Applications* 

Weight change +

Waist circumference change 0

Physical activity 0

Booth, 200887 On-line weight 

reduction 

program 

including 

dietary advice 

plus exercise 

Exercise only 

program 

Energy intake 0

Short term weight change: Baseline 2 

wk period 

0

Short term weight change: Postbaseline 8 wk period 

+

Long term weight changes (24 wks) +

Long term weight changes (40 wks) +

Self-reported Caloric intake +

Burnett-Kent, 

198590 Computer 

Assisted 

method of 

providing 

feedback 

Self-reported physical activity +

Weight change 0

BMI 0

Exercise energy expenditure 0

Cussler, 

200886 Internet group 

Energy intake 0

Frenn, 2005 Physical Activity + 88 Internet based 

interactive 

model Diet +

Body weight +

BMI +

Waist circumference +

Hunter, 

2008‡85

Behavioral 

Internet 

treatment(BIT) 

Body fat percentage +

Total fat intake +

Saturated fat intake 0

Kroeze, 2008 

†80

Interactive - 

tailored 

condition 

Print - tailored 

condition 

Energy intake +

BMI change at 12 months 0

Obesity 

 

11 

McConnon, 

200781 Internet 

intervention Loss of 5% or more body weight (12 

months)

0
51 

Table 9. Results of studies on CHI applications impacting intermediate outcomes related to 

obesity (N=11) (continued).

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Applications* 

Physical activity 

(mean steps/day) 3 months

+

Physical activity 

(mean steps/day) 6 months

+

Energy intake (kJ/day) 3 months +

Morgan , 

200991 Internet-based 

weight-loss 

program 

Energy intake (kJ/day) 6 months +

Weight Loss (Post-treatment 12weeks – 

Pretreatment)

Taylor, 1991 0 89 Computer 

Assisted 

Therapy Weight Loss (followup at 6months – 

Pretreatment)

0

Body weight +

Body composition +

Weight loss behavior 0

Williamson, 

2006§83

Interactive 

nutrition 

education 

program and 

Internet 

counseling 

behavioral 

therapy for the 

intervention 

group 

BMI +

Weight change percent ‐ 

Obesity 

(continued) 

Womble, 

2004║84 ediets.com 

Weight change (kg) ‐ 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 positive impacts (where indicated) only at 3months post-intervention, at 6 months post-intervention all impacts were 

insignificant 

‡

 A positive impact indicates a decrease in any of the four listed outcomes 

§

 positive impacts (where indicated) only at 12 months post-intervention, at 24 months post-intervention all impacts were 

insignificant 

║

 A negative impact indicates an increase in any of the two listed outcomes 

BMI=body mass index; kJ/day = kilojoules per day; kg = kilogram; wk = week 
52 

Table 10. Grade of the body of evidence addressing CHI impact on intermediate outcomes in 

obesity. 

1 Protection against risk of bias (relates to study design, study quality, reporting bias) High 

2 Number of studies 11 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Moderate 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

Outcomes

 Weight-loss behavior. Williamson et al83 presented graphs on dieting change, exercise 

change, overeating change, and avoidance of fat food change, none of which favored the 

intervention, in either the teens or their parents. Cussler et al86 similarly showed equivalent 

exercise energy expenditure in controls (mean164 [kcal/day], SD 268[kcal/day]) and 

interventions (mean 123 [kcal/day], SD 265 [kcal/day]) and equivalent change in energy intakes 

of 91 kcal/day (SD 33) and 74 kcal/day (SD 371) in the two groups, respectively. Kroeze and 

colleagues80 measured food intake and found a decrease at 1 month equal to or greater than the 

effect of a printed resource. For instance, for total fat intake, the regression-coefficient 

confidence intervals (CIs) were (-18.6, -3.23) and (-15.59, -0.04) respectively. There were 

similar effects for saturated fat and energy. The effects were statistically indistinguishable from 0 

at 6 months. Print resources were more effective for high-risk consumers, with effects lasting 6 

months, and with the Internet group showing no statistically significant improvement. Booth et 

al87 measured weight-loss behavior through changes in physical activity (number of steps 

counted per day) and changes in energy intake. Both the exercise-only and the online exercise 

and diet advice groups showed a significant increase in the number of daily steps taken. Both 

groups showed a decrease in energy intake at the 12-week measuring period, but the differences 

were not significant. Frenn et al 88 demonstrated a significant improvement in physical activity 

and significant reductions in dietary fat intake from an 8-session interactive Web-based 

intervention (p=0.05). Burnett-Kent et al90 found that a laptop based computer assisted therapy 

system could enable participants to achieve a significantly higher mean weight loss at 8 week 

follow up (p<0.05) as compared to controls not using the computer assisted therapy system. The 

effect size was reported to be rm =0.75. The significant enhancement of weight loss by the 

computer assisted therapy was also found at 24 and 40 months (p<0.2 and p<0.5 respectively). 

Effect sizes were not reported for these longer term findings. The computer system did not have 
53 

a significant effect on self-reported caloric intake and physical activity. Finally Morgan et al91

demonstrated a significant increase in physical activity and significant reductions in energy 

intake as compared to baseline in both the Internet based program and information session and 

program booklet as well as the information session and program-booklet-only control group at 

the 6-month followup (Appendix G, Evidence Table 19).

Weight change. Cussler et al86 showed no difference in weight change: 1 kg (SD 4.6) loss 

for control, 0.7 kg (5.4) loss for the intervention. Hunter et al85 documented a statistically 

significant difference in BMI change: in the internet group , a decrease of 1.3 kg/m2 at 6 months, 

with an increase in the control groups of 0.5 kg/m2

 (initial BMI ≥ 27 kg/m2) and 0.9 kg/m2 

(initial BMI ≤ 27 kg/m2

) (p value not stated). Womble et al84 reported percent change in weight 

from baseline. Again, the effects were small (1-4 percent), with overlapping confidence intervals. 

Four studies reported BMI changes. Cussler et al86 reported identical changes of 2 kg/m2

 at 4 and 

at 16 months. Hunter et al85 also showed no change in BMI at 6 months (change statistically 

indistinguishable from 0 and overlapping CIs). McConnon et al81 reported a mean change of 0.3 

kg/m2

 (CI -0.5 to 1 kg/m2, p=0.4) in favor of the Internet intervention, but not statistically 

significant. Williamson et al83 also found a change of about 1 kg/m2

 for the two groups (1.2 

kg/m2 loss for the control group, 0.73 kg/m2 loss for the intervention group, statistically not 

significantly different from each other) that became statistically nonsignificant at 18 months. 

Booth 87 reported that weight change in the exercise-only group had a higher percentage weight 

loss than online diet and exercise program group at 12 weeks; the difference between the two 

groups was not significant. Taylor et al89 found no effect of a computer-assisted therapy 

application on weight loss at 12 weeks or at the 6-month followup. Finally, Morgan et al91 found 

significant increases in weight loss from baseline in the Internet-based program and information 

session and program booklet as well as the information session and program-booklet-only 

control group at the 6-month followup (Appendix G, Evidence Table 19).

 Body composition. Hunter et al85 reported on body fat percentages. These, too, showed no 

difference between the control group (mean 34.7, SD 7.0) and the intervention group (mean 33.9, 

SD 7.3) at 6 months. Similarly, Williamson and colleagues83 reported an increase in body fat, as 

measured by dual-energy x-ray absorptiometry (DXA), of 0.84 percent (SD 0.72) for the control 

group and a decrease of 0.08 percent (SD 0.71) for the intervention group. Results of the Booth 

study87 found the exercise-only group had a greater change in waist circumference, but the 

difference between the two groups was not significant. Finally, Morgan et al demonstrated 

significant changes in body weight, waist circumference, and BMI as compared to baseline in 

both the Internet-based program plus information session and program booklet as well as the 

information session and program-booklet-only control group at the 6-month followup (Appendix 

G, Evidence Table 19).
54 

Diabetes 

Summary of Findings 

Seven studies examined the effect of a CHI application on intermediate outcomes such as 

health knowledge and health behavior in people with diabetes mellitus (Table 11). One of the 

seven studies also included patients with heart disease and chronic lung disease. All studies were 

RCTs, but the studies had low study quality scores and did not always directly address one of our 

key questions. The findings were inconsistent across studies regarding the impact of a CHI 

application on intermediate outcomes related to diabetes, with four studies suggesting a benefit 

in terms of self-care, knowledge, physical activity adherence and satisfaction and three other 

studies indicating mostly a lack of benefit (Appendix G, Evidence Table 1). 

Strengths and Limitations of the Evidence 

Seven studies evaluated a wide range of effects of CHI applications on intermediate 

outcomes related to diabetes (Appendix G, Evidence Tables 20-22). All studies were RCTs, but 

the comparisons being made were not all directly relevant to our key question. All seven of the 

studies received low to very low study quality scores. This is a result of the difficulty in blinding 

participants, and often investigators, regarding the assignment to the control and intervention 

groups. Additionally, one study did not explain withdrawals.4

 The overall strength of the body of 

this evidence (Table 12) was graded as low based on a modified version of the GRADE criteria5

and Chapter 11 of the EPC Manual.6

General Study Characteristics 

Four of the studies were limited to type 2 diabetes, one was limited to gestational diabetes,92

and two included both type 1 and type 2 diabetes.93,94 Four of the studies evaluated an interactive 

consumer Web site,92,93,95,96 two evaluated a personal monitoring and feedback device,39,97

Another study evaluated an interactive computer program.94 Comparisons were generally made 

between a control group and an intervention group that was exposed to a CHI application. 

However, in the study by Wangberg,93 both groups received an Internet-based intervention, with 

one group receiving an intervention targeted at the area of self-care for which reported selfefficacy was lowest, and the other group receiving an intervention targeted at the area of selfcare for which reported self-efficacy was highest (Appendix G, Evidence tables 20 and 21). 
55 

Table 11. Results of studies on CHI applications impacting intermediate outcomes in diabetes 

(N=6). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

applications* 

Glasgow, 

200397 Tailored selfmanagement 

Kristal Fat and Fiber Behavior scale - 

Self-efficacy (Diabetes Empowerment 

Scale (DES)) 

Homko, 2007 + 92 Telemedicine 

Satisfaction and readiness to change + 

McKay, 2001 Moderate-to-vigorous exercise 0 95 Internet-based 

physical activity 

intervention 

Walking 0 

Total Step + 

Bout Steps + 

Satisfaction + 

Usefulness + 

Adherence (Likelihood of wearing a 

pedometer) 

+ 

Richardson, 

200739 Computerized 

feedback 

mechanism 

Adherence (Mean hours of wearing a 

pedometer) 

+ 

Summary of Diabetes Self Care 

Activities 

+ 

Perceived competence scale - 

Wangberg, 

2006†93

Low selfefficacy 

High selfefficacy 

Minutes activity per day 0 

Diabetes 6 

Wise, 198694 Interactive 

computer 

assessment 

Knowledge score + 

Change in health distress (0-5) + 

Change in self-reported global health(0-

5) 

0 

Change in illness intrusiveness 0 

Diabetes 

with with 

heart 

disease 

and chronic 

lung 

disease 

1 Lorig, 2006‡96 Online 

intervention 

Change in self-efficacy 0 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 study compares CHI targeting low self-efficacy items with CHI targeting high self-efficacy items: (+) indicates that there was 

an increase in self efficacy in both groups; (-) indicates a decrease in both groups ‡

 study measures the use of a personal monitoring device with tailored self –management compared with no tailored selfmanagement 
56 

Table 12. Grade of the body of evidence addressing CHI impact on intermediate outcomes in 

diabetes. 

1 Protection against risk of bias (relates to study design, study quality, reporting bias) Moderate 

2 Number of studies 6 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

-1 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

-1 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

0 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2).

Outcomes 

 Self-efficacy, self-care, and self-management. Homko et al evaluated the feasibility of 

monitoring glucose control in indigent women with gestational diabetes mellitus (GDM) over the 

Internet. Women with GDM were randomized to either the Internet group (n=32) or the 

control group (n = 25). Patients in the Internet group were provided with computers and/or 

Internet access if needed. A Web site was established for documentation of glucose values and 

communication between the patient and the health care team. Women in the control group 

maintained paper logbooks. The results of this study indicate that women in the Internet group 

demonstrated significantly higher feelings of self-efficacy at the study’s end 92 (Appendix G, 

Evidence Table 22). 

In the Wangberg study,93 the author assessed whether self-efficacy(SE) could function as a 

moderator of the effect of a tailored Internet-based intervention aimed at increasing self-reported 

diabetes self-care behaviors. There was a significant overall main effect of the intervention on 

self-care, F(1,25) = 5.56, p=0.026. A significant interaction between change in self-care and 

baseline self-efficacy was found, F(1,25) = 4.67, p=0.040, with lower baseline self-efficacy 

being related to greater improvements in self-care. A significant interaction between time and 

gender was observed, F(1,25) = 4.78, p=0.038, with men having greater improvements in selfcare than women93 (Appendix G, Evidence Table 22). 

 Lorig et al96 evaluated the impact on self-efficacy of an Internet-based tailored chronic 

disease self-management program. The results indicate that the intervention group increased their 

self-efficacy significantly more than controls (0.40 [SD 1.98] p=0.051) This study also found 

that the mean Health Distress Score decreased significantly more in the intervention group 

(0.377 [SD 1.11] p=0.013) compared to controls96 

 Wise et al94 compared the effects of an interactive computer program, graphic animations and 

personalized feedback vs. knowledge assessment and printed feedback vs. knowledge assessment 
57 

alone on knowledge and insulin control among insulin dependant and non insulin dependant 

diabetics (IDDM and NIDDM respectively). Among IDDM patients at 4-6 month follow up the 

printed feedback group and the computer program group showed significant increased in 

knowledge (p<0.05 and p<0.01 respectively). The same was also true among NIDDM patients 

(0<0.1 and p<0.05 respectively). In terms of glucose control all three treatment groups resulted 

in significant reductions in HbA1c (knowledge assessment only [9.1± 0.2 percent to 8.4±0.1 

percent, p<0.05], knowledge assessment and feedback [9.3±to 8.1±0.4 percent, p<0.05] and 

interactive computer program [9.3±0.2 percent to 8.6±0.3 percent, p<0.05 percent]). Finally 

among NIDDM patients significant reductions in HbA1c were only seen in the knowledge 

assessment group and the feedback groups (knowledge assessment [9.6±0.4 percent to 8.8±0.3 

percent, p<0.05] and feedback [9.2±0.4 percent to 7.9±0.4 percent, p<0.01]) (Appendix G, 

Evidence Table 22). 

Physical activity. McKay et al95 evaluated an Internet-based supplement (D-Net) to usual 

care that focused on providing support for sedentary patients with type 2 diabetes to increase 

their physical activity levels. The intervention group received goal-setting and personalized 

feedback, identified and developed strategies to overcome barriers, received and could post 

messages to an online “personal coach,” and were invited to participate in peer group support 

areas. Results of this intervention indicate a significant increase in moderate to vigorous physical 

activity (minutes/day) (p<0.001) and walking (minutes/day) (p<0.001).95 In a 10-month followup 

evaluation of the McKay intervention (D-Net), the data indicate significant improvements in the 

intervention group for physical activity (p<0.000)97 (Appendix G, Evidence Table 22). 

 A study by Richardson39 evaluated a pedometer hooked up to interactive computer-based 

feedback. The study failed to demonstrate an effect on actual steps taken, but did demonstrate a 

significant effect on patient satisfaction (p=0.006), usefulness (p=0.03), likelihood of wearing a 

pedometer (p=<0.001), and mean hours of wearing a pedometer (p=0.038) (Appendix G, 

Evidence Table 22). 

 Dietary habits. Glasgow et al97reports on additional dietary outcomes using the D-Net 

intervention described by McKay et al above. 10 month follow up evaluation of the intervention 

indicate significant improvements on the Kristal Fat and Fiber Behavior (FFB) scale 

(P<0.000), in daily dietary fat consumption (p<0.000), CES-D Depression scale scores 

(p<0.000), total cholesterol (p<0.000), LDL cholesterol (p<0.000), triglycerides (p<0.000) and 

Lipid ratios (p<0.000). The intervention did not significantly improve HDL cholesterol or 

HbA1c levels. (Appendix G, Evidence Table 22). 

Mental Health 

Summary of the Findings 

 Eight studies evaluated the impact of CHI applications on intermediate outcomes in 

mental health (Table 13). Studies evaluated the impact of CHI on three broad aspects of mental 

health. These included: 1) depression/anxiety, 2) phobia, and 3) stress. Across the three domains 

of mental health, the scientific evidence suggested that CHI applications may have a beneficial 
58 

Table 13. Results of studies on CHI applications impacting intermediate outcomes of mental 

health (N=8). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

applications 

Center for Epidemiologic depression 

scale 

+

Automatic thoughts +

Medical literacy +

Psychological literacy +

Lifestyle literacy +

Christensen, 

200499 Blue Pages: 

Web site 

MoodGYM: 

Computer 

based CBT 

Cognitive behavior therapy literacy +

Neil, 2009 Warpy thoughts score 106 MoodGYM 

internet-based 

CBT 

No. of exercises completed (0—28) +

Depression (BDI) +

Anxiety (BAI) +

Work and Social Adjustment scale +

ASQ,CoNeg +

Proudfoot, 

200498 Computerized 

Therapy 

ASQ,CoPos +

Depression (CES – D) +

Anxiety using HADS +

QoL using EQ5D +

Depression/ 

anxiety 

4 

Warmerdam, 

2008107 Interactive 

computer tool 

based on CBT 

Depression (CES – D) Proportion reaching 

clinically significant change

0

Phobia 1 Schneider, Main Problem(self-rating) +

2005108 Computer 

aided cognitive 

behavior 

therapy with 

self-help 

exposure 

Main Goal(self-rating) +

Chiauzzi, 

2008101 MyStudent 

Body–Stress 

Perceived Stress Scale 0

Self-rated stress management +

Self rated sleep quality +

Self rated mental energy +

Self rated concentration ability +

Self rated social support +

Stress 2 

Hasson, 

2005100 Web-based 

stress 

management 

system 

Biological marker +

Perceived Stress Scale 

Hospital Anxiety and Depression Scale 

HADS 

+

Anxiety +

Depression +

LE (Life Events) (Holmes and Rahes 

Scale) 

0

Perceived Social Support PS-family 0

Stress 

management 

1 Zetterqvist , 

2003105 Interactive self 

help stress 

management 

program 

Perceived Social Support PS-friends 0

ASQ=Attributional style questionnaire; BAI=Beck anxiety inventory; BDI= Beck depression inventory; CBT=cognitive 

behavioral therapy; CoNeg=composite index for negative situations; CoPos=composite index for positive situations; CES–D = 

Center for Epidemiologic Studies Depression Scale; EQ5D = EuroQoL; HADS = Hospital Anxiety and Depression Scale; QoL = 

quality of life; PS+ perceived social support system 
59 

Table 14. Grade of the body of evidence addressing CHI impact on intermediate outcomes in 

mental health. 

1 Protection against risk of bias (relates to study design, study quality, reporting bias) Moderate 

2 Number of studies 8 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

-1 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2).

effect on depression/anxiety, phobias, and stress (Table 14, and Appendix G, Evidence Tables 

23-25). 

Strengths and Limitations of the Evidence 

 The volume of the literature in this area was small. Four studies evaluated several domains of 

CHI impact on intermediate outcomes related to depression or anxiety, 98,99 two studies evaluated 

the impact on stress,100 one evaluated the impact on stress management, and 101one study 

evaluated the impact on social phobia102 (Appendix G, Evidence Tables 23-25). The quality of 

these eight trials was variable, ranging from moderate to very low study quality scores,4 with 

several studies lacking in one or more methodological domains of RCT quality as measured by 

the Jadad criteria (Appendix G, Evidence Table 1). Postintervention evaluations ranged from as 

little as 1 month to as many as 6 months. The overall strength of the body of this evidence (Table 

14) was graded as low, based on a modified version of the GRADE criteria5

 and Chapter 11 of 

the EPC Manual.6

 

General Study Characteristics 

 These studies involved predominately married or cohabitating female adults of varying race 

and ethnicities. They ranged from 13 to 75 years of age with widely varying educational 

backgrounds. Outcomes of interest included impact on depressive symptoms,98 99 103,104 impact 

on anxiety levels, 98,103,104 change in the degree to which problems affect one’s ability to conduct 

normal activities,98 impact on dysfunctional thoughts,99 improvements in knowledge of therapy 

including cognitive-behavioral theory (CBT),99 changes in perceived stress scores,101 self-rated 

self-management,100,105 self-rated sleep quality,100 self-rated mental energy,100 self-rated 

concentration,100 self-rated social support,100 quality of life, 104 and change in measured biologic 
60 

marker levels.100 Samples sizes were relatively small, ranging from 78101 to 182100 subjects per 

arm of the study (Appendix G, Evidence Tables 23 and 24). 

Outcomes 

 Depression/anxiety. Proudfoot et al98 evaluated the impact of Web-based cognitivebehavioral therapy (CBT) on patients with diagnoses of depression, anxiety, and/or mixed 

depression with anxiety. Use of the “Beating the Blues” online CBT intervention was associated 

with improvements on the Beck depression inventory (BDI) (p=0.0006),98 Beck anxiety 

inventory (BAI) (p=0.06),98 Work and Social Adjustment Scale (p=0.002),98 and Attributional 

Style questionnaire (p<0.001 for negative situations and p<0.008 for positive situations).98 

 Christensen et al99 also evaluated the impact of a Web-based CBT application among patients 

who scored above 22 on the Kessler psychological distress scale and who were not currently 

receiving any treatment. The MoodGYM CBT intervention was associated with improvements in 

depressive symptoms on the CES-D scores (p=0.05) and dysfunctional thoughts via the 

Automatic Thoughts Questionnaire (p=0.05) compared to controls (Appendix G, Evidence Table 

25). 

 Neil et al106 evaluated the impact of adherence to interactive consumer Web site-based 

therapy among depressed and/or anxious youth. The first adolescent sample consisted of 1000 

school students who completed the MoodGYM program in a classroom setting over five weeks 

as part of n RCT. The second sample consisted of 7207 adolescents who accessed the 

MoodGYM program spontaneously and directly through the open Web-based access. The results 

of this evaluation indicate that adolescents in the school-based sample completed significantly 

more online exercises (mean = 9.38, SD = 6.84) than adolescents in the open-access community 

sample (mean = 3.10, SD = 3.85; t1088.62= −28.39, p<0.001). 

 Warmerdam et al 107 evaluated the effectiveness of Internet-based Cognative Behaviroal 

Therapy (CBT) vs. Internet-based Problem Solving Therapy (PST) on Depressive symptoms 

among community dwelling adults. Outcomes were evaluated at 5, 8 and 12 weeks post 

intervention. The results indicate significant improvements in between-group effect sizes for 

depressive symptoms, 0.54 for CBT after 8 weeks (95 percent confidence interval (CI): 0.25 - 

0.84) and 0.47 for PST after 5 weeks (95 percent CI: 0.17 - 0.77) as compared to wait list 

controls. These effects were further improved at 12 weeks in both treatment groups (CBT: 0.69, 

95 percent CI: 0.41 - 0.98; PST: 0.65, 95 percent CI: 0.36 - 0.95). 

 Phobia. FearFighter is an online CHI application designed to reduce symptoms of 

phobia/panic disorders (agoraphobia, social phobia, and specific phobias). 108 In this study 

FearFighter was compared to guided Internet-based self-help relaxation therapy (Managing 

Anxiety group [MA]). Both arms also received periodic phone or email followup from a 

therapist. At 1 month, patients in the FearFighter group scored better than those in the MA group 

on several phobia subscales as assessed by self-report and blinded raters using the main problems 

and goals subscale (p<0.001), FQ global phobias subscale (p<0.001), and FQ global impression 

score (p<0.001) (Appendix G, Evidence Table 25). 

Stress. MyStudentBody is a Web-based CHI application, which is designed to reduce 

symptoms of stress among college students. Chiauzzi et al101 evaluated the effects of this 

application as compared to use of a control Web site and a non-Internet Web site control group. 

No significance between group differences in perceived stress was detected at 6-month followup. 
61 

 Hasson et al100 conducted an evaluation of a Web-based health promotion tool on mental and 

physical well-being and stress-related biological markers. At 6-month postintervention followup, 

the intervention group had improved significantly compared to the reference group on ratings of 

ability to manage stress (p=0.001), sleep quality (p=0.04), mental energy (p=0.002), 

concentration ability (p=0.038), and social support (p=0.049). The anabolic hormone 

dehydroepiandosterone sulphate (DHEA-S) decreased significantly in the reference group as 

compared to unchanged levels in the intervention group (p=0.04). Neuropeptide Y (NPY) 

increased significantly (p=0.002), and Chromogranin A (CgA) decreased significantly in the 

intervention group (p=0.001) as compared to the reference group, while tumor necrosis factor α

(TNFα) decreased significantly in the reference group compared to the intervention group 

(p<0.016). These results were consistent with a beneficial effect of this CHI application on 

several indicators of well-being and stress-related biomarkers (Appendix G, Evidence Table 25). 

 Zetterqvist et al 105 evaluated the effects of an internet-based self-help stress management 

program. The program was entirely delivered via the internet and included applied relaxation, 

problem solving, time management, and cognitive restructuring. The results of this investigation 

indicate that no measureable intervention effect was found in that both the treatment and control 

groups improved significantly at follow up in terms of perceived stress scores and the Hospital 

Anxiety and Depression Scale. In addition, participant attrition was significant. 

Asthma and Chronic Obstructive Pulmonary Disease 

Summary of the Findings 

Three studies evaluated the impact of CHI applications on intermediate outcomes in 

asthma8,10,109 and one in chronic obstructive pulmonary disease (COPD).110 Outcomes of interest 

included adherence, change in behavior in relation to rescue inhaler availability, asthma 

knowledge, change in asthma knowledge, dyspnea knowledge, and self-efficacy in managing 

dyspnea (Table 15). Across these studies, the body of the scientific evidence suggested that most 

CHI applications intended for use by individuals with asthma or COPD had variable results. 

Significant changes were noticed in the areas of change in knowledge. 

Strengths and Limitations of the Evidence 

 Overall the volume of the literature in this area is small. There were only three studies on 

asthma and one on COPD. They evaluated several domains of CHI impact on intermediate 

outcomes. Studies addressing intermediate outcomes in asthma had a wide range of study 

participants, ranging from very low (<30 participants per arm)8

 to low (>70 participants per 

arm).10,109 The one study addressing intermediate outcomes of CHI applications on COPD had a 

small sample size (<30 participants per arm)110(Appendix G, Evidence Tables 26-28). The 

quality of these four trials was moderate to low. All studies lacked information on blinding, were 

single blinded, and/or used inappropriate blinding methods as measured by the Jadad criteria4

(Appendix G, Evidence Table 1). Consumer postintervention evaluations ranged from as little as 

12 weeks to as many as 6 months. The overall strength of the body of this evidence (Table 16) 

was graded as low based on a modified version of the GRADE criteria5 and Chapter 11 of the 

EPC Manual.6

 
62 

General Study Characteristics 

 Studies that evaluated the impact of CHI applications on asthma-related intermediate 

outcomes looked at individuals under the age of 17 years, and/or their caregivers. The population 

of interest in the study addressing COPD was much older–greater than 68 years old. Information 

regarding gender across these studies was reported and can be found in Appendix G, Evidence 

Table 26. Information on race/ethnicity was reported in only one study8 where the population 

was identified as mainly white, non-Hispanic. The education level of participants (children) in 

studies addressing asthma was not reported. In one study where caregivers were under 

evaluation,10 over 50 percent of the caregivers had a high school diploma or below. The 

education level of caregivers in the other study8

 was not reported; education levels of the 

children were reported, but were not of value for this report (Appendix G, Evidence Tables 26 

and 27). 

Outcomes 

 Adherence. The impact of CHI applications on adherence was measured in two of the three 

articles addressing asthma. Jan et al10 evaluated Blue Angel for Asthma Kids, an Internet-based 

interactive asthma educational and monitoring program. The intervention group was taught to 

monitor their peak expiratory flows (PEF) and asthma symptoms daily on the Internet. The also 

received an interactive response consisting of a self-management plan from the Blue Angel 

monitoring program. The control group received a traditional asthma care plan consisting of a 

written asthma diary supplemented with instructions for self-management. The results of this 

study indicate that the intervention group experienced significantly decreased nighttime 

(p=0.028) and daytime symptoms (p= 0.009); improved morning (p=0.017) and night peak 

expiratory flow (p=0.010); increased adherence rates (p<0.05); improved well-controlled asthma 

rates (p<0.05); improved knowledge regarding self-management (p<0.05); and improved quality 

of life (p<0.05) when compared with conventional management. 

 Joseph et al109 evaluated a multimedia, Web-based asthma management program to 

specifically target urban high school students. The program uses “tailoring,” in conjunction with 

theory based models, to alter behavior through individualized health messages based on the 

user’s beliefs, attitudes, and personal barriers to change. The control group was given access to a 

generic asthma Website. The results of this investigation indicate that at 12 month follow up, the 

intervention group reported fewer symptom-days (p= 0.003), fewer symptom-nights (p=0.009), 

fewer school days missed (p=0.006), fewer restricted activity days (p=0.02) and fewer 

hospitalizations for asthma (p=0.01) when compared with control (Appendix G, Evidence Table 

28).
63 

Table 15. Results of studies on CHI applications impacting intermediate outcomes in asthma and 

COPD (N=4). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Applications* 

Monitoring adherence (peak flow meter 

technique score) 

0

Monitoring adherence (asthma diary 

entries per month)

+

Therapeutic adherence (DPI or MDI plus 

spacer technique score

0

Therapeutic adherence 0

Jan et al 

700710 Asthma 

education and 

an interactive 

asthma 

monitoring 

system 

Therapeutic adherence (adherence to 

inhaled corticosteroid)

0

Controller medication adherence: 0

Positive, no negative, or negative 

behavior change

0

Joseph, et al 

2007109 “Puff City” 

Internet 

intervention 

Rescue inhaler availability: positive 

behavior, no negative, or negative 

behavior change

0

Asthma knowledge score (caregivers of 

children 0-6 17years old) 

+

Asthma knowledge score (caregivers of 

children 7-17 17years old)

+

Asthma knowledge score (children 7-17 

17years old) † +

Change in knowledge (caregivers of 

children 0-6 17years old) † +

Change in knowledge (caregivers of 

children 7-17years old)

+

Asthma 3 

Krishna et al 

20038 Internetenabled 

asthma 

education 

program 

Change in knowledge (children 7-

1717years old)

+

Dyspnea knowledge score (range 0-15 

17years old) 

COPD 1 Nguyen et al +

20089 Internet-based 

dyspnea selfmanagement Self-efficacy score for managing 

dyspnea (range 0-10 17years old)

0

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 while the CHI application showed positive impact in knowledge scores across groups, the change in scores was most significant 

in these two groups using the application 

DPI=dry powder inhaler; MDI=metered dose inhaler 
64 

Table 16. Grade of the body of evidence addressing CHI impact on intermediate outcomes in 

asthma/COPD.

1 Protection against risk of bias (relates to study design, study quality, reporting bias) Moderate 

2 Number of studies 4 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2).

 Knowledge. Krishna et al8 evaluated whether health outcomes of children who have asthma 

can be improved through the use of an Internet-enabled interactive multimedia asthma education 

program. Children and caregivers in both the intervention and control groups received 

traditional patient education. Intervention group participants also received self-management 

education through the Interactive Multimedia Program for Asthma Control and Tracking. 

Results indicate that the intervention significantly increased asthma knowledge of children 

(p<0.001) as compared to controls. 

 Nguyen et al 111 measured the efficacy of an Internet-based and face-to-face self 

management program in people living with COPD. The content of the two programs was similar, 

focusing on education, skills training, and ongoing support for dyspnea self-management. The 

only difference was the mode of administration (Internet/personal digital assistant (PDA) or faceto-face) of the education sessions, reinforcement contacts, and peer interactions. The results 

indicate that there were improvements in knowledge of dyspnea management strategies in both 

groups, however there were no significant group by time differences. (Appendix G, Evidence 

Table 28). 

Self efficacy. Nguyen et al 111 also measured the efficacy of an Internet-based and face-toface self management program to increase self efficacy among people living with COPD. As 

outlined above, the content of the two programs were similar, focusing on education, skills 

training, and ongoing support for dyspnea self-management. The only difference was the mode 

of administration (Internet/personal digital assistant [PDA] or face-to-face) of the education 

sessions, reinforcement contacts, and peer interactions. The results indicate that there were 

improvements in self-efficacy for managing dyspnea in both groups, however there were no 

significant group by time differences (Appendix G, Evidence Table 28).
65 

Miscellaneous Intermediate Outcomes 

Summary of the Findings 

 Sixteen studies evaluated the impact of CHI applications on intermediate outcomes across 13 

other categorical diseases and health issues. These included cardiovascular disease,112,113

arthritis,114 back pain,115 behavioral risk factor management,116 cancer,15,117, caregiver decisionmaking,118 health behavior change,119 headache,120 HIV/AIDS,121 menopause/hormone 

replacement therapy (HRT),122,123 fall prevention,124 adolescent risk behavior, 125 and 

contraception9 (Appendix G, Evidence Tables 29-31). Across these studies, the CHI applications 

had varying effects on intermediate outcomes. The studies were too heterogeneous and the 

volume of studies on any single topic too few to support a conclusion about the effectiveness of 

CHI applications for these conditions.

Strengths and Limitations of the Evidence 

 The volume of the literature in this area is at a very early and incomplete stage of 

development. With the exception of studies focusing on cardiovascular disease, cancer, and 

menopause/HRT, which had two studies each, all other health issues had only one study 

evaluating an intermediate outcome for that topic area (Appendix G, Evidence Tables 29-31). 

The quality of these trials was variable with several studies lacking in one or more 

methodological domains of RCT quality as measured by the Jadad criteria4 (Appendix G, 

Evidence Table 1). The overall grade of this body of evidence was insufficient based on a 

modified version of the GRADE criteria5 and Chapter 11 of the EPC Manual.6

General Study Characteristics 

 Most of these studies involved adults of varying race and ethnicities, from 22 to 89 years of 

age, with widely varying educational backgrounds. One study125 involved adolescent teens aged 

13-17. Outcomes of interest included self-efficacy,112,114,124 medication compliance,126 activity 

limitation,114 self-reported global health,114 arthritic pain,114 fat intake,116 physical activity,116

satisfaction with care,15 receipt of Pap smear,117 caregiver decisional confidence,118,121

decisionmaking skill,118,121 social isolation,118,121 preventive care uptake,119 headache 

symptoms,120 depressive symptoms,120 anxiety,120 reduced health status decline,121

knowledge,9,122,123 satisfaction with decisions,122 reduced decisional conflict,122 realistic health 

expectations,123 high cigarette use, frequent marijuana use, high alcohol use, problems at home, 

often feeling sad or upset, feeling sad or down lately, taking meds, having a love interest, having 

sex, desiring contraceptive information,125 and behavioral intention.127 Postintervention 

followup time ranged from immediate postintervention to 1 year. Samples sizes were generally 

small, ranging from 8 to 344 subjects per arm of the study, except for two larger studies which 

had sample sizes of 827 and 930 in each arm,116 and 940 and1066 in each arm119 (Appendix G, 

Evidence Tables 30 and 31). 
66 

Outcomes

 Adolescent risk behavior. Paperny et al125 evaluated the effect of a written Personalized 

Health Risk Assesment (HRA) (controls) that is shared with a clinician to a computerized HRA 

that was (intervention #2) or was not (intervention number 1) shared with a clinician. Over 75 

percent of the participants were White or of Asian descent, 52 percent were males, and 

approximately 10 percent were receiving financial assistance. The results indicated that 

significant postintervention reductions in high cigarette use (p=<0.01/p=<0.03); reductions in 

frequent marijuana use (p=<0.04/p=<0.03); reductions in problems with parents 

(p=0.001/p=0.001); and reductions in often sad, upset, or unhappy feelings (p=0.001/p=0.007) 

were achieved in both treatment groups (did not share computerized HRA with clinician/shared 

computerized HRA with clinician) as compared to controls (written HRA shared with clinicians). 

Significant reductions in high alcohol use (p=<0.02/NS), feeling sad or down lately 

(p=<0.04/NS), and has a current lover (p=<0.03/NS) were only significant in the group that did 

not share their HRA with the clinician. Finally there was no measureable effect of the 

intervention on having sexual intercourse (NS/NS) or taking medications (NS/NS). 

 Arthritis. Lorig et al114 conducted an evaluation of an Internet-based arthritis selfmanagement program among patients with rheumatoid arthritis, osteoarthritis, or fibromyalgia. 

At 1-year postintervention, patients in the intervention group demonstrated significant 

improvement in health distress (p< 0.001), activity limitation (p< 0.001), self-reported global 

health (p=0.004), and pain (p<0.001) and self-efficacy (p=0.018). No impact was seen on health 

care utilization or health behaviors (Appendix G, Evidence Table 31).

Back pain. Buhrman et al115 investigated the impact of an Internet-based cognitivebehavioral intervention with telephone support for chronic back pain. At 3-month 

postintervention followup evaluation there was significant improvement for several Coping 

Strategies Subscale items including praying and hoping (p=0.032), catastrophizing (p=0.005), 

control of pain (p<0.001), and ability to decrease pain (p<0.0001). In addition, significant 

improvement was also found on Multidimensional Pain Inventory subscales for life control 

(p<0.001) and decrease of punishing responses (p<0.05). Results on the Pain Impairment Rating 

Scale showed a significant reduction (p<0.01), while a significant decrease was also found on the 

Hospital and Anxiety Depression Scale (p<0.001) (Appendix G, Evidence Table 31).

 Behavioral risk factor control. Oenema et al116 evaluated the impact of an Internetdelivered, computer-tailored lifestyle intervention targeting saturated fat intake, physical activity 

(PA), and smoking cessation. At 1-month postintervention followup the intervention group had a 

significantly lower self-reported saturated fat intake (p<0.01) and a higher likelihood of meeting 

the physical activity guidelines among respondents who were insufficiently active at baseline 

(OR, 1.34, 95 percent CI, 1.001–1.80). No significant effects were found for self-reported 

smoking status (Appendix G, Evidence Table 31).

Contraception. Chewning 9

 et al conducted a study to evaluate a computer-based 

contraceptive decision aid among young women. At 1-year postintervention followup, 

intervention participants demonstrated higher oral contraceptive knowledge than controls 

(p=0.00) (Appendix G, Evidence Table 31).

Cardiovascular disease. Kukafka et al112 investigated if a tailored, Web-based, 

cardiovascular disease educational system could influence self-efficacy regarding a patient’s 

likelihood of acting appropriately in response to acute myocardial infarction symptoms. At 3-

months postintervention followup evaluation, patients in the Web-based intervention arm of the 
67 

study demonstrated significant increases in self-efficacy to label symptom sensations (p<0.001), 

self-efficacy to respond to symptom sensations (p<0.05), and cognitive control self-efficacy 

(p<0.001) (Appendix G, Evidence Table 31).

Cancer. Jones et al15 conducted an investigation to compare the use and effect of a 

computer-based personalized information system for cancer patients using each patient’s medical 

record with a computer system providing only general information and with information 

provided in booklets. At postintervention followup, patients in the personalized computer 

intervention group were more likely to learn something new (p=0.03), thought that the 

information was relevant (p=0.02), and had higher satisfaction scores (p=0.04) than patients in 

the general computer information group. In addition, patients who used the printed booklets were 

more likely to feel overwhelmed by the information (p<0.001) and felt that the information was 

too limited (p<0.001). Finally, at 3-months postintervention, patients who used the printed 

booklets were less likely to prefer the computer to a 10-minute, in-person consultation (p<0.001). 

Campbell et al117 assessed the impact of computer-generated printed feedback on cervical 

screening among women who were under-screened for cervical cancer. Significant 6-month 

postintervention screening rates were demonstrated only among under-screened women between 

50-70 years of age (p<0.5) (Appendix G, Evidence Table 31).

 Caregiver decision. Brennan et al118 evaluated CompuLink, which is an online support 

application, designed to enhance decisionmaking confidence and skill by provision of 

information, decision-support tools, and communication (email). An evaluation of this 

application documented an association between CompuLink and significantly improved 

decisionmaking confidence (p<0.01). However no change was seen in terms of decisionmaking 

skill, social isolation, or health status (Appendix G, Evidence Table 31).

 Fall prevention. Yardley et al124 conducted an evaluation of an interactive Web-based 

program that provides tailored advice about undertaking SBT activities among seniors 65-97 

years of age. Postintervention evaluation suggests that there was a significant difference between 

the tailored and control groups on ratings of the personal relevance of the advice (p =0.014), selfefficacy for carrying out SBT (p=0.047), and intention to carry out strength and balance training 

(p=0.039). The intervention did not exert any measurable effects on reports of the advice being 

more suitable or interesting or expectation that the recommended activities would improve their 

balance (Appendix G, Evidence Table 31). 

 Health behavior change. Harari et al119 conducted an RCT to evaluate the impact on health 

behaviors and use of preventive health care services of a computer-generated, tailored, health 

education system. At 1-year followup evaluation there were no significant differences in selfreported health risk behavior, except for a small but statistically significant difference in 

adherence with recommended levels of physical activity (at least 5 times per week moderate to 

strenuous) (P = 0.03). In terms of preventive health care uptake, there was a significant increase 

in pneumococcal vaccination rates (P=0.04) among patients enrolled in the computer-based 

intervention (Appendix G, Evidence Table 31).

 Headache. Devineni et al 120 evaluated an Internet-delivered behavioral intervention versus a 

symptom monitoring waiting list control group among patients with chronic headache. Twomonth postintervention evaluations indicated significant reductions in headache index scores 

(p<0.05). There were also significant improvements on the Headache Symptom Questionnaire 

(p<0.01) and the Headache Disability Inventory (p<0.05) (Appendix G, Evidence Table 31).
68 

HIV/AIDS. Brennan et al121 conducted a second study of CompuLink (see above) among 

persons living with HIV/AIDS. This investigation suggested an association between using 

CompuLink and reduced levels of social isolation (p<0.01) and improved decisionmaking 

confidence (p<0.0). However no change was seen in terms of decisionmaking skill or health 

status as compared to controls. 

 Menopause/HRT utilization. Shapira et al122 conducted an RCT of a computer-based 

hormone therapy (HT) decision-aid versus print material among postmenopausal women. At 3-

months postintervention followup evaluation, there was no measurable difference between 

groups with respect to knowledge, satisfaction with decision, decisional conflict, or hormone 

therapy use. Rostom et al 123 conducted an investigation to compare the efficacy of a 

computerized decision aid compared to an audio booklet among women considering long-term 

HRT. The results of a postintervention evaluation indicated that the computerized decision aid 

intervention significantly increased realistic expectations (p=0.015) and knowledge (p=0.019) 

among women considering long term HRT (Appendix G, Evidence Table 31). 

Key Question 1c: What evidence exists that consumer health 

informatics applications impact relationship-centered 

outcomes?

Summary of the Findings 

 Eight studies evaluated the impact of CHI applications on various aspects of relationshipcentered outcomes (Tables 17 and 19, and Appendix G, Evidence Tables 32-34). Outcomes of 

interest include social support, quality of life, health information competence, decision 

confidence, improved decision making skill, reduced social isolation, level of positive interaction 

with the provider, and satisfaction with care. These outcomes were examined in the context of 

five health problems, which include breast cancer, caregiver decision making, osteoarthritis, 

newborn birth and delivery, and HIV/AIDS. Across these studies, the body of the scientific 

evidence indicated that most CHI applications evaluated to date had equivocal effects on 

relationship-centered health outcomes. 
69 

Table 17. Studies of CHI applications impacting relationship-centered outcomes in women with 

breast cancer (N=4). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Decision confidence 

Improved decision making skill 

 Caregiver 

decision making 

1 Brennan, 

1995118 Experimental 

Isolation 

Improved decision making confidence 

Improved decision making skill 

Reduced social isolation 

HIV/AIDS 1 FlatleyBrennan, 

1998121

Computer Link 

Differential decline in health status 

Patient overall satisfaction score 

with the osteoarthritis care they 

are receiving 

Arthritis 1 Sciamanna, 

2005131 Patient satisfaction after 

intervention 

Satisfaction with care Peak consumption: max number of 

drinks per drinking day 

DCS at followup 

Difference between groups in total 

score on DCS (decision vs. 

usual care) 

Odds ratio for caesarean (elective 

& emergency) vs. vaginal 

decision vs. usual care 

Satisfaction with decision (decision 

analysis vs. usual care) 

Mode of delivery - elective 

caesarean 

Delivery - emergency caesarean 

Vaginal or csection delivery 1 Montgomery, 

2007130 Information 

Decision analysis 

Delivery - vaginal birth 

CHESS = Comprehensive Health Enhancement Support System; DCS= decisional conflict scale; IVD= interactive video disc system 

Strengths and Limitations of the Evidence 

 Eight studies evaluated several domains of CHI impact on relationship-centered care 

outcomes. With the exception of breast cancer, for which there were four studies regarding 

relationship-centered outcomes,13,14,128,129 all other topics were evaluated by a single study: 

HIV/AIDS,121 newborn delivery,130 osteoarthritis,131 and Alzheimer’s disease and caregiver 

decisionmaking.118 Only one study130 was large, with 147 to 201 subjects per arm of the study; 

all other studies relied on very small sample sizes (< 80 subjects per arm) (Appendix G, 

Evidence Tables 32-34). The quality of these eight trials was variable with several studies 

lacking in one or more methodological domains of RCT quality as measured by the Jadad4

criteria (Appendix G, Evidence Table 32-34). Patient postintervention evaluations ranged from 

as little as 2 months to as many as 12 months. The overall strength of the body of this evidence 

(Table 18) was graded as moderate for studies on breast cancer based on a modified version of 

the GRADE criteria5

 and Chapter 11 of the EPC Manual.6
70 

Table 18. Grade of the body of evidence addressing CHI impact on relationship-centered 

outcomes in breast cancer. 

1 Protection against risk of bias (relates to study design, study quality, reporting bias Moderate 

2 Number of studies 4 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the 

people, interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and 

outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low): if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

General Study Characteristics

 The studies that were evaluations of the impact of CHI applications on relationship-centered 

care generally were tested among an adult, non-elderly population. The mean age of study 

participants across studies was 32 to 52 years. One additional study was among participants with 

an average age of 64. Information regarding gender across these studies was generally not 

reported. Because 4 of the studies were conducted in the context of breast cancer13,14,128,129 and a 

fifth study was conducted in the context of newborn birth decision,130 it can be inferred that these 

six studies were completely among female participants. Only six studies reported on the 

race/ethnicity of study participants. Of these, 4 studies included only non-Hispanic white 

participants. One additional study included whites and African Americans while a final study 

included whites, African-Americans, Asian/Pacific Islanders and Hispanics (Appendix G, 

Evidence Tables 32 and 33). 

Outcomes

 Breast cancer. When evaluating social support, quality of life, and health confidence among 

women with breast cancer, Gustafson et al14 found that the Comprehensive Health Enhancement 

Support System (CHESS) provided significantly more social support (p=0.003) and enabled 

greater quality of life (p=0.029) and health information competence (p=0.007) than Internet 

access alone at 2 months. The effect of CHESS remained for social support (p=0.027) and 

quality of life (p=0.047) at 4 months, while no effects of CHESS were observed at 9 months for 

social support, quality of life, or health information confidence. 

 Gustafson13 also evaluated the effectiveness of the CHESS among younger underserved 

women. At the 2-month postintervention followup, CHESS had significant impact on patient 
71 

information competence (p<0.05), level of comfort with the health care system (p<0.01), and 

increased confidence in doctors (p<0.05).

 Maslin et al129 studied the effectiveness of a shared decision making computer program 

(interactive video disc) for women with early breast cancer contemplating surgical and 

chemotherapeutic options. Use of the interactive video disk did not have significant effect on the 

treatment decisions made by women participating in the study. 

 Green et al128 compared the effectiveness of counseling alone versus counseling preceded by 

use of a computer-based decision aid among women referred to genetic counseling for a family 

or personal history of breast cancer. Postintervention evaluations suggested that participants 

rated 11 of 12 specific attributes of the effectiveness of the counseling sessions significantly 

higher (P < 0.0001) compared with the counselors. Overall, computer program use resulted in 

shorter face-to-face counseling sessions among women at low risk for carrying breast cancer 

gene mutations (p=0.027) (Table 19, and Appendix G, Evidence Table 34). 

Table 19. Results of studies on CHI applications impacting relationship-centered outcomes in 

breast cancer (N=4). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Applications 

Alter content of discussions +

Change the way they used their time +

Used time more efficiently +

Skip material typically present +

Effectiveness of counseling session +

Green, 

2005128 Counseling 

and Computer 

Shorter counseling sessions +

Social support + 

Quality of life + 

Gustafson, 

200814 Internet 

CHESS Health competence + 

Information competence + 

Participation + 

Gustafson, 

200113 CHESS 

Confidence in doctors + 

Anxiety and depression + 

Breast cancer 4 

Maslin, 

1998129 IVD shared decision 

making 

Satisfaction with treatment decision 0 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

CHESS = Comprehensive Health Enhancement Support System; IVD = interactive videodisc system 

 Caregiver decisionmaking (Alzheimer’s disease). CompuLink118is an online support 

application designed to enhance decisionmaking confidence and skill by provision of 

information, decision support tools, and communication (email). An evaluation of this 

application documented an association between use of CompuLink and significantly improved 

decisionmaking confidence (p<0.01). No change was seen in terms of decisionmaking skill, 

social isolation, or health status (Appendix G, Evidence table 34). 

 HIV/AIDS. In another study of CompuLink 121 among persons living with HIV/AIDS, data 

suggested an association between use of CompuLink and reduced levels of social isolation 

(p<0.01) and improved decisionmaking confidence (p<0.0). No change was seen in terms of 

decisionmaking skill or health status as compared to controls (Appendix G, Evidence table 34).
72 

 Osteoarthritis. Sciamanna et al131 evaluated the effect of a Web-based osteoarthritis 

educational application on patients’ perceptions of the quality of their osteoarthritis care. This 

application failed to produce a measurable effect on patient satisfaction with osteoarthritis care 

as compared to controls (Appendix G, Evidence table 34). 

 Newborn delivery. Montgomery et al130 investigated the effects of two computer-based 

decision aids (an information program and individualized decision analysis) on decisional 

conflict and actual mode of delivery among a group of pregnant women with one previous 

caesarean section. The results of this study indicate that there was no significant effect of either 

of these computer-based decision aids on decisional conflict or mode of delivery (Appendix G, 

Evidence table 34). 

Key Question 1d: What evidence exists that consumer health 

informatics applications impact clinical outcomes? 

Breast Cancer 

Summary of the Findings 

 Three studies addressed the impact of CHI applications on breast cancer clinical outcomes. 

Outcomes of interest include quality of life, well-being, physical functioning, and anxiety (Table 

20). All three studies were RCTs and the quality of these studies varied from very low to low. 

Across these studies the body of the scientific evidence suggests that CHI applications intended 

for use by individuals with breast cancer have a neutral to positive impact. 

Table 20. Results of studies on CHI applications impacting clinical outcomes in breast cancer 

(N=3) 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Social/family well being (quality of life) 0 

Emotional well-being (quality of life) 0 

Functional well-being (quality of life) 0 

Gustafson, 

200113 CHESS 

Breast cancer concerns (quality of life) 0 

Gustafson, 

200814 CHESS Quality of life 0 

Anxiety and depression† + 

Breast 

Cancer 

3 

Maslin, 

1998129 IVD shared 

decision 

programme 

Physical functioning + 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 significant impact of CHI was seen in this outcome 

CHESS= Comprehensive Health Enhancement Support System; IVD= interactive video disc system 
73 

Strengths and Limitations of the Evidence 

 Overall the volume of the literature in this area is small (three studies). Many domains of 

CHI application impact on clinical outcomes with individuals with breast cancer were measured. 

The three studies had low13 to very low14,129 numbers of study participants. Followup periods 

were either shot (2 months) or not reported (Appendix G, Evidence Tables 35-37). None of the 

studies contained any information on blinding as measured by the Jadad criteria4

 (Appendix G, 

Evidence Table 1). The overall strength of the body of this evidence (Table 21) was graded as 

low based on a modified version of the GRADE criteria5 and Chapter 11 of the EPC Manual6

General Study Characteristics 

 Studies that evaluated the impact of CHI applications on breast cancer clinical outcomesoutcomes looked at individuals between the ages of 44 and 52; age was only reported in two 

studies.14,129 Information regarding gender across these studies was not reported. Information on 

race/ethnicity was reported in only in two studies as predominantly white, non-Hispanic. 14,129 

(Appendix G, Evidence Tables 36 and 37). 

Outcomes 

To assess the impact of a computer-based patient support system on quality of life in younger 

women with breast cancer, 246 newly diagnosed breast cancer patients under age 60 were 

randomized to a control group or an experimental group that received Comprehensive Health 

Enhancement Support System (CHESS), a home-based computer system providing information, 

decision-making, and emotional support. At 5-month followup, no statistical difference was 

shown in quality of life between the control and CHESS group.13 No significant improvement in 

quality of life was demonstrated by the same authors in another study in 257 breast cancer 

patients after 9-month followup.14

 Another study evaluated the usefulness of a shared decisionmaking program for women with 

early breast cancer; looking at surgical and adjuvant treatment options (chemotherapy) using a 

personalized computerized interactive video system.129 One hundred patients were randomized to 

an intervention group (n=51) or control group (n=49). The study showed improvement in the 

following clinical outcomes: a significant fall in anxiety after 9 months measured by the Hospital 

Anxiety and Depression Scale (p<0.001), improvement in the physical functioning sub-score of 

general quality of life measured by the Medical Outcomes Study Short Form 36 questionnaire 

(Table 22, and Appendix G, Evidence Table 37). 
74 

Table 21. Grade of the body of evidence addressing CHI impact on clinical outcomes in 

individuals with breast cancer. 

1 Protection against risk of bias Low 

2 Number of studies 3 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the people, 

interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ Very low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

Diabetes Mellitus 

Summary of the Findings 

 Three studies addressed the impact of CHI applications on clinical outcomes in individuals 

with diabetes mellitus. Outcomes of interest were the use of insulin therapy, and measures of 

hemoglobin A1c (HbA1c), total glucose, triglycerides, and fasting blood glucose. (Table 22) All 

three studies were RCTs and the quality of these studies was low (Appendix G, Evidence Table 

1). There was no indication of significant impact of the CHI application on outcomes in two 

studies.92,132 One study94 showed a positive impact on HbA1c. 

Strengths and Limitations of the Evidence 

 The volume of this literature is small. All three studies had a small number (<30) 

participants. Followup periods ranged from 37 weeks92 to 12 months132 (Appendix G, Evidence 

Tables 35-37). Blinding, as measured by the Jadad criteria,4 was not reported in any of the 

studies (Appendix G, Evidence Table 1). The overall strength of the body of this evidence (Table 

23) was graded as low based on a modified version of the GRADE criteria5

 and Chapter 11 of 

the EPC Manual6
75 

Table 22. Results of studies on CHI applications impacting clinical outcomes in diabetes mellitus 

(N=3). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Insulin therapy 0 

FBS 0 

Homko, 

200792 Telemedicine 

A1c at time of delivery 0 

A1C (%) 0 

FBG (MMOL/L) 0 

TC (MMOL/L 0 

Tjam, 2006132 Individuals with 

interactive 

Internet 

program TG (MMOL/L) 0 

Diabetes 3 

Wise et al 94 Interactive 

computer 

assessment 

HbA1c + 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 significant impact of CHI was seen in this outcome 

FBG = fasting blood glucose; FBS = fasting blood sugar; HbA1c = hemoglobin A1c; MMOL/L = millimoles per litre; 

TC = total cholesterol; TG = triglycerides 

Table 23. Grade of the body of evidence addressing CHI impact on clinical outcomes in 

individuals with diabetes mellitus. 

1 Protection against risk of bias Low 

2 Number of studies 3 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the people, 

interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

0 

 Overall grade of evidence‡ low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low): if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

Outcomes 

To demonstrate the feasibility of monitoring glucose control among indigent women with 

GDM over the Internet, women with GDM were randomized to either the Internet group (n=32) 

or the control group (n=25).92 Patients in the Internet group were provided with computers and/or 

Internet access if needed. A Web site was established for documentation of glucose values and 

communication between the patient and the health care team. Women in the control group 

maintained paper log books, which were reviewed at each prenatal visit. There was no difference 

between the two groups in regards to either fasting or postprandial blood glucose values, 
76 

although more women in the Internet group received insulin therapy (31 percent vs. 4 percent; 

P<0.05). There were also no significant differences in pregnancy and neonatal outcomes between 

the two groups. 92

Another study compared physiological outcomes between an interactive diabetes Internet 

program and the Diabetes Education Centers with respect to followup care for on-going diabetes 

management. Participants were followed for 1 year and were assessed at baseline, 3 months, 6 

months, and 1 year. Triglyceride levels improved significantly in the intervention group from 

baseline to followup. Hemoglobin A1c levels were also significantly improved in the 

intervention group at 3 months, but this improvement was not sustained to the 6-month or 1-year 

time points. 

 Wise et al94 evaluated the impact of an interactive computer program on process and clinical 

outcomes among insulin-dependent and noninsulin-dependent patients with diabetes. At 4-6 

months, this application significantly improved HBA1c among both insulin dependent and noninsulin dependent (Appendix G, Evidence Table 37). 

Diet, Exercise, Physical Activity, not Obesity 

Summary of the Findings 

 Five studies addressed the impact of CHI applications on clinical outcomes related to diet, 

exercise, or physical activity, not obesity. Clinical outcomes of interest were weight loss, change 

in body weight, and change in body fat (Table 23). All of the studies were RCTs and four of the 

five had low study quality (Appendix G, Evidence Table 1). One study by Tate et al44 received a 

Jadad score of high due to the fact that it was blinded. Overall the studies showed results 

indicating either no impact or a positive impact on one of the outcomes. 

Strengths and Limitations of the Evidence 

 The volume of this literature is small, including only five studies addressing clinical 

outcomes in CHI applications focused on in diet, exercise, physical activity, not obesity. Four of 

the studies has small sample sizes of under 80 participants17,81,83,85 On study had a large number 

of study participants; over 200.44 Followup periods ranged from 6 to 24 months. Blinding, as 

measured by the Jadad criteria,4

 was reported in one44 study but not in the remaining 4 of the 

other studies (Appendix G, Evidence Table 1). The overall strength of the body of this evidence 

(Table 24) was graded as low based on a modified version of the GRADE criteria5

 and Chapter 

11 of the EPC Manual.6

Outcomes 

To assess computer-tailored feedback, 192 adults with a mean age of 49.2 years (SD 9.8) and 

a mean BMI of 32.7 (SD 3.5) were randomized to one of three Internet treatment groups: no 

counseling, computer-automated feedback, or human email counseling. All participants received 

one weight loss group session, coupons for meal replacements, and access to an interactive Web 

site. The human email counseling and computer-automated feedback groups also had access to 

an electronic diary and message board. The human email counseling group received weekly 
77 

Table 23. Results of studies on CHI applications impacting clinical outcomes in diet, exercise, 

physical activity, not obesity (N=5). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Adachi, 

2007†17 Computer 

tailored program 

with 6-mos 

weight and 

targeted 

behavior’s selfmonitoring, 

(Group KM) 

Computer 

tailored program 

only, 

(Group K) 

Percent weight loss + 

Hunter, 

200885 BIT Body weight (kg) + 

McConnon, 

200781 Internet group Loss of 5% or more body weight (12 

months) 

0

Tate, 200644 Tailored 

ComputerAutomated 

Feedback 

Human Email 

Counseling 

Weight loss + 

Body weight ‡(kg) 0 

Diet/exercise/ 

physical 

activity 

5 

Williamson, 

200683 Interactive Nutrition 

education 

program and 

Internet 

counseling 

behavioral 

therapy for the 

intervention 

group 

Body fat ║

(%) + 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 the greatest effect of the intervention was seen at the 1-month post intervention time interval 

‡

 both parents and children showed a decrease in bodyweight at 6months, at the end of the followup period of 2 years all weight 

lost was regained and there was no difference between intervention and control. 

║

 positive impact of reduction of body fat was greater in children and was only reported for the first 6 months post-intervention 

BIT= behavioral Internet treatment; BMI= body mass index: ; kg = kilogram
78 

Table 24. Grade of the body of evidence addressing CHI impact on clinical outcomes related to 

diet, exercise, or physical activity, not obesity. 

1 Protection against risk of bias Low 

2 Number of studies 5 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the people, 

interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

-1 

6 Did the studies show strong evidence of association between intervention and outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

1 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

e-mail feedback from a counselor, and the computer-automated feedback group received 

automated, tailored messages. At 3 months, weight loss was greater for completers in both the 

computer-automated feedback group (mean 5.3 kg, SD 4.2 kg) and human email counseling 

group (mean 6.1 kg, SD 3.9 kg) compared with the no-counseling group (mean 2.8 kg, SD 3.5 

kg), and the two intervention groups did not differ from each other. At 6 months, weight loss was 

significantly greater in the human email counseling group (mean 7.3 kg, SD 6.2 kg) than in the 

computer-automated feedback group (mean 4.9 kg, SD 5.9 kg) or no-counseling group (mean 

2.6, SD 5.7 kg). Intent-to-treat analyses using single or multiple imputation techniques showed 

the same pattern of significance. Providing automated computer-tailored feedback in an Internet 

weight loss program was as effective as human email counseling at 3 months. 44

Another study examined the long-term effects of a new behavioral weight control program 

(Kenkou-tatsujins, KT program) consisting of interactive communications twice in a month 

communications including computer-tailored personal advice on treatment needs and behavioral 

modification. Two hundred and five overweight Japanese women were recruited in an RCT 

comparing Group KM (KT program with 6-month weight and targeted behavior selfmonitoring), Group K (KT program only), Group BM (an untailored self-help booklet with 7-

month self-monitoring of weight and walking), and Group B (the self-help booklet only). 

Significant weight loss was observed in all groups. At 1 month, weight loss was greatest for 

Groups KM and K, but at 7 months, the mean weight loss was significantly more in Group KM 

than the other three groups. 17

 To evaluate the efficacy of an Internet-based program for weight loss and weight-gain 

prevention, 446 overweight individuals (222 men; 224 women) with a mean age of 34 years and 

a mean BMI of 29 were recruited from a military medical research center with a population of 

17,000 active-duty military personnel. Participants were randomly assigned to receive a 6-month 

behavioral Internet treatment (BIT, n=227) or usual care (n=224). After 6 months, completers 

who received BIT lost a mean of 1.3 kg while those assigned to usual care gained a mean of 0.6 

kg (<0.001). 85
79 

To determine the effectiveness of an Internet-based resource for obesity management, an 

RCT was conducted in a community setting, where obese volunteers were randomly assigned to 

an Internet group (n = 111) or usual care group (n =110). Data were collected at baseline, 6 

months, and 12 months. Based on analysis conducted on all available data, the Internet group lost 

a mean of 1.3 kg, compared with a 1.9 kg weight loss in the usual care group at 12 months, a 

nonsignificant difference (difference = 0.6 kg; 95 percent CI: -1.4 to 2.5, p = 0.56). This trial 

failed to show any additional benefit of this Web site in terms of weight loss compared with 

usual care. 81

To test the efficacy of an Internet-based lifestyle behavior modification program for African 

American girls over a 2-year period of intervention, 57 overweight African American girls (mean 

BMI percentile, 98.3; mean age, 13.2 years) were randomly assigned to an interactive behavioral 

Internet program or an Internet health education program, the control condition. Overweight 

parents were also participants in the study. Forty adolescent-parent dyads (70 percent) completed 

the 2-year trial. In comparison with the control condition, adolescents in the behavioral program 

lost more mean body fat (BF) (-1.12 percent, SD 0.47 percent vs. 0.43 percent, SD 0.47 percent , 

p < 0.05), and parents in the behavioral program lost significantly more mean body weight (-2.43 

kg, SD 0.66 kg vs. -0.35 kg, SD 0.64 kg, p<0.05) during the first 6 months. This weight loss was 

regained over the next 18 months. After 2 years, differences in BF for adolescents (mean -0.08 

percent, SD 0.71 percent vs. mean 0.84 percent, SD 0.72 percent) and weight for parents (mean 

-1.1 kg, SD 0.91 vs. mean -0.60 kg, SD 0.89 kg) did not differ between the behavioral and 

control programs. An Internet-based weight management program for African American 

adolescent girls and their parents resulted in weight loss during the first 6 months but did not 

yield long-term loss due to reduced use of the Web site over time 83(Table 23, and Appendix G, 

Evidence Table 37). 

Mental Health 

Summary of the Findings 

 Seven studies addressed the impact of CHI applications on mental health clinical outcomes 

(Table 25). Outcomes of interest include depression, anxiety, and serological measures. All of 

these studies were RCTs and received low scores according to the Jadad criteria (Appendix G, 

Evidence table 1). All of the studies indicated a positive impact of the CHI application on at least 

one of the reported outcomes. One study by Orbach et al133 showed a positive impact on anxiety 

but no impact on the Hem reasoning test or general self efficacy. 

Strengths and Limitations of the Evidence 

 The volume of this evidence is small, including only seven studies. Sample sizes in these 

studies ranged from very small (<20) in March et al134to greater than 100 participants in 

Chrstensen et al99, Ker et al135 and Hasson et al100 Followup periods were not reported in all 

seven studies, but where they were reported, they ranged from 6 weeks99 up to 12 months.135

Blinding, as measured by the Jadad criteria,4

 was not reported in any of the studies (Appendix G, 

Evidence Table 1). The overall strength of the body of this evidence (Table 26) was graded as 

low based on a modified version of the GRADE criteria5

 and Chapter 11 of the EPC Manual6
80 

Table 25. Results of studies on CHI applications impacting clinical outcomes in mental health 

(N=7). 

Target 

condition N Author, year Interventions Primary outcomes measured 

Effect of CHI 

Application* 

Christensen, 

2004†99 Blue Pages: 

Computer 

based psycho 

education Web 

site offering 

information 

about 

depression 

MoodGYM: 

Computer 

based Cognitive 

Behavior 

therapy 

Center for Epidemiologic depression 

scale

+ 

DHE-S + 

NPY + 

CgA + 

Hasson, 

2005100 Web-based 

stress 

management 

system TNFα + 

Kerr, 

2008135 PACEi CESD score + 

March, 

2008134 Web based 

intervention 

Reduction in childhood anxiety + 

Test Anxiety Inventory + 

Anxiety Hierarchy Questionnaire + 

Heim Reasoning Test 0 

Orbach, 

2007133 Cognitive 

Behavior 

Therapy group 

(CBT) General Self-Efficacy Scale 0 

BDI + 

BAI + 

Proudfoot, 

2003137 beating the 

blues 

Work and social adjustment scale + 

Mental 

Health 

7 

Spek, 

2008136 Group CBT 

Internet based 

intervention 

Treatment response after 1 yr + 

* (+) positive impact of the CHI application on outcome; (-) negative impact of the CHI application on outcome; (0) no impact or 

not a significant of the CHI application on outcome 

†

 positive impact was seen in both intervention groups, but was significant only in the MoodGym group 

BDI= Beck depression inventory; BAI= Beck anxiety inventory; CBT = Cognitive behavioral therapy; CESD= Center for 

Epidemiological Studies Depression ; CgA=chromogranin A; DHE-S=dehydroeoiandosterone sulphate; NPY=nueropeptide Y; 

PACEi= Patient-centered Assessment and Counseling for exercise and nutrition via the Internet; TNFα= tumor necrosis factor 

α
81 

Table 26. Grade of the body of evidence addressing CHI impact on clinical outcomes in mental 

health. 

1 Protection against risk of bias Low 

2 Number of studies 7 

3 Did the studies have important inconsistency? 

y (-1); n (0) 

0 

4 Was there some (-1) or major (-2) uncertainty about the directness or extent to which the people, 

interventions and outcomes are similar to those of interest? 

Some (-1); major (-2); none (0) 

0 

5 Were the studies sparse or imprecise? 

y (-1); n (0) 

0 

6 Did the studies show strong evidence of association between intervention and outcome? 

“strong*” (+1); “very strong†

” (+2); No (0) 

1 

 Overall grade of evidence‡ Low 

* if significant relative risk or odds ratio > 2 based on consistent evidence from 2 or more studies with no plausible confounders †

 if significant relative risk or odds ratio > 5 based on direct evidence with no major threats to validity 

‡

 (high, moderate, low):if above score is (+), increase grade; if above score is (-), decrease grade from high to moderate (-1) or 

low (-2). 

Outcomes 

A total of 191 women and 110 men (mean age 55 years, SD 4.6) with sub-threshold 

depression were randomized into Internet-based treatment, group CBT (Lewinsohn’s Coping 

with Depression Course) or a waiting-list control condition.136 The main outcome measure was 

treatment response after 1 year, defined as the difference in pretreatment and followup scores on 

the BDI. Simple contrasts showed a significant difference between the waiting-list condition and 

Internet-based treatment (p=0.03) and no difference between both treatment conditions 

(p=0.08).136

Another study assessed depressive symptoms in 401 participants in an RCT of a 12-month 

primary care, phone, and Internet-based behavioral intervention for overweight women. A oneway analysis of variance examining the mean change in Center for Epidemiological Studies 

Depression (CESD) score from baseline to 12 months, controlling for age, education, marital 

status, and employment, showed that those receiving the intervention significantly decreased 

their CESD scores (p<0.03) more than those receiving standard care.135

To evaluate the efficacy of an Internet-based cognitive-behavioral therapy (CBT) approach to 

the treatment of child anxiety disorders, 73 children with anxiety disorders, aged 7 to 12 years, 

and their parents were randomly assigned to either an Internet-based CBT (NET) or wait-list 

(WL) condition. The NET condition was reassessed at 6-month followup. At posttreatment 

assessment, children in the NET condition showed small but significantly greater reductions in 

anxiety symptoms and increases in functioning than WL participants. These improvements were 

enhanced during the 6-month followup period, with 75 percent of NET children free of their 

primary diagnosis. The conclusion was that Internet delivery of CBT for child anxiety offered 

promise as a way of increasing access to treatment for this population.134

To assess possible effects on mental and physical well-being and stress-related biological 

markers of a Web-based health promotion tool, 303 employees (187 men and 116 women, age 

23–64 years) from four information technology and two media companies were enrolled. Half of 
82 

the participants were offered Web-based health promotion and stress management training 

(intervention) lasting for 6 months. All other participants constituted the reference group. 

Clinical outcomes consisted of different biological markers measured to detect possible 

physiological changes. After 6 months, the intervention group had improved statistically 

significantly compared to the reference group on ratings of ability to manage stress, sleep 

quality, mental energy, concentration ability, and social support. The anabolic hormone 

dehydroepiandosterone sulphate (DHEA-S) decreased significantly in the reference group as 

compared to unchanged levels in the intervention group. Neuropeptide Y (NPY) increased 

significantly in the intervention group compared to the reference group. Chromogranin A (CgA) 

decreased significantly in the intervention group as compared to the reference group. Tumour 

necrosis factor α (TNFα) decreased significantly in the reference group compared to the 

intervention group.100

To test the hypothesis that CBT, available on the Internet, could reduce test anxiety, 90 

university students were randomly allocated to CBT or a control program, both on the Internet. 

Before and after treatment, the participants completed the Test Anxiety Inventory (TAI), an 

Anxiety Hierarchy Questionnaire (AHQ), the Exam Problem-Solving Inventory (EPSI), the 

General Self-Efficacy Scale (GSES) and the Heim reasoning tests (AH) as a measure of test 

performance. Of the CBT and control groups 28 percent and 35 percent, respectively, withdrew. 

According to the TAI, 53 percent of the CBT group showed a reliable and clinically significant 

improvement with treatment but only 29 percent of the control group exhibited such a change. 

On the AHQ, 67 percent of the CBT group and 36 percent of the control group showed a 

clinically significant improvement, more than two standard deviations above the mean of the 

baseline, a change in favor of CBT. Both groups improved on the GSES, in state anxiety during 

exams retrospectively assessed, and on the AHQ tests. The study supported use of CBT on the 

Internet for the treatment of test anxiety. 133

 A study by Christensen et al99 studied the impact of two different Internet interventions 

(MoodGym and BluePages) on community-dwelling individuals with symptoms of depression. 

To measure symptom change after the intervention, the 20-item CESD score was the primary 

outcome measure. The mean change in score was greater in the Internet intervention groups than 

in the control group. The difference was significant in the MoodGym group but not the 

BluePages group.

To measure the impact of the “beating the Blues” (BtB) interactive multimedia CBT program 

on anxiety and depression, Proudfoot et al 2003137 compared this program with usual treatment 

(or treatment as usual) for depression and anxiety. Three measures were used: the BDI, the BAI, 

and the Work and Social Adjustment (WSA) Scale. There was a significantly greater drop (of 5 

points) in the BDI score in the BtB group compared to the usual care group. This drop was seen 

at 1 month post-intervention and was maintained over the six month followup period. 

Significance was not reported. A similar result was seen in the BAI score with a difference in 

reduction in score between the BtB group and usual care of 3 points. This change was sustained 

over the 6 month followup period. No significance was reported. Again, similar results were seen 

in the WSA score with a difference in reduction in score between the BtB group and usual care 

of 3 points. This change was sustained over the 6 month followup period. No significance was 

reported (Table 24, and Appendix G, Evidence Table 37). 
83 

Miscellaneous Outcomes 

Summary of the Findings 

Ten studies evaluated the impact of CHI applications on clinical outcomes in various other 

health conditions (Table 27). Outcomes of interest included quality of life and disease-specific 

clinical outcomes. These outcomes were examined in the context of the following health 

problems: Alzheimer’s disease, arthritis, asthma, back pain, chronic adult aphasia, COPD, 

headache, HIV/AIDS, general pain, and obesity. The quality of these 10 studies was moderate to 

low. 

Strengths and Limitations of the Evidence 

The literature in this area had significant limitations. There were only a few studies for each 

particular condition. The disease-specific clinical outcomes evaluated for the same condition in 

different studies were not fully comparable. The same problem was true of the general quality of 

life measures used across various conditions in different studies. This limited the possibility of 

cross-study comparisons. The quality of these trials was variable with some studies lacking in 

one or more methodological domains of RCT quality as measured by the Jadad4

 criteria 

(Appendix G, Evidence Tables 35-37). The majority of the studies did not fully comply with 

CONSORT138guidelines or had low study quality scores. Several studies were based on a very 

small sample size or relied on a short follow up period. Sample size varied from as little as 16 in 

an entire study to 651. Postintervention evaluations ranged from as little as 6 weeks to as many 

as 24 months. Although there was sparse data for each target condition within this category of 

outcomes, we felt that grading the evidence was important due to the large number f studies. The 

overall strength of the body of this evidence was not graded as it was too heterogeneous. 

Outcomes 

 Alzheimer’s disease. This was a 24-week study of 46 mildly impaired patients suspected of 

having Alzheimer’s disease receiving stable treatment with cholinesterase inhibitors (ChEIs). 

The patients were divided into three groups: 1) those who received three weekly, 20-min 

sessions of interactive multimedia Internet-based system (IMIS) in addition to eight hours per 

day of an integrated psychostimulation program (IPP); 2) those who received only IPP sessions; 

and 3) those who received only ChEI treatment. The primary outcome measure was the 

Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog). Secondary outcome measures 

were: Mini-Mental State Examination (MMSE), Syndrome Kurztest, Boston Naming Test, 

Verbal Fluency, and the Rivermead Behavioral Memory Test story recall subtest. Although both 

the IPP and IMIS improved cognition in patients with Alzheimer’s disease, the IMIS program 

provided an improvement above and beyond that seen with IPP alone, which lasted for 24 

weeks139(Appendix G, Evidence Table 37).

 Arthritis. To determine the efficacy of an Internet-based Arthritis Self-Management 

Program (ASMP), randomized intervention participants were compared with usual care controls 

at 6 months and 1 year using repeated-measures analyses of variance. Patients with rheumatoid 
84 

Table 27. Studies of CHI applications impacting miscellaneous clinical outcomes (N=10). 

Target 

condition 

Number of 

studies 

Author, year Interventions Primary outcomes measured 

Alzheimer’s 

disease 

1 Tarraga, 2006139 IMIS,IPP, 

ChEIs 

IPP,ChEIs 

Alzheimer’s Disease Assessment 

Scale-Cognitive 

Health distress 

Activity limitation

Self reported global health

Pain

Arthritis 1 Lorig, 2008114 Online intervention 

Self efficacy

Symptom score at nighttime 

Symptom score at daytime

Morning PEF

Asthma 1 Jan, 200710 Participants 

received asthma 

education and with 

interactive asthma 

monitoring system Night PEF

CSQ-Catastrophizing 

CSQ-Ability to decrease pain

Back Pain 1 Buhrman, 2004115 Cognitive Behavior 

Intervention 

CSQ-Control over pain

Porch Index of Communicative 

Ability (percentiles): Overall 

Porch Index of Communicative 

Ability (percentiles): Verbal

Western Aphasia Battery Aphasia 

"Quotient"

Chronic Adult 

Aphasia 

1 Katz, 

1997140 Computer reading 

treatment 

Computer stimulation 

Western Aphasia Battery Aphasia 

"Repetition"

COPD 1 Nguyen, 2008110 Electronic dyspnea self 

management program 

Score on CRQ subscale for 

dyspnea with ADLs 

Frequency 

Duration

Intensity

Headache 1 Trautman, 2008141 CBT 

Pain catastrophizing

Active life 

Social support 

 HIV/AIDS 1 Gustafson, 

1999142 CHESS 

Participation in health care 

Change in body weight at 3 and 6 

months 

Change in waist circumference at 

3 and 6 months 

BMI at 3 and 6 months 

Systolic blood pressure at 3 and 6 

months 

Diastolic blood pressure at 3 and 

6 months 

Obesity 1 Morgan, 200991 Tailored Web-site 

Resting heart rate at 3 and 6 

months 

Pain 1 Borckardt, 2007143 CACIS Cold Pressor Tolerance 

IMIS=interactive multi-media Internet-based system; IPP= integrated psychostimulation program; ChEIs = cholinesterase 

inhibitors; PEF= peak expiratory flow; CSQ= coping strategies questionnaire; CRQ= chronic respiratory questionnaire; ADL= 

activities of daily living; FBS= fasting blood sugar; FBG= fasting blood glucose; TC = total cholesterol; TG= triglycerides; BIT= 

behavioral Internet treatment; BMI= body mass index: CHESS= Comprehensive Health Enhancement Support System; CACIS 

computer assisted cognitive imagery system 
85 

arthritis, osteoarthritis, or fibromyalgia and Internet and email access (n=855) were randomized 

to an intervention (n=433) or usual care control (n=422) group. Measures included six clinical 

outcomes: pain, fatigue, activity limitation, health distress, disability, and self-reported global 

health. At 1 year, the intervention group significantly improved in four of six clinical outcomes 

as compared to baseline: health distress (p<0.001), activity limitation (p < 0.001), self-reported 

global health (p<0.004), and pain (p<0.001). The Internet-based ASMP proved effective in 

improving clinical outcomes in arthritis patients114 (Appendix G, Evidence Table 37).

 Asthma. To assess an Internet-based interactive asthma educational and monitoring program 

used in the management of asthmatic children, 164 pediatric patients with persistent asthma were 

enrolled and randomized into two study groups for a 12-week controlled trial. The intervention 

group had 88 participants who were taught to monitor their peak expiratory flows (PEF) and 

asthma symptoms daily on the Internet. Clinical outcomes were assessed by weekly averaged 

peak expiratory flow (PEF) values, symptom scores, asthma control tests, and quality of life. At 

the end of trial, the intervention group decreased the nighttime symptom score (range: 0=no 

asthma symptoms, 1=symptoms occurred several times but do not interfere with daily activities, 

2=symptoms interfere with daily activities, 3= symptoms interfere with all activities), (mean 

change -0.08, SD 0.33 vs. 0.00, SD 0.20, p<0.028) and daytime symptom score (mean change -

0.07, SD 0.33 vs. 0.01, SD 0.18, p<0.009); improved morning PEF (mean change 241.9 L/min, 

SD 81.4 vs. 223.1L/min, SD 55.5, p<0.017) and night PEF (mean change 255.6 L/min, SD 86.7 

vs. 232.5 L/min, SD 55.3, p<0.010); improved the rate of having well-controlled asthma (70.4 

percent vs. 55.3 percent, p<0.05); and improved quality of life on a 7-point scale (mean 6.5, SD 

0.5 vs. 4.3, SD 1.2, p<0.05) when compared with conventional management. The Internet-based 

asthma telemonitoring program improved clinical outcomes in pediatric asthma patients10

(Appendix G, Evidence Table 37).

 Back pain. To investigate the effects of an Internet-based CBI with telephone support for 

chronic back pain, 56 subjects with chronic back pain were randomly assigned to either an 

Internet-based cognitive-behavioral self-help treatment or to a waiting-list control condition. The 

study period lasted 8 weeks and consisted of 1 week of self-monitoring prior to the intervention, 

6 weeks of intervention, and 1 week of postintervention assessment. Treatment consisted of 

education, cognitive skill acquisition, behavioral rehearsal, generalization, and maintenance. The 

study showed statistically significant improvements in catastrophizing, control over pain, and 

ability to decrease pain. The findings indicated that Internet-based self-help with telephone 

support, based on established psychological treatment methods, holds promise as an effective 

approach for treating disability in association with pain 115 (Appendix G, Evidence Table 37).

 Chronic adult aphasia. To examine the effects of computer-provided reading activities on 

language performance in chronic aphasic patients, 55 aphasic adults were assigned randomly to 

one of three conditions: computer reading treatment, computer stimulation, or no treatment. 

Subjects in the computer groups used a computer 3 hours each week for 26 weeks. Computer 

reading treatment software consisted of visual matching and reading comprehension tasks. 

Computer simulation software consisted of nonverbal games and cognitive rehabilitation tasks. 

Language measures were administered to all subjects at entry and after 3 and 6 months. 

Significant improvement over the 26 weeks occurred on five language measures for the 

computer reading treatment group, on one language measure for the computer stimulation group, 

and on none of the language measures for the no-treatment group. The computer reading 

treatment group displayed significantly more improvement on the Porch Index of 

Communicative Ability "Overall" and "Verbal" modality percentiles and on the Western Aphasia 
86 

Battery "Quotient" and "Repetition" subtest than the other two groups140 (Appendix G, Evidence 

Table 37).

 COPD. One study tested the efficacy of two 6-month dyspnea self-management programs, 

Internet-based (eDSMP) and face-to-face (fDSMP), on dyspnea with activities of daily living 

(ADL) in people living with COPD. Fifty participants with moderate to severe COPD who were 

current Internet users were randomized to either the eDSMP (n = 26) or fDSMP (n = 24) group. 

The content of the two programs was similar, focusing on education, skills training, and ongoing 

support for dyspnea self-management, including independent exercise. The only difference was 

the mode (Internet/personal digital assistant [PDA] or face-to-face) in which the education 

sessions, reinforcement contacts, and peer interactions took place. The primary clinical outcome 

was dyspnea with ADL that was measured with the Chronic Respiratory Questionnaire. The 

study was stopped early due to multiple technical challenges with the eDSMP, but followup was 

completed on all enrolled participants. Analysis of data available from the remaining 39 

participants did not show significant differences between intervention and control groups110

(Appendix G, Evidence Table 37). 

 Headache. Sixteen participants participated in a study to compare the efficacy of an on-line 

cognitive behavioral treatment (CBT) program with an Internet-based psychoeducational 141

intervention using chat groups (control) on pediatric headache. The main outcome measures 

were frequency, duration, intensity, and pain catastrophization. There were no significant 

differences in changes between the groups for all of the outcome measures. However, the 

frequency of headaches in the CBT group postintervention decreased. Headache duration and 

intensity did not change significantly for the CBT group. Pain catastrophizing was reduced 

significantly post treatment. At the 6-month followup, the treatment effects had not diminished 

(Appendix G, Evidence Table 37).

 HIV/AIDS. To test a computerized system (CHESS: Comprehensive Health Enhancement 

Support System), which provided HIV-positive patients with information, decision support, and 

connections to experts and other patients, 204 HIV-positive patients (90 percent male, 84 percent 

White, most having at least some college education, and 65 percent experiencing HIV-related 

symptoms) were randomized to an intervention group (CHESS computers in experimental 

subjects’ homes for 3 or 6 months) or control group (no intervention). The following quality of 

life sub-scores were significantly different between control and intervention groups in 6-month 

followup: active life (1.37 vs. 1.66, p<0.034), social support (4.24 vs. 4.47, p<0.017), and 

participation in health care (3.64 vs. 4.15, p<0.020)142 (Appendix G, Evidence Table 37). 

 Pain. This study was designed to compare the effectiveness of a computerized pain 

management program over a distraction control. A computer-assisted cognitive/imagery system 

(CACIS) was used to assist subjects in pain management.143 The control group used an identical 

system as the intervention group; the difference between the two being the control group group 

received a prerecorded story about migratory bird patterns with no animation in the visual 

presentation. The intervention group heard a male voice framing the experience as unpleasant 

instead of painful. An individual’s pain was animated on the screen. Each group was subjected to 

an ice water bath for up to 150 seconds, depending on pain tolerance. The intervention group was 

able to tolerate the cold for 13 seconds longer than the control group, but this was not a 

significant difference (Appendix G, Evidence Table 37). 
87 

Key Question 1e. What evidence exists that consumer health 

informatics applications impact economic outcomes? 

Summary of the Findings 

Three studies evaluated the impact of CHI applications on economic outcomes (Table 28). 

These outcomes were examined in the context of 3 health problems including asthma, cancer 

(breast, cervical prostate and laryngeal), and obesity. Studies were very heterogeneous in respect 

to their target areas of interest and outcomes. They will be discussed individually below. 

Table 28. Studies of CHI applications impacting economic outcomes (N=3). 

Target 

condition 

Number of 

studies 

Author, year Interventions Primary outcomes measured 

Asthma 1 Joseph, 200715 Treatment Cost of program delivery to 

developers 

Cost of the computer information 

system—Manual extraction of 

Patient data 

Cost of the computer information 

system—use of electronic 

patient record

Cancer, breast, 

cervical 

prostate, and 

laryngeal 

1 Jones, 1999109 General computer 

information 

Tailored computer 

information 

Materials cost

Obesity 1 McConnon, Total costs to user 

200781 Web site (Internet 

group) Incremental cost-effectiveness

Strengths and Limitations of the Evidence 

Three studies evaluated six domains of CHI impact on economic outcomes. All of the studies 

addressing economic outcomes had relatively large sample sizes: greater than 300 for the study 

on asthma,109 152 in the control arm,162 in the intervention arm; greater than 450 in the study on 

cancer,15 162 in the control arm, 143 in the general information arm, and 162 in the tailored 

intervention; and more than 100 participants in the study on obesity81 (Appendix G, Evidence 

Tables 38-40). The quality of these 3 trials was low to moderately low with studies lacking in 

one or more methodological domains of RCT quality as measured by the Jadad criteria 4

(Appendix G, Evidence Table 1). The body of evidence was not graded for this sub-question due 

to the small number of studies. 

General Study Characteristics 

The studies that were evaluations of the impact of CHI applications on economic outcomes 

generally were tested on an adult population (mean age: 47.4-48.1 years) in the study addressing 

obesity,81 on a juvenile population in the study on asthma (mean age 15.3 years),109 and not 

specified in the study on cancer.15 Information regarding gender was only reported in the paper 

on asthma where 63 percent of the population was female.109 None of the studies reported on the 

race/ethnicity, or socioeconomic status (SES) of study participants. The asthma study109 reported 

on smoking status of greater than or equal to 2 cigarettes per day in 5.2 percent of the population, 
88 

and the obesity study81 reported on weight, BMI, quality of life, and physical activity in each of 

the study groups (Appendix G, Evidence Tables 38 and 39). 

Outcomes 

 Asthma. The economic measure in the study on asthma was identified as the “cost of 

program delivery.” At the end of the study period (12 months), the cost of the referral 

coordinator (the only measurable cost of the study) was $6.66 per treatment per student. There 

was no cost estimate for the control group 109 (Appendix G. Evidence Table 40).

 Cancer: breast, cervical, prostate, and laryngeal. There were three measures of cost in the 

study on cancer. The first measure was the actual cost of implementing the computer information 

system using manual entry of patient data. The authors found that the cost to manually extract 

patient data into a computer information system would cost 9 times as much as the control or a 

general information site. The next measure identified the cost of importing the electronic patient 

record into the tailored information system. There was no difference found in cost between the 

general information system and the tailored system using this method. The final measure of cost 

studied was material cost. The control group used paper (books) and the cost per book was 

estimated at £7. The cost of the general information system was estimated to be 40 percent of 

this, or £2.8 per patient. No information was provided for per user cost of the tailored 

information system15 (Appendix G. Evidence Table 40).

 Obesity. The obesity study measured total costs and incremental cost effectiveness. The total 

cost for the control group was £276.12 compared to the total cost for the Web site intervention 

group of £992.40. The authors pointed out that the difference in cost was due to the cost of 

developing the Web site. They stated that when this fixed cost was removed, the total costs of the 

intervention were lower. However, the actual estimate was not reported. Incremental costeffectiveness was calculated for the intervention group, and was reported as £39,248 per qualityadjusted life-year81 (Appendix G. Evidence Table 40).

Key Question 2: What are the barriers that clinicians, 

developers, and consumers and their families or caregivers 

encounter that limit implementation of consumer health 

informatics applications? 

 Thirty-one studies were reviewed that addressed the barriers to CHI applications, with a 

focus on studies that reported on CHI applications that were individualized to the consumers’ or 

caregivers’ needs. Documented barriers to CHI applications were identified, extracted, and 

tabulated. 

Disease/Problem Domain 

 The CHI applications focused on a specific disease or problem domain. Two studies 

addressed more than one disease (breast cancer – all cancers144; HIV/AIDS – STDs145), but the 

remaining 20 studies focused on only one disease or problem domain. Diseases included breast 

cancer (4), 144,146-148 mental health (3),149-151 physical activity/diet/obesity (4),36,152-154 diabetes 
89 

(3),93,155,156 HIV/AIDS (2),145,157 prostate cancer (1),158 all cancers (1),144 hypertension (1), 159

STDs (1).145 Problem domains included use of a personal health record (3)160-162 and review of 

systems (1).163

 For the purpose of further analysis, the study focusing on breast cancer and all cancers 144

was collapsed under “all cancers” (leaving three breast cancer related studies) and the study 

dealing with HIV/AIDS and STDs145 under HIV/AIDS (leaving no study on STDs) (Appendix 

G, Evidence Tables 41-43). 

Methodology 

 The methodology used to identify barriers varied across studies (Tables 29 and 30). There 

were four categories including validated survey, nonvalidated survey, qualitative research, and 

empirical research. Five studies used more than one methodology. 36,145,150,153,160 If a study used 

either a validated survey or empirical research, it was collapsed under “Validated survey / 

Empirical.” Otherwise, it was assigned “Nonvalidated survey / Qualitative” as the research 

methodology (Appendix G, Evidence Tables 41-43). 

 CHI applications require participation of consumers, their caregivers, clinicians, and 

developers. Barriers can apply to any of the participants, and the type and impact of the barrier 

may vary significantly between providers, developers, patients, and their caregivers. Thus, an 

analysis of the barriers must include those that impede participation of any of the above groups. 
90 

Table 29. The distribution of methodologies for identifying barriers to the use of consumer health 

informatics by disease /problem domain. 

Methodology 

Disease 

Non-validated survey / 

Qualitative 

Validated survey / 

Empirical Total 

All Cancers 2 2 

Breast Cancer 1 2 3 

Diabetes 5 5 

HIV/AIDS 3 3 

Hypertension 1 1 

Mental Health 3 3 

Personal health record 2 1 3 

Physical Activity / Diet / 

Obesity 6 1 7 

Prostate Cancer 1 1 

Review of systems 1 1 

Smoking Cessation 2 2 

Total 24 7 31 

Table 30. The distribution of methodology by barrier type. 

Methodology

Barrier 

Non-validated survey / 

Qualitative 

Validated survey / 

Empirical Total 

Systems & User Level 6 1 7 

Systems level 3 2 5 

User level 15 4 19 

Total 24 7 31 

Barriers 

 Barriers were divided between system-level and the individual-level barriers (Table 31): 

1. System-level barriers were further divided into technical or health care system issues. 

Technical barriers included usability, work flow issues, and data security concerns. 

Health care system issues included the reimbursement system and incompatibility 

between patient applications and legacy systems in health care institutions. 

2. Individual level barriers pertained to either the clinician or the consumer. Clinician 

endorsement affects consumer choice, and thus negative attitudes of clinicians may be a 

barrier to consumer use. Consumer issues included lack of access to the application (e.g., 

no home Internet access), concerns about privacy, limited literacy and knowledge, 

language hurdles, cultural issues, and lack of technologic skills (Appendix G, Evidence 

Tables 41-43). 
91 

Table 31. The distribution of barrier levels by disease/problem domain. 

Barrier 

Disease/Problem 

Domain 

Both levels Systems level User level Total 

All Cancers 1 1 2 

Breast Cancer 3 3 

Diabetes 2 3 5 

HIV/AIDS 1 2 3 

Hypertension 1 1 

Mental Health 1 1 1 3 

Personal health record 2 1 3 

Physical Activity/ Diet / Obesity 3 4 7 

Prostate Cancer 1 1 

Review of systems 1 1 

Smoking Cessation 2 2 

Total 7 5 19 31 

 System-level barriers. 

Technical system-level barriers. Nine studies explored lack of Internet access at home or in 

the community and six found this to be a barrier147,152,153,156,159,160 (Appendix G, Evidence Table 

43). One study identified hardware requirements as a systems level barrier.164 and another study 

identified mobile device shape/design/configuration as a systems level barrier.165

 Health care system-level barriers. Five studies cited incompatibility with current care as a 

barrier145,157,159,160,163 (Appendix G, Evidence Table 43).

 Individual-level barriers. 

Clinicians. One study noted that the clinic staff feared more work. 151 Of note, the 

applications that were included in the literature review were applications that are operated 

independently by consumers, so there are no applications that require the physician to interact 

directly with the consumer through a CHI application (Appendix G, Evidence Table 43).

Developers. One study cited lack of built-in social support in the CHI application as a barrier. 

93 One study noted that patients forgot their passwords by the time they had their followup visit. 

151 One study cited lack of training and guidance in the use of the application. 160 Along the same 

lines, one study reported that electronic tools for data entry were a problem for users 144, whereas 

another cited the lack of automated data entry as a problem. 155 In one study users complained 

about a design that did not allow for back entry of old data.165 Two studies discussed lack of user 

customization or making the content more relevant to the consumer and his or her community as 

a barrier93,154 (Appendix G, Evidence Table 43). Two studies focused on the “substantial 

investment” required for the development and maintenance of CHI resources.75,166

Consumers and their caregivers. Nineteen studies queried application usability or userfriendliness and all nineteen found evidence of this barrier36,147-149,151-158,160,161,163,167-169(Appendix 

G, Evidence Table 43).

 Eleven studies explored patient knowledge, literacy, and skills to use the CHI application. 

Deficits in these areas were found by one study not to be a barrier. 146 The other ten, plus one 

study that had not initially considered these barriers in the study design, did find these deficits to 

be barriers 144,148,150,151,156,157,159-163 (Appendix G, Evidence Table 43). 

 Six studies considered the possibility that users would find the application too timeconsuming and five of these reported this barrier in the results section. 152 In the same vein, one 
92 

study cited too many emails to participants as a barrier.169One study queried consumers about the 

acceptability of fees for use of an interactive portal and found that most participants were not 

willing to pay any fee for the service.166

 Five studies sought information about privacy concerns and four reported concerns over 

privacy as a barrier in their finding.144,145,151,161 The same four studies queried and found 

concerns over the control of information or lack of trust to be barriers144,145,151,161(Appendix G, 

Evidence Table 43). 

 Two studies queried for cultural barriers and only one study found evidence of this. 146

One study found the language of the CHI application to be a barrier. 161

 The expectations of consumers figured prominently in the barriers analysis. The terms 

acceptability, usefulness, credibility, expectations, and goals were mentioned often and the lack 

thereof was found to indicate barriers in eight studies 20,93,165,167 36,147,151,157(Appendix G, 

Evidence Table 43). 

 One study of an interactive Web portal did not identify a barrier regarding usefulness, but 

found that most participants who had not used the portal expected a number of features to be 

useful, but less users of the portal actually rated these features as useful.166

 Cost was mentioned as a barrier in only one study.165

 Three studies investigated consumer disability, generally grouped as physical or cognitive. 

One did find evidence that physical or cognitive impairment resulted in barriers to the use of CHI 

applications. 162 One found that not reacting to visual preferences was a barrier. 158 

Anxiety over the use of computers, complaints about lack of personal contact with clinicians and 

the belief that IT would not be an improvement to current care were mentioned in two studies as 

barriers159,162 (Appendix G, Evidence Table 43). 

Key Question 3: What knowledge or evidence deficits exist 

regarding needed information to support estimates of cost, 

benefit, and net value with regard to consumer health 

informatics applications? 

 Upon review of the results of the systematic review presented above, several important 

knowledge gaps became evident. In general, the literature was at a very early stage of 

development. Many questions have only been evaluated by one study. Thus, confirmatory 

studies have generally not been done. In addition, no high quality studies have been conducted 

regarding several important questions. Broadly, these questions can be grouped into at least one 

of the following four categories: patient-related questions, CHI utilization factors, 

technology/hardware/software/platform-related issues, and health-related questions. The major 

questions and outstanding issues of concern for each of these sections will be outlined below. 

Patient-related Questions 

 Many questions about CHI applications at the patient level remain. The results of our review 

suggested that the literature is relatively silent on the question of whether or not significant 

differences in patient preferences, knowledge, attitudes, beliefs, needs, utilization, and potential 

benefits exists across gender, age, and race/ethnicity. Intuitively, we suspect some differences 

exist, especially as they relate to the senior population compared to the adolescent population. 
93 

However, these differences have not been definitively characterized, and the clinical and public 

health implications of these differences are largely unknown. The same could be said for 

potential gender- and race or ethnicity-based differences. Early evidence suggests that potentially 

significant differences exist that could have important health implications as we move toward a 

more technology-saturated society.170,171 Beyond these potential demographic differences, the 

emerging field of CHI is developing within the context of a societal and even global emergence 

of technology-based realities, including Web 2.0/Web 3.0 and ubiquitous computing, which are 

enabling an unprecedented level of user-determined interactivity and functionality. The degree 

to which this functionality could be harnessed for the health benefit of consumers is largely 

unknown. Along these lines, with the predominance of chronic diseases and the burgeoning of 

the senior population in this country, there is an increasing reliance on nonprofessional family, 

community, and low-skilled caregivers providing ever increasing levels of care to patients. As 

such, the target users of CHI applications must increasingly be focused on more than just the 

index patient. Our review suggested that the majority (but not all) of the current RCT CHI 

literature is focused on the patient as the CHI user. Finally, given the increasing role of family 

members, friends, and other caregivers, sociocultural and community factors will likely exert 

significant impact on access, usability, desirability, and benefit of CHI applications. Issues 

related to trust, security, and confidentiality need to be further explored. 

 

CHI Utilization-related Factors 

 Given the ubiquity of the Internet, the overwhelming majority of current and developing CHI 

applications will likely be reliant at least in part on the Internet. Increasingly this will require that 

consumers have broadband access to the Internet to take advantage of the full functionality that 

CHI applications potentially have to offer. Despite a rapid increase in both the availability and 

access to broadband services among all population groups, age groups, and geographic regions 

of the country (eHealth Solutions for Health Care Disparities Gibbons (ed) 2007 Springer Pub), 

differential access to broadband Internet access may have significant implications in terms of 

health benefits that may be derived from these tools and applications. Of equal concern, while 

many in the younger generations become very technically savvy at an early age, many 

Americans still have limited health literacy (eHealth Solutions for Health Care Disparities 

Gibbons (ed) 2007 Springer Pub). The combination of low technology expertise and low health 

literacy may pose insurmountable barriers for some individuals. The ability of these individuals 

to use and benefit from CHI applications, even when adequate access exists, should be evaluated. 

Taken together then, these CHI utilization factors suggest the need for a more robust evaluation 

and explication of the epidemiology of broadband access and technology literacy in the US. 

Technology/Hardware/Software/Platform-Related Issues 

 The results of our review suggest that the majority of currently evaluated CHI tools and 

applications are designed for use on personal computers (desktop, laptop) as Web-based 

applications. While these technology platforms have certainly not been exhaustively studied, 

many more potential platforms exist, including interactive webTV, Video On Demand, 

smartphones, and health gaming to name a few. In the domestic literature, the potential of these 

platforms has not been evaluated. In addition, it appears that the CHI applications evaluated to 

date have been designed primarily by health care practitioners without sufficient training or 
94 

expertise in critical design areas such as human factors and user-centered design. As such, 

currently available tools may not be the best possible tools and may yield disappointing results 

despite well-designed evaluation studies. Emerging evidence from the Robert Wood Johnson 

Foundation’s Project HealthDesign and other similar projects is suggesting that the CHI tools 

and applications and functionality that consumers want and need are not always what health care 

practitioners think they need.172 Furthermore, many health care practitioners and entrepreneurs 

are likely ill-equipped to integrate the appropriate data, as suggested above, into the design 

process. As a result, important sociocultural and human computer interface design elements may 

not get incorporated adequately into emerging CHI applications and therefore may lead to CHI 

applications with limited efficacy. 

Health-related Factors 

 Finally, the results of our review suggested that several important health-related questions 

remain regarding the potential utility of CHI applications. To date, most CHI applications that 

have been evaluated tend to focus on one or more domains of chronic disease management. 

While this is very important and clearly needed, insufficient attention has been given to the role 

of CHI applications in the acute exacerbation of symptomatology or other urgent and emergent 

problems that may occur in home- and community-based settings. While it remains clear that 

professional expertise is increasingly needed as the acuity of the problem increases, with the 

growing dominance of home- and community-based care and self-management, telephone and/or 

ambulance transfer to an emergency room may not represent the most efficient and cost effective 

way to access professional health care personnel and services. Along these lines, the role of CHI 

applications in primary, secondary, and tertiary prevention needs to be more adequately 

explored. Given the prevalence of mental health and psychiatric issues, the value of CHI 

applications in the context of mental health, coping, and stress should be evaluated. Finally 

sociocultural factors are increasingly important determinants of health care outcomes. The 

potential impact on social factors including social isolation and social support and perhaps even 

broader social determinants of health need to be evaluated and may prove useful in helping 

patients address select health concerns in the home- and community-based setting. 

Key Question 4: What critical information regarding the 

impact of consumer health informatics applications is 

needed in order to give consumers, their families, clinicians, 

and developers a clear understanding of the value 

proposition particular to them? 

 The results of the current review suggest that several critical information needs still exist that 

must be filled to enable a clear understanding of the value proposition of CHI applications. It is 

likely that the knowledge gaps needed to establish a value proposition, while overlapping, are not 

identical across all potential stakeholders. We will address this question from 2 perspectives, that 

of the clinician or provider and that of the patient, family and caregiver. 
95 

Clinician and Provider Value Proposition Information Needs 

 While this review focused on CHI applications that are not dependent on a clinical provider, 

they at times may involve providers. It is well known that provider recommendations and 

support can be an important motivator for some patients to engage in a certain behavior. It may 

be that provider recognition and support of patient use of CHI could play an important role in the 

adoption and use of CHI applications by some patients. Because providers are often most 

concerned about clinical outcomes and costs, it seems reasonable that questions of the impact of 

CHI applications on provider or health care processes, costs, and outcomes as addressed in this 

report will need to be more definitively characterized. There is at least one additional critical 

knowledge need that is pertinent to providers. It is the potential liability a provider might incur 

from a patient using a CHI application. It is not clear at this point that any liability would exist 

under current law, particularly for those CHI applications that do not involve a health care 

professional. Yet it may be that this question will need some further clarification prior to 

widespread endorsement of CHI applications by many health care providers. 

Patient, Family, and Caregiver Value Proposition Information Needs 

 While it is tempting to believe that patients want the exact same thing as their providers and 

health care practitioners, we know that this is not always the case. Indeed the growth of the 

Internet and its utilization first by consumers and then providers can be very instructive 

regarding value proposition needs of consumers.173 This data and experience suggest that 

patients and caregivers are, except in the cast of an emergent problem, often most concerned 

with well-being issues, health care processes, costs, and then clinical outcomes. Patients most 

often cite convenience and anonymity as the primary reasons the Internet has become such a 

major source of health information.173 Interestingly, both of these characteristics are largely 

lacking from our health care system today. It is likely that the more these elements can be 

incorporated into emerging CHI applications, the more likely they will be considered of value by 

consumers. Other related factors such as usability, portability, and patient-centered functionality 

are likely important characteristics of CHI applications that may help drive utilization. It has 

been suggested that the degree to which technology becomes “invisible,” or becomes 

incorporated into an individual’s lifestyle rather than creating additional tasks or processes, is the 

degree to which these tools will become more powerful. Those technologies that exist and enable 

consumers to accomplish tasks (empower) without further complicating individuals lives may 

ultimately prove to be the most widely used and valued CHI applications. Finally, by expanding 

the list of available platforms from which consumers can utilize CHI tools and applications (TV, 

WebTV, satellite, On Demand, health gaming), CHI applications may become more appealing to 

a broader consumer base and thus prove valuable to those consumers who could most benefit, 

but may not otherwise use a more traditional CHI application. 

Research in Progress 

 Based on a search string developed early in the development of the project (see Appendix C), 

a similar search string was developed to search the grey literature for ongoing research (Health 

Services Research Projects in Progress database). Our search identified 180 titles that were 

reviewed for relevance to our study topic. Four ongoing and continuing research studies were 
96 

identified. The outcomes in these studies may provide additional information about the success 

of a consumer-centered approach to health care. All these studies were designed to develop 

Internet-based health informatics that in the end will be helpful in improving the quality of care 

and creating a more informed consumer. The results of these studies have not been published yet. 

 One study by Christakis,171 is an ongoing study to develop an Internet-based patient-centered 

asthma management system. A critical feature of the study is to improve the quality of asthma 

care delivery by health care providers. The study will gauge the effectiveness of AsthmaNet, a 

Web-based asthma patient activation system, which will provide tailored clinical information to 

parents as well as give them decision aids to share with their providers. 

 In another study, which was completed in 2008, Lorig et al174 evaluated the usefulness of 

translating evidenced-based small-group diabetes education on to an Internet platform. The main 

aims of the 2-year RCT were to: 1) develop, implement, and evaluate an Internet Diabetes SelfManagement Program (IDSMP) compared with usual care; 2) compare the effects of the IDSMP 

with and without email discussion group reinforcement; 3) conduct cost-benefit analysis of the 

IDSMP compared with usual care, and the IDSMP with and without reinforcement; and 4) 

conduct a process evaluation of the use of the sections of the IDSMP and how usage, changes in 

behaviors, changes in self-efficacy, and patient characteristics are associated with intervention 

effects (health status and health care utilization) at 6 months and 2 years. 

 Another completed study completed in 2005, by Col175 was designed to address the issues 

involved with menopause. The immediate goal was to develop a technology comprehensive 

Menopause Interactive Decision Aid System (MIDAS) that provides personalized feedback 

about menopausal symptoms, risks for common conditions, and the effects of different treatment 

options on the short- and long-term consequences of menopause. The main hypotheses of this 

study are that MIDAS can: 1) lead to better decisions and improve the quality of menopausal 

counseling; 2) improve compliance with a chosen menopausal plan; and 3) reduce medical errors 

associated with the use of menopausal therapies. The specific aims are to: 1) develop and 

optimize the utilization of MIDAS; 2) evaluate the impact of MIDAS on the decisionmaking 

process, including decisional conflict, knowledge, risk perception, anxiety, patient-physician 

communication, satisfaction with decisionmaking, the quality of menopause counseling, and 

medical errors related to menopausal therapy; and 3) evaluate the long-term impact of MIDAS 

on outcomes related to menopause. 

 In another study, which was completed in 2008, Sciamanna,176 studied the efficacy of a 

computer program that creates: 1) patient-specific physical activity self-help reports for 

individuals, and 2) patient-specific reports to prompt and guide physician advice. The study was 

designed to assess the effects of the computer-generated physical activity reports (patient and 

physician) on the patients' physical activity and endurance fitness over a 6-month period as 

compared with usual care. 
Chapter 4. Discussion 

1

Summary of Key Findings 

 We have presented here the results of a systematic review of the literature regarding the 

impact of CHI applications. The CHI field is new and still evolving. As such, the literature in this 

field is very heterogeneous and challenging to summarize in well-described categories. Our 

review identified a total of 162 articles, of which 137 addressed Key Question 1 and 31 

addressed Key Question 2. Overall, despite the heterogeneity and limited nature of the literature, 

the following themes emerged. 

 First, while there may be a role for CHI applications to reach consumers at a low cost and 

obviate the need for some activities currently performed by humans, it is likely that a more 

important role is to enhance the efficacy of interventions currently delivered by humans. Several 

studies compared the use of a CHI application with traditional therapy against traditional therapy 

alone. Many found that both groups exerted a significant effect on the outcome of interests, yet 

the CHI group had even more benefit that traditional therapy alone. 

 Secondly, in the aggregate, the studies evaluated in this review tended to support the finding 

that at least three critical elements are most often found in those CHI applications that exert a 

significant impact on health outcomes. These three factors are 1) individual tailoring, 2) 

personalization, and 3) behavioral feedback. Personalization involves designing the intervention 

to be delivered in a way that makes it specific for a given individual. Tailoring refers to building 

an intervention, in part, on specific knowledge of actual characteristics of the individual 

receiving the intervention. Finally, behavioral feedback refers to providing consumers with 

messages regarding their status, wellbeing, or progression through the intervention. These 

messages may come in many different forms. They can be motivational (You did great today!) or 

purely data driven (You completed 80 percent of your goal today). Interestingly, it is not clear 

from this literature that CHI-derived behavioral feedback is any better than feedback originating 

from human practitioners or others. Rather, it appears that the feedback must happen with an 

appropriate periodicity, in a format that is appealing and acceptable to the consumer, not just the 

provider. 

 This systematic review found that RCT evaluations to date suggest that CHI applications 

may positively impact healthcare processes such as medication adherence among asthmatics. 

CHI applications may also positively impact intermediate outcomes across a variety of clinical 

conditions and health behaviors, including cancer, diabetes mellitus, mental health disorders, 

smoking, diet, and physical activity. CHI applications may not have much impact on 

intermediate outcomes among individuals who are obese or suffer with asthma or COPD. The 

currently available RCT evidence is more equivocal regarding the impact of CHI applications on 

relationship-centered outcomes, while the evidence appears relatively strong in support of the 

positive impact of CHI on selected clinical outcomes. (Mental Health) The data are insufficient 

to determine the impact of CHI on economic outcomes. 

 Of note, studies have identified several barriers to utilization of CHI applications. The 

barriers include incompatibility with current care practices, professional staff perceptions of 

increased workload, poor social support, limited IT knowledge and literacy of consumers, 

cultural issues, and concerns about time, privacy, security, and control. 

 

1

 Appendixes and evidence tables cited in this report are available at: http://www.ahrq.gov/clinic/tp/chiapptp.htm.

97 
 While the use of CHI applications offers significant promise and potential, the nascent 

literature has important knowledge gaps that currently preclude claims of proven efficacy or 

unquestionably support a value proposition for the use of CHI applications. In the final analysis, 

the early work cited in this review is encouraging, but clearly more research is needed to 

substantiate these early findings and close the identified gaps in knowledge. 

Limitations 

 This review has several important limitations. First our initial search for eligible studies 

proved to be challenging because of inconsistent use of terminology in the literature. We 

minimized this problem by searching multiple databases and supplementing our search with a 

review of selected journals and querying experts. The most important limitation was marked 

heterogeneity of interventions, populations and outcomes, making synthesis across studies 

difficult, and precluding meta-analysis. Inconsistent definitions and reporting of outcome 

measures further limited our ability to synthesize data, as many studies did not report enough 

data to support calculation of effect sizes. Another limitation is related to the design of CHI tools 

and applications. Because development involves an iterative process, it is sometimes difficult to 

synthesize results across studies. Two studies my have evaluated the same CHI tool or 

application however the tool itself may have been adapted or otherwise changed during the 

period of time after the first study but prior to the second study. Methodologic limitations of 

many of the RCTs limit the strength of conclusions. We evaluated the quality of the study using 

the criteria proposed by Jadad.4

 We also graded the strength of the body of the scientific 

evidence on each section. For a variety of reasons, the strength of the body of evidence was often 

graded as low. Because the distinction between CHI and patient-centered HIT has not been 

clearly articulated, it was at times challenging to distinguish between consumer HIT and patientcentered HIT. Patient centered HIT studies were excluded because they will be addressed in a 

separate evidence report. Finally, as indicated in the Research in Progress section of the Results 

chapter, several studies of CHI applications have been initiated or completed but not yet 

reported. The evidence report may need to be updated when the results of these studies are 

available. 

Future Research Needs 

 The results of this review indicate that the scientific evidence base regarding the impact of 

CHI applications is at a nascent and evolving state. As such, several future research needs can be 

identified. More work needs to be done to confirm the preliminary findings identified in this 

review. In many areas, only one study has been done on a given question or issue, precluding 

definitive conclusions. Across studies, the reporting of the evaluations is non-uniform, often with 

critical features of the evaluation methodology or application details entirely lacking. To 

facilitate uniform reporting and improve the quality of the work in this field, consideration 

should be given to development of a national CHI applications design and development registry 

and CHI applications trials registry with uniform reporting requirements. However, the 

developers of these applications come from a wide and diverse array of backgrounds. Some have 

significant technical expertise while others do not. Furthermore, these studies are reported in a 

variety of journals with editors and editorial boards of widely differing technical expertise and 

reporting requirements. Research in this multidisciplinary field would be greatly enhanced by an 

98 
accepted vocabulary, nomenclature, or ontology. Currently there is much confusion and blurring 

of the lines between the technical platform upon which the application is built along with the 

technical specifications of the CHI application in question with both the goals and functions of 

the application and the educational or behavioral content included in the application. While a 

strict rendering of the current definitions of these elements allows for little conceptual overlap, 

the literature is replete with examples of investigators who describe the technical platform 

employed in a CHI application (cell phone) when describing the application, which by itself, 

sheds little light, regarding the nature of the CHI application. More work will need to be done to 

explicate the role of human factors, socio cultural factors, human computer interface issues, 

literacy, and gender. 

 The findings of this review indicate that most CHI research is being primarily conducted 

among white/Caucasian adult patients, and it is not clear how the findings apply to non-white 

populations. The importance of this limitation is heightened by the fact that the internet will be 

the primary means of the consumer’s ability to use and take advantage of CHI tools. While 

technological platforms may vary, most CHI applications will, in one way or another, rely on the 

internet to perform its functions. Consumer internet familiarity and utilization trends will have 

significant impact on the ability of CHI applications to be successful across all consumer 

populations. Recent data suggests the internet and technology experiences of whites may not be 

the same as individuals from other racial/ethnic backgrounds. Differential experiences across 

racial groups may be associated with differential efficacy of a given CHI application and result 

in outcomes that are unexpected or unseen among white consumer groups. The evidence 

suggests, for example, that Internet and technology utilization has not yet become as essential or 

appealing to African-Americans as to whites. Just 36 percent of African-Americans with Internet 

access go online on a typical day compared to 56 percent of whites. Whites and blacks even have 

differing attitudes toward the internet with online African-Americans not being as fervent in their 

appreciation of the Internet as online whites.173 African-American Internet users are also 

somewhat more likely than whites to have their Internet access come exclusively through their 

jobs. Finally, while online privacy has become a significant concern for a majority of Internet 

users, African-Americans tend to be less trusting than whites. They are also more concerned 

about their online privacy than whites and these heightened privacy concerns are reflected in 

what they choose to do online. Online African-Americans are less likely to participate in hightrust activities like auctions or to give their credit card information to an online vendor. They are 

also less likely than white Internet users to trade their personal information for access to a Web 

site. 173 The CHI and health implications of these findings are unclear. 

 The problem extends beyond African Americans. Fifty-six percent of Latinos in the U.S. use 

the Internet. This compares to 71 percent of non- Hispanic whites and 60 percent of nonHispanic blacks who use the internet. 173 Among Latinos, the information and communications 

revolution is not limited to the computer screen. Some Latinos who do not use the internet are 

connecting to the communications superhighway via cell phone. Almost 60 percent (59 percent) 

of Latino adults have a cell phone and 49 percent of Latino cell phone users send and receive text 

messages on their phone.173 

 Finally, the issue is not just one of under-utilization or access. Asian-Americans who speak 

English are the most wired racial or ethnic group in America. They are also the Internet’s 

heaviest and most experienced users. Over 5 million Asian Americans (75 percent) have used the 

internet. This compares to 58 percent of whites, 43 percent of African- Americans, and 50 

percent of English-speaking Hispanics. 173 Typically Asians spend more time online than other 

99 
racial and ethnic groups. In addition, they engage the internet at a much higher level of intensity 

on a typical day than other groups and, as such, the internet represents an extremely important 

and fundament component of daily living for Asian-Americans. Overall, Asian-American men 

engage in online activities more than Asian-American women.173 Even beyond race and ethnicity 

issues that may affect CHI mediated health outcomes; the importance of family, neighborhood, 

and environmental determinants of many clinical health outcomes is increasingly realized. We 

need to understand how these factors (social determinants) may impact CHI access, utilization, 

efficacy, costs, and/or outcomes at the individual level and healthcare disparities at the 

population level. The results of this review indicate that the realities and implications of these 

differences have not been adequately evaluated in the current scientific literature and much more 

formative and experimental work needs to be done to fill these critical knowledge gaps. 

The results of this review also indicate that because most of the evaluative research being done is 

being conducted among middle aged adult populations, significant opportunities exist for 

additional research among other age groups of consumers. It may even be that the impact of CHI 

applications may be greater among non middle aged adult consumers because these consumers 

may be most likely to adopt CHI applications (children, adolescents, and young adults) and they 

may have the most to gain from using effective CHI applications (elderly). 

 Similarly, the results of this review indicate that most CHI applications evaluated to date are 

designed to run on desktop computers. More work will need to be done to understand the role of 

other technological platforms including cell phones, PDA’s, TV, satellite, on Demand, Health 

Gaming platforms (Wii, XBOX, Gamecube etc). Related to technological platforms used for 

CHI applications is the potential role of social networking applications. Very few currently 

evaluated CHI applications explored the dynamics and potential utility of using social 

networking applications (Skype, Twitter, MySpace, Facebook, You Tube, blogs, Second life, 

Yoville and Farmville etc) to support behavior change or improve health outcomes. While it may 

be challenging to envision the elderly twittering, use of these applications may open 

opportunities to address health problems impacted by trust, social isolation, cognitive stimulation 

and low literacy) This type of research may inevitably lead to a broader array of interactivity 

among patients and their caregivers with measurable psychological and physiological health 

benefits for users and patients. In so doing, CHI applications may accrue greater appeal and 

effectiveness among patients because these applications are assisting patients to address real life 

issues that in the past may have been unrecognized barriers to achieving optimal health. 

Implications 

 The results of this review have several important implications. In terms of the currently 

engaged and activated consumer, CHI applications and tools may in the future provide additional 

tools to facilitate efforts to optimize their health status. The rapid growth and development of the 

internet combined with the rapid rise in the use of the internet to search for health related 

information suggest that individuals are drawn to use convenient and anonymous technologies 

for health purposes. If CHI applications and tools become available in a wider array of platforms, 

it may become easier to engage more people who are not actively managing their health. 

Although CHI tools and applications, as we have defined them, do not require the involvement 

of a healthcare provider, it is likely that significant growth in the utilization of CHI tools will 

necessitate increasing provider and healthcare system competency with these emerging tools. 

Consumers will increasingly want more interactivity and functionality and the ability to work 

100 
101 

interactively with traditionally collected health information at the time and place of their 

choosing. Providers and healthcare systems that are seen as not equipped to handle or address 

these issues are unlikely to be seen as the highest quality or highest performing providers and 

systems. 

 There are may be important implications for health policy decision makers, such as the 

National Coordinator of IT. To the extent that CHI applications help improve healthcare process 

and clinical outcomes, they cannot be considered outside the domain of the healthcare system or 

direct medical care. Growth in this area may necessitate the development of policy positions 

which support diffusion of HIT tools and applications among providers and healthcare systems, 

but also facilitate the diffusion of CHI tools and applications among healthcare consumers. In 

like fashion many state officials and governments have or are currently considering supporting 

regional Health Information Exchanges, state wide Electronic Medical Records systems and 

other medical technologies. These state level health leaders may soon need to consider 

supporting patient use of CHI tools as one strategy to facilitate health promotion. Yet, as the 

results of this review indicate, the current state of the scientific literature is promising, but 

largely preliminary and thus not able to provide evidence based guidance regarding cost effective 

utilization of scarce public or private resource dollars with respect to CHI. 
103

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Appendix A 
Appendix A: List of Acronyms 

 

Acronym Definition 

ADAS-Cog Alzheimer’s disease assessment scale-cognitive 

AHQ Anxiety hierarchy questionnaire 

AHRQ Agency for Healthcare Research and Quality 

AMIA American medical informatics association 

ANCOVA Analysis of covariance 

APHA America public health association 

ASMP Arthritis self-management program 

ASQ Attributional style questionnaire 

BAI Beck Anxiety inventory 

BDI Beck Depression inventory 

BMI Body mass index 

BtB Beating the Blues 

CBT Cognitive behavioral theory 

CESD Center for Epidemiologic Studies-Depression 

CES-D Center for Epidemiologic Studies Depression 

CgA Chromogranin A 

ChEIs Cholinesterase inhibitors 

CHESS Comprehensive health enhancement support system 

CHI Consumer health informatics 

CI Confidence interval 

CoNeg Composite index for positive situations 

CoPos Composite index for negative situations 

DHEA-S Dehydroepiandosterone sulphate 

DSMP Dyspnea self-management programs 

DXA Dual-energy x-ray absorptiometry 

EPC Evidence-based Practice Center 

EPSI Exam problem-solving inventory 

FFB Kristal Fat and Fiber Behavior 

HDS Health distress scale 

IEEE Institute of Electrical and Electronics Engineers 

IET Industrial engineering technology 

IMIS Interactive multimedia internet-based system 

IPP Integrated psychostimulation program 

ISI International standards institute 

IT Information technology 

MeSH Medical subject heading 

MMSE Mini-mental state examination 

NET Internet-based CBT 

NPY Neuropeptide Y 

PCS Perceived competence scales 

PDA Personal digital assistant 

PDF Portable document format 

RCT Randomized controlled trial 

SB2-BED Student bodies 2-binge eating disorder 

SD Standard deviation 

SDSCA Summary of Diabetes Self Care Activities 

TAI Test anxiety inventory 

TEP Technical expert panel 

TNFα Tumor necrosis factor α

WHO World Health organization 

WSAS Work and Social Adjustment Scale 

A-1 
Appendix B 
Appendix B: Technical Experts and Peer Reviewers 

B-1 

Patricia Flatley Brennan, R.N., Ph.D. 

Chair, Industrial & Systems Engineering 

College of Engineering 

Moehlman Bascom Professor School of 

Nursing University of Wisconsin-Madison 

1513 University Avenue 

Madison, WI 53706 

Carol H. Cain, Ph.D. 

Care Management Institute 

One Kaiser Plaza, 16th floor 

Oakland, CA 94612 

Sara Czaja, Ph.D. 

University of Miami Miller 

 School of Medicine 

McArthur Engineering Building 

Miami, FL 33136 

Gunther Eysenbach, M.D., M.P.H. 

Centre for Global eHealth Innovation 

R. Fraser Elliott Building 

190 Elizabeth Street 

Toronto, ON M5G 2C4 

Kenneth W. Goodman, Ph.D. 

University of Miami Ethics Programs 

P.O. Box 016960 (M-825) 

Miami, FL 33101 

Holly B. Jimison, Ph.D. 

Oregon Health & Science University 

BICC 

Department of Medical Informatics & 

Clinical Epidemiology 

Portland, OR 97239 

Rita Kukafka, Dr.P.H., M.A. 

Columbia University 

Department of Biomedical Informatics 

New York, NY 10032 

Musa Mayer 

250 West 82 Street, #42 

New York, NY 10024 

Maryann Napoli 

Center for Medical Consumers 

239 Thompson Street. 

 NY 10012-5030 

Eugene C. Rich, M.D., F.A.C.P. 

Scholar in Residence 

Association of American Medical Colleges 

2450 N Street, NW 

Washington, DC 20037-1126 

Earl Steinberg, M.D., M.P.P. 

Resolution Health 

Columbia, MD 21044 
Appendix C 
Appendix C: Detailed Search Strategies 

Database Terms Date Returns 

PubMed ((("Medical Informatics Applications"[Mesh] OR "Informatics"[Mesh] OR 

"medical informatics"[mh] OR telemedicine[mh] OR informatics[tiab] OR 

internet[tiab] OR internet[mh] OR "Consumer Health Information"[Mesh] 

OR "Support systems"[tiab]) AND (consumer[tiab] OR "Patients"[Mesh] 

OR patients[tiab] OR patient[tiab] OR parents[mh] OR parents[tiab] OR 

parent[tiab] OR "age groups"[mh] OR Caregivers[mh] OR caregiver[tiab] 

OR "care giver"[tiab] OR "persons"[mh] OR persons[tiab] OR 

person[tiab] OR people[tiab] OR individual[tiab] OR individuals[tiab]) 

AND English[lang] AND ("randomized controlled trial"[pt] OR 

"randomized controlled trials as topic"[mh] OR "randomized controlled 

trial"[tiab] OR "randomised controlled trial"[tiab] OR "controlled trial"[tiab] 

OR "clinical trial"[tiab]) NOT (editorial[pt] OR letter[pt] OR comment[pt]) 

NOT (animal[mh] NOT human[mh]) AND (("1900/01/01"[PDat] : 

"2009/06/01"[PDat]))) 

OR 

(("Medical Informatics Applications"[Mesh] OR "Informatics"[Mesh] OR 

"medical informatics"[mh] OR telemedicine[mh] OR informatics[tiab] OR 

internet[tiab] OR "internet"[MeSH Terms] OR "Consumer Health 

Information"[Mesh] OR "Support systems"[tiab]) AND (consumer[tiab] 

OR "Patients"[Mesh] OR patients[tiab] OR patient[tiab] OR 

"parents"[MeSH Terms] OR parents[tiab] OR parent[tiab] OR "age 

groups"[mh] OR "caregivers"[MeSH Terms] OR caregiver[tiab] OR "care 

giver"[tiab] OR "persons"[mh] OR persons[tiab] OR person[tiab] OR 

people[tiab] OR individual[tiab] OR individuals[tiab]) AND (Access[tiab] 

OR barrier[tiab] OR facilitator[tiab] OR compatibility[tiab] OR 

incompatibility[tiab] OR "user-centered"[tiab] OR "user centered"[tiab] 

OR "work flow"[tiab] OR workflow[tiab] OR "reimbursement 

mechanisms"[mh] OR reimbursement[tiab] OR "attitude to 

computers"[mh] OR attitude[tiab] OR "health knowledge, attitudes, 

practice"[mh] OR "computer literacy"[mh] OR (computer[tiab] AND 

literacy[tiab])) AND English[lang] NOT (editorial[pt] OR letter[pt] OR 

comment[pt]) NOT ("animals"[MeSH Terms] NOT "humans"[MeSH 

Terms]) AND (("1900/01/01"[PDat] : "2009/06/01"[PDat]))) AND 

(("1900/01/01"[PDat] : "2009/06/01"[PDat]))) 

June1st, 

2009 

14561 

EMBASE (('informatics':ti,ab OR telemedicine:ti,ab OR internet:ti,ab OR 'consumer 

health information':ti,ab) AND (consumer:ti,ab OR 'patients':ti,ab OR 

parents:ti,ab OR 'age groups':ti,ab OR caregivers:ti,ab) AND 

('randomized controlled trial':ti,ab OR (controlled:ti,ab AND trial:ti,ab) OR 

(clinical:ti,ab AND trial:ti,ab))) OR (('informatics':ti,ab OR 

telemedicine:ti,ab OR internet:ti,ab OR 'consumer health 

information':ti,ab) AND (consumer:ti,ab OR 'patients':ti,ab OR 

parents:ti,ab OR 'age groups':ti,ab OR caregivers:ti,ab) AND 

(access:ti,ab OR barrier:ti,ab OR facilitator:ti,ab OR compatibility:ti,ab 

OR incompatibility:ti,ab OR 'user centered':ti,ab OR 'work flow':ti,ab OR 

reimbursement:ti,ab OR attitude:ti,ab OR (computer:ti,ab AND 

literacy:ti,ab))) AND ([article]/lim OR [editorial]/lim OR [review]/lim) AND 

[english]/lim AND [humans]/lim 

1421 

Cochrane 

Library 

(("Medical Informatics applications":ti,ab,kw or "Informatics":ti,ab,kw or 

(telemedicine):ti,ab,kw or (internet):ti,ab,kw or "Consumer Health 

Information":ti,ab,kw or “Support systems”:ti,ab,kw) AND 

((consumer):ti,ab,kw or "Patients":ti,ab,kw or (parents):ti,ab,kw or "age 

groups":ti,ab,kw or (Caregivers):ti,ab,kw) AND ((randomized controlled 

trial):ti,ab,kw or (controlled trial):ti,ab,kw or (clinical trial):ti,ab,kw)) 

OR 

(("Medical Informatics applications":ti,ab,kw or "Informatics":ti,ab,kw or 

(telemedicine):ti,ab,kw or (internet):ti,ab,kw or "Consumer Health 

Information":ti,ab,kw or “Support systems”:ti,ab,kw) AND 

((consumer):ti,ab,kw or "Patients":ti,ab,kw or (parents):ti,ab,kw or "age 

groups":ti,ab,kw or (Caregivers):ti,ab,kw) AND ((Access):ti,ab,kw or 

3716 

C-1 
Appendix C: Detailed Search Strategies 

C-2 

(barrier):ti,ab,kw or (facilitator):ti,ab,kw or (compatibility):ti,ab,kw or 

(incompatibility):ti,ab,kw or "user centered":ti,ab,kw or "work 

flow":ti,ab,kw or Reimbursement:ti,ab,kw or "attitude to 

computers":ti,ab,kw or “computer literacy”:ti,ab,kw)) 

SCOPUS ((TITLE-ABS-KEY("Medical Informatics applications") OR TITLE-ABSKEY(telemedicine) OR TITLE-ABS-KEY(internet) OR TITLE-ABSKEY("Consumer Health Information")) AND (TITLE-ABS-KEY(consumer) 

OR TITLE-ABS-KEY("Patients") OR TITLE-ABS-KEY(caregivers)) AND 

(TITLE-ABS-KEY("randomized controlled trial") OR TITLE-ABSKEY(“clinical trial”))) OR ((TITLE-ABS-KEY("Medical Informatics 

applications") OR TITLE-ABS-KEY(telemedicine) OR TITLE-ABSKEY(internet) OR TITLE-ABS-KEY("Consumer Health Information")) 

AND (TITLE-ABS-KEY(consumer) OR TITLE-ABS-KEY("Patients") OR 

TITLE-ABS-KEY(caregivers)) AND (TITLE-ABS-KEY(access) OR TITLEABS-KEY(barrier) OR TITLE-ABS-KEY(facilitator) OR TITLE-ABSKEY("user centered") OR TITLE-ABS-KEY("attitude to computers") OR 

TITLE-ABS-KEY(“computer literacy”) OR TITLE-ABS-KEY(“health 

knowledge, attitudes, practice”))) AND (LIMIT-TO(DOCTYPE, "ar") OR 

LIMIT-TO(DOCTYPE, "re") OR LIMIT-TO(DOCTYPE, "rp")) AND (LIMITTO(LANGUAGE, "English")) 

5577 

CINAHL ((TX "Informatics" or TX telemedicine or TX internet or TX "Consumer 

Health Information" or TX “Support systems”) AND (TX consumer or TX 

"Patients" or TX parents or TX "age groups" or TX Caregivers) AND (TX 

"randomized controlled trial" or TX “controlled trial” or TX “clinical trial”) ) 

OR ((TX "Informatics" or TX telemedicine or TX internet or TX 

"Consumer Health Information" or TX “Support systems”) AND (TX 

consumer or TX "Patients" or TX parents or TX "age groups" or TX 

Caregivers) AND (TX Access or TX barrier or TX facilitator or TX 

compatibility or TX incompatibility or TX "user centered" or TX "work 

flow" or TX Reimbursement or TX Attitude or TX “computer literacy”) 

)NOT ((PT editorial )or (PT letter) or (PT comment)) 

1462 
Appendix D: Grey Literature Detailed Search Strategies 

Database Terms 

Health Services Research Projects in Progress (((informatics OR internet OR consumer health 

information) AND (consumer OR patients OR parents 

OR caregivers) AND (randomized controlled trial OR 

clinical trial)) OR ((informatics OR internet OR 

consumer health information) AND (consumer OR 

patients OR parents OR caregivers) AND (access OR 

barrier OR facilitator OR compatibility OR user 

centered))) 

IEEE CNF IEEE Conference Proceeding 

IET CNF IET Conference Proceeding 

((((((informatics or internet or consumer health information) 

and (consumer or patients or parents or caregivers) and 

(randomized controlled trial or clinical trial)) or ((informatics or 

internet or consumer health information) and (consumer or 

patients or parents or caregivers) and (access or barrier or 

facilitator or compatibility or user centered))))<in>metadata)) 

<and> (pyr >= 1990 <and> pyr <= 2009) 

Proceedings of the American Society for 

Information Science and Technology (Wiley 

InterScience) 

informatics OR “health information” OR “consumer health 

information” OR internet 

WHO –International Clinical Trials Registry 

Platform 

informatics applications OR consumer health information OR 

internet 

American Public Health Association (APHA) 

2000-2008

Consumer health information OR health information OR 

consumer 

OpenSIGLE - System for Information on Grey 

Literature in Europe 

(((informatics OR internet OR consumer health information) 

AND (consumer OR patients OR parents OR caregivers) 

AND (randomized controlled trial OR clinical trial)) OR 

((informatics OR internet OR consumer health information) 

AND (consumer OR patients OR parents OR caregivers) 

AND (access OR barrier OR facilitator OR compatibility OR 

user centered))) 

The New York Academy of Medicine – Grey 

Literature 

informatics OR "consumer health information" OR "health info

rmation application" 

D-1 
Appendix E 
Previewing Only: You cannot submit data from this form

Previewing at Level 2 

Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording 

family histories of cancer via the Internet, Genet Med, 10(12), 2008, p.895-902 

State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

Submit Data

Key Question 1: What evidence exisits 

that CHI applications impact: 

a. health care processes (e.g., receipt 

of appropriate treatment) 

b. intermediate outcomes (e.g., selfmanagement, knowledge, health 

behaviors) 

c. relationship-centered outcomes 

(e.g., shared decision making, 

clinician-patient communication) 

d. clinical outcomes (e.g., quality of 

life) 

e. economic outcomes (e.g., cost, 

access to care)

Key Question 2: What are the barriers/facilitators that 

clinicians, developers, and consumers and their families or 

caregivers encounter that limit implimentation of CHI 

applications?

1. Does the abstract POTENTIALLY apply to Key Question 1 OR Key Question 2?

Yes (go to Question 2)

No (Go to Question 3 and optionally 4)

Unclear or No Abstract available (Go to Question 5)

Clear Selection

2. This abstract POTENTIALLY applies to:

Key Question 1 (must be an RCT to apply to KQ1)

Key Question 2 (addresses DIRECT barriers to CHI)

Key Question 2 (addresses barriers NOT specific to CHI)

If you have chosen any of the answers to question 2 (reasons for inclusion), SUBMIT. If you believe the abstract should be EXCLUDED, or you are 

UNCLEAR/or no abstract is available, please proceed.

3. Reason for Exclusion

No health informatics application

Health informatics application does not apply to the consumer

Health informatics application is for general information only (e.g., general website, message 

board, survey, etc.) AND is not tailored to the individual consumer

Study of a "point of care" device (requires a clinician to use or obtain and is part of the regular 

provision of care; e.g., device or telemedicine used at the point of care)

No original data (letter to the editor, comment, systematic review)

NOT a randomized controlled trial (this is ONLY an exclusion for KQ1, any article that may apply 

to KQ2 should NOT be excluded based on study design)

Other

Non-English (specify language)

4. FLAG excluded article: 

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(these answers are optional and should only be chosen if one of the above reasons for exclusion have been 

identified)

Article of interest: use for background information

Review of a relevant topic: pull for further evaluation of relevance to this review

Other article of intereste: team members may flag personal articles of interest

Clear Selection

If you have chosen any of the answers to question 3 or optionally 4 (reasons for exclusion), SUBMIT. If you are UNCLEAR/or no abstract is 

available, please proceed.

5. Relevance to Key question 1 OR 2 is UNCLEAR or no abstract is available.

Unable to determine eligibility based on the abstract alone: INCLUDE (move to next level for assessment)

No Abstract: Title may apply to one of the Key Questions: INCLUDE (move to next level for assessment)

No Abstract: Based on title, journal, and number of pages, this is a letter tot the editor, commentary, or other publication type 

that does not contain peer-reviewed data. EXCLUDE

Clear Selection

6. Comments

Enlarge Shrink 

Submit Data

SRS Form Page 2 of 2

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Previewing Only: You cannot submit data from this form

Previewing at Level 4 

Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording family histories of cancer via the Internet, Genet Med, 10(12), 2008, p.895-902

State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

Save to finish later Submit Data

GENERAL study and population characteristics 1. After full review of this article, does it apply to, and contain abstractable data to answer either Key Question1, or Key Question 2, or both? If you answer "no" please contact Renee (rwilsob@jhmi.edu) immeditately with the refID.

Yes Key Question 1 (go to question 2)

Yes Key Question 2 (go to question 3)NO--does nto apply to either key question (contact Renee)

2. 

If this article applies to Key Question 1 (outcomes), please identify the subquestion it applies to:

a. Healthcare process outcomes (e.g., diagnosis, treametn, prevetnion)

b. Intermediate outcomes (e.g., self management, health knowledge, health behaviors)c. Relationship-centered outcomes (e.g., shared decision makig, communication)d. Clinical outcomes (e.g., quailty of life, safety)e. Economic outcomes (e.g., cost, access, reimbursement)f. Other (specifiy)

3. If this article applies to Key Question 2 (barriers), please identify the type or types of barriers it applies to:

system-level barriers (e.g., not user-centric, inefficient workflow, incompatible with other systems, lack of or inadequate reinmbursement)

Individual-level barrier (e.g., negative or opposing attitudes, lack of access, lack o for inadequate reimbursement, lack of knowledge, limited literacy)Other (specify)

4. Study design

RCT (ALL KQ 1 articles MUST be RCTs)

Other: define as identified by study authors)

Clear Selection5. Study location

Home/residence

Remote location (e.g, library, internet cafe); specifyClinician officeNot specifiedOther; specifcy

6. 

Year data collection began

Year

Not specifiedDuration

Clear Selection7. 

Who is the consumer?

Individual interested in their own health care (add details if necessary) 

Non-medical caregiver (add details)

8. Identify the CHI application type:

Patient kiosk

Personal monitoring deviceDisease specific sensorInteractive consumer websiteDisease risk calculatorPersonalized health risk assessment toolElectronic medication reminderOther (specify)

Clear Selection9. Identify the target condition, behavior, or barrier of interest. 

(barriers should be listed as free text at teh end of the list of choices)

Obesity

SmokingCancer (breast)DiabetesHypertensionAsthmaMental healthDepressionSubstance abuseAlcohol abuseother (specify)Breast (other)menopause/HRTDiet/exercise/pysical activity NOT obesityHIV/AIDSBARRIER

Study participant inclusion/exclusion criteria (as defined in the article):

Inclusion Exclusion Not specified

10. Age (specify)

11. Race (specify)12. Gender (specify)13. Other (specify)14. Other (specify)15. Other (specify)16. Other (specify)17. Other (specify)18. Other (specify)19. Other (specify)20. Other (specify)

Specify ALL OUTCOMES and ALL TIME POINTS measured in this study. Be carful to categorize oucomes properly; thi swill impact which form reviewers will fill out next. 

Are CATEGORICAL outcomes being studied?Describe below

Yes

No

Clear Selection

Are CONTINUOUS outcomes being studied?Describe below

Yes

No

Clear Selection

Identify (define) the timepoints where outcomes are measured.always use time point 1 as the baseline measure always use time point 6 as the final measure 

Cat outcome 1

Cat outcome 2Cat outcome 3Cat outcome 4Cat outcome 5Cat outcome 6Cat outcome 7Cat outcome 8Cat outcome 9Cat outcome 10

Cont outcome 1

Cont outcome 2Cont outcome 3Cont outcome 4Cont outcome 5Cont outcome 6Cont outcome 7Cont outcome 8Cont outcome 9Cont outcome 10

Time point 1: always define as baseline

Time point 2: defineTime point 3: defineTime point 4: defineTime point 5 defineTime point 6: define

SRS Form 

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Form Creation Date: Not available Form Last Modified: Mar 27 2009 4:00PM

Define ALL Study Arms

Define No control group

26. ARM A (control group) Clear27. ARM B (intervention) Clear28. ARM C (intervention) Clear29. ARM D (intervention) Clear

Study population characteristics:

ARM answers from above questions will populate these cells

Age Race/Ethnicity 

if information is provided and it does not fit the NIH criteria, fill in the "other" categories at the bottom of the cell

Annual Incomealways identify units, always identify income range as reported in the article OR mean, meadian, SD

Education Socioeconomic status Other 1 Other 2 Other 3 

ARM A (control group)

Define

No control group

Clear Selection

Mean

MedianRangeSD

Race not stated

White, non-hispanic, nWhite, non-hispanic, %Black, non-hispanic, nBlack, non-hispanic, %Latino/hispanic, nLatino/hispanic, %Asian/Pacific Islander, nAsian/Pacific Islander, %American Indian/Alaska Native, nAmerican Indian/Alaska Native, %Other, nOther, %Other, nOther, %

Not specified

UNITSincome range, n (%)income range, n (%)income range, n (%)income range, n (%)income range, n (%)Mean incomeMedian incomeSD

Not reported

Less than 8 years, n(%)8-12 years, n(%)12-16 years, n(%)>16 years, n(%)MeanMedianSD

Not specified

Low {define}, n(%)Middle {define}, n(%)High {define}, n(%)

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Define

category 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

ARM B (intervention)

Define

Clear Selection

Mean

MedianRangeSD

Race not stated

White, non-hispanic, nWhite, non-hispanic, %Black, non-hispanic, nBlack, non-hispanic, %Latino/hispanic, nLatino/hispanic, %Asian/Pacific Islander, nAsian/Pacific Islander, %American Indian/Alaska Native, nAmerican Indian/Alaska Native, %Other, nOther, %Other, nOther, %

Not specified

UNITSincome range, n (%)income range, n (%)income range, n (%)income range, n (%)income range, n (%)Mean incomeMedian incomeSD

Not reported

Less than 8 years, n(%)8-12 years, n(%)12-16 years, n(%)>16 years, n(%)MeanMedianSD

Not specified

Low {define}, n(%)Middle {define}, n(%)High {define}, n(%)

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Define

category 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

ARM C (intervention)

Define

Clear Selection

Mean

MedianRangeSD

Race not stated

White, non-hispanic, nWhite, non-hispanic, %Black, non-hispanic, nBlack, non-hispanic, %Latino/hispanic, nLatino/hispanic, %Asian/Pacific Islander, nAsian/Pacific Islander, %American Indian/Alaska Native, nAmerican Indian/Alaska Native, %Other, nOther, %Other, nOther, %

Not specified

UNITSincome range, n (%)income range, n (%)income range, n (%)income range, n (%)income range, n (%)Mean incomeMedian incomeSD

Not reported

Less than 8 years, n(%)8-12 years, n(%)12-16 years, n(%)>16 years, n(%)MeanMedianSD

Not specified

Low {define}, n(%)Middle {define}, n(%)High {define}, n(%)

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Define

category 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

ARM D (intervention)

Define

Clear Selection

Mean

MedianRangeSD

Race not stated

White, non-hispanic, nWhite, non-hispanic, %Black, non-hispanic, nBlack, non-hispanic, %Latino/hispanic, nLatino/hispanic, %Asian/Pacific Islander, nAsian/Pacific Islander, %American Indian/Alaska Native, nAmerican Indian/Alaska Native, %Other, nOther, %Other, nOther, %

Not specified

UNITSincome range, n (%)income range, n (%)income range, n (%)income range, n (%)income range, n (%)Mean incomeMedian incomeSD

Not reported

Less than 8 years, n(%)8-12 years, n(%)12-16 years, n(%)>16 years, n(%)MeanMedianSD

Not specified

Low {define}, n(%)Middle {define}, n(%)High {define}, n(%)

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Define

category 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

Definecategory 1, n(%)category 2, n(%)category 3, n(%)category 4, n(%)MeanMedianSD

70. CommentEnlarge Shrink

Save to finish later Submit Data

Click a link below to review this article at these other levels.5. KQ 1 CHI (categorical variables)

6. KQ 1 CHI (continuous variables)7. KQ 2 CHI barriers

8. Jadad -- RCT quality

SRS Form 

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Previewing at Level 5 

Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording family histories of cancer via the Internet, Genet Med, 10(12), 2008, p.895-902State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

Save to finish later Submit Data

KEY QUESTION 1

Report CATEGORICAL variables 

What evidence exists that consumer health informatics applications impact health care process outcomes, intermediate outcomes, relationship-centered o

economic outcomes of its users? 

Description of all CATEGORICAL outcomes being studied Identify (define) the timepoints where outcomes are measured.

always use time point 1 as the baseline measure always use time point 4 as the final measure 

1. 

Cat outcome 1

Cat outcome 2Cat outcome 3Cat outcome 4Cat outcome 5Cat outcome 6

2. 

Baseline

Time point 2: defineTime point 3: defineTime point 4: define

Time pint 5: define (ALWAYS use this timepoint as the last/main 

measure timepoint when abstracting data)

CATEGORICAL Outctomes see answers to question 1 Cat Outcome 1

ARM Total N in ARM n with outcome % with outcome 95% CI 

ARM A (control)

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM B

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at 

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

SRS Form 

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final/main 

measure

ARM C

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM D

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

Cat Outcome 2

ARM Total N in ARM n with outcome % with outcome 95% CI 

ARM A (control)

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM B

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main 

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 4

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

SRS Form 

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measure 95% CI 

at final/main measure

ARM C

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM D

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

Cat Outcome 3

ARM Total N in ARM n with outcome % with outcome 95% CI 

ARM A (control)

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM B

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at 

n at baselinen at time point 2n at time point 3n at timepoint 4

% at baseline% at time point 2% at time point 3% at time point 4% at 

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI 

P at baseli

P at tipoint P at tipoint P at tipoint P at 

SRS Form 

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final/main 

measure

n at final/main measure

final/main measure

at time point 495% CI at final/main measure

final/mmeas

ARM C

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM D

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

Cat Outcome 4

ARM Total N in ARM n with outcome % with outcome 95% CI 

ARM A (control)

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM B

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time 

n at baseline

n at time point 2n at time point 3n at 

% at baseline

% at time point 2% at time point 3% at time 

95% CI at baseline

95% CI at time point 295% CI at time 

P at baseli

P at tipoint P at tipoint P at ti

SRS Form 

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point 4

N at final/main measure

timepoint 4n at final/main measure

point 4% at final/main measure

point 395% CI at time point 495% CI at final/main measure

point P at final/mmeas

ARM C

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM D

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

Cat Outcome 5

ARM Total N in ARM n with outcome % with outcome 95% CI 

ARM A (control)

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM B

Define

N randomized to this ARM

N at baseline

N at time point 2N at time 

n at baselinen at time point 2n at time 

% at baseline% at time point 2% at time 

95% CI at baseline

95% CI at time 

P at baseli

P at tipoint P at ti

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point 3

N at time point 4N at final/main measure

point 3n at timepoint 4n at final/main measure

point 3% at time point 4% at final/main measure

point 295% CI at time point 395% CI at time point 495% CI at final/main measure

point P at tipoint P at final/mmeas

ARM C

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM D

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

Cat Outcome 6

ARM Total N in ARM n with outcome % with outcome 95% CI 

ARM A (control)

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM B

Define N at baseline n at baseline % at baseline 95% CI at P at baseli

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Form took 1.890625 seconds to render

Form Creation Date: Mar 24 2009 12:27PM Form Last Modified: Apr 14 2009 9:45AM

N randomized to this ARM

N at time point 2N at time point 3N at time point 4N at final/main measure

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at time point 2% at time point 3% at time point 4% at final/main measure

baseline95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM C

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

ARM D

Define

N randomized to this ARM

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at timepoint 4n at final/main measure

% at baseline

% at time point 2% at time point 3% at time point 4% at final/main measure

95% CI at baseline

95% CI at time point 295% CI at time point 395% CI at time point 495% CI at final/main measure

P at baseli

P at tipoint P at tipoint P at tipoint P at final/mmeas

171. COMMENTSEnlarge Shrink

Save to finish later Submit Data

Click a link below to review this article at these other levels.4. GENERAL study and population characteristics

6. KQ 1 CHI (continuous variables)

7. KQ 2 CHI barriers

8. Jadad -- RCT quality

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Previewing at Level 6 

Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording family histories of cancer via the Internet, Genet Med, 10(12), 2008, p.895-902State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

Save to finish later Submit Data

KEY QUESTION 1 Report CONTINUOUS variables What evidence exists that consumer health informatics applications impact health care process outcomes, intermediate outcomes, relationship-centered outcomes, clinical outcomes, or economic outcomes of its users? 

Description of all CONTINUOUS outcomes being studied Identify (define) the timepoints where outcomes are measured.

always use time point 1 as the baseline measure always use time point 4 as the final measure 

1. 

Cont outcome 1

Cont outcome 2Cont outcome 3Cont outcome 4Cont outcome 5Cont outcome 6Cont outcome 7Cont outcome 8

2. 

Time point: baseline

Time point 2: defineTime point 3: defineTime point 4: defineTime point: final/main measure

CONTINUOUS Outctome 1 (see answers to question 2)

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4

n at baseline

n at time point 2n at time point 3n at time point 4

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4

mean at baseline

median at baselinerange at baselineSD at baseline

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time 

SRS Form 

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N at 

final/main measure

n at final/main measure

value at final/main measure

mean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at 

n at baseline

n at time point 2n at time point 3n at time point 4n at 

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at 

mean at baseline

median at baselinerange at baselineSD at baselinemean at time 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3

SRS Form 

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final/main 

measure

final/main measure

final/main measure

point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

39. Comments , outcome 1Enlarge ShrinkCONTINUOUS Outctome 2 (see answers to question 2) 

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

SRS Form 

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ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM D

SRS Form 

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Define

N randomized to this Arm

N at baselineN at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baselinemedian at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

76. Comments , outcome 2Enlarge ShrinkCONTINUOUS Outctome 3 (see answers to question 2) 

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

SRS Form 

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final/main 

measurerange at final/main measureSD at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

SRS Form 

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measure

range at final/main measureSD at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

113. Comments , outcome 3Enlarge ShrinkCONTINUOUS Outctome 4 (see answers to question 2)

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

SRS Form 

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range at time 

point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

SRS Form 

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point 4

SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

150. Comments , outcome 4Enlarge ShrinkCONTINUOUS Outctome 5 (see answers to question 2)

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

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time point 3

range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

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time point 3

range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

187. Comments , outcome 5Enlarge ShrinkCONTINUOUS Outctome 6 (see answers to question 2)

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at 

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main 

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at 

mean at baseline

median at baselinerange at baselineSD at baselinemean at time 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR 

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final/main 

measure

measure final/main 

measure

point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

(specify) at time point 4RR or OR (specify) at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at 

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main 

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at 

mean at baseline

median at baselinerange at baselineSD at baselinemean at time 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR 

SRS Form 

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final/main 

measure

measure final/main 

measure

point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

(specify) at time point 4RR or OR (specify) at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

224. Comments , outcome 6Enlarge ShrinkCONTINUOUS Outctome 7 (see answers to question 2)

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

SRS Form 

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ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM C

SRS Form 

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Define

N randomized to this Arm

N at baselineN at time point 2N at time point 3N at time point 4N at final/main measure

n at baselinen at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baselinemedian at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

261. Comments , outcome 7

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Enlarge Shrink

CONTINUOUS Outctome 8 (see answers to question 2)

ARM Total N in ARM n in ARM with outcome Units Value Mean, Median, Range, SD RR or OR (spec

ARM A (control)

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM B

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

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final/main 

measurerange at final/main measureSD at final/main measure

ARM C

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baseline

value at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main measurerange at final/main measureSD at final/main measure

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

ARM D

Define

N randomized to this Arm

N at baseline

N at time point 2N at time point 3N at time point 4N at final/main measure

n at baseline

n at time point 2n at time point 3n at time point 4n at final/main measure

Units (define)

value at baselinevalue at time point 2value at time point 3value at time point 4value at final/main measure

mean at baseline

median at baselinerange at baselineSD at baselinemean at time point 2median at time point 2range at time point 2SD at time point 2mean at time point 3median at time point 3range at time point 3SD at time point 3mean at time point 4median at time point 4range at time point 4SD at time point 4mean at final/main measuremedian at final/main 

RR or OR (specify) at baselineRR or OR (specify) at time point 2RR or OR (specify) at time point 3RR or OR (specify) at time point 4RR or OR (specify) at final/main measure

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measurerange at final/main measureSD at final/main measure

298. Comments , outcome 8Enlarge Shrink

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Click a link below to review this article at these other levels.4. GENERAL study and population characteristics

5. KQ 1 CHI (categorical variables)

7. KQ 2 CHI barriers

8. Jadad -- RCT quality

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Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording family histories of cancer via the Internet, Genet Med, 10(12), 2008, 

State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

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KEY QUESTION 2

What are the barriers that clinicians, developers, and consumers and their families or caregivers encounter that limit implementation of consumer health informatics ap

1. This study provides evidence for:

Existence of user-level barriers

Existence of systems-level barriers Existence of other barriers (define)

User-level barrier: poor access to internet from home or community, lack of knowledge, poor literacy, culture, language, and other things which are not amenable to Systems-level barrier: design is not user-centered, poor workflow, incompatible with existing healthcare information management systems, no reimbursement for ot

patients. 

Condition of interest Barriers considered by authors (as described 

in the purpose or methods)

Barriers reported by authors as important (these may differ from previous column)

How were the 

Alcohol abuse

AsthmaBreast cancerCancer, other 

than breast (specify)

DepressionDiabetesEating disorderHeadacheHIV/AIDSHypertensionMenopaus/HRT 

(specify)

Mental health 

(specify)

ObesityPhysical 

activity/diet (specify)

Smoking/smoking 

cessation

Other

Application usability 

(user friendliness)

Care giver 

preferences (define)

CHI application not 

designed for general use (only designed for the sick)

CHI application not 

designed for general use (only designed for the healthy)

CHI application use 

too time consuming

Confidentiality/privacyControl of information 

(trust)

Cost (patient)CulturalDisabilityIncompatibility with 

current care

Knowledge literacy 

(Care giver's lack of skill 

Application usability 

(user friendliness)

Care giver 

preferences (define)

CHI application not 

designed for general use (only designed for the sick)

CHI application not 

designed for general use (only designed for the healthy)

CHI application use 

too time consuming

Confidentiality/privacyControl of information 

(trust)

Cost (patient)CulturalDisabilityIncompatibility with 

current care

Knowledge literacy 

(Care giver's lack of skill 

Empirical 

based on trial data (e.g., log ins, # completed modules)

Validated 

survey (e.g., patient or caregiver report, scales of skills or other characteristics)

Non-validated 

survey

Observational 

(e.g., adminstrative data, review of cost, objective testing of usability, objective testing of access)

Biologic 

outcome

Qualitative 

(e.g., focus group, structured interview)

Other

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re: CHI application)

Knowledge literacy 

(Patient 's lack of skill re: CHI application)

Lack of insurance for 

services recommended by CHI application

Lack of 

reimbursement (provider)

Lack of technical 

infrastructure (home or community)

LanguagePatient preferences 

(define)

OtherOtherOther

re: CHI application)

Knowledge literacy 

(Patient 's lack of skill re: CHI application)

Lack of insurance for 

services recommended by CHI application

Lack of 

reimbursement (provider)

Lack of technical 

infrastructure (home or community)

LanguagePatient preferences 

(define)

OtherOtherOther

OtherOther

7. CommentEnlarge Shrink

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Click a link below to review this article at these other levels.4. GENERAL study and population characteristics

5. KQ 1 CHI (categorical variables)

6. KQ 1 CHI (continuous variables)8. Jadad -- RCT quality

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Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording 

family histories of cancer via the Internet, Genet Med, 10(12), 2008, p.895-902 

State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

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QUALITY FORM

JADAD (quality of controlled trials) 

1. Was the study described as randomized (this includes the use of words such as randomly, random, and 

randomization)? In other words, was the allocation concealed?

Yes (go to question 2)

No (-1)

Unspecified (0)

Clear Selection

2. If the answer to question #1 was "yes," then answer the following:

Was the method used to generate the sequence of randomization described and was it appropriate? (+1)

Was the method of randomization described but inappropriate? (-1)

unspecified (0)

Clear Selection

3. Was the study described as double blind? In other words, were the outcome assessors blind in addition to the 

patients?

Yes (go to question 4)

No (-1)

unspecified (0)

Clear Selection

4. If the answer to #3 is "Yes" then answer the following:

The method of double blinding was described and appropriate (+1)

the study was described as being blind, but the method of blinding was inapproriate (-1)

unspecified (0)

Clear Selection

5. Was there a description of withdrawals and dropouts?

Yes (+1)

No (-1)

Clear Selection

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Click a link below to review this article at these other levels.

4. GENERAL study and population characteristics

5. KQ 1 CHI (categorical variables)

6. KQ 1 CHI (continuous variables)

7. KQ 2 CHI barriers

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Refid: 1, Simon, C., Acheson, L., Burant, C., Gerson, N., Schramm, S., Lewis, S., and Wiesner, G., Patient interest in recording 

family histories of cancer via the Internet, Genet Med, 10(12), 2008, p.895-902 

State: Ok, Level: KQ 1 CHI (categorical variables), KQ 1 CHI (continuous variables), Jadad -- RCT quality

Save to finish later Submit Data

1. Does this article POTENTIALLY apply to ANY of the key questions?

Key Question 1: What evidence exists that consumer health informatics impacts: a) health care process outcomes (e.g., receiving appropriate 

treatment); b) intermediate outcomes (e.g., self-management, health care knowledge), c) relationship-centered outcomes (e.g., shared decision 

making), d) clinical outcomes (e.g., quality of life), or e) economic outcomes (e.g., cost, or access to care)? 

Key question 2: What are the barriers that clinicians, developers, and consumers and their families and caregivers encounter that limit 

implementation of consumer health informatics applications?

Yes

No

Unclear

Clear Selection

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Appendix F 
Appendix F: List of Excluded Articles 

A new strategy to empower people in Africa. World Health 

97;(6):4-5 

No health informatics application 

Adler K G. Web portals in primary care: an evaluation of 

patient readiness and willingness to pay for online services. 

J Med Internet Res 2006;8(4):e26 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Ahmad F, Hogg-Johnson S, Skinner H A. Assessing patient 

attitudes to computerized screening in primary care: 

psychometric properties of the computerized lifestyle 

assessment scale. J Med Internet Res 2008;10(2):e11 

Study of a point of care device 

Allenby A, Matthews J, Beresford J et al. The application 

of computer touch-screen technology in screening for 

psychosocial distress in an ambulatory oncology setting. 

Eur J Cancer Care (Engl) 2002;11(4):245-53 

No health informatics application; 

Study of a point of care device 

An J. Correlates and predictors of consumers' health 

information and services usage behavior on the Internet: A 

structural equation modeling approach. New York 

University, 2005. (Doctoral dissertation) 

No original data; 

Other* 

An L C, Schillo B A, Saul J E et al. Utilization of smoking 

cessation informational, interactive, and online community 

resources as predictors of abstinence: cohort study. J Med 

Internet Res 2008;10(5):e55 

Not a RCT and not a study addressing barriers 

Anderson PF, Wilson B. Rapid development of a 

craniofacial consumer health Web site: part one, what 

happens before content and coding.. Journal of Consumer 

Health on the Internet 2007;11(2):13-31 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Andersson G, Bergstrom J, Hollandare F et al. Internetbased self-help for depression: randomised controlled trial. 

Br J Psychiatry 2005;187456-61 

Study of a point of care device 

Andersson G, Lundstrom P, Strom L. Internet-based 

treatment of headache: does telephone contact add 

anything?. Headache 2003;43(4):353-61 

Study of a point of care device 

Anhoj J, Nielsen L. Quantitative and qualitative usage data 

of an Internet-based asthma monitoring tool. J Med Internet 

Res 2004;6(3):e23 

Study of a point of care device 

Apkon M, Mattera J A, Lin Z et al. A randomized 

outpatient trial of a decision-support information 

technology tool. Arch Intern Med 2005;165(20):2388-94 

Study of a point of care device 

Balmford J, Borland R, Benda P. Patterns of use of an 

automated interactive personalized coaching program for 

smoking cessation. J Med Internet Res 2008;10(5):e54 

Not a RCT, and not a study addressing barriers; 

Other* 

Bandy M. Health information for patients and consumers.: 

Health information for patients and consumers.. 2000;325-

350 

No health informatics application; 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Barnason S, Zimmerman L, Nieveen J et al. Impact of a 

home communication intervention for coronary artery 

bypass graft patients with ischemic heart failure on selfefficacy, coronary disease risk factor modification, and 

functioning. Heart Lung 2003;32(3):147-58 

No health informatics application; 

Study of a point of care device 

Barrera M, Glasgow R E, McKay H G et al. Do Internetbased support interventions change perceptions of social 

support?: An experimental trial of approaches for 

supporting diabetes self-management. Am J Community 

Psychol 2002;30(5):637-54 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Study of a point of care device 

Barry K L, Fleming M F. Computerized administration of 

alcoholism screening tests in a primary care setting. J Am 

Board Fam Pract 90;3(2):93-8 

Other* 

F-1 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Bechtel-Blackwell D A. Computer-assisted self-interview 

and nutrition education in pregnant teens. Clin Nurs Res 

2002;11(4):450-62 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Berman R L, Iris M A, Bode R et al. The effectiveness of 

an online mind-body intervention for older adults with 

chronic pain. J Pain 2009;10(1):68-79 

Other* 

Bernhardt J M, Lariscy R A W, Parrott R L et al. Perceived 

barriers to internet-based health communication on human 

genetics. 2002;7(4):325-340 

Other* 

Bexelius C, Honeth L, Ekman A et al. Evaluation of an 

internet-based hearing test--comparison with established 

methods for detection of hearing loss. J Med Internet Res 

2008;10(4):e32 

Not a RCT, and not a study addressing barriers; 

Other* 

Birru M S, Monaco V M, Charles L et al. Internet usage by 

low-literacy adults seeking health information: an 

observational analysis. J Med Internet Res 2004;6(3):e25 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Blas M M, Alva I E, Cabello R et al. Internet as a tool to 

access high-risk men who have sex with men from a 

resource-constrained setting: a study from Peru. Sex 

Transm Infect 2007;83(7):567-70 

No health informatics application 

Health informatics application does not apply to the 

consumer 

Block G, Sternfeld B, Block C H et al. Development of 

Alive! (A Lifestyle Intervention Via Email), and its effect 

on health-related quality of life, presenteeism, and other 

behavioral outcomes: randomized controlled trial. J Med 

Internet Res 2008;10(4):e43 

Other* 

Bonniface L, Green L. Finding a new kind of knowledge on 

the HeartNET website. Health Information & Libraries 

Journal 2007-; 2467-76 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Not a RCT, and not a study addressing barriers 

Borbolla D, Giunta D, Figar S et al. Effectiveness of a 

chronic disease surveillance systems for blood pressure 

monitoring. Stud Health Technol Inform 2007;129(Pt 

1):223-7 

No health informatics application 

Health informatics application does not apply to the 

consumer 

Borckardt J J, Younger J, Winkel J et al. The computerassisted cognitive/imagery system for use in the 

management of pain. Pain Res Manag 2004;9(3):157-62 

Health informatics application does not apply to the 

consumer 

Borland R, Balmford J, Hunt D. The effectiveness of 

personally tailored computer-generated advice letters for 

smoking cessation. Addiction 2004;99(3):369-77 

No health informatics application 

Borland R, Balmford J, Segan C et al. The effectiveness of 

personalized smoking cessation strategies for callers to a 

Quitline service. Addiction 2003;98(6):837-46 

No health informatics application 

Study of a point of care device 

Borzekowski D L, Rickert V I, VI. Urban girls, internet 

use, and accessing health information. J Pediatr Adolesc 

Gynecol 2000;13(2):94-5 

No health informatics application; 

Other* 

Bosworth K, Gustafson D H. CHESS: Providing Decision 

Support for Reducing Health Risk Behavior and Improving 

Access to Health Services. 91;21(3):93-104 

Not a RCT, and not a study addressing barriers; 

Other* 

Bouhaddou O, Lambert J G, Miller S. Consumer health 

informatics: knowledge engineering and evaluation studies 

of medical HouseCall. Proc AMIA Symp 98;612-6 

Not a RCT, and not a study addressing barriers; 

Other* 

Boukhors Y, Rabasa-Lhoret R, Langelier H et al. The use 

of information technology for the management of intensive 

insulin therapy in type 1 diabetes mellitus. Diabetes Metab 

2003;29(6):619-27 

No health informatics application 

F-2 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Braithwaite S R, Fincham F D. A randomized clinical trial 

of a computer based preventive intervention: replication 

and extension of ePREP. J Fam Psychol 2009;23(1):32-8 

Other* 

Brennan P F. Health informatics and community health: 

support for patients as collaborators in care. Methods Inf 

Med 99;38(4-5):274-8 

No original data 

Brug J. Dutch research into the development and impact of 

computer-tailored nutrition education. European Journal of 

Clinical Nutrition 1999;53(SUPPL. 2): S78-82 

No original data 

Bukachi F, Pakenham-Walsh N. Information technology 

for health in developing countries. Chest 

2007;132(5):1624-30 

No health informatics application; 

Study of a point of care device 

Bull S S, Phibbs S, Watson S et al. What do young adults 

expect when they go online? Lessons for development of 

an STD/HIV and pregnancy prevention website. J Med 

Syst 2007;31(2):149-58 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Campbell M K, DeVellis B M, Strecher V J et al. 

Improving dietary behavior: The effectiveness of tailored 

messages in primary care settings. American Journal of 

Public Health 1994; 84(5):783-787 

No health informatics application 

Campbell M K, Tessaro I, DeVellis B et al. Effects of a 

tailored health promotion program for female blue-collar 

workers: Health works for women. Preventive Medicine 

2002;34(3):313-323 

Other* 

Campbell R. Older women and the internet. J Women 

Aging 2004;16(1-2):161-74 

No health informatics application 

Campbell RJ, Wabby J. The elderly and the Internet: a case 

study.. Internet Journal of Health 2003;3(1):11p 

Not a RCT, and not a study addressing barriers 

Campbell SE. Individualised computer generated nutrition 

information plus interative feedback reduced dietary fat and 

increased fruit and vegetable intake [commentary on Brug 

J, Glanz K, Van Assema P, et al. The impact of computertailored feedback and iterative feedback on fat, fruit, and 

vegetable intake. HEALTH EDUC BEHAV 1998 

Aug;25:517-31].. Evidence-Based Nursing 99;2(3):83 

No original data 

Carlbring P, Smit F. Randomized trial of internet-delivered 

self-help with telephone support for pathological gamblers. 

J Consult Clin Psychol 2008;76(6):1090-4 

Other* 

Carlfjord S, Nilsen P, Leijon M et al. Computerized 

lifestyle intervention in routine primary health care: 

evaluation of usage on provider and responder levels. 

Patient Educ Couns 2009;75(2):238-43 

Other* 

Casper G R, Brennan P F, Burke L J et al. HeartCareII: 

Patients' Use of a Home Care Web Resource. Stud Health 

Technol Inform 2009;146139-43 

Other* 

Chan D S, Callahan C W, Hatch-Pigott V B et al. Internetbased home monitoring and education of children with 

asthma is comparable to ideal office-based care: results of a 

1-year asthma in-home monitoring trial. Pediatrics 

2007;119(3):569-78 

Study of a point of care device 

Chandra A, Rutsohn P, Carlisle MB. Utilization of the 

Internet by rural West Virginia consumers.. Journal of 

Consumer Health on the Internet 2004;8(2):45-59 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Chiang M F, Starren J. Evaluation of consumer health 

website accessibility by users with sensory and physical 

disabilities. Stud Health Technol Inform 2004;107(Pt 

2):1128-32 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Chinman M, Young A S, Schell T et al. Computer-assisted 

self-assessment in persons with severe mental illness. J 

Clin Psychiatry 2004;65(10):1343-51 

No health informatics application;

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

F-3 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Cho J H, Lee H C, Lim D J et al. Mobile communication 

using a mobile phone with a glucometer for glucose control 

in Type 2 patients with diabetes: as effective as an Internetbased glucose monitoring system. J Telemed Telecare 

2009;15(2):77-82 

No health informatics application; 

Study of a point of care device 

Christensen H, Griffiths K, Groves C et al. Free range users 

and one hit wonders: Community users of an internet-based 

cognitive behaviour therapy program. Australian and New 

Zealand Journal of Psychiatry 2006;40(1):59-62 

Not a RCT, and not a study addressing barriers 

Cintron A, Phillips R, Hamel M B. The effect of a webbased, patient-directed intervention on knowledge, 

discussion, and completion of a health care proxy. J Palliat 

Med 2006;9(6):1320-8 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Civan A, Skeels M M, Stolyar A et al. Personal health 

information management: consumers' perspectives. AMIA 

Annu Symp Proc 2006;156-60 

Other* 

Clarke G, Eubanks D, Reid E et al. Overcoming Depression 

on the Internet (ODIN) (2): a randomized trial of a self-help 

depression skills program with reminders. J Med Internet 

Res 2005;7(2):e16 

Study of a point of care device 

Clayton A E, McNutt L A, Homestead H L et al. Public 

health in managed care: a randomized controlled trial of the 

effectiveness of postcard reminders. Am J Public Health 

99;89(8):1235-7 

No health informatics application 

Cobb N K, Graham A L, Bock B C et al. Initial evaluation 

of a real-world internet smoking cessation system. Nicotine 

Tob Res. 2005;7(2):207-216 

Not a RCT, and not a study addressing barriers 

Coile R C. E-health: Reinventing healthcare in the 

information age. Journal of Healthcare Management. 

2000;45(3):206-210 

No health informatics application 

Col N F, Ngo L, Fortin J M et al. Can computerized 

decision support help patients make complex treatment 

decisions? A randomized controlled trial of an 

individualized menopause decision aid. Med Decis Making 

2007;27(5):585-98 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Cook R F, Billings D W, Hersch R K et al. A field test of a 

web-based workplace health promotion program to 

improve dietary practices, reduce stress, and increase 

physical activity: Randomized controlled trial. Journal of 

Medical Internet Research 2007;9:e17 

Other* 

Cox A, Boehm M, Summers R et al. Patient perspective. 

Using a virtual community to support healthcare. Quality in 

Primary Care 2003;11(2):143-145 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Cummings E, Turner P. Patient self-management and 

chronic illness: evaluating outcomes and impacts of 

information technology. Stud Health Technol Inform 

2009;143229-34 

No health informatics application; 

Study of a point of care device 

Cummings E, Turner P. Patient self-management and 

chronic illness: evaluating outcomes and impacts of 

information technology. Stud Health Technol Inform 

2009;143229-34 

Other* 

Cunningham J A, Humphreys K, Koski-Jannes A. 

Providing personalized assessment feedback for problem 

drinking on the internet: A pilot project. 2000;61(6):794-

798 

Not a RCT and not a study addressing barriers; 

Other* 

Cunningham J A, Selby P, van Mierlo T. Integrated online 

services for smokers and drinkers? Use of the check your 

drinking assessment screener by participants of the Stop 

Smoking Center. Nicotine Tob Res 2006;8 Suppl 1S21-5 

No health informatics application; 

Study of a point of care device 

Curioso W H, Kurth A E. Access, use and perceptions 

regarding Internet, cell phones and PDAs as a means for 

health promotion for people living with HIV in Peru. BMC 

Med Inform Decis Mak 2007;724 

F-4 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Curioso W H, Kurth A E. Access, use and perceptions 

regarding Internet, cell phones and PDAs as a means for 

health promotion for people living with HIV in Peru. BMC 

Med Inform Decis Mak 2007;724 

Other* 

Damster G, Williams J R. The Internet, virtual 

communities and threats to confidentiality. S Afr Med J 

99;89(11):1175-8 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Danaher B G, Boles S M, Akers L et al. Defining 

participant exposure measures in Web-based health 

behavior change programs. J Med Internet Res 

2006;8(3):e15

No health informatics application; 

Other* 

Dart J, Gallois C, Yellowlees P. Community health 

information sources--a survey in three disparate 

communities. Aust Health Rev 2008;32(1):186-96 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Dart J. The internet as a source of health information in 

three disparate communities. Aust Health Rev 

2008;32(3):559-69 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Davison B J, Degner L F. Feasibility of using a computerassisted intervention to enhance the way women with breast 

cancer communicate with their physicians. Cancer Nurs 

2002;25(6):417-24 

Other* 

Dawson A J, Konkin D, Riordan D et al. Education about 

genetic testing for breast cancer susceptibility: Patient 

preferences for a computer program or genetic counselor. 

American Journal of Medical Genetics 2001;103(1):24-31 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Not a RCT, and not a study addressing barriers 

De Bourdeaudhuij I, Stevens V, Vandelanotte C et al. 

Evaluation of an interactive computer-tailored nutrition 

intervention in a real-life setting. Annals of Behavioral 

Medicine 2007;33(1):39-48 

Other* 

Demiris G, Finkelstein S M, Speedie S M. Considerations 

for the design of a Web-based clinical monitoring and 

educational system for elderly patients. Journal of the 

American Medical Informatics Association 2001;8(5):468-

472 

No original data 

Demiris G, Rantz M, Aud M et al. Older adults' attitudes 

towards and perceptions of "smart home" technologies: a 

pilot study. Med Inform Internet Med 2004;29(2):87-94 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Detailed report on physician and patient use of the Web. 

Internet Healthc Strateg 2003;5(5):5-6 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Dilts D, Ridner S H, Franco A et al. Patients with cancer 

and e-mail: implications for clinical communication. 

Support Care Cancer 2008; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Dimeff L A, McNeely M. Computer-enhanced primary 

care practitioner advice for high-risk college drinkers in a 

student primary health-care setting. Cognitive and 

Behavioral Practice 2000;7(1):82-100 

Not a RCT, and not a study addressing barriers; 

Other* 

Dini E F, Linkins R W, Sigafoos J. The impact of 

computer-generated messages on childhood immunization 

coverage. Am J Prev Med 2000;18(2):132-9 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Dolezal-Wood S, Belar C D, Snibbe J. A Comparison of 

Computer-Assisted Psychotherapy and CognitiveBehavioral Therapy in Groups. Journal of Clinical 

Psychology in Medical Settings 98;5(1):103-115 

F-5 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Other* 

Doumas D M, McKinley L L, Book P. Evaluation of two 

Web-based alcohol interventions for mandated college 

students. J Subst Abuse Treat 2009;36(1):65-74 

Other* 

Draper S, Coffman S. Logging on: what it takes to provide 

patients with computer access. Biomed Instrum Technol 

2004-2005;Suppl17-9 

No original data 

Dreault RT. Information anxiety. Mississippi RN 

2000;62(3):11-14 

No health informatics application 

Edwards A, Thomas R, Williams R et al. Presenting risk 

information to people with diabetes: evaluating effects and 

preferences for different formats by a web-based 

randomised controlled trial. Patient Educ Couns 

2006;63(3):336-49 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Ellison G L, Weinrich S P, Lou M et al. A randomized trial 

comparing web-based decision aids on prostate cancer 

knowledge for African-American men. J Natl Med Assoc 

2008;100(10):1139-45 

Not a RCT, and not a study addressing barriers 

Emmons K M, Wong M, Puleo E et al. Tailored computerbased cancer risk communication: Correcting colorectal 

cancer risk perception. Journal of Health Communication

2004;9(2):127-141

Other* 

Enterprise scheduling may improve patient access. 

''Transparent'' registration is the goal. Patient Focus Care 

Satisf 98;6(7):83-6 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Ervin N E, Berry M M. Community readiness for a 

computer-based health information network. J N Y State 

Nurses Assoc 2006;37(1):5-11 

No health informatics application 

Escoffery C, McCormick L, Bateman K. Development and 

process evaluation of a web-based smoking cessation 

program for college smokers: innovative tool for education. 

Patient Educ Couns 2004;53(2):217-25 

Other* 

Etter J F, Perneger T V. Post-intervention effect of a 

computer tailored smoking cessation programme. J 

Epidemiol Community Health 2004;58(10):849-51 

No health informatics application;

Other* 

Eysenbach G. From intermediation to disintermediation 

and apomediation: new models for consumers to access and 

assess the credibility of health information in the age of 

Web2.0. Stud Health Technol Inform 2007;129(Pt 1):162-6 

No health informatics application; 

Study of a point of care device 

Feldstein A C, Smith D H, Perrin N et al. Improved 

therapeutic monitoring with several interventions: a 

randomized trial. Arch Intern Med 2006;166(17):1848-54 

No health informatics application 

Ferrer-Roca O, C+írdenas A, Diaz-Cardama A et al. 

Mobile phone text messaging in the management of 

diabetes. Journal of Telemedicine and Telecare 

2004;10(5):282-285 

Other* 

Ferriman A. Patients get access to evidence based, online 

health information. BMJ 2002;325(7365):618 

No original data 

Finfgeld-Connett D, Madsen R. Web-based treatment of 

alcohol problems among rural women. J Psychosoc Nurs 

Ment Health Serv 2008;46(9):46-53 

Health informatics application does not apply to the 

consumer; 

Other* 

Fitzgibbon M L, Stolley M R, Schiffer L et al. Two-year 

follow-up results for Hip-Hop to Health Jr.: A randomized 

controlled trial for overweight prevention in preschool 

minority children. Journal of Pediatrics 2005;146(5):618-

625 

No health informatics application 

Ford P. Brief report. Is the Internet changing the 

relationship between consumers and practitioners? Journal 

for Healthcare Quality: Promoting Excellence in Healthcare 

2000;22(5):41-43 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

F-6 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Franklin V L, Greene A, Waller A et al. Patients' 

engagement with "Sweet Talk" - a text messaging support 

system for young people with diabetes. J Med Internet Res 

2008;10(2):e20 

No health informatics application; 

Other* 

Friedman D B, Kao E K. A comprehensive assessment of 

the difficulty level and cultural sensitivity of online cancer 

prevention resources for older minority men. Prev Chronic 

Dis 2008;5(1):A07 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Fung V, Ortiz E, Huang J et al. Early experiences with ehealth services (1999-2002): promise, reality, and 

implications. Med Care 2006;44(5):491-6 

Study of a point of care device 

Garth McKay H, Glasgow R E, Feil E G et al. Internetbased diabetes self-management and support: Initial 

outcomes from the diabetes network project. Rehabilitation 

Psychology 2002;47(1):31-48 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Gerressu M, French R S. Using the Internet to promote 

sexual health awareness among young people. J Fam Plann 

Reprod Health Care 2005;31(4):267, 269-70 

No original data 

Glasgow R E, Barrera M, Mckay H G et al. Social support, 

self-management, and quality of life among participants in 

an Internet-based diabetes support program: A multidimensional investigation. Cyberpsychology and Behavior 

99;2(4):271-281 

Not a RCT, and not a study addressing barriers; 

Other* 

Goulis D G, Giaglis G D, Boren S A et al. Effectiveness of 

home-centered care through telemedicine applications for 

overweight and obese patients: a randomized controlled 

trial. Int J Obes Relat Metab Disord 2004;28(11):1391-8 

Study of a point of care device; 

Other* 

Graham A L, Bock B C, Cobb N K et al. Characteristics of 

smokers reached and recruited to an internet smoking 

cessation trial: a case of denominators. Nicotine Tob Res 

2006;8 Suppl 1S43-8 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Graham W, Smith P, Kamal A et al. Randomised controlled 

trial comparing effectiveness of touch screen system with 

leaflet for providing women with information on prenatal 

tests. BMJ 2000;320(7228):155-160 

No health informatics application;

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Gruber K, Moran P J, Roth W T et al. Computer-Assisted 

Cognitive Behavioral Group Therapy for Social Phobia. 

Behavior Therapy 2001;32(1):155-165 

Study of a point of care device; 

Not a RCT, and not a study addressing barriers 

Gustafson D H, Bosworth K, Chewning B et al. Computerbased health promotion: combining technological advances 

with problem-solving techniques to effect successful health 

behavior changes. Annual Review of Public health 87 

;(8)387-415 

No original data 

Gustafson D H, Hawkins R P, Boberg E W et al. CHESS: 

ten years of research and development in consumer health 

informatics for broad populations, including the 

underserved. Stud Health Technol Inform 2001;84(Pt 

2):1459-563 

No original data 

Gustafson D H, McTavish F M, Boberg E et al. 

Empowering patients using computer based health support 

systems. Quality in Health Care 99;8(1):49-56 

No original data 

Gustafson D H, McTavish F M, Stengle W et al. Reducing 

the digital divide for low-income women with breast 

cancer: a feasibility study of a population-based 

intervention. J Health Commun 2005;10 Suppl 1173-93 

Study of a point of care device 

Gustafson D H, McTavish F M, Stengle W et al. Use and 

Impact of eHealth System by Low-income Women With 

Breast Cancer. J Health Commun 2005;10 Suppl 1195-218 

Not a RCT, and not a study addressing barriers 

Gustafson D H, Robinson T N, Ansley D et al. Consumers 

and evaluation of interactive health communication 

applications. American Journal of Preventive Medicine 

99;16(1):23-29 

F-7 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Gutteling J J, Busschbach J J, de Man R A et al. Logistic 

feasibility of health related quality of life measurement in 

clinical practice: results of a prospective study in a large 

population of chronic liver patients. Health Qual Life 

Outcomes 2008;6(1):97 

Health informatics application does not apply to the 

consumer 

Haerens L, Deforche B, Maes L et al. Evaluation of a 2-

year physical activity and healthy eating intervention in 

middle school children. Health Education Research 

2006;21(6):911-921 

No health informatics application 

Hanauer D, Dibble E, Fortin J et al. Internet use among 

community college students: implications in designing 

healthcare interventions. J Am Coll Health 2004;52(5):197-

202 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Hartmann C W, Sciamanna C N, Blanch D C et al. A 

website to improve asthma care by suggesting patient 

questions for physicians: qualitative analysis of user 

experiences. J Med Internet Res 2007;9(1):e3 

Other* 

Harvey K, Churchill D, Crawford P et al. Health 

communication and adolescents: what do their emails tell 

us?. Fam Pract 2008;25(4):304-11 

No health informatics application 

Harvey-Berino J, Pintauro S, Buzzell P et al. Does using 

the Internet facilitate the maintenance of weight loss? 

International Journal of Obesity 2002;26(9):1254-1260 

Not a RCT and not a study addressing barriers; 

Other* 

Harvey-Berino J, Pintauro S, Buzzell P et al. Effect of 

internet support on the long-term maintenance of weight 

loss. Obes Res 2004;12(2):320-9 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Study of a point of care device 

Hayashi A, Kayama M, Ando K et al. Analysis of 

subjective evaluations of the functions of tele-coaching 

intervention in patients with spinocerebellar degeneration. 

NeuroRehabilitation 2008;23(2):159-69 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Heidenreich P A, Chacko M, Goldstein M K et al. ACE 

inhibitor reminders attached to echocardiography reports of 

patients with reduced left ventricular ejection fraction. Am 

J Med 2005;118(9):1034-7 

No health informatics application 

Hibbard J H, Peters E, Dixon A et al. Consumer 

competencies and the use of comparative quality 

information: it isn't just about literacy. Med Care Res Rev 

2007;64(4):379-94 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Hill W, Weinert C, Cudney S. Influence of a computer 

intervention on the psychological status of chronically ill 

rural women: preliminary results. Nurs Res 2006;55(1):34-

42 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Study of a point of care device 

Holmes-Rovner M, Stableford S, Fagerlin A et al. 

Evidence-based patient choice: a prostate cancer decision 

aid in plain language. BMC Med Inform Decis Mak 

2005;516 

No health informatics application 

Hopper K D, Zajdel M, Hulse S F et al. Interactive method 

of informing patients of the risks of intravenous contrast 

media. Radiology 94;192(1):67-71 

Health informatics application does not apply to the 

consumer; 

Other* 

Huber J T, Huggins D W. Assessing electronic information 

access and use in long-term care facilities in north Texas. 

Bull Med Libr Assoc 2000;88(2):187-9 

Health informatics application does not apply to the 

consumer 

Hughes S, Dennison C R. Progress in prevention: how can 

we help patients seek information on the World Wide 

F-8 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Web?: an opportunity to improve the "net effect". J 

Cardiovasc Nurs 2008;23(4):324-5 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Hung S H, Hwang S L, Su M J et al. An evaluation of a 

weight-loss program incorporating E-learning for obese 

junior high school students. Telemed J E Health 

2008;14(8):783-92 

Not a RCT, and not a study addressing barriers 

Huss K, Salerno M, Huss R W. Computer-assisted 

reinforcement of instruction: effects on adherence in adult 

atopic asthmatics. Research in nursing & health 

91;14(4):259-267 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Irvine A B, Ary D V, Grove D A et al. The effectiveness of 

an interactive multimedia program to influence eating 

habits. Health Education Research 2004;19(3):290-305 

No health informatics application 

Izenberg N, Lieberman D A. The Web, communication 

trends, and children's health. Part 3: The Web and health 

consumers. Clin Pediatr (Phila) 98;37(5):275-85 

No original data 

Jackson S J. Access to medical information: essential for 

better patient care. J Tenn Med Assoc 72;65(10):902-6 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Jacobs A D, Ammerman A S, Ennett S T et al. Effects of a 

tailored follow-up intervention on health behaviors, beliefs, 

and attitudes. Journal of Women’s Health 2004;13(5):557-

568 

Study of a point of care device 

Jansa M, Vidal M, Viaplana J et al. Telecare in a structured 

therapeutic education programme addressed to patients 

with type 1 diabetes and poor metabolic control. Diabetes 

Res Clin Pract 2006;74(1):26-32 

Study of a point of care device 

Jibaja-Weiss M L, Volk R J. Utilizing computerized 

entertainment education in the development of decision 

aids for lower literate and naive computer users. J Health 

Commun 2007;12(7):681-97 

No health informatics application; 

Other* 

Jones J. Patient education and the use of the World Wide 

Web. Clin Nurse Spec 2003;17(6):281-3 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Jones R B, Atkinson J M, Coia D A et al. Randomised trial 

of personalised computer based information for patients 

with schizophrenia. BMJ 2001;322(7290):835-40 

Health informatics application does not apply to the 

consumer 

Jones R, Labajo R, Soler-Lopez Alonso et al. "Evaluation 

of a Scottish touch-screen public-access health information 

system in rural Spain." In Current Perspectives in 

Healthcare Computing Conference, Harrogate 20-22 March 

2000, 45-54. Guildford, UNITED KINGDOM: British 

Computer Society Health Informatics Committee, 2000

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Jones R, Pearson J, Cawsey A et al. Information for 

patients with cancer. Does personalization make a 

difference? Pilot study results and randomised trial in 

progress. Proc AMIA Annu Fall Symp 96;423-7 

No original data; 

Not a RCT and not a study addressing barriers 

Kaldo V, Levin S, Widarsson J et al. Internet versus group 

cognitive-behavioral treatment of distress associated with 

tinnitus: a randomized controlled trial. Behav Ther 

2008;39(4):348-59 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Kaphingst K A, Zanfini C J, Emmons K M. Accessibility 

of web sites containing colorectal cancer information to 

adults with limited literacy (United States). Cancer Causes 

Control 2006;17(2):147-51 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Kaufman DR, Rockoff ML. Increasing access to online 

information about health: a program for inner-city elders in 

community-based organizations. Generations 

2006;30(2):55-57 

F-9 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

No original data 

Kennedy M G, Kiken L, Shipman J P. Addressing 

underutilization of consumer health information resource 

centers: a formative study. J Med Libr Assoc 

2008;96(1):42-9 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Kenwright M, Liness S, Marks I. Reducing demands on 

clinicians by offering computer-aided self-help for 

phobia/panic. Feasibility study. Br J Psychiatry 

2001;179456-9 

Study of a point of care device; 

Not a RCT, and not a study addressing barriers 

Kerr C, Murray E, Stevenson F et al. Interactive health 

communication applications for chronic disease: patient 

and carer perspectives. J Telemed Telecare 2005;11 Suppl 

132-4 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Khoo K, Bolt P, Babl F E et al. Health information seeking 

by parents in the Internet age. J Paediatr Child Health 

2008;44(7-8):419-23 

Health informatics application does not apply to the 

consumer 

Kim E H, Stolyar A, Lober W B et al. Usage patterns of a 

personal health record by elderly and disabled users. AMIA 

Annu Symp Proc 2007;409-13 

Health informatics application does not apply to the 

consumer 

Kim H S, Yoo Y S, Shim H S. Effects of an Internet-based 

intervention on plasma glucose levels in patients with type 

2 diabetes. J Nurs Care Qual 2005;20(4):335-40 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Kim J, Trace D, Meyers K et al. An empirical study of the 

Health Status Questionnaire System for use in patientcomputer interaction. Proc AMIA Annu Fall Symp 97;86-

90 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Kim S, Chung D S. Characteristics of cancer blog users. J 

Med Libr Assoc 2007;95(4):445-50 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

King A C, Friedman R, Marcus B et al. Ongoing Physical 

Activity Advice by Humans Versus Computers: The 

Community Health Advice by Telephone (CHAT) Trial. 

Health Psychol. 2007;26(6):718-727 

Other* 

Kingston J. Web-based support for patients with type 2 

diabetes in West Norfolk Primary Care Trust. A district 

model of diabetes care. Practical Diabetes International 

2005;22(8):302 

No original data 

Kiropoulos L A, Klein B, Austin D W et al. Is internetbased CBT for panic disorder and agoraphobia as effective 

as face-to-face CBT?. J Anxiety Disord 2008;22(8):1273-

84 

Study of a point of care device; 

Other* 

Kiss G R, Walton H J, Farvis K M et al. An adaptive, online computer program for the exploration of attitude 

structures in psychiatric patients. Int J Biomed Comput 

74;5(1):39-50 

Study of a point of care device 

Klein B, Richards J C, Austin D W. Efficacy of internet 

therapy for panic disorder. J Behav Ther Exp Psychiatry 

2006;37(3):213-38 

Study of a point of care device 

Koivunen M, Hatonen H, Valimaki M. Barriers and 

facilitators influencing the implementation of an interactive 

Internet-portal application for patient education in 

psychiatric hospitals. Patient Educ Couns 2008;70(3):412-9 

Health informatics application does not apply to the 

consumer 

Koonce T Y, Giuse D A, Beauregard J M et al. Toward a 

more informed patient: bridging health care information 

through an interactive communication portal. J Med Libr 

Assoc 2007;95(1):77-81 

F-10 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Kreuter M W, Strecher V J. Do tailored behavior change 

messages enhance the effectiveness of health risk 

appraisal? Results from a randomized trial. Health 

Education Research 96;11(1):97-105 

No health informatics application 

Krukowski R A, Harvey-Berino J, Ashikaga T et al. 

Internet-based weight control: the relationship between web 

features and weight loss. Telemed J E Health 

2008;14(8):775-82 

Other* 

Kypri K, Langley J D, Saunders J B et al. Randomized 

controlled trial of web-based alcohol screening and brief 

intervention in primary care. Arch Intern Med 

2008;168(5):530-6 

Health informatics application does not apply to the 

consumer 

Lai T Y, Larson E L, Rockoff M L et al. User acceptance of 

HIV TIDES--Tailored Interventions for Management of 

Depressive Symptoms in persons living with HIV/AIDS. J 

Am Med Inform Assoc 2008;15(2):217-26 

Not a RCT, and not a study addressing barriers 

Lange A, van de, Ven J P et al. Interapy, treatment of 

posttraumatic stress through the Internet: a controlled trial. 

J Behav Ther Exp Psychiatry 2001;32(2):73-90 

Study of a point of care device 

Lee C J. Does the internet displace health professionals?. J 

Health Commun 2008;13(5):450-64 

Health informatics application does not apply to the 

consumer 

Lee D M, Fairley C K, Sze J K et al. Access to sexual 

health advice using an automated, internet-based risk 

assessment service. Sex Health 2009;6(1):63-6 

Other* 

Legare F, Dodin S, Stacey D et al. Patient decision aid on 

natural health products for menopausal symptoms: 

randomized controlled trial. Menopause Int 

2008;14(3):105-10 

No health informatics application 

Lemire M, Pare G, Sicotte C et al. Determinants of Internet 

use as a preferred source of information on personal health. 

Int J Med Inform 2008;77(11):723-34 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Lemire M, Pare G, Sicotte C et al. Determinants of Internet 

use as a preferred source of information on personal health. 

Int J Med Inform 2008;77(11):723-34 

No original data 

Leong T Y, Aronsky D, Shabot M M. Computer-based 

decision support for critical and emergency care. J Biomed 

Inform 2008;41(3):409-12 

No original data 

Leung K Y, Lee C P, Chan H Y et al. Randomised trial 

comparing an interactive multimedia decision aid with a 

leaflet and a video to give information about prenatal 

screening for Down syndrome. Prenat Diagn 

2004;24(8):613-8 

Health informatics application does not apply to the 

consumer 

Levetan C S, Dawn K R, Robbins D C et al. Impact of 

computer-generated personalized goals on HbA(1c). 

Diabetes care 2002;25(1):2-8 

Other* 

Lewis D, Gunawardena S, El Saadawi G. Caring 

connection: developing an Internet resource for family 

caregivers of children with cancer. Comput Inform Nurs 

2005;23(5):265-74 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

No original data 

Liaw S T, Radford A J, Maddocks I. The impact of a 

computer generated patient held health record. Aust Fam 

Physician 98;27 Suppl 1S39-43 

Health informatics application does not apply to the 

consumer; 

Not a RCT, and not a study addressing barriers 

Lim J E, Choi O H, Na H S et al. A context-aware fitness 

guide system for exercise optimization in U-health. IEEE 

Trans Inf Technol Biomed 2009;13(3):370-9 

Health informatics application does not apply to the 

consumer; 

Not a RCT, and not a study addressing barriers 

F-11 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Lim J E, Choi O H, Na H S et al. A context-aware fitness 

guide system for exercise optimization in U-health. IEEE 

Trans Inf Technol Biomed 2009;13(3):370-9 

Other* 

Lindsay S, Smith S, Bellaby P et al. The health impact of 

an online heart disease support group: a comparison of 

moderated versus unmoderated support. Health Educ Res 

2009;24(4):646-54 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Other* 

Linke S, Brown A, Wallace P. Down your drink: A webbased intervention for people with excessive alcohol 

consumption. Alcohol and Alcoholism 2004;39(1):29-32 

Not a RCT, and not a study addressing barriers 

Linke S, Murray E, Butler C et al. Internet-based 

interactive health intervention for the promotion of sensible 

drinking: Patterns of use and potential impact on members 

of the general public. Journal of Medical Internet Research 

2007;9: e10 

Not a RCT, and not a study addressing barriers 

Lipkus I M, Rimer B K, Halabi S et al. Can tailored 

interventions increase mammography use among HMO 

women?. Am J Prev Med 2000;18(1):1-10 

No health informatics application 

Lorence D P, Greenberg L. The zeitgeist of online health 

search. Implications for a consumer-centric health system. J 

Gen Intern Med 2006;21(2):134-9 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Lorence D, Park H. Group disparities and health 

information: a study of online access for the underserved. 

Health Informatics J 2008;14(1):29-38 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Macdougall J. Community access to health information in 

Ireland. Health Libr Rev 99;16(2):89-96 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Magee J C, Ritterband L M, Thorndike F P et al. Exploring 

the Relationship between Parental Worry about their 

Children's Health and Usage of an Internet Intervention for 

Pediatric Encopresis. J Pediatr Psychol 2008; 

Health informatics application does not apply to the 

consumer; 

Other* 

Mahabee-Gittens E M, Gordon J S, Krugh M E et al. A 

smoking cessation intervention plus proactive quitline 

referral in the pediatric emergency department: a pilot 

study. Nicotine Tob Res 2008;10(12):1745-51 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Majumdar BB. The effectiveness of a culturally sensitive 

educational programme of self-perception of health, 

happiness, self-confidence, and loneliness in Southeast 

Asian seniors. 1995 (Doctoral Dissertation) 

Other*

Malone M, Mathes L, Dooley J et al. Health information 

seeking and its effect on the doctor-patient digital divide. J 

Telemed Telecare 2005;11 Suppl 125-8 

No health informatics application 

Marceau L D, Link C, Jamison R N et al. Electronic diaries 

as a tool to improve pain management: is there any 

evidence?. Pain Med 2007;8 Suppl 3S101-9 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Marcus B H, Lewis B A, Williams D M et al. Step into 

Motion: a randomized trial examining the relative efficacy 

of Internet vs. print-based physical activity interventions. 

Contemp Clin Trials 2007;28(6):737-47 

Other* 

Masucci M M, Homko C, Santamore W P et al. 

Cardiovascular disease prevention for underserved patients 

using the Internet: bridging the digital divide. Telemed J E 

Health 2006;12(1):58-65 

Study of a point of care device 

Matano R A, Koopman C, Wanat S F et al. A pilot study of 

an interactive web site in the workplace for reducing 

alcohol consumption. J Subst Abuse Treat 2007;32(1):71-

80 

No health informatics application; 

F-12 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Other* 

Mattila E, Parkka J, Hermersdorf M et al. Mobile diary for 

wellness management--results on usage and usability in 

two user studies. IEEE Trans Inf Technol Biomed 

2008;12(4):501-12 

No original data 

Mayben J K, Giordano T P. Internet use among lowincome persons recently diagnosed with HIV infection. 

AIDS Care 2007;19(9):1182-7 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

McClure L A, Harrington K F, Graham H et al. Internetbased monitoring of asthma symptoms, peak flow meter 

readings, and absence data in a school-based clinical trial. 

Clinical Trials 2008;5(1):31-37 

Health informatics application does not apply to the 

consumer 

McCoy M R, Couch D, Duncan N D et al. Evaluating an 

Internet weight loss program for diabetes prevention. 

Health Promotion International 2005;20(3):221-228 

Not a RCT, and not a study addressing barriers; 

Other* 

McKee B. Electronic access to consumer health 

information.. Health Libraries Review 89;6(2):119-121 

No original data 

McMahon G T, Gomes H E, Hickson Hohne S et al. Webbased care management in patients with poorly controlled 

diabetes. Diabetes Care 2005;28(7):1624-9 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

McTavish F M, Pingree S, Hawkins R et al. Cultural 

differences in use of an electronic discussion group. Journal 

of Health Psychology 2003;8(1):105-117 

Other* 

Mead N, Varnam R, Rogers A et al. What predicts patients' 

interest in the Internet as a health resource in primary care 

in England?. J Health Serv Res Policy 2003;8(1):33-9 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Meingast M, Roosta T, Sastry S. Security and privacy 

issues with health care information technology. Conf Proc 

IEEE Eng Med Biol Soc 2006;15453-8 

No health informatics application 

Health informatics application does not apply to the 

consumer 

Mennell S, Murcott A, van Otterloo A H. The sociology of 

food: eating, diet and culture. Sociology Abstracts 1992; 

Newbury Park, CA: Sage Publications 

Other* 

Mermelstein R, Turner L. Web-based support as an adjunct 

to group-based smoking cessation for adolescents. Nicotine 

Tob Res 2006;8 Suppl 1S69-76 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Mitchell J E, Myers T, Swan-Kremeier L et al. 

Psychotherapy for bulimia nervosa delivered via 

telemedicine. European Eating Disorders Review 

2003;11(3):222-230 

Study of a point of care device; 

Other* 

Molenaar S, Sprangers M A, Postma-Schuit F C et al. 

Feasibility and effects of decision aids. Med Decis Making 

2000;20(1):112-27 

No original data 

Montani S, Bellazzi R, Quaglini S et al. Meta-analysis of 

the effect of the use of computer-based systems on the 

metabolic control of patients with diabetes mellitus. 

Diabetes Technology and Therapeutics 2001;3(3):347-356 

No original data 

Montelius E, Astrand B, Hovstadius B et al. Individuals 

appreciate having their medication record on the web: a 

survey of attitudes to a national pharmacy register. J Med 

Internet Res 2008;10(4):e35 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Moore T J, Alsabeeh N, Apovian C M et al. Weight, blood 

pressure, and dietary benefits after 12 months of a Webbased Nutrition Education Program (DASH for health): 

longitudinal observational study. J Med Internet Res 

2008;10(4):e52 

F-13 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Not a RCT, and not a study addressing barriers 

Moore T J, Alsabeeh N, Apovian C M et al. Weight, blood 

pressure, and dietary benefits after 12 months of a Webbased Nutrition Education Program (DASH for health): 

longitudinal observational study. J Med Internet Res 

2008;10(4):e52 

Other* 

Moran W P, Nelson K, Wofford J L et al. Computergenerated mailed reminders for influenza immunization: a 

clinical trial. J Gen Intern Med 92;7(5):535-7 

No health informatics application 

Study of a point of care device 

Mustafa Y. E-health centre: a web-based tool to empower 

patients to become proactive customers. Health Info Libr J 

2004;21(2):129-33 

No original data 

Newton N C, Andrews G, Teesson M et al. Delivering 

prevention for alcohol and cannabis using the internet: a 

cluster randomised controlled trial. Prev Med 

2009;48(6):579-84 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Nguyen H Q, Carrieri-Kohlman V, Rankin S H et al. Is 

Internet-based support for dyspnea self-management in 

patients with chronic obstructive pulmonary disease 

possible? Results of a pilot study. Heart Lung 

2005;34(1):51-62 

Study of a point of care device 

Nix S T, Ibanez C D, Strobino B A et al. Developing a 

computer-assisted health knowledge quiz for preschool 

children. Journal of School Health 99;69(1):9-11 

Health informatics application does not apply to the 

consumer 

Noell J, Glasgow R E. Interactive technology applications 

for behavioral counseling: Issues and opportunities for 

health care settings. American Journal of Preventive 

Medicine 99;17(4):269-274 

No original data 

Norman C D, Skinner H A. eHealth Literacy: Essential 

Skills for Consumer Health in a Networked World. J Med 

Internet Res 2006;8(2):e9 

No original data 

Norman C. CATCH-IT Report: Evaluation of an Internetbased smoking cessation program: Lessons learned from a 

pilot study. Journal of Medical Internet Research 2004;6(4) 

No original data 

Nwosu CR, Cox BM. The impact of the Internet on the 

doctor-patient relationship.. Health Informatics Journal 

2000;6(3):156-161 

No health informatics application 

O'Connor A M, Rostom A, Fiset V et al. Decision aids for 

patients facing health treatment or screening decisions: 

systematic review. BMJ 99;319(7212):731-4 

No original data; 

Not a RCT, and not a study addressing barriers 

Oenema A, Brug J. Feedback strategies to raise awareness 

of personal dietary intake: Results of a randomized 

controlled trial. Preventive Medicine 2003;36(4):429-439 

Other* 

Olver I N, Whitford H S, Denson L A et al. Improving 

informed consent to chemotherapy: a randomized 

controlled trial of written information versus an interactive 

multimedia CD-ROM. Patient Educ Couns 2009;74(2):197-

204 

Other* 

Ornstein S M, Garr D R, Jenkins R G et al. Computergenerated physician and patient reminders. Tools to 

improve population adherence to selected preventive 

services. J Fam Pract 91;32(1):82-90 

Study of a point of care device 

Osman L M, Abdalla M I, Beattie J A et al. Reducing 

hospital admission through computer supported education 

for asthma patients. Grampian Asthma Study of Integrated 

Care (GRASSIC). BMJ 94;308(6928):568-71 

No health informatics application 

Otsuki M. Social connectedness and smoking behaviors 

among Asian American college students: An electronic 

diary study. Nicotine Tob Res 2009;11(4):418-26 

No health informatics application; 

Other* 

Papadaki A, Scott J A. Follow-up of a web-based tailored 

intervention promoting the Mediterranean diet in Scotland. 

Patient Educ Couns 2008;73(2):256-63 

Not a RCT, and not a study addressing barriers; 

Other* 

F-14 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Parlove AE, Cowdery JE, Hoerauf SL. Acceptability and 

appeal of a Web-based smoking prevention intervention for 

adolescents.. International Electronic Journal of Health 

Education 2004;71-8 

No health informatics application; 

Other* 

Partin M R, Nelson D, Flood A B et al. Who uses decision 

aids? Subgroup analyses from a randomized controlled 

effectiveness trial of two prostate cancer screening decision 

support interventions. Health Expect 2006;9(3):285-95 

No health informatics application 

Patrick K, Raab F, Adams M A et al. A text message-based 

intervention for weight loss: randomized controlled trial. J 

Med Internet Res 2009;11(1):e1 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer;

Other* 

Patten C A, Rock E, Meis T M et al. Frequency and type of 

use of a home-based, Internet intervention for adolescent 

smoking cessation. J Adolesc Health 2007;41(5):437-43 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Other* 

Penn DL, Simpson LE, Leggett S et al. The development of 

a Web site to promote the mental and physical health of 

sons and daughters of Vietnam veterans of Australia.. 

Journal of Consumer Health on the Internet 2006;10(4):45-

63 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Pennbridge J, Moya R, Rodrigues L. Questionnaire survey 

of California consumers' use and rating of sources of health 

care information including the Internet. West J Med 

99;171(5-6):302-5 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Other* 

Pingree S, Hawkins R P, Gustafson D H et al. Will HIVpositive people use an interactive computer system for 

information and support? A study of CHESS in two 

communities. Proc Annu Symp Comput Appl Med Care 

93;22-6 

Study of a point of care device 

Plotnikoff R C, McCargar L J, Wilson P M et al. Efficacy 

of an e-mail intervention for the promotion of physical 

activity and nutrition behavior in the workplace context. 

American Journal of Health Promotion 2005;19(6):422-429 

No health informatics application 

Polzien K M, Jakicic J M, Tate D F et al. The efficacy of a 

technology-based system in a short-term behavioral weight 

loss intervention. Obesity (Silver Spring) 2007;15(4):825-

30 

No health informatics application; 

Study of a point of care device 

Port K, Palm K, Viigimaa M. Daily usage and efficiency of 

remote home monitoring in hypertensive patients over a 

one-year period. J Telemed Telecare 2005;11 Suppl 134-6 

No health informatics application 

Porter S C, Silvia M T, Fleisher G R et al. Parents as direct 

contributors to the medical record: validation of their 

electronic input. Ann Emerg Med 2000;35(4):346-52 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Prochaska J O, Velicer W F, Redding C et al. Stage-based 

expert systems to guide a population of primary care 

patients to quit smoking, eat healthier, prevent skin cancer, 

and receive regular mammograms. Prev Med 

2005;41(2):406-16 

No health informatics application 

Proudfoot J, Swain S, Widmer S et al. The development 

and beta-test of a computer-therapy program for anxiety 

and depression: Hurdles and lessons. 2003;19(3):277-289 

Not a RCT, and not a study addressing barriers; 

Other* 

Quinn P, Goka J, Richardson H. Assessment of an 

electronic daily diary in patients with overactive bladder. 

BJU Int 2003;91(7):647-52 

No health informatics application 

Ralston J D, Hirsch I B, Hoath J et al. Web-based 

collaborative care for type 2 diabetes: a pilot randomized 

trial. Diabetes Care 2009;32(2):234-9 

Study of a point of care device 

Ran D, Peretz B. Assessing the pain reaction of children 

receiving periodontal ligament anesthesia using a 

F-15 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

computerized device (Wand). J Clin Pediatr Dent 

2003;27(3):247-50 

No health informatics application; 

Study of a point of care device 

Raphael C, Cornwell J L. Influencing support for 

caregivers. Am J Nurs 2008;108(9 Suppl):78-82; quiz 82 

No health informatics application; 

No original data 

Recabarren M, Nussbaum M, Leiva C. Cultural illiteracy 

and the Internet. Cyberpsychol Behav 2007;10(6):853-6 

No health informatics application 

Renahy E, Parizot I, Chauvin P. Health information seeking 

on the Internet: a double divide? Results from a 

representative survey in the Paris metropolitan area, 

France, 2005-2006. BMC Public Health 2008;869 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Resnik D B. Patient access to medical information in the 

computer age: ethical concerns and issues. Camb Q Healthc 

Ethics 2001;10(2):147-54; discussion 154-6 

No health informatics application; 

No original data 

Rigby M, Draper R, Hamilton I. Finding ethical principles 

and practical guidelines for the controlled flow of patient 

data. Methods Inf Med 99;38(4-5):345-9 

No health informatics application; 

No original data 

Rizo C A, Lupea D, Baybourdy H et al. What Internet 

services would patients like from hospitals during an 

epidemic? Lessons from the SARS outbreak in Toronto. J 

Med Internet Res 2005;7(4):e46 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Study of a point of care device 

Rogers J L, Haring O M, Goetz J P. Changes in patient 

attitudes following the implementation of a medical 

information system. QRB Qual Rev Bull 84;10(3):65-74 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Rosenman S J, Levings C T, Korten A E. Clinical utility 

and patient acceptance of the computerized Composite 

International Diagnostic Interview. Psychiatr Serv 

97;48(6):815-20 

Study of a point of care device 

Ross S E, Nowels C T, Haverhals L M et al. Qualitative 

assessment of Diabetes-STAR: a patient portal with disease 

management functions. AMIA Annu Symp Proc 2007;1097 

No original data; 

Other* 

Rosser W W, Hutchison B G, McDowell I et al. Use of 

reminders to increase compliance with tetanus booster 

vaccination. CMAJ 92;146(6):911-7 

No health informatics application; 

Study of a point of care device 

Rothert K, Strecher V J, Doyle L A et al. Web-based 

weight management programs in an integrated health care 

setting: a randomized, controlled trial. Obesity (Silver 

Spring) 2006;14(2):266-72 

Study of a point of care device 

Rotondi A J, Sinkule J, Spring M. An interactive Webbased intervention for persons with TBI and their families: 

use and evaluation by female significant others. J Head 

Trauma Rehabil 2005;20(2):173-85 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Rovniak L S, Hovell M F, Wojcik J R et al. Enhancing 

theoretical fidelity: An e-mail-based walking program 

demonstration. American Journal of Health Promotion 

2005;20(2):85-95 

No health informatics application 

Rozmovits L, Ziebland S. What do patients with prostate or 

breast cancer want from an Internet site? A qualitative 

study of information needs. Patient Educ Couns 

2004;53(1):57-64 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Rybarczyk B, Lopez M, Schelble K et al. Home-based 

video CBT for comorbid geriatric insomnia: a pilot study 

using secondary data analyses. Behav Sleep Med 

2005;3(3):158-75 

No health informatics application 

Saitz R, Helmuth E D, Aromaa S E et al. Web-based 

screening and brief intervention for the spectrum of alcohol 

problems. Preventive Medicine 2004;39(5):969-975 

F-16 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Not a RCT, and not a study addressing barriers 

Schinke S, Di Noia J, Schwinn T et al. Drug abuse risk and 

protective factors among black urban adolescent girls: a 

group-randomized trial of computer-delivered motherdaughter intervention. Psychol Addict Behav 

2006;20(4):496-500 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Schinke S, Schwinn T. Gender-specific computer-based 

intervention for preventing drug abuse among girls. Am J 

Drug Alcohol Abuse 2005;31(4):609-16 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Schmidt R, Norgall T, Morsdorf J et al. Body Area 

Network BAN--a key infrastructure element for patientcentered medical applications. Biomed Tech (Berl) 2002;47 

Suppl 1 Pt 1365-8 

No original data; 

Other* 

Schumann A, John U, Ulbricht S et al. Computer-generated 

tailored feedback letters for smoking cessation: theoretical 

and empirical variability of tailoring. Int J Med Inform 

2008;77(11):715-22 

No original data; 

Other* 

Scott C, Byng S. Computer assisted remediation of a 

homophone comprehension disorder in surface dyslexia. 

Aphasiology 89;3(3):301-320 

Health informatics application does not apply to the 

consumer; 

Not a RCT and not a study addressing barriers; 

Other* 

Secnik K, Pathak D S, Cohen J M. Postcard and telephone 

reminders for unclaimed prescriptions: a comparative 

evaluation using survival analysis. J Am Pharm Assoc 

(Wash) 2000;40(2):243-51; quiz 330-1 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Seematter-Bagnoud L, Santos-Eggimann B. Sources and 

level of information about health issues and preventive 

services among young-old persons in Switzerland. Int J 

Public Health 2007;52(5):313-6 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Shachak A, Shuval K, Fine S. Barriers and enablers to the 

acceptance of bioinformatics tools: a qualitative study. J 

Med Libr Assoc 2007;95(4):454-8 

Health informatics application does not apply to the 

consumer 

Shah A, Kuo A, Zurakowski D et al. Use and satisfaction of 

the internet in obtaining information on brachial plexus 

birth palsies and its influence on decision-making. J Pediatr 

Orthop 2006;26(6):781-4 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Shaw M J, Beebe T J, Tomshine P A et al. A randomized, 

controlled trial of interactive, multimedia software for 

patient colonoscopy education. Journal of Clinical 

Gastroenterology 2001;32(2):142-147 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Shepperd S, Charnock D, Gann B. Helping patients access 

high quality health information. BMJ 99;319(7212):764-6 

No original data 

Shigaki C L, Smarr K L, Yang Gong et al. Social 

interactions in an online self-management program for 

rheumatoid arthritis. Chronic Illn 2008;4(4):239-46 

Other* 

Silvia K A, Ozanne E M, Sepucha K R. Implementing 

breast cancer decision aids in community sites: barriers and 

resources. Health Expect 2008;11(1):46-53 

Health informatics application does not apply to the 

consumer 

Simoes A A, Bastos F I, Moreira R I et al. Acceptability of 

audio computer-assisted self-interview (ACASI) among 

substance abusers seeking treatment in Rio de Janeiro, 

Brazil. Drug Alcohol Depend 2006;82 Suppl 1S103-7 

Health informatics application does not apply to the 

consumer 

F-17 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Siva C, Smarr K L, Hanson K D et al. Internet use and email communications between patients and providers: a 

survey of rheumatology outpatients. J Clin Rheumatol 

2008;14(6):318-23 

Health informatics application does not apply to the 

consumer; 

Other* 

Skinner C S, Strecher V J, Hospers H. Physicians' 

recommendations for mammography: Do tailored messages 

make a difference? American Journal of Public Health 

94;84(1):43-49 

No health informatics application 

Skinner H, Morrison M, Bercovitz K et al. Using the 

Internet to engage youth in health promotion. 

Promotion & education 1997;4(4):23-25 

Health informatics application does not apply to the 

consumer; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Smaglik P, Hawkins R P, Pingree S et al. The quality of 

interactive computer use among HIV-infected individuals. 

Journal of Health Communication 1998;3(1):53-68 

Not a RCT, and not a study addressing barriers; 

Other* 

Smith D T, Carr L J, Dorozynski C et al. Internet-delivered 

lifestyle physical activity intervention: limited 

inflammation and antioxidant capacity efficacy in 

overweight adults. J Appl Physiol 2009;106(1):49-56 

Other* 

Spallek H, Butler B S, Schleyer T K et al. Supporting 

emerging disciplines with e-communities: needs and 

benefits. J Med Internet Res 2008;10(2):e19 

Health informatics application does not apply to the 

consumer 

Staccini P, Joubert M, Fieschi D et al. Confidentiality 

issues within a clinical information system: moving from 

data-driven to event-driven design. Methods Inf Med 

99;38(4-5):298-302 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Staccini P, Joubert M, Fieschi D et al. Confidentiality 

issues within a clinical information system: moving from 

data-driven to event-driven design. Methods Inf Med 

99;38(4-5):298-302 

No original data 

Steele R, Mummery W K, Dwyer T. Using the Internet to 

promote physical activity: a randomized trial of 

intervention delivery modes. J Phys Act Health 

2007;4(3):245-60 

No health informatics application; 

Study of a point of care device 

Stevens V J, Glasgow R E, Toobert D J et al. One-year 

results from a brief, computer-assisted intervention to 

decrease consumption of fat and increase consumption of 

fruits and vegetables. Prev Med 2003;36(5):594-600 

Study of a point of care device 

Stock S E, Davies D K, Davies K R et al. Evaluation of an 

application for making palmtop computers accessible to 

individuals with intellectual disabilities. J Intellect Dev 

Disabil 2006;31(1):39-46 

No health informatics application 

Stoddard J L, Augustson E M, Moser R P. Effect of adding 

a virtual community (bulletin board) to smokefree.gov: 

randomized controlled trial. J Med Internet Res 

2008;10(5):e53 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Stoddard J L, Delucchi K L, Munoz R F et al. Smoking 

cessation research via the internet: A feasibility study. 

Journal of Health Communication 2005;10(1):27-41 

Not a RCT and not a study addressing barriers; 

Other* 

Strecher V J, Kreuter M, Den Boer et al. The effects of 

computer-tailored smoking cessation messages in family 

practice settings. Journal of Family Practice 

1994;39(3):262-270 

No health informatics application; 

Other* 

Strom L, Pettersson R, Andersson G. Internet-based 

treatment for insomnia: a controlled evaluation. J Consult 

Clin Psychol 2004;72(1):113-20 

No health informatics application; 

Study of a point of care device 

Suggs L S, McIntyre C. Are We There Yet? An 

Examination of Online Tailored Health Communication. 

Health Educ Behav 2007; 

No health informatics application; 

F-18 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Svetkey L P, Stevens V J, Brantley P J et al. Comparison of 

strategies for sustaining weight loss: the weight loss 

maintenance randomized controlled trial. JAMA 

2008;299(10):1139-48 

Study of a point of care device 

Takahashi Y, Satomura K, Miyagishima K et al. A new 

smoking cessation programme using the Internet. Tobacco 

Control 1999;8(1):109-110 

Study of a point of care device; 

No original data 

Tan R L. Medicare beneficiaries'' use of computers and 

Internet: 1998-2005. Health Care Financing Review 

2007;28(2):45-51 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Tate D F, Wing R R, Winett R A. Using Internet 

technology to deliver a behavioral weight loss program. 

JAMA 2001;285(9):1172-7 

Study of a point of care device 

Taub S J. The Internet's role in patient/physician 

interaction: bringing our understanding in line with online 

reality. Compr Ophthalmol Update 2006;7(1):25-30 

No original data 

Taylor D P, Bray B E, Staggers N et al. User-centered 

development of a Web-based preschool vision screening 

tool. AMIA Annu Symp Proc 2003;654-8 

Other* 

Ten Wolde G B, Dijkstra A, van Empelen P et al. Longterm effectiveness of computer-generated tailored patient 

education on benzodiazepines: a randomized controlled 

trial. Addiction 2008;103(4):662-70 

Study of a point of care device 

Tetzlaff L. Consumer informatics in chronic illness. J Am 

Med Inform Assoc 97;4(4):285-300 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Other* 

Thobaben M. Technology and informatics. Accessibility, 

quality, and readability of health information on the 

internet: implication for home health care professionals.. 

Home Health Care Management & Practice 

2002;14(4):295-296 

Health informatics application does not apply to the 

consumer; 

No original data 

Thompson D, Baranowski T, Cullen K et al. Food, fun, and 

fitness internet program for girls: pilot evaluation of an eHealth youth obesity prevention program examining 

predictors of obesity. Prev Med 2008;47(5):494-7 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Tiller K, Meiser B, Gaff C et al. A randomized controlled 

trial of a decision aid for women at increased risk of 

ovarian cancer. Med Decis Making 2006;26(4):360-72 

No health informatics application 

Titov N, Andrews G, Choi I et al. Shyness 3: randomized 

controlled trial of guided versus unguided Internet-based 

CBT for social phobia. Aust N Z J Psychiatry 

2008;42(12):1030-40 

Other* 

Tjora A, Tran T, Faxvaag A. Privacy vs usability: a 

qualitative exploration of patients' experiences with secure 

Internet communication with their general practitioner. J 

Med Internet Res 2005;7(2):e15 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Study of a point of care device 

Torsney K. Advantages and disadvantages of 

telerehabilitation for persons with neurological disabilities. 

NeuroRehabilitation 2003;18(2):183-185 

No original data 

Tsang M W, Mok M, Kam G et al. Improvement in 

diabetes control with a monitoring system based on a handheld, touch-screen electronic diary. J Telemed Telecare 

2001;7(1):47-50 

Not a RCT, and not a study addressing barriers; 

Other* 

Tugwell P S, Santesso N A, O'Connor A M et al. 

Knowledge translation for effective consumers. Phys Ther 

2007;87(12):1728-38 

F-19 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

No health informatics application; 

Other* 

Tuil W S, Verhaak C M, Braat D D et al. Empowering 

patients undergoing in vitro fertilization by providing 

Internet access to medical data. Fertil Steril 

2007;88(2):361-8 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Underhill C, Mckeown L. Getting a second opinion: health 

information and the Internet. Health Rep 2008;19(1):65-9 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

van den, Berg M H, Ronday H K et al. Using internet 

technology to deliver a home-based physical activity 

intervention for patients with rheumatoid arthritis: A 

randomized controlled trial. Arthritis Rheum 

2006;55(6):935-45 

Study of a point of care device 

van der, Meer V, van Stel H F et al. Internet-based selfmanagement offers an opportunity to achieve better asthma 

control in adolescents. Chest 2007;132(1):112-9 

Study of a point of care device 

Van Voorhees B W, Fogel J, Reinecke M A et al. 

Randomized clinical trial of an Internet-based depression 

prevention program for adolescents (Project CATCH-IT) in 

primary care: 12-week outcomes. J Dev Behav Pediatr 

2009;30(1):23-37 

Other* 

van Wier M F, Ariens G A, Dekkers J C et al. Phone and email counselling are effective for weight management in an 

overweight working population: a randomized controlled 

trial. BMC Public Health 2009;96 

Other* 

van Zutphen M, Milder I E, Bemelmans W J. Integrating an 

eHealth program for pregnant women in midwifery care: a 

feasibility study among midwives and program users. J 

Med Internet Res 2009;11(1):e7 

Not a RCT, and not a study addressing barriers 

Vandelanotte C, De Bourdeaudhuij I, Brug J. Acceptability 

and feasibility of an interactive computer-tailored fat intake 

intervention in Belgium. Health Promotion Internation 

2004;19(4):463-470 

Not a RCT, and not a study addressing barriers; 

Other* 

Wade SL, Wolfe CR, Brown TM et al. Can a Web-based 

family problem-solving intervention work for children with 

traumatic brain injury?. Rehabilitation Psychology 

2005;50(4):337-345 

No health informatics application; 

Health informatics application does not apply to the 

consumer 

Wagner T H, Greenlick M R. When parents are given 

greater access to health information, does it affect pediatric 

utilization?. Med Care 2001;39(8):848-55 

No health informatics application; 

Not a RCT, and not a study addressing barriers 

Walker S N, Pullen C H, Boeckner L et al. Clinical trial of 

tailored activity and eating newsletters with older rural 

women. Nurs Res 2009;58(2):74-85 

No health informatics application 

Wantland D. Content and Functional Assessment of A 

HIV/AIDS Tailored Web-Based Symptom Self Assessment 

and Self Management Tool. Stud Health Technol Inform 

2009;146820-1 

Health informatics application does not apply to the 

consumer 

Wantland D. Content and Functional Assessment of A 

HIV/AIDS Tailored Web-Based Symptom Self Assessment 

and Self Management Tool. Stud Health Technol Inform 

2009;146820-1 

Other* 

Warmerdam L, van Straten A, Cuijpers P. Internet-based 

treatment for adults with depressive symptoms: the 

protocol of a randomized controlled trial. BMC Psychiatry 

2007;772 

Study of a point of care device 

Weinert C, Cudney S, Hill W. Retention in a computerbased outreach intervention for chronically ill rural women. 

Appl Nurs Res 2008;21(1):23-9 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Study of a point of care device 

Weingart S N, Rind D, Tofias Z et al. Who uses the patient 

internet portal? The PatientSite experience. J Am Med 

Inform Assoc 2006;13(1):91-5 

Health informatics application does not apply to the 

consumer; 

F-20 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

What children think about computers. Future Child 

2000;10(2):186-91 

No health informatics application; 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

White M A, Martin P D, Newton R L et al. Mediators of 

weight loss in a family-based intervention presented over 

the internet. Obes Res 2004;12(7):1050-9 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

White M. Enhancing process efficiency through remote 

access. Wireless implementation and remote access enable 

medical oncology practice to improve patient and clinician 

confidence while achieving ROI. Health Manag Technol 

2004;25(3):42-3 

No original data 

Whitten P, Mickus M. Home telecare for COPD/CHF 

patients: outcomes and perceptions. J Telemed Telecare 

2007;13(2):69-73 

Study of a point of care device 

Williams A. Surfing over sixty. Making Internet access 

available helps residents stay connected. Provider 

99;25(8):69, 71-2 

No original data 

Williams G C, Lynch M, Glasgow R E. Computer-assisted 

intervention improves patient-centered diabetes care by 

increasing autonomy support. Health Psychol 

2007;26(6):728-34 

Other* 

Williams M L, Freeman R C, Bowen A M et al. The 

acceptability of a computer HIV/AIDS risk assessment to 

not-in-treatment drug users. AIDS Care 98;10(6):701-11 

Study of a point of care device 

Williams R B, Boles M, Johnson R E. A patient-initiated 

system for preventive health care. A randomized trial in 

community-based primary care practices. Arch Fam Med 

98;7(4):338-45 

Health informatics application does not apply to the 

consumer; 

Study of a point of care device 

Wilson C, Flight I, Hart E et al. Internet access for delivery 

of health information to South Australians older than 50. 

Aust N Z J Public Health 2008;32(2):174-6 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer; 

Other* 

Wilson E V, Lankton N K. Modeling patients' acceptance 

of provider-delivered e-health. J Am Med Inform Assoc 

2004;11(4):241-8 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Wilson P. Searching for the needle in the haystack -- or -- 

quality criteria for health-related websites.. Health IT 

Advisory Report 2001;3(1):20-23 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Wilson-Steele G. Improving healthcare through patient 

education, patient relationship management. Internet 

Healthc Strateg 2003;5(3):8 

Study of a point of care device; 

No original data 

Winett R A, Anderson E S, Wojcik J R et al. Guide to 

health: nutrition and physical activity outcomes of a grouprandomized trial of an Internet-based intervention in 

churches. Ann Behav Med 2007;33(3):251-61 

Health informatics application does not apply to the 

consumer 

Wong B M, Yung B M, Wong A et al. Increasing Internet 

use among cardiovascular patients: new opportunities for 

heart health promotion. Can J Cardiol 2005;21(4):349-54 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

Woolf S H, Krist A H, Johnson R E et al. A practicesponsored Web site to help patients pursue healthy 

behaviors: an ACORN study. Ann Fam Med 

2006;4(2):148-52 

Other* 

Wright J H, Wright A S, Albano A M et al. Computerassisted cognitive therapy for depression: maintaining 

efficacy while reducing therapist time. Am J Psychiatry 

2005;162(6):1158-64 

F-21 

*Please see a list of other reasons at the end of this document
Appendix F: List of Excluded Articles 

F-22 

*Please see a list of other reasons at the end of this document

No health informatics application; 

Study of a point of care device 

Zanchetta MS. Understanding functional health literacy in 

experiences with prostate cancer: older men as consumers 

of health information.. Online Brazilian Journal of Nursing 

2004;3(2):9p 

Health informatics application is for general 

information only AND is not tailored to the individual 

consumer 

List of “other” reasons: 

1. Based on data from 2 other trials 

2. Book 

3. Case study 

4. Chess discussion board 

5. Cohort study, no control group, no comparison to other methods of testing hearing 

6. Commentary--not a study no info 

7. Comparing paper based consent form with cd-rom, no tailored info 

8. Contact with therapist 

9. Contains learning modules only--no tailoring 

10. Data not abstractable 

11. Description and pilot test 

12. Description of the system 

13. Development study 

14. Development survey 

15. Discussion board 

16. Doctoral dissertation 

17. Electronic diaries only to measure. The study does not examine its impact. 

18. Evaluate an internet-based hearing test, no control grp 

19. Evaluate the use of a computerized concept for lifestyle 

20. Evaluation 

21. Focus group--does not address any real barriers 

22. Group therapy, no tailored, therapist presence 

23. It seems to talk more on lines with group discussion and moderation of group. 

24. Descriptive 

25. No data comparing experimental and control group; no barriers nor facilitators 

26. No data--usage study 

27. No health outcomes, just a satisfaction assessment with the system 

28. No outcome data available 

29. No quantitative data are reported 

30. No result 

31. No tailored information provided to patients from this application, only to physicians 

32. No tailoring required 

33. No usable data 

34. No usable outcomes, and i really don't think this is a tailored in tailored out study 

35. Not about the application but about the parent 

36. Not computerized tailored 

37. Not tailored and can not isolate the impact of the application from impact of the community chat 

38. Not tailored and intervention includes human counseling 

39. Not tailored or interactive 

40. Not tailored, and there was clinician interaction 

41. Only abstract 

42. Output is not applied to the patient 

43. Overview of a type of full-body sensor 

44. Participants had no access to application 

45. Patient-centered care 

46. Pilot testing of the instrument. Does not look like a study 

47. Presence of health care provider -web-based tool to help people to manage their health and improve their 

communication with their health care provide 
Appendix F: List of Excluded Articles 

48. Qualitative analysis of website usage; does not provide barriers nor facilitators 

49. Survey on access to the internet 

50. Survey on general use 

51. Tailoring not required 

52. Tailoring occurs at the level of chat group and therapist 

53. Tailoring was accomplished in this study by providing flexibility in the number and timing of receipt of message each 

day. 

54. Telecommunication 

55. Telemedicine 

56. This is a pilot beta-test 

57. This is a study of "use" not any real outcomes 

58. Usage study 

59. Video informed consent 

60. Web based management of diabetes, physician presence 

61. Web-based intervention with e-mail counseling 

62. Website rating 

63. Workplace 

F-23 

*Please see a list of other reasons at the end of this document
Appendix G 
Evidence Table 1. Jadad criteria for RCT quality 

G-1 

Author, year Target RCT Approp of 

rand. 

Blinding Approp of 

blinding 

WD SCORE 

Key Question 1 a (healthcare process outcomes) 

Bartholomew et 

al.1 1 0 -1 -1 -1 

Guendelman et 

al.,2 1 0 0 1 2 

Jan, 

20073 1 0 -1 1 1 

Krishna, 

20034

Asthma 

1 1 1 3 

Chewining, 

19995 Oral contraception 

use 

1 -1 0 0 

Key Question 1 b (intermediate outcomes) 

Gustafson, 

20086 1 1 0 2 

Gustafson, 20017 1 0 1 

Jones, 

19998

Breast cancer 

1 -1 1 1 

Adachi, 20079 1 -1 0 0 

Anderson, 200110 1 -1 0 1 1 

Bruge, 199611 1 1 2 

Brug, 199812 1 0 0 -1 0 

Brug, 199913 1 0 0 1 2 

Campbell, 199414 1 0 0 -1 0 

Campbell, 199915 1 0 0 1 2 

Campbell, 200416 1 1 0 -1 1 

Haerens, 200517 1 1 0 -1 1 

Haerens, 200718 1 1 -1 -1 0 

Haerens, 200919 1 1 0 -1 1 

Hurling, 200620 1 1 0 -1 1 

Hurling, 200721 1 1 0 2 

Jones, 200822 1 0 0 -1 1 1 

King, 200623 1 1 0 -1 1 

Kristal, 200024 1 1 0 1 3 

Low, 200625 1 1 0 2

Lewis, 200826 1 1 0 1 3 

Marcus, 200727 1 1 1 3 

Mangunkusumo, 

200728 1 0 1 

Napolitano, 

200329 1 -1 0 0 

Oenema, 200130 1 1 -1 -1 0 

Richardson, 

200731 1 0 0 1 2 

Silk, 

200832 1 -1 0 

Smeets, 200733 1 -1 -1 -1 

Spittaels, 200734 1 -1 1 1 

Spittaels, 200735 1 0 0 1 2 

Tate, 200636 1 1 1 1 0 4 

Vandelanotte, 

200537

Diet, exercise, 

physical activity 

(not obesity) 

1 0 1 
Evidence Table1. Jadad criteria for RCT quality (continued) 

G-2 

Author, year Target RCT Approp of 

rand. 

Blinding Approp of 

blinding 

WD SCORE 

Verheijden, 

200438 1 0 -1 0 0 

Winzelberg, 

200039 1 -1 0 

Wylie-Rosett, 

200140 1 0 0 -1 0 

Cunningham , 

200541 1 -1 1 1 

Hester , 200542 1 1 -1 1 2 

Kypri , 199943 1 1 1 1 0 4 

Lieberman, 

200644 1 -1 -1 -1 

Neighbors , 

200445 1 0 1 

Riper, 200846 1 1 -1 1 2 

Riper, 200847

Alcohol 

1 0 0 1 2 

An, 200848 1 1 -1 1 2 

Brendryen, 2008 

49 1 0 0 1 2 

Curry, 1995 50 1 0 0 -1 0 

Dijkstra, 2005 51 1 0 0 -1 0 

Hang, 2009 52 1 1 0 1 3 

Japuntich, 200653 1 1 2 

Pattents, 200654 1 0 1 

Prochaska, 1993 

55 1 0 0 1 2 

Prokhorov, 2008 

56 1 0 0 1 2 

Schiffmans, 

200057 1 1 1 3 

Schumann, 

200658 1 -1 -1 -1 

Schumann, 

200859 1 1 -1 1 2 

Severson, 200860 1 1 2 

Strecher, 1994 61

Study 1 

1 0 0 1 2 

Strecher, 200562 1 -1 -1 -1 

Strecher, 2005 63 1 0 0 1 

Strecher, 200664 1 -1 0 0 

Strecher, 200865 1 -1 -1 0 -1 

Swartz, 200666

Smoking cessation 

1 1 -1 0 1 

Booth, 200867 1 0 1 

Burnett, 

198568 -1 -1 -1 -3 

Cussler, 200869 1 1 2 

Frenn, 200570 1 -1 -1 0 -2 

Hunter, 200871 1 1 -1 1 2 

Kent, 198568 -1 -1 -1 -3 

Kroeze, 2008 72 1 1 1 3 

McConnon, 

200773

Obesity 

1 1 -1 0 1 
Evidence Table1. Jadad criteria for RCT quality (continued) 

G-3 

Author, year Target RCT Approp of 

rand. 

Blinding Approp of 

blinding 

WD SCORE 

Morgan , 200974 1 1 0 1 3 

Taylor, 

199175 1 -1 0 

Williamson, 

200676 1 1 -1 1 2 

Womble, 200477 1 0 1 2 

Glasgow, 200378 1 -1 0 0 

Homko, 200779 1 -1 1 1 

McKay, 200180 1 1 1 3 

Richardson, 

200731 1 1 -1 1 2 

Wangberg, 200681 1 1 2 

Wise, 198682

Diabetes 

1 1 0 -1 1 

Lorig, 200683 Diabetes, heart 

disease 

1 -1 1 1 

Chiauzzi, 

200884 1 1 -1 -1 0 

Christensen, 

200485 1 1 -1 1 2 

Hasson, 

200586 1 1 -1 1 2 

Neil, 200987 1 0 0 1 2 

Proudfoot, 200488 1 1 1 3 

Schneider, 

200589 1 -1 1 1 

Warmerdam, 

200890 1 1 0 1 3 

Zetterqvist , 

200391

Mental health 

1 0 0 1 2 

Jan, 20073 1 0 -1 1 1 

Joseph, 200792 1 1 -1 1 2 

Krishna, 20034

Asthma 

1 1 1 3 

Nguyen, 200893 COPD 1 1 -1 1 2 

Paperny, 199094 Adolescent risk 

behavior 

1 1 0 1 3 

Lorig, 

200895 Arthritis 1 -1 1 1 

Buhrman, 

200496 Back pain 1 1 2 

Oenema, 

200897 Behavioral risk 

factors 

1 1 0 1 3 

Chewning, 19995 Contraception use 1 -1 0 0 

Kukafka, 

200298 Cardio-vascular 

disease 

1 -1 -1 -1 

Jones. 19998 1 1 1 3 

Campbell, 

199799

Cancer, general 

1 -1 -1 -1 

Brennan, 

1995100 Caregiver decision 

making 

1 1 2 

Yardley, 

2007101 Prevention of falls 

in the elderly 

1 1 1 1 1 5 

Harari, Change in health 1 1 -1 0 1 
Evidence Table1. Jadad criteria for RCT quality (continued) 

G-4 

Author, year Target RCT Approp of 

rand. 

Blinding Approp of 

blinding 

WD SCORE 

2008102 behavior 

Devineni, 2005103 Headache 1 1 2 

Flatley-Brennan, 

1998104 HIV/AIDS 1 -1 0 0 

Schapira, 

2007105 1 1 -1 1 2 

Rostom, 

2002106

Menopause/HRT 

1 1 -1 -1 0 

Key Question 1 c (relationship-centered outcomes) 

Green, 2005107 1 -1 -1 -1 

Gustafson, 20017 1 0 1 

Maslin, 1998108 1 -1 -1 -1 

Gustafson, 20086

Breast cancer 

1 1 0 2 

Brennan, 1995100 Caregiver decision 

making 

1 1 2 

Flatley-Brennan, 

1998104 HIV/AIDS 1 -1 0 0 

Sciamanna, 

2005109 Osteoarthritis 1 -1 -1 -1 

Montgomery, 

2007110 Newborn delivery 1 1 1 3 

Key Question 1 d (clinical outcomes) 

Gustafson, 

20017 1 0 1 

Gustafson, 

20086 1 1 0 2 

Maslin, 

1998108

Breast cancer 

1 -1 -1 -1 

Homko, 

200779 1 -1 1 1 

Tjam, 2006111 1 1 -1 0 1 

Wise, 1986 82 

Diabetes 

1 1 0 -1 1 

Adachi, 

20079 1 -1 0 0 

Hunter, 

200871 1 1 -1 1 2 

McConnon, 

200773 1 1 -1 0 1 

Tate, 

200636 1 1 1 1 0 4 

Williamson, 

200676

Diet, exercise, 

physical activity 

(not obesity) 

1 1 -1 1 2 

Christensen, 

200485 1 1 -1 1 2 

Hasson, 200586 1 1 -1 1 2 

Kerr, 

2008112 1 0 1 

March, 

2008113 1 1 -1 1 2 

Orbach, 

2007114 1 1 -1 0 1 

Proudfoot, 

2003115 1 1 1 3 

Spek, 

2008116

Mental health 

1 1 2 
Evidence Table1. Jadad criteria for RCT quality (continued) 

G-5 

Author, year Target RCT Approp of 

rand. 

Blinding Approp of 

blinding 

WD SCORE 

Tarraga, 2006117 Alzheimers 1 -1 0 0 

Lorig, 

200895 Arthritis 1 -1 1 1 

Jan, 

20073 Asthma 1 0 -1 1 1 

Buhrman, 

200496 Back pain 1 1 2 

Katz, 

1997118 Chronic adult 

aphasia 

1 -1 1 1 

Nguyen, 

200893 COPD 1 1 -1 1 2 

Trautman, 2008119 Headache 1 1 1 3 

Gustafson, 

1999120 HIV/AIDS 1 1 -1 1 2 

Morgan, 200974 Obesity 1 1 0 1 3 

Borckardt, 2007121 Pain 1 -1 0 

Key Question 1 e (economic outcomes) 

Jones, 19998 Cancer 1 -1 1 1 

Joseph, 200792 Asthma 1 1 -1 1 2 

McConnon, 

200773 Obesity 1 1 -1 0 1 

Key Question 2 (Barriers) 

Wangberg, 

200881 Diabetes 1 1 2 

Mangunkusumo, 

200728 Diet, exercise 1 0 1 
Evidence Table1. Jadad criteria for RCT quality (continued) 

G-6 

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G-11 

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G-12 

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G-13 

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Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes

G‐65

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

diet/exercise/physical activity NOT obesity 

Control 50 Mean, 65.1 

SD, 6.4 

1 month mean, -3 SD, 0.9 

3 month mean, -1.1 SD, 1.5 

 7 month: 

mean, -1.4 SD, 2.4 

 

Computer tailored program with 6-month weight and targeted behavior’s selfmonitoring, 

36 Mean, 65.3 

SD, 6.4 

1 month mean, -1.1 SD, 1.2 

3 month mean, -2.3 SD, 2 

 7 month: 

mean, -2.9 SD, 2.7 

 

Computer tailored program only, 

44 Mean, 64.8 

SD, 6.5 

1 month mean, -0.9 SD, 1.1 

3 month mean, -1.7 SD, 1.9 

 7 month: 

mean, -2.2 SD, 3 

 

Body Weight 

Untailored self-help booklet with 7-month selfmonitoring of weight and walking 

53 Mean, 63.4 

SD, 5.5 

1 month mean, -0.5 SD, 0.8 

3 month mean, -1.3 SD, 1.5 

 7 month: 

mean, -1.6 SD, 2.1 

 

Adachi, 20071

BMI Control 50 Mean, 26.1 

SD, 1.6 

1 month mean, -0.14 SD, 0.38 

3 month mean, -0.44 SD, 0.6 

 7 month 

mean, -0.57 SD, 0.93 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐66

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Computer tailored program with 6-month weight and targeted behavior’s selfmonitoring 

36 Mean, 26.2 

SD, 1.4 

1 month mean, -0.47 SD, 0.49 

3 month mean, -0.93 SD, 0.85 

 7 month 

mean, -1.22 SD, 1.16 

 

Computer tailored program only 

44 Mean, 26.2 

SD, 1.5 

1 month mean, -0.38 SD, 0.42 

3 month mean, -0.69 SD, 0.73 

 7 month 

mean, -0.86 SD, 1.15 

 

Untailored self-help booklet with 7-month selfmonitoring of weight and walking, 

53 Mean, 26.1 

SD, 1.5 

1 month mean, -0.2 SD, 0.34 

3 month mean, -0.53 SD, 0.64 

 7 month 

mean, -0.68 SD, 0.88 

 

Control 50 1 month 

mean, -0.05 SD, 1.4 

3 month mean, -1.6 SD, 2.3 

 7 month 

mean, -2.2 SD, 3.5 

 

% weight loss 

Computer tailored program with 6-month weight and targeted behavior’s self36 1 month mean, -1.8 SD, 1.9 3 month mean, -3.6 SD, 3.3 7 month mean, 4.7 SD, 4.5 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐67

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

monitoring, Computer tailored program only 

44 1 month 

mean, -1.5 SD, 1.6 

3 month mean, -2.6 SD, 2.8 

 7 month 

mean, -3.3 SD, 4.3 

 

Untailored self-help booklet with 7-month selfmonitoring of weight and walking 

53 1 month 

mean, -0.8 SD, 1.3 

3 month mean, -2 SD, 2.5 

 7 month 

mean, -2.6 SD, 3.4 

 

Control 137 32.74 (6.85) 33.19 (6.93) 

n 90 

Fat (% Ncalories) Composites Scores mean (SD) 

Intervention 124 33.24 (7.28) 31.00 (6.42) 

n 72 

N

Control NS 9.00(3.32) 9.21(3.26) n 

90 

Fiber N(g/1,000kcals) mean (SD) 

Intervention NS 8.97 (2.57) 10.61 (3.37) 

n 72 

N

Control 136 2.85 (1.34) 2.5 (1.18) n 

90 

Fruit and Nvegetables (servings/1000kcals) mean (SD) 

Intervention 124 2.78 (1.06) 3.35 (1.56) n 

72 

N

Anderson, 20012

Self Efficacy/ Control 139 6.70 (1.79) 6.68 (1.73) 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐68

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Low-Fat Meals mean (SD) 

Intervention 125 6.89 (1.82) 7.01 (1.67) NControl 139 7.24 (1.67) 7.18 (1.61) n 

132 

Self-Efficacy/ NLow-Fat Snacks mean (SD) Intervention 125 7.29 (1.86) 7.38 (1.80) n 

98 

N

Control 139 7.29 (1.78) 7.38 (1.67) n 

132 

Self- NEfficacy/Fruit, Vegetables, Fiber mean (SD) 

Intervention 125 7.50 (1.58) 7.58 (1.73) n 

98 

N

Control 139 3.92 (0.90) 3.94 (0.91) n 

132 

Outcome NExpectations/Appetite Satisfaction mean (SD) 

Intervention 125 3.97 (0.93) 4.13 (0.88) n 

98 

N

Control 139 3.39 (1.09) 3.40 (1.07) n 

132 

Outcome NExpectations/Budgetary Outcomes mean (SD) 

Intervention 125 3.40 (1.10) 3.39 (1.14 ) n 

98 

N

Control 139 4.29 (0.66) 4.32 (0.63) n 

132 

Outcome NExpectations/Health Outcomes mean (SD) 

Intervention 125 4.37 (0.59) 4.40 (0.58) n 

98 

N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐69

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

General Information 

220 28.2 (5.2) 27.5 (5.6) N

Tailored + Iterative feedback 

215 28.3 (5.4) 25.6 (4.6) N

Fat (fat points per day) 

Tailored Feedback 

211 28.0 (5.3) 26.2 (5.2) N

General Information 

220 2.09 (1.75) 2.02 (1.59) N

Tailored + Iterative feedback 

215 2.13 (1.70) 2.45 (1.69) N

Fruit (servings per day) 

Tailored Feedback 

211 2.18 (1.72) 2.18 (1.47) N

General Information 

220 1.02 (0.36) 1.08 (0.41) N

Brug, 19983

Vegetables (servings per day) 

Tailored + Iterative feedback 

215 1.06 (0.38) 1.20 (0.36) N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐70

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Tailored Feedback 

211 1.06 (0.41) 1.15 (0.41) N

Fat score Comparison 163 27.4 25.9 N

Experimental 152 26.5 26.2 NComparison 163 1.04 1.13 N

Servings of vegetables 

Experimental 152 1.14 1.07 N

Servings of Comparison 163 1.61 1.91 Nfruit 

Experimental 152 1.62 2.02 N

Intention to Comparison 163 .11 .50 Nreduce fat (Range: 23 (very surely not) to 13 (very sure). 

Experimental 152 .26 .37 NComparison 163 -.91 -.47 N

Brug, 19994

Intention to increase vegetables Range: 23 (very surely not) to 13 (very sure). 

Experimental 152 -1.21 -.51 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐71

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Intention to Comparison 163 -0.40 -0.13 Nincrease fruit dRange: 23 (very surely not) to 13 (very sure). 

Experimental 152 -0.31 -0.17 Nno message NS 41,1 (2,1) 39,8(1,9) Ntailored message 

NS 45,6 (2,6) 35,3(1,7) N

Fat (g/day)Mean (SE) 

Non tailored message 

NS 40,4 (2,4) 36,8(1,7) N

no message NS 16,3 (,98) 15.8 (,81) Ntailored message 

NS 18,7(1,1) 13,9 (.72) N

Saturated Fat (g/day) Mean (SE) 

Non tailored message 

NS 16,1 (.93) 14.4 (.72) N

no message NS 3,6 (.20) 3.3 (.20) N

Campbell, 19945

Vegetable/Fruit (servings/day) Mean (SE) tailored 

message 

NS 3,6 (.19) 3,3 (,19) N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐72

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Non tailored message 

NS 3,6 (.20) 3,3 (.19) N

Computertailored intervention 

NS 40.7 ± 14.4 31.6 ± 12.4 N

Standard intervention 

NS 35.6 ± 14.2 33.3 ± 12.9 N

No intervention 

NS 100.3 ± 39.9 97.1 ± 40.3 N

Computertailored intervention 

NS 109.2 ± 40.7 85.0 ± 34.5 N

Total fat intake (grams/ day) a , b 

Standard intervention 

NS 87.5 ± 35.9 81.8 ± 33.3 N

Control 212 4.13 (0.08) N 

Sig 

 4.33 (0.08) 

P<0.001 

Knowledge Nscore of low fat foods 

Intervention 165 4.29 (0.09) N 

Sig 

 5.08 (0.09) 

P<0.001 

N

Control 212 3.53 (0.08) N 

Sig 

 3.83 (0.07) 

N Sig 

Self-efficacy N

Intervention 165 3.55 (0.09) N 

Sig 

 3.94 (0.08) N 

Sig 

N

Campbell, 19996

Fat score (g) Control 212 101.6 (4.2) 

P<0.001 

 65.5 (2.8) N 

Sig 

N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐73

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Intervention 165 83.8 (4.7) 

P<0.001 

 64.1 (3.2) N 

Sig 

N

Control 212 31 (14.6) 18 (8.5) –

Stage of changePrecontemplation n (%) 

Intervention 165 14 (8.5) 9 (5.4) –

Stage of Control 212 72 (34.0) 47 (22.1) –changeContemplation n (%) 

Intervention 165 71 (43.0) 26 (15.8) –

Stage of Control 212 30 (14.2) 39 (18.3) +changePreparation n (%) 

Intervention 165 35 (21.2) 41 (24.8) +

Stage of Control 212 79 (37.3) 109 (51.2) +changeAction/maintenance n (%) 

Intervention 165 45 (27.3) 89 (53.9) +

P cnspsp
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐74

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rapin intigaescethcg

Control-No Intervention 

Total Low-fat 166 1.86(1.2) 2.63 (0.55) Nknowledge score 

Computer based interactive nutrition education 

141 1.94(1.2) 2.76 (0.46) N

Control-No Intervention 

166 2.25 (0.86) 2.40 (0.75) N

Campbell, 20047

Total Infant feeding knowledge score Computer 

based interactive nutrition education 

141 2.29 (0.82) 2.62 (0.62)* N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐75

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Control-No Intervention 

Total self- 166 17.08 18.48 Nefficacy score 

Computer based interactive nutrition education 

141 17.68 18.93 

(significant increase at Immediate Follow-up 19.51, P < .05 

N

control condition 

(n 655 pupils) 

108 ± 46 104 ± 45 N

intervention with parental support 

(n 1055 pupils) 

111 ± 48 105 ± 49 N

Fat intake (g day21) 

intervention alone 

 (n 685 pupils) 

130 ± 54 127 ± 56 N

Haerens, 20058

Fruit intake (pieces week) 

control condition 

(n 655 pupils) 

6.5 ± 5.0 6.0± 4.9 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐76

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

intervention with parental support 

(n 1055 pupils) 

5.3 ± 5.3 5.4 ± 5.3 N

intervention alone 

 (n 685 pupils) 

4.6 ±5.0 4.4 ± 4.7 N

control condition 

(n 655 pupils) 

2.5 ± 2.2 2.6± 2.4 N

intervention with parental support 

(n 1055 pupils) 

3.1± 2.4 3.1 ±2.5 N

Soft drinks (glasses day) 

intervention alone 

 (n 685 pupils) 

3.5 ± 2.5 3.9 ± 2.8 N

control condition 

(n 655 pupils) 

Water (glasses 3.7 ± 2.6 4.0 ±2.8 Nday21) 

intervention with parental support 

(n 1055 pupil

3.4 ± 2.7 3.7 ± 2.8 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐77

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

s) 

intervention alone 

(n 685 pupils) 

3.1 ± 2.7 3.5 ± 2.9 N

control condition 

(n 392 pupils) 

99 ± 39 95 ± 40 N

intervention with parental support 

(n 432 pupils) 

97 ± 38 85 ± 35 N

Pre- and posttest intake levels (mean ^ SD) for fat intake in girls 

intervention alone 

(n 108 pupils) 

108 ± 46 98 ± 40 N

control condition 

(n 392 pupils) 

38.7 ± 15.8 36.1 ± 15.5 N

intervention with parental support 

(n 432 pupils) 

37.5 ± 15.0 31.9 ± 13.6 N

Pre- and posttest intake levels (mean ^ SD) for % energy from fat in girls 

intervention (n 

108 

41.1 ± 16.8 36.6 ± 15.2 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐78

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

alone pupil

s) 

 Control 84 Mean, 110 

SD, 42.2 

 3 months 

after 50 minute intervention: mean, 107.3 SD, 41.5 

 

Intervention 90 Mean, 120.9 

SD, 48.7 

 3 months 

after 50 minute intervention: mean, 108.2 SD, 43.9 

Dietary fat intake (self-report) (g/day) 

Intervention students who had read the intervention message 

65 Mean, 118.4 

SD, 50.1 

 3 months 

after 50 minute intervention: mean, 102 SD, 43.8 

 Control 67 Mean, 118.8 

SD, 50.8 

 3 months 

after 50 minute intervention mean, 110.5 SD, 47 

 

Intervention 63 Mean, 109.7 

SD, 51.6 

 3 months 

after 50 minute intervention mean, 99.6 SD, 51.3 

Haerens, 20079

Dietary fat intake (technicalvocational) 

Intervention students who had read the intervention message 

46 Mean, 97.8 

SD, 38.9 

 3 months 

after 50 minute intervention mean, 86.2 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐79

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

SD, 39.3 

Generic Feedback 

Cycling for 543 76 (112) 105 (140) Ntransportation 

Tailored feedback 

511 78 (111) 110 (153) N

Generic Feedback 

Walking for 543 75 (120) 98 (155) Ntransportation 

Tailored feedback 

511 68 (119) 95 (170) N

Generic Feedback 

Walking in 543 42 (96) 60 (139) Nleisure time 

Tailored feedback 

511 38 (99) 61 (156) N

Generic Feedback 

543 618 (527) 642 (573) N

Haerens, 200910

Total moderate to vigorous activity 

Tailored feedback 

511 604 (482) 642 (598) N

Hurling, 200611

Hypothesis 1: %of 

Control 22 At 3 weeks 

70 

 At 10 weeks 

43 

N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐80

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

participants logging into system after 10 week test period 

Intervention 25 At 3 weeks 

70 

 At 10 weeks 

75 

N

Hypothesis 2: Control 22 NS NS Nchange in perception of exercise as boring; Too much effort

Intervention 25 NS F(2, 57) 3.19; 

F(2, 57) 2.26, 

N

Control 22 NS 0.6; .38 N

Hypothesis 3: Change in ratings of Expectation; satisfaction with motivation to exercise; 

Intervention 25 Not clear 3.13; 3.6 N

The mean change (from 

Control 22 NS -0.05 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐81

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

the beginning of the study period to 7 months after the 10-week intervention) in ratings of the statement ‘‘I am very satisfied with my current level of motivation to do exercise’’

Intervention 25 NS +0.64 NControl 30 9 weeks: 

mean, 4.0 SD, 4.1 

MET min/week 

Had access to the internet and mobile phone 

47 9 weeks: 

mean, 12 SD, 3.1 

 

Control 30 3 weeks 9 weeks 

mean, -5.5 SD, 3.5 

Hurling, 200712

Change in weekly hours spent sitting (Met min/week leisure time) 

Had access to internet and mobile phone 

47 3 weeks 9 weeks 

mean, 4.1 SD, 2.6 

 

King, Total PA 

(kcals/kg/hr) M 

generic health risk 

161 55.5 (31.7) 53.6 (27.6) N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐82

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

appraisal CD-ROM 

(SD) 

Interactive CD-ROM 

174 58.4 (33.7) 64.6 (39.1) N

generic health risk appraisal CD-ROM 

161 15.7 (19.1) 14.9 (17.8) N

200613

Moderate PA (kcals/kg/hr) M (SD) 

Interactive CD-ROM 

174 17.2 (20.6) 22.9 (26.4) N

Fat-related diet Control 604 2.30 ± 0.49 -0.00 ± 0.40 Nhabit 

Intervention 601 2.29 ± 0.49 -0.09 ± 0.38 NControl 604 3.47 ± 1.41 0.14 ± 1.80 N

Kristal, 200014

Fruit and vegetables (svg/day) Intervention 601 3.62 ± 1.49 0.47 ± 1.83 N

Standard Internet 

median NS NS 38 Nnumber of logins 

Motivationally-Tailored Internet 

NS NS 50 N

Lewis, 200815

5-itemWebsite Standard NS NS 11.64 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐83

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Quality Internet Questionnaire Motivationally

-Tailored Internet 

NS NS 16.76 N

Control 14 Mean, 4.0 

SD, 5.6 

Post Intervention: mean,5.0 SD,4.6 

 8-9 month 

follow-up: mean, 5.5 SD, 5.7 

Student bodies with a moderated discussion group 

14 Mean, 2.5 

SD, 6 

Post Intervention: mean,2.0 SD,2.0 

 8-9 month 

follow-up: mean, 2.3 SD, 5.6 

 

Unmoderated discussion group 

19 Mean, 2.3 

SD, 3.4 

Post Intervention: mean,2.3 SD,2.3 

 8-9 month 

follow-up: median, 1.2; SD, 1.5 

Eating Disorder Inventory (EDI) -Drive for Thinness 

Program alone 

14 Mean, 4 

SD, 5 

Post Intervention: mean,3.7 SD,3.6 

 8-9 month 

follow-up: mean, 3.7 SD, 4.6 

Control 14 Mean, 1.2 

SD, 1.6 

Post Intervention: mean,1.1 SD, 1.0 

 8-9 month 

follow-up mean, 2 SD, 1.9 

Student bodies with a moderated discussion group 

14 Mean, 1.4 

SD, 4.2 

Post Intervention: mean,1.7 SD, 1.7 

 8-9 month 

follow-up mean, 0.46 SD, 1.9 

 

Unmoderated discussion group 

19 Mean, 1.4 

SD, 2.2 

Post Intervention: mean,0.85 SD, 0.86 

 8-9 month 

follow-up mean, 0.42 SD, 0.84 

 

Low,  200616

EDI- Bulimia 

Program 14 Mean, 1.2 Post 8-9 month 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐84

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

alone SD, 1.5 Intervention: 

mean,0.47 SD, 0.53 

follow-up mean, 1.3 SD, 1.6 

Control 14 Mean, 9.4 

 SD, 8 

Post Intervention: mean,7.9 SD, 8.2 

 8-9 month 

follow-up mean, 9.4 SD, 7.8 

 

Student bodies with a moderated discussion group 

14 Mean, 8.1 

 SD, 6.8 

Post Intervention: mean,7.6 SD, 7.6 

 8-9 month 

follow-up mean, 7 SD, 4.9 

Unmoderated discussion group 

19 Mean, 7.9 

 SD, 6.4 

Post Intervention: mean, 5.9 SD, 5.9 

 8-9 month 

follow-up mean, 5.2 SD, 4.2 

 

 EDI-Body Dissatisfaction 

Program alone 

14 Mean, 9 

 SD, 6.7 

Post Intervention: mean,7.1 SD, 7.1 

 8-9 month 

follow-up mean, 6.3 SD, 7.8 

Control 14 Mean, 37 

SD, 22.3 

Post Intervention: mean,41.8 SD, 22.8 

 8-9 month 

follow-up mean, 43.2 SD, 21.1 

 

Student bodies with a moderated discussion group 

14 Mean, 33.8 

SD, 22.4 

Post Intervention: mean, 32.2 SD, 33.8 

 8-9 month 

follow-up mean, 29.9 SD, 23.1 

Unmoderated discussion group 

19 Mean, 29.5 

SD, 16.6 

Post Intervention: mean, 28.5 SD, 29.3 

 8-9 month 

follow-up mean, 27.5 SD, 14.5 

 

 Weight and Shape Concerns 

Program alone 

14 Mean, 38.3 

SD,17.0 

Post Intervention: mean, 38.2 SD, 26.7 

 8-9 month 

follow-up mean, 34.6 SD, 16.7 

Mangunkus Evaluation of Control 465 2-4 months: 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐85

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

mean, 4.1; median, 4; range, 4.0-5.0; SD, 0.7 

Health Behavior mode (easy to use) (Likert Scale) 

Internet Group 

444 2-4 months: 

mean, 4.2; median, 4.0 ; range, 4.0-5.0; SD, 0.7 

 

Control 418 2-4 months 

mean, 3.7 median, 4 range, 3.0-4.0 SD, 0.7 

Evaluation of Fruit Advice (pleasant) (Likert Scale 

Internet 381 2-4 months 

mean, 3.8 median, 4 range, 3.0-4.0 SD, 0.7 

 

Control 417 2-4 months 

mean, 3.3 median, 3 range, 3.0-4.0 SD, 1 

Acceptability (Was fruit advice targeted to you?) 

Internet Group 

376 2-4 months 

mean, 3.5 median, 4 range, 3.0-4.0 SD, 0.9 

 

umo, 200717

Acceptability (Did you enjoy it) 

Control 417 2-4 months 

mean, 3.2 median, 3 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐86

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

range, 3.0-4.0 SD, 1 

Internet Group 

376 2-4 months 

mean, 3.4 median, 4 range, 3.0-4.0 SD, 0.9 

 

Control 417 2-4 months: 

mean, 3.3: median, 4: range, 3.0-4.0: SD, 1 

Quality of Intervention (relevant) 

Internet Group 

376 2-4 months: 

mean, 3.5: median, 4: range, 3.0-4.0: SD, 1 

 

Control 417 2-4 months 

mean, 3.8 median, 4 range, 4.0-4.0 SD, 0.8 

Quality of Intervention (credible) 

Internet Group 

376 2-4 months 

mean, 3.6 median, 4 range, 3.0-4.0 SD, 0.9 

 

Quality of Intervention (useful) 

Control 417 2-4 months 

mean, 3.7 median, 4 range, 3.0-4.0 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐87

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

SD, 0.9 

Internet 376 2-4 months 

mean, 3.6 median, 4 range, 3.0-4.0 SD, 0.9 

 

Physical activity per week 

Tailored print 86 Minutes of 

physical activity per week (using PAR interview) 

6 months mean, 112.5 

 12 months: 

mean, 90 

 

 Tailored 

internet 

81 Minutes of 

physical activity per week (using PAR interview) 

6 months mean, 120 

 12 months: 

mean, 90 

 

 Standard 

internet 

82 Minutes of 

physical activity per week (using PAR interview) 

6 months mean, 90 

 12 months 

mean, 80 

 

Improvement in functional capacity (estimated v02 at 85% of predicted maximum heart rate) (ml/kg per minute) 

Tailored print 86 6 months 

mean, 25.8 SD, 6.8 

 12 months 

mean, 26.2 SD, 6.9 

 

Marcus, 200718

 Tailored 

internet 

81 6 months 

mean, 26.5 

 12 months 

mean, 26.1 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐88

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

SD, 6.6 SD, 6.9 

 Standard 

internet 

82 6 months 

mean, 25.4 SD, 6.6 

 12 months 

mean, 25.7 SD, 6 

 

150 minutes of physical activity per week (150 minutes) 

Tailored print 86 6 months, 

(37.2) 

 12months, 

(32.6) 

 

 Tailored 

internet 

81 6 months, 

(44.4) 

 12months, 

(39.5) 

 

 Standard 

internet 

82 6 month, 

(36.6) 

 12 months, 

(30.5) 

 

Control 31 Mean, 80.86 

SD, 77.8 

1 month mean, 96.82 SD, 93.7 

 3 month: 

mean, 82 SD, 87.3 

Minutes moderate physical activity Internet 

intervention 

21 Mean, 68.79 

SD, 58.1 

1 month mean, 98.33 SD, 53.9 

 3 month: 

mean, 112 SD, 75.7 

 

Control 31 Mean, 87.57 

SD, 177.4 

1 month mean, 83.79 SD, 121.1 

 3 month 

mean, 68.39 SD, 85.2 

Napolitano, 200319

Minutes, walking 

Internet intervention 

21 Mean, 57.24 

SD, 56.9 

1 month mean, 87.29 SD, 46 

 3 month 

mean, 99.75 SD, 68.3 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐89

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Stage of Control 31 1 month 3 month change, progression 

Internet intervention 

21 1 month 3 month 

Control 102 Postintervention: 

mean, 0.29 SD, 1.26 

 

Intention to eat less fat 

Web based tailored nutrition education 

96 Postintervention: 

mean, 0.72 SD, 1.21 

 

Control 102 Mean, -0.44 

SD, 0.77 

 Postintervention 

mean, -0.33 SD, 0.74 

 

Self-rated fat intake compared to others 

Web based tailored nutrition education 

96 Mean, -0.31 

SD, 0.7 

 Postintervention 

mean, -0.05 SD, 0.8 

 

Control 102 Mean, -0.51 

 SD, 0.98 

 Postintervention 

mean, -0.49 SD, 0.97 

 

Self-rated fruit intake 

Web based tailored nutrition education 

96 Mean, -0.49 

 SD, 0.91 

 Postintervention 

mean, -0.27 SD, 0.93 

 

Oenema, 200120

Self rated fat Control 102 Post- 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐90

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Mean, -0.23 SD, 0.77 

intervention mean, 0.01 SD, 0.81 

intake 

Tailored 96 Mean, 0.03 

SD, 0.73 

 Postintervention 

mean, 0.17 SD, 0.68 

 

Control 102 Mean, -0.34 

 SD, 1.01 

 Postintervention: 

mean, -0.34  SD, 0.96 

 

Self rated fruit intake compared to others 

Tailored 96 Mean, -0.31 

 SD, 0.93 

 Postintervention: 

mean, -0.16  SD, 0.89 

 

Control 102 Mean, 0.37 

SD, 0.73 

 Postintervention 

mean, 0.3 SD, 0.76 

Self-rated vegetable intake 

Tailored 96 Mean, 0.2 

SD, 0.71 

 Postintervention 

mean, 0.08 SD, 0.74 

 

Control 102 Mean, 0.3 

SD, 0.78 

 Postintervention 

mean, 0.27 SD, 0.72 

Self-rated vegetable intake compared to other Tailored 96 Mean, 0.18 

SD, 0.74 

 Postintervention 

mean, 0.08 SD, 0.82 

 

Oenema, 200521

Fat intake Control 232 Mean, 20.3 

SD, 6.2 

 3 weeks after 

sending intervention 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐91

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

materials: mean, 19.9 SD, 6.2 

 General 

information 

196 Mean, 

20.0SD, 5.9 

 3 weeks after 

sending intervention materials: mean, 19.2 SD, 6.0 

 Tailored 

Information 

188 Mean, 19.8 

SD, 6.1 

 3 weeks after 

sending intervention materials: mean, 19.2 SD, 6.2 

Vegetable intake 

Control 232 Mean, 1.9 

SD, 1.0 

 3 weeks after 

sending intervention materials mean, 1.8 SD, 0.9 

 General 

information 

196 Mean, 1.8 

SD, 0.8 

 3 weeks after 

sending intervention materials Mean,1.7 SD,0.8 

 Tailored 

Information 

188 Mean, 1.8 

SD, 0.8 

 3 weeks after 

sending intervention materials mean, 1.9 SD, 0.9 

Fruit intake Control 232 Mean, 2.1 

 SD, 1.4 

 3 weeks after 

sending intervention materials mean, 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐92

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

median, 2.0 range, SD, 1.6 

 General 

information 

196 Mean, 2.1 

 SD, 1.4 

 3 weeks after 

sending intervention materials mean, 2.0 SD, 1.2 

 Tailored 

intervention 

188 Mean, 2.2 

 SD, 1.6 

 3 weeks after 

sending intervention materials mean, 2.3 SD, 1.6 

Fat self-rated intake 

Control 232 Mean, -0.11 

SD, 0.81 

 3 weeks after 

sending intervention materials mean, -0.18 SD, 0.73 

 General 196 Mean, -.16 

SD, 0.70 

 3 weeks after 

sending intervention materials mean, -0.22 SD, 0.71 

 Tailored 188 Mean, -0.25 

SD,0.68 

 3 weeks after 

sending intervention materials mean, -0.12 SD, 0.76 

Vegetable selfrated intake Control 232 Mean, 0.42 SD, 0.71 3 weeks after sending 

intervention materials: mean, 0.37 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐93

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

 SD, 0.70 

 General 

information 

196 Mean, 0.44 

 SD, 0.68 

 3 weeks after 

sending intervention materials: mean, 0.37  SD, 0.71 

 Tailored 188 Mean, 0..35 

 SD, 0.67 

 3 weeks after 

sending intervention materials: mean, 0.16  SD, 0.69 

Fruit self-rated intake 

Control 232 Mean, -0.22 

SD, 0.99 

 3 weeks after 

sending intervention materials mean, -0.26 SD, 0.91 

 General 

information 

196 Mean, -0.26 

SD, 0.95 

 3 weeks after 

sending intervention materials mean, -0.16 SD, 0.89 

 Tailored 

intervention 

188 Mean, -0.19 

SD, 1.09 

 3 weeks after 

sending intervention materials mean, -0.19 SD, 1.05 

Fat Intention to Change 

Control 232 Mean, -0.21 

SD, 1.01 

 3 weeks after 

sending intervention materials mean, -0.24 SD, 1.00 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐94

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

 General 196 Mean, -0.13 

SD, 0.95 

 3 weeks after 

sending intervention materials mean, -0.13 SD, 0.99 

 Tailored 188 Mean, -

0.23SD, 0.92 

 3 weeks after 

sending intervention materials mean, 0.01 SD, 0.97 

Vegetable Intention to Change 

Control 232 Mean, 0.85 

SD, -0.31 

 3 weeks after 

sending intervention materials: mean, -0.26 SD, 0.89 

 General 196 Mean, -0.32 

SD, 0.83 

 3 weeks after 

sending intervention materials: mean, -0.25 SD, 0.81 

 Tailored 188 Mean, -0.38 

SD, 0.78 

 3 weeks after 

sending intervention materials: mean, -0.04 SD, 0.93 

Fruit Intention to Change 

Control 232 Mean,-0.08 

SD,0.93 

 3 weeks after 

sending intervention materials: mean, -0.13 SD, 0.85 

 General 196 Mean,0.02 

SD,0.90 

 3 weeks after 

sending 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐95

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

intervention materials: mean, -0.04 SD,0.92 

 Tailored 188 Mean,-0.10 

SD,0.88 

 3 weeks after 

sending intervention materials: mean, -0.01 SD,1.01 

LG 17 4,157 ± 

1,737 

 6,279 ± 

3,306 

Total Steps 

SG 13 5,171 ± 

1,769 

 6,868 ± 

3,751 

 

LG 17 286 ± 599 2,070 ± 

2,814 

Bout Steps 

SG 13 516 ± 801 2,616 ± 

2,706 

 

LG 17 2,122 ± 

3,179 

Richardson, 200722

Change 

SG 13 1,783 ± 

2,741 

Control 1410 Mean, 18.7 

SD, 6.2 

 Post test at 3 

month: mean, -1 SD, 0.05 

Smeets, 200723

Fat consumption (gm) 

Intervention group, receiving one tailored letter 

1417 Mean, 18.7 

SD, 6.2 

 Post test at 3 

month: mean, -2.5 SD, -0.05 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐96

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Control 1410 Mean, 2.7 

SD, 1.7 

 Post test at 3 

month mean, -0.2 SD, 0.06 

Fruit consumption (pieces/day) 

Intervention group, receiving one tailored letter 

1417 Mean, 2.7 

SD, 1.7 

 Post test at 3 

month mean, 0.04 SD, 0.06 

 

Control 1410 Mean, 139 

 SD, 140 

 Post test at 3 

month mean, -10.4 

Vegetable consumption (gm/day) 

Intervention group, receiving one tailored letter 

1417 Mean, 139 

SD 140 

 Post test at 3 

month mean, -0.48 

 Control 1410 Mean, 5 

SD, 3.6 

 Post test at 3 

month mean, -1.1 

Physical activity (action moments/wk) 

Intervention group, receiving one tailored letter 

1417 Mean, 5 

SD, 3.6 

 Post test at 3 

month mean, -0.7 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐97

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

No Intervention 

104 201 (254) 233 (273) +

Website with computer tailored feedback 

103 292 (285) 420 (337) +

Spittaels, 200724

Total MVPA scores=Moderate- to vigorousintensity physical activity for completers 

Website without computer tailored feedback 

78 290 (319) 352 (357) +

 Control 141 Mean, 622 

SD, 462 

 6 month: 

mean, 708 SD, 514 

On-line tailored PA advice+ stage based reinforcement emails 

116 Mean, 696 

SD, 510 

 6 month: 

mean, 776 SD, 540 

 

Increase in total physical activity 

On-line tailored physical activity advice 

122 Mean, 640 

SD, 422 

 6 month: 

mean, 682 SD, 452 

Control 141 Mean, 376 

SD, 325 

 6 month 

mean, 428 SD, 374 

Spittaels, 200725

Increase in moderate to vigorous physical activity 

On-line tailored PA advice+ 

116 Mean, 438 

SD, 373 

 6 month 

mean, 479 SD, 376 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐98

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

stage based reinforcement emails On-line tailored physical activity advice only 

122 Mean, 362 

SD, 292 

 6 month 

mean, 397 SD, 310 

Control 141 Mean, 151 

 SD, 152 

 6 month 

mean, 185 SD, 161 

On-line tailored PA advice+ stage based reinforcement emails 

116 Mean, 174 

 SD, 191 

 6 month 

mean, 211 SD, 220 

 

Increase in physical activity in leisure time 

On-line tailored physical activity advice only 

122 Mean, 154 

 SD, 150 

 6 month 

mean, 190 SD, 188 

Control 54 Mean, 

1869.7 SD, 778.9 

3 month: mean, 1544.2 SD,651.7 

 6 month: 

mean, 1603.5 SD, 793.7 

Tailored ComputerAutomated Feedback 

40 Mean, 1911 

SD, 770.9 

3 month: mean,1381.7 SD,448.2 

 6 month: 

mean, 1488.7 SD, 580.2 

 

Dietary intake (kcal/day) 

Human Email Counseling (HC) 

52 Mean, 

2042.6 SD, 875.6 

3 month: mean,1468.2 SD,449.1 

 6 month: 

mean, 1484.3 SD, 574.3 

 

Control 54 Mean, 38.4, 

SD, 7.1 

3 month: mean,36.0 SD,7.0 

 6 month 

mean, 37.3 SD, 6.6 

Tate, 200626

Fat intake (% day) 

Tailored 40 Mean, 37.5 3 month: 6 month 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐99

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

ComputerAutomated Feedback 

SD, 6.1 mean,33.5 

SD,4.9 

mean, 34 SD, 6.5 

Human Email Counseling (HC) 

52 Mean,38.8 

SD, 6.2 

3 month: mean, 32.8 SD,5.0 

 6 month 

mean, 33.1 SD,4.9 

 

Control 54 Mean, 

1188.7  SD, 1286.8 

3 month: mean, 1335.8 SD,1540 

 6 month 

mean, 1064.4 SD, 1139.5 

Tailored ComputerAutomated Feedback 

40 Mean, 

1210.9  SD, 1234.9 

3 month: mean,1525.1 SD,1368.9 

 6 month 

mean, 1335.1 SD, 1410.1 

 

Physical activity (kcal/week) 

Human Email Counseling (HC) 

52 Mean, 

1283.9  SD, 1969.3 

3 month: mean, 1537.2 SD,1113 

 6 month 

mean, 1377.1 SD, 1163.8 

 

Control 204 Minutes of 

pa/week mean, 720 SD, 485 

 6 months: 

mean, 734 SD, 516 

Sequential interactive computer tailored intervention 

180 Minutes of 

pa/week mean, 514 SD, 367 

 6 months: 

mean, 727 SD, 492 

Increase Physical activity 

Simultaneous interactive computer tailored intervention 

189 Minutes of 

pa/week mean, 532 SD, 519 

 6 months: 

mean, 705 SD, 519 

Control 195 Grams/week: 

mean, 101 SD, 39 

 6 months 

mean, 94 SD, 33 

Vandelanotte, 200527

Decrease fat intake 

Sequential interactive 

194 Gram/week: 

mean, 110 

 6 months 

mean, 85 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐100

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

computer tailored intervention 

SD, 39 SD, 30 

Simultaneous interactive computer tailored intervention 

176 Grams/week: 

mean, 118 SD, 43 

 6 months 

mean, 85 SD, 28 

Control 73 Mean score, 

range, 1-7, higher score, better outcome mean, 5.7 SD, 1.2 

Change after 4 month mean, -0.08 

 Change after 

8 month: mean, -0.07 SD, 

 

Perceived support 

Web-Based Targeted Nutrition Counseling and Social Support 

24 Mean score, 

range,1-7, higher score, better outcome mean, 5.7 SD, 1.3 

Change after 4 month mean, 0.11 

 Change after 

8 month: mean, -0.17 SD, 

 

Control 73 Mean score, 

range,1-7, higher score, better outcome: mean, 3.5 SD, 0.5 

Change after 4 month mean, 0.04 SD, 

 Change after 

8 month mean, 0.07 SD, 

 

Social network 

Web-Based Targeted Nutrition Counseling and Social Support 

24 Mean score, 

range,1-7, higher score, better outcome: mean, 3.5 SD, 0.5 

Change after 4 month mean, -.0.06 SD, 

 Change after 

8 month mean, 0.01 SD, 

 

Verheijden, 200428

BMI ( kg/m2) Control 73 BMI, kg/m2 

mean, 29.2 

Change after 4 month 

 Change after 

8 month 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐101

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

 SD, 4.5 mean, -0.21 

SD, 

mean, -0.01 SD, 

Web-Based Targeted Nutrition Counseling and Social Support 

24 BMI, kg/m2 

mean, 29.5  SD, 5.2 

Change after 4 month mean, 0.08 SD, 

 Change after 

8 month mean, -0.02 SD, 

 

Control 73 Systolic 

blood pressure mean, 136 SD, 18 

Change after 4 month mean, -2.1 SD, 

 Change after 

8 month mean, -5.2 SD, 

 

Systolic blood pressure 

Web-Based Targeted Nutrition Counseling and Social Support 

24 Systolic 

blood pressure mean, 134 SD, 14 

Change after 4 month mean, -0.4 

 Change after 

8 month mean, -1.9 SD, 

Tp0tip0

Control 73 Diastolic 

blood pressure: mean, 80  SD, 11 

Change after 4 month: mean, -1.4  SD, 

 Change after 

8 month: mean, -3.2  SD, 

 

Diastolic blood pressure 

Web-Based Targeted Nutrition Counseling and Social Support 

24 Diastolic 

blood pressure: mean, 81  SD, 9 

Change after 4 month: mean, -0.2  SD, 

 Change after 

8 month: mean, -2.5  SD, 

 

Total cholesterol 

Control 73 Total 

cholesterol mean, 5.4 SD, 1.2 

Change after 4 month mean, -0.06 SD, 

 Change after 

8 month mean, -0.11 SD, 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐102

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Web-Based Targeted Nutrition Counseling and Social Support 

24 Total 

cholesterol mean, 5.5 SD, 0.9 

Change after 4 month mean, 0.03 SD, 

 Change after 

8 month mean, -0.08 SD, 

 

Work book only 

97 NS -397.9±55.3 N

Computer tailored feedback 

183 NS -283±41.8 N

Dietary Intake (Kcal/d) 

Computer tailored feedback plus staff consultation 

194 NS -323.6±43.1 N

Work book only 

97 NS 5.9±1.10 N

Computer tailored feedback 

183 NS 5.1±0.79 N

Exercise (Blocks walked daily) 

Computer tailored feedback plus staff consultation 

194 NS 3.9±0.79 N

WylieRosett, J, 200129

Exercise (min walked 

Work book 97 NS 5.10±1.10 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐103

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

only Computer tailored feedback 

183 NS 5.11±1.13 N

continuously) 

Computer tailored feedback plus staff consultation 

194 NS 4.96±1.09 N

Work book only 

97 NS -2.2±1.26 N

Computer tailored feedback 

183 NS -4.7±1.02 N

Weight (lb) 

computer tailored feedback plus staff consultation 

194 NS -7.4±1.15 N

Work book only 

97 NS -0.4±0.21 N

Computer tailored feedback 

183 NS -0.8±0.17 N

BMI 

computer tailored 

194 NS -1.2±0.19 N
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐104

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

feedback plus staff consultation 

Eating disorder 

Control 20 Mean, 104 

SD, 36 

Post Intervention mean, 107 SD, 39 

 3months: 

mean, 101 SD, 44 

Body shape measure 

Intervention group 

24 Mean, 118 

SD, 34 

Post Intervention mean, 104 SD, 33 

 3months: 

mean, 93 SD, 25 

 

Control 20 Mean, 24 

SD, 8 

Post Intervention mean, 26 SD, 9.4 

 3months 

mean, 24.8 SD, 9.9 

EDI-drive for thinness 

Intervention 24 Mean, 27.6 

SD, 9.7 

Post Intervention mean, 25.1 SD, 8.8 

 3months 

mean, 23.3 SD, 9.1 

 

Control 20 Mean, 14 

 SD, 4.9 

Post Intervention mean, 14.8 SD, 6 

 3months 

mean, 13.8 SD, 6.7 

EDI-Bulimia 

Intervention 24 Mean, 15.9 

 SD, 8.4 

Post Intervention mean, 14.1 SD, 7 

 3months 

mean, 12.6 SD, 5.7 

 

Control 20 

Mean, 2.5 SD, 1.3 

Post Intervention mean, 2.7 SD, 1.6 

 3months 

mean, 2.5 SD, 1.6 

Winzelberg, 200030

EDE-Q Weight Concerns 

Intervention 24 

Mean, 2.8 SD, 1.4 

Post Intervention mean,2.5 SD,1.3 

 3months 

mean, median, 2.3 range, SD, 1.2 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐105

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Control 20 Mean, 2.7 

 SD, 1.5 

Post Intervention: mean, 3  SD, 1.6 

 3months: 

mean, 2.6  SD, 1.7 

EDE-Q Shape Concern 

Intervention 24 Mean, 3.3 

 SD, 1.4 

Post Intervention: mean, 2.8  SD, 1.3 

 3months: 

mean, 2.5  SD, 1.3 

 

Nutrition intervention Bruge, 199631

Fat (points per day) 

Control 169 Fat points/ 

day mean, 28 SD, 5.3 

 3 weeks after 

receiving nutrition letter: mean, 27.2 SD, 5.5 

 

 Tailored 

feedback 

178 Fat points/ 

day mean, 29 SD, 5 

 3 weeks after 

receiving nutrition letter: mean, 26.9 SD, 4.9 

 

 Vegetables 

(servings per day) 

Control 169 Servings/day: 

mean, 1 SD, 0.31 

 3 weeks after 

receiving nutrition letter mean, 1.06 SD, 0.37 

 

 Tailored 

feedback 

178 Mean, 1.03 

SD, 0.36 

 3 weeks after 

receiving nutrition letter mean, 1.07 SD, 0.36 

 

 Fruit (servings 

per day) 

Control 169 Servings/ day 

mean, 1.61  SD, 1.14 

 3 weeks after 

receiving nutrition letter mean, 1.57 SD, 1.19 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐106

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

 178 Servings/ day 

mean, 1.49  SD, 1 

 3 weeks after 

receiving nutrition letter mean, 1.57 SD, 0.96 

 

 Positive 

attitude to increasing vegetables and fruits 

Control 169 3 weeks after 

receiving dietary information , .11 

 Tailored 

feedback 

178 3 weeks after 

receiving dietary information, .39 

 

Pamphlet 57 Likeability 

was measured using 9 items on a 5-point Likert-type scale. mean, no measure SD, 

 10-12days: 

mean, 3.99 SD, 0.66 

 

Website 51 Likeability 

was measured using 9 items on a 5-point Likert-type scale. 

 10-12days: 

mean, 4.29 SD, 0.45 

 

Silk, 200832 Likeability of learning 

materials (hypothesis 1) [authors identify 3 subscales -- attention, understanding, intention] 

Video game 47 Likeability 

was measured using 9 items 

 10-12days: 

mean, 4.06 SD, 0.66 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐107

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

on a 5-point Likert-type scale. 

Pamphlet 57 Knowledge 

questions were based on the EFNEP Evaluation and Reporting System developed by USDA for EFNEP at the federal level (33 items) 

 10-12days 

mean, 24.75 SD, 4.76 

 

Website 51 Knowledge 

questions were based on the EFNEP Evaluation and Reporting System developed by USDA for EFNEP at the federal level (33 items) 

 10-12days 

mean, 25.59 SD, 3.56 

 

Nutrition literacy scores (hypothesis 2) [authors identify 6 subscales: My Pyramid, Food groups, Food servings, Serving size, Food safety, Food cost] 

Video game 47 Knowledge 

questions were based on the EFNEP 

 10-12days 

mean, 23.17 SD, 4.95 

 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐108

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

Evaluation and Reporting System developed by USDA for EFNEP at the federal level (33 (Items) 

Overweight and binge eating 

Control 43 Mean, 30.64 

SD, 5.97 

Post Treatment mean, 29.99 SD, 5.92 

 16weeks: 

mean, 31.17 SD, 6.33 

BMI (kg/m2)

SB2-BED 44 Mean, 30.58 

SD, 4.9 

Post Treatment mean, 28.76 SD, 4.72 

 16weeks: 

mean, 29.76 SD, 5.34 

 

Control 43 Mean, 1.79 

SD, 0.51 

Post Treatment mean, 1.68 SD, 0.54 

 16weeks 

mean, 1.76 SD, 0.57 

BMIzScore 

SB2-BED 44 Mean, 1.81 

SD, 0.47 

Post Treatment mean, 1.56 SD, 0.59 

 16weeks 

mean, 1.6 SD, 0.62 

 

Control 43 No. of 

episodes mean, 8.42  SD, 18.74 

Post Treatment mean, 6.98 SD, 17.55 

 16weeks 

mean, 2.74 SD, 8.6 

Binge eating (OBEs and SBEs) 

SB2-BED 44 No. of 

episodes mean, 15.16  SD, 20.78 

Post Treatment mean, 0.95 SD, 3.88 

 16weeks 

mean, 2.29 SD, 7.67 

 

Jones, 200833

Binge eating (OOEs) 

Control 43 No. of 

episodes mean, 7.53 

Post Treatment mean, 2.34 

 16weeks 

mean, 1.07 SD, 2.80 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐109

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Rap

 SD, 14.28 SD, 5.25 

SB2-BED 44 No. of 

episodes mean, 7.89  SD, 14.28 

Post Treatment mean, 2.05 SD, 6.98 

 16weeks 

mean, 2.16 SD, 9.33 

 

Control 43 Score 

mean, 1.35 SD, 0.92 

Post Treatment mean, 1.27 SD, 0.78 

 16weeks 

mean, 1.14 SD, 0.72 

Weight and shape concerns 

SB2-BED 44 Score: 

mean, 1.3 SD, 0.80 

Post Treatment mean,1.05 SD, 0.64 

 16weeks 

mean, 0.81 SD, 0.67 

 

Control 43 Score: 

mean, 22.06  SD, 10.73 

Post Treatment: mean, 20.05  SD, 7.49 

 16weeks: 

mean, 17.33  SD, 7.57 

Dietary fat intake 

SB2-BED 44 Score: 

mean, 24.54  SD, 8.63 

Post Treatment: mean, 18.88  SD, 6.56 

 16weeks: 

mean, 18.25  SD, 6.95 

 

Control 43 Score: 

mean, 15.63 SD, 10.33 

Post Treatment mean, 12.57 SD, 10.10 

 16weeks 

mean, 10.49 SD, 11.21 

Depressed mood 

SB2-BED 44 Score: 

mean, 14.26 SD, 9.43 

Post Treatment mean, 9.63 SD, 8.30 

 16weeks 

mean, 12.42 SD, 11.59 

 

NS = Not Significant, BL = baseline, SD =Standard Deviation, BMI= Body Mass Index 

Reference List 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐110

 1 Adachi Y, Sato C, Yamatsu K, Ito S, Adachi K, Yamagami T. A randomized controlled trial on the long-term effects of a 1-month 

program assisted by computer tailored advice. Behav Res Ther 2007; 45(3):459-70. 

 2 Anderson ES, Winett RA, Wojcik JR, Winett SG, Bowden T. A computerized social cognitive intervention for nutrition behavior: 

effects on fat, fiber, fruits, and vegetables, self-efficacy, and outcome expectations among food shoppers. 2001; 23(2):88-

 3 Brug J, Glanz K, Van Assema P, Kok G, Van Breukelen GJP. The Impact of Computer-Tailored Feedback and Iterative Feedback 

Vegetable Intake. 1998; 25(4):517-31. 

 4 Brug J, Steenhuis I, Van Assema P, Glanz K, De Vries H. Computer-tailored nutrition education: Differences between two interve

56. 

 5 Campbell MK, DeVellis BM, Strecher VJ, Ammerman AS, DeVellis RF, Sandler RS. Improving dietary behavior: The effectivene

primary care settings. 1994; 84(5):783-7. 

 6 Campbell MK, Honess-Morreale L, Farrell D, Carbone E, Brasure M. A tailored multimedia nutrition education pilot program for 

receiving food assistance. 1999; 14(2):257-67. 

 7 Campbell MK, Carbone E, Honess-Morreale L, Heisler-MacKinnon J, Demissie S, Farrell D. Randomized trial of a tailored nutriti

program for women receiving food assistance. 2004; 36(2):58-66. 

 8 Haerens L, De Bourdeaudhuij I, Maes L, Vereecken C, Brug J, Deforche B. The effects of a middle-school healthy eating interven

and fruit intake and soft drinks consumption. 2007; 10(5):443-9. 

 9 Haerens L, Deforche B, Maes L, Brug J, Vandelanotte C, De Bourdeaudhuij I. A computer-tailored dietary fat intake intervention f

a randomized controlled trial. Ann Behav Med 2007; 34(3):253-62. 

10 Haerens L, Maes L, Vereecken C, De Henauw S, Moreno L, De Bourdeaudhuij I. Effectiveness of a computer tailored physical act

adolescents compared to a generic advice. Patient Educ Couns 2009. 

11 Hurling R, Fairley BW, Dias MB. Internet-based exercise intervention systems: Are more interactive designs better? 2006; 21(6):7

12 Hurling R, Catt M, Boni MD et al. Using internet and mobile phone technology to deliver an automated physical activity program: 

trial. J Med Internet Res 2007; 9(2):e7. 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐111

13 King DK, Estabrooks PA, Strycker LA, Toobert DJ, Bull SS, Glasgow RE. Outcomes of a multifaceted physical activity regimen a

management intervention. 2006; 31(2):128-37. 

14 Kristal AR, Curry SJ, Shattuck AL, Feng Z, Li S. A randomized trial of a tailored, self-help dietary intervention: The puget sound 

2000; 31(4):380-9. 

15 Lewis B, Williams D, Dunsiger S et al. User attitudes towards physical activity websites in a randomized controlled trial. Prev Me

16 Low KG, Charanasomboon S, Lesser J et al. Effectiveness of a computer-based interactive eating disorders prevention program at 

Disord 2006; 14(1):17-30. 

17 Mangunkusumo R, Brug J, Duisterhout J, de Koning H, Raat H. Feasibility, acceptability, and quality of Internet-administered ado

in a preventive-care setting. Health Educ Res 2007; 22(1):1-13. 

18 Marcus BH, Lewis BA, Williams DM et al. A comparison of Internet and print-based physical activity interventions. Arch Intern M

19 Napolitano MA, Fotheringham M, Tate D et al. Evaluation of an internet-based physical activity intervention: a preliminary invest

2003; 25(2):92-9. 

20 Oenema A, Brug J, Lechner L. Web-based tailored nutrition education: results of a randomized controlled trial. Health Educ Res 2

21 Oenema A, Tan F, Brug J. Short-term efficacy of a web-based computer-tailored nutrition intervention: main effects and mediators

29(1):54-63. 

22 Richardson CR, Mehari KS, McIntyre LG et al. A randomized trial comparing structured and lifestyle goals in an internet-mediate

people with type 2 diabetes. Int J Behav Nutr Phys Act 2007; 4:59. 

23 Smeets T, Kremers SP, Brug J, de Vries H. Effects of tailored feedback on multiple health behaviors. Ann Behav Med 2007; 33(2)

24 Spittaels H, De Bourdeaudhuij I, Vandelanotte C. Evaluation of a website-delivered computer-tailored intervention for increasing p

general population. 2007; 44(3):209-17. 

25 Spittaels H, De Bourdeaudhuij I, Brug J, Vandelanotte C. Effectiveness of an online computer-tailored physical activity interventio

Health Educ Res 2007; 22(3):385-96. 
Evidence Table 10. All outcomes KQ1b, impact of CHI application on intermediate outcomes (continued)

G‐112

26 Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored counselin

an Internet weight loss program. Arch Intern Med 2006; 166(15):1620-5. 

27 Vandelanotte C, De Bourdeaudhuij I, Sallis JF, Spittaels H, Brug J. Efficacy of sequential or simultaneous interactive computer-tai

increasing physical activity and decreasing fat intake. Ann Behav Med 2005; 29(2):138-46. 

28 Verheijden M, Bakx JC, Akkermans R et al. Web-based targeted nutrition counselling and social support for patients at increased c

general practice: randomized controlled trial. J Med Internet Res 2004; 6(4):e44. 

29 Wylie-Rosett J, Swencionis C, Ginsberg M et al. Computerized weight loss intervention optimizes staff time: The clinical and cost 

clinical trial conducted in a managed care setting. 2001; 101(10):1155-62. 

30 Winzelberg AJ, Eppstein D, Eldredge KL et al. Effectiveness of an Internet-based program for reducing risk factors for eating diso

Psychol 2000; 68(2):346-50. 

31 Brug J, Steenhuis I, Van Assema P, De Vries H. The impact of a computer-tailored nutrition intervention. 1996; 25(3):236-42.

32 Silk KJ, Sherry J, Winn B, Keesecker N, Horodynski MA, Sayir A. Increasing nutrition literacy: testing the effectiveness of print, 

modalities. J Nutr Educ Behav 2008; 40(1):3-10. 

33 Jones M, Luce KH, Osborne MI et al. Randomized, controlled trial of an internet-facilitated intervention for reducing binge eating 

adolescents. Pediatrics 2008; 121(3):453-62. 
Evidence table 11. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) 

G-113 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Alcohol Abuse Cunningham, 20051

Individuals interested in their own health care 

Interactive consumer website, 

Home/ residence 

2 yr After 

intervention, did not receive additional self-help information by postal mail 

Hester, 20052

Individuals interested in their own health care 

Personalized health risk assessment tool 

Home/ residence 

NS <21 yr old, Minimum 

score of 8 on Alcohol Use Disorders Inventory Test, At least 8th grade reading level, Available and willing significant other to corroborate selfreport of drinking 

Current alcohol treatment, Severe uncontrolled thought disorder, Presence of a medical condition for which alcohol use would be contraindicated 

Delayed treatment 

Kypri , 19993 Individuals 

interested in their own health care 

Personalized health risk assessment tool 

Home/ residence 

2002/ NS 

17-26 yr old, Score of 8 or more on the Alcohol Use Disorders Identification Test, Consuming more than 4/6 standard drinks (F/M) on one more occasions in the preceding 4 weeks 

Leaflet on health effects of alcohol 

Lieberman, 20064

Individuals interested in their own health care 

Interactive consumer website 

NS 18 months Text website 

Neighbors, Individuals 

interested 

Interactive consumer 

Remote location: 

NS At least one heavy 

drinking episode at 

 No 

intervention 
Evidence table 11. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (contin

G-114 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

20045 in their own 

health care 

website "Controlled 

setting on campus" 

one sitting in the previous month 

Riper, 20086

Adult alcohol drinkers 

Web-based self help intervention 

Data from other RCT 

NS Men who were 

drinking more than 21 standard units per week, women who were drinking over 14 units per week, age 18-65, access to the internet, no previous professional help for problem drinking 

Insufficient alcohol use, above age 65, alcohol-related medication, professional help, in other alcohol study; incomplete data, non-response, in same household 

Control condition (an online psycho educational brochure on alcohol use that could be read in 10 minutes) 

Riper, 20087

Individuals interested in their own health care 

Interactive consumer website 

NS Yr 2003 18–65 yr, 

Men were selected who were drinking either more than 21 units per week (excessive drinking) or 6 or more units at least 1 day per week for the past 3 months (hazardous drinking). Women were included if they drank over 14 units a week or 4 or more units at least 1 day a week for the past 3months. Access to the internet. Not receiving professional help for problem drinking at 

 Control 

condition 
Evidence table 11. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (contin

G-115 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

the start of the study. 

Yr = year, NS= Not Specified 

Reference List 

 1 Cunningham JA, Humphreys K, Koski-Jannes A, Cordingley J. Internet and paper self-help materials for problem drinking: is ther

Addict Behav 2005; 30(8):1517-23. 

 2 Hester RK, Squires DD, Delaney HD. The Drinker's Check-up: 12-month outcomes of a controlled clinical trial of a stand-alone so

problem drinkers. J Subst Abuse Treat 2005; 28(2):159-69. 

 3 Kypri K, Saunders JB, Williams SM et al. Web-based screening and brief intervention for hazardous drinking: a double-blind rand

Addiction 2004; 99(11):1410-7. 

 4 Lieberman DZ. Effects of a personified guide on adherence to an online program for alcohol abusers. Cyberpsychol Behav 2006; 9 5 Neighbors C, Larimer ME, Lewis MA. Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered p

feedback intervention. J Consult Clin Psychol 2004; 72(3):434-47. 

 6 Riper H, Kramer J, Keuken M, Smit F, Schippers G, Cuijpers P. Predicting successful treatment outcome of web-based self-help fo

secondary analysis from a randomized controlled trial. J Med Internet Res 2008; 10(4):e46. 

 7 Riper H, Kramer J, Smit F, Conijn B, Schippers G, Cuijpers P. Web-based self-help for problem drinkers: a pragmatic randomized 

103(2):218-27. 
Evidence Table 12. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in alcohol abu

G-116 

Author, year 

Control Interventions Age Race, n (%) Income Education, n (%) SES 

Gender, n (%) 

MaritStatu

alcohol abuse 

After intervention, did not receive additional selfhelp information by postal mail 

Cunningham , 20051

Internet plus book 

Baseline characteristics not reported 

Delayed treatment 

Hester , 20052

DCU/ Immediate treatment group 

Baseline characteristics not reported 

Leaflet on health effects of alcohol 

Mean, 20.4 SD, 1.8 

NS NS NS NR 

Kypri , 19993

Web-based assessment and personalized feedback on their drinking 

Mean, 19.9 SD, 1.4 

NS NS NS NR 

Text website Mean, 37.2 

SD, 11.8 

White non 

Hispanic, (87) 

Black nonHispanic, (1.7) Latino/Hispanic, 

(7) 

API, (2.3) AIAN , (2.3) Other, 6.5 

NS NS NR F, (37.2) 

Lieberman, 20064

Multimedia website 

Mean, 36 SD, 12.1 

White nonHispanic, (86.8) Black nonNS NS NR F, (31) 
Evidence Table 12. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in alcohol abu

G-117 

Author, year 

Control Interventions Age Race, n (%) Income Education, n (%) SES 

Gender, n (%) 

MaritStatu

Hispanic, (1.6) 

Latino/Hispanic, 

(4.1) 

API, (4.1) AIAN, (2.5) 

No response, (5.0 ) 

No intervention NS NS NS NS NR M, 54 

F, 72 

Neighbors , 20045

Intervention (personalized normative feedback) 

NS NS NS NS NR M, 50 

F, 76 

Riper, 20086

Control: alcohol information brochure Intervention: 

Control: mean 46.2 SD,9.2 Experiment

NS NS 

Paid employment control: 96 (73.3) Paid 

Control low 38 (29.0) High 93 (71.0) Low 41 (31.5) 

NS NS 

F 64(48.9) F 64(49.2) 

 
Evidence Table 12. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in alcohol abu

G-118 

Author, year 

Control Interventions Age Race, n (%) Income Education, n (%) SES 

Gender, n (%) 

MaritStatu

drinking less (free-access, Web-based self-help intervention without therapist guidance. 

al : mean 45.9 SD,8.9 

employment control: 96 (73.3) 

High 89 (68.5) 

Control condition (PBA) 

Mean, 46.2 SD, 9.2 

NS NS Unskilled, 38 (29.0) 

Vocational, 55 (42.0) Academic, 38 (29.0) 

NR F, 64 

(48.9) 

 

Riper, 20087

Experimental condition (DL) 

Mean, 45.9 SD, 8.9 

NS NS Unskilled, 41 (31.5) 

Vocational, 52 (40.0) Academic, 37 (28.5) 

NR F, 64 

(49.2) 

 

NR= Not Reported, NS= Not specified, SD= Standard Deviation, SES= Socioeconomic Status, AIAN= American Indian/Alaska Native, A

F = female, M = Male 
Evidence Table 12. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in alcohol abu

G-119 

Reference List 

 1 Cunningham JA, Humphreys K, Koski-Jannes A, Cordingley J. Internet and paper self-help materials for problem drinking: is ther

Addict Behav 2005; 30(8):1517-23. 

 2 Hester RK, Squires DD, Delaney HD. The Drinker's Check-up: 12-month outcomes of a controlled clinical trial of a stand-alone so

problem drinkers. J Subst Abuse Treat 2005; 28(2):159-69. 

 3 Kypri K, Saunders JB, Williams SM et al. Web-based screening and brief intervention for hazardous drinking: a double-blind rand

Addiction 2004; 99(11):1410-7. 

 4 Lieberman DZ. Effects of a personified guide on adherence to an online program for alcohol abusers. Cyberpsychol Behav 2006; 9 5 Neighbors C, Larimer ME, Lewis MA. Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered p

feedback intervention. J Consult Clin Psychol 2004; 72(3):434-47. 

 6 Riper H, Kramer J, Keuken M, Smit F, Schippers G, Cuijpers P. Predicting successful treatment outcome of web-based self-help fo

secondary analysis from a randomized controlled trial. J Med Internet Res 2008; 10(4):e46. 

 7 Riper H, Kramer J, Smit F, Conijn B, Schippers G, Cuijpers P. Web-based self-help for problem drinkers: a pragmatic randomized 

103(2):218-27. 
Evidence table 13. All outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b)

G‐120

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at final time point 

Ratiotime p

Alcohol Abuse 

 Internet alone 

29 Mean, 21 

SD, 16.6 

 3 months 

mean, 17.4 SD, 17.7 

Mean drinks per typical week 

Internet plus self help book 

19 Mean, 29.1 

SD, 23.2 

 3 months 

mean, 18.4 SD, 25.8 

 

 Internet alone 

29 Mean, 15.6 

SD, 8.9 

 3 months 

mean, 12.6 SD, 7.8 

Mean AUDIT test score 

Internet plus book 

19 Mean, 19.8 

SD, 10.3 

 3 months 

mean, 11.9 SD, 9.9 

 

Internet alone 29 Mean, 2.4 

SD, 1.9 

 3 months 

Mean, 1.9 SD, 1.6 

Cunningham, 20051

Mean # of alcohol consequences 

Internet plus book 

19 Mean, 2.9 

SD, 1.8 

 3 months 

mean, 1.5 SD, 1.6 

 

DCU/4 week Delayed treatment group 

21 Mean, 5.64 

SD, 4.66 

4 weeks mean, 4.13 SD, 2.61 

8 weeks mean, 3.56 SD, 2.8 

12 months median, 2.5 SD, 2.58 

Average drinks per day 

DCU/ Immediate treatment group 

29 Mean, 5.69 

SD, 5.44 

4 weeks mean, 2.71 SD, 2.84 

8 weeks mean, 2.31 SD, 2.23 

12 months mean, 2.07 SD, 2.19 

 

 DCU/4 week Delayed treatment group 

21 Mean, 5.57 

SD, 2.55 

4 weeks mean, 5.66 SD, 2.6 

8 weeks mean, 4.86 SD, 2.4 

12 months mean, 4.14 SD, 2.72 

Drinks per drinking day 

DCU/Immediate treatment group 

29 Mean, 8.84 

SD, 6.36 

4 weeks mean, 5.64 SD, 4.09 

8 weeks mean, 6.66 SD, 6.12 

12 months mean, 5.5 SD, 4.63 

 

 DCU/4 week Delayed treatment group 

21 Mean, 0.161 

 SD, 0.132 

4 weeks mean, 0.149 SD, 0.106 

8 weeks mean, 0.1 SD, 0.079 

12 months mean, 0.073 SD, 0.063 

 

Hester , 20052

Average peak BAC 

DCU/ 29 Mean, 0.174 4 weeks 8 weeks 12 months 
Evidence table 13. All outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (con

G‐121

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at final time point 

Ratiotime p

Immediate treatment group 

 SD, 0.107 mean, 0.096 

SD, 0.087 

mean, 0.118 SD, 0.126 

mean, 0.078 SD, 0.058 

Control 47 N of drinking days 

in last 2 weeks 

6 weeks median, 4 range, 0-13 

 6 months: 

median, 4 range, 0-14 

Kypri , 19993 Drinking 

frequency 

Computerized assessment and behavioral intervention 

47 N of drinking days 

in last 2 weeks 

6 weeks median, 3 range, 0-9 

 6 months 

median, 3 range, 0-8 

 

 Control NS Number of 

modules (1-4) 

 After 

completing each of 4 modules mean, 3.69 

Number of modules completed 

Multimedia NS After 

completing each of 4 modules mean, 3.9 

 

 Control Helpfulness 

scores (rating the 4 modules) 

 After 

completing each of 4 modules mean, 12.1 

Lieberman, 20064

Perceived helpfulness of the modules 

Multimedia Helpfulness 

scores (rating the 4 modules) 

 After 

completing each of 4 modules mean, 12.2 

 

Control 126 Effect Size 3 months 

mean, .17 

 6 months 

mean, .2 

Effect size in perceived norms Computerized 

normative feedback 

126 Effect size 3 months 

mean, .46 

 6 months 

mean, .48 

 

Control 126 Effect Size 3 months 

mean, .05 

 6 months 

mean, .03 

Neighbors , 20045

Effect size in reduction in alcohol Computerized 126 Effect Size 3 months 6 months 
Evidence table 13. All outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (con

G‐122

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at final time point 

Ratiotime p

consumption normative 

feedback 

mean, .24 mean, .22 

Effect size in Control 126 Effect Size 3 months 6 months reduction in alcohol consumption 

Computerized normative feedback 

 126 Effect Size 3 months 6 months 

Follow up at 6 month n 81 (61.8) 

 Follow up at 

12 month n 92(70.2) 

Control: alcohol information brochure 

131 

 Loss to follow-up at 6 month n 50 (38.2) 

 Loss to 

follow up at 12 month n 39(29.8) 

Riper, NS 20086

Mean alcohol consumption difference between baseline and 6months and 12month follow up period. 

Intervention: drinking less(freeaccess, Webbased selfhelp intervention without therapist 

130 Follow up at 6 

month n 70 (53.8) 

 Follow up at 

12 month n 71(54.6) 

NS 
Evidence table 13. All outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (con

G‐123

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at final time point 

Ratiotime p

guidance. Loss to follow-up 

at 6 month n 60 (46.2) 

 Loss to 

follow up at 12 month n 59(45.4) 

 Control 81 Weekly alcohol 

intake in std units mean, 43.5 SD, 22.3 

 6months 

mean, 39.2 

Differmean(95) (16.94

Riper, 2008 

7 Weekly alcohol 

consumption (second outcomes) 

Intervention condition DL 

70 Weekly alcohol 

intake in std units mean, 43.7 SD, 21 

 6months 

mean, 28.7 

SD = Standard deviation, BL = baseline, CI = confidence interval, DCU = Drinker’s Check-up, NR = Not reported 
Evidence table 13. All outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (con

G‐124

Reference List 

 1 Cunningham JA, Humphreys K, Koski-Jannes A, Cordingley J. Internet and paper self-help materials for problem drinking: is ther

Addict Behav 2005; 30(8):1517-23. 

 2 Hester RK, Squires DD, Delaney HD. The Drinker's Check-up: 12-month outcomes of a controlled clinical trial of a stand-alone so

problem drinkers. J Subst Abuse Treat 2005; 28(2):159-69. 

 3 Kypri K, Saunders JB, Williams SM et al. Web-based screening and brief intervention for hazardous drinking: a double-blind rand

Addiction 2004; 99(11):1410-7. 

 4 Lieberman DZ. Effects of a personified guide on adherence to an online program for alcohol abusers. Cyberpsychol Behav 2006; 9 5 Neighbors C, Larimer ME, Lewis MA. Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered p

feedback intervention. J Consult Clin Psychol 2004; 72(3):434-47. 

 6 Riper H, Kramer J, Keuken M, Smit F, Schippers G, Cuijpers P. Predicting successful treatment outcome of web-based self-help fo

secondary analysis from a randomized controlled trial. J Med Internet Res 2008; 10(4):e46. 

 7 Riper H, Kramer J, Smit F, Conijn B, Schippers G, Cuijpers P. Web-based self-help for problem drinkers: a pragmatic randomized 

103(2):218-27. 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) 

G-125 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Smoking An, 20081 Individuals 

interested in their own health care 

Interactive consumer website 

University of Minnesota internet health screening 

2004 ≥18 yr, 

Smoked cigarettes in the past 30 days, indicated that they intended to be in school for the next two semesters 

 Control 

group 

Brendryen, 2008 2

Smokers in Norway 

Internet and cell phone 

Online 2006 / 

February 2006 to March, 2007 

18 years or older, Willing to quit on March 6, 2006, currently smoking five cigarettes or more a day, willing to quit without using NRT, owning a mobile phone, owning a Norwegianregistered phone number and postal address, and having daily access to the Internet and email. 

NR Self-help 

booklet 

Curry, 1995 3

Random sample of group health cooperative enrollees 

Computer generated tailored feedback 

Residence NS / 21 

months 

Self-identified smoker 

 No treatment 

Dijkstra, 2005 4

Students who are smokers 

Information Laboratory 

at university 

NR / One session 

Student who is a smoker 

NR Standard 

information 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-126 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Hang, 2009 5 Daily smoker SMS messaging University 2007 / August to 

December 

Daily smoker, use SMS (text messaging) at least weekly 

NR No 

intervention 

Japuntich, 20066

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Recruitment took place from October 2001 to July 2002. 

≥18 yr, Smoke at least 10 cigarettes per day, Have a traditional telephone line, Literate in English 

Current depression, current use of psychiatric medication, medical conditions contraindicating bupropion SR (e.g., history of seizure disorder), current use of a smoking cessation product or treatment, Being pregnant or likely to become pregnant during the treatment phase of the study 

bupropion plus counseling alone 

Pattens, 20067

Individuals interested in their own health care, adolescent smokers 

Interactive consumer website 

Clinician office 

March 2000 to November 2003 

11-18 yr , gave written consent or received consent from parent/guardian, 18 yr, Smoked 10 or more cigarettes in last 30 days, Cigarettes were primary tobacco product used, Willing and able to 

Homeless, Alcohol or drug abuse in the last 3 months 

Brief office intervention 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-127 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

complete treatment assessment visits 

Prochaska, 19938

Volunteer smokers in Rhode Island 

Tailored manuals and computer reports 

Residence NS / 18 

months 

Smokers who responded to advertisement 

 ALA + 

(standard self-help manual) 

Prokhorov, 20089

Students in culturally diverse high schools 

Interactive CD-ROM 

High school NS / 4 years 10th grade 

Speaks, reads and writes English 

 Clearing the 

Air self-help booklet 

Schiffman, 200010

Individuals interested in their own health care 

Computer tailored mailings via computer assisted automated telephone interviews 

Home/ residence 

1996/ NS >18 yr, 

Current cigarette smoker, Purchased 2 or 4 mg nicotine prolacrilex gum, Were attempting to quit smoking cigarettes, Target quit date was within 7 days of enrollment, Agreed to be contacted at follow up at 6 and 12 weeks 

 User Guide 

only 

Schumann, 200611

Smokers drawn from representative population 

Computer generated tailored feedback 

Residence 2002 – 2004 

/ 24 months 

Provided written informed consent and said yes to a question about currently smoking 

 No 

intervention 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-128 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

of 20-79 year olds living in Western Pomeania, GERMANY 

Schumann, 200812

Individuals interested in their own health care 

Letters entered into a system with PHI and generating tailored information for the consumer 

NS Started in 

April 2002 

20-79 yr, Currently smoke cigarettes, Currently smoke cigars or cigarillos, Currently a pipe smoker 

 Assessmentonly control 

group 

Severson, 200813

Individuals interested in their own health care 

Interactive consumer website 

 Online NS At least 18 yr old, 

Male, A resident of the US or Canada, E-mail address checked at least weekly, any ST user (defined as having used ST for at least 1 year and used at least at in a week), and willing to provide his or her name, mailing address, and phone number 

Text-based website (Basic Condition) 

Strecher, 1994 14

Study 1 

Adult cigarette smokers in North Carolina 

Computer generated tailored feedback 

Residence Study 1: 

1990 / 4 months 

40-65 years old, seen by family physician in last 6 months, telephone available and working, not sharing household with other 

 Standardize

d generic letter 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-129 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

subject, mentally and physically capable of being interviewed. 

Strecher, 2005 15

Callers to NCI CIS call centers 

 18 yrs or older, 

English as a primary language, smoked at least five cigarettes per day, interested in quitting, not currently in another cessation program, not currently undergoing or planning cancer treatment 

Strecher, 200516

Individuals interested in their own health care 

Interactive consumer website 

NS ≥18 yr, 

Target quit date within 7 days, Valid email address, Internet access, Smoke more than 10 cig/day Purchased NiQuitin CQ 21 mg, Agreed to contact for FU email and questionnaire at 6 and 12 weeks 

Strecher, 200617

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS ≥ 18 yr, 

Smokers in the United Kingdom and Republic of Ireland who purchased NiQuitin CQ 21-mg patch and connected to a Web site to enroll for free behavioral support 

Non-tailored Web-based cessation material 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-130 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

materials, Had a target quit date that was within seven days from the enrollment date, Provided a valid email address and had Internet access for the duration of the study, Were attempting to quit smoking cigarettes (i.e., not smokeless tobacco), Had been smoking more than 10 cigarettes per day, had purchased NiQuitin CQ 21 mg (21 mg of nicotine; indicated for those who smoke at least 10 cigarettes per day), Agreed to be contacted for followup e-mail and Webbased questionnaires at 6 and 12 weeks 

Strecher, 200818

Individuals interested in their own health care 

Interactive consumer website 

NS September 

2004 

21–70 yr, had smoked at least 100 cigarettes in his or her lifetime, Currently smoked at least 10 cigarettes/ day, and had smoked in the past 7 days, was seriously 

Medical contraindications for NRT, Not currently enrolled in the HMO, Lack of adequate Internet/e-mail access, Already enrolled in 

Low-tailored 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-131 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

considering quitting in the next 30 days, was a member of either Group Health or HFHS, had home or work access to the Internet and an email account that he or she used at least twice weekly, was not currently enrolled in another formal smokingcessation program or was not currently using pharmacotherapy for smoking cessation, had no medical contraindications for NRT 

another smokingcessation program, Medical contraindications for NRT, Currently using pharmacotherapy to quit smoking 

Swartz, 200619

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence Remote: work site 

NS >18 yr, 

Daily smoker, Wish to quit in the next 30 days, Ability to access website 

<18 yr 90 day wait 

period for access to website 

NS = not specified, yr = year, NRT = nicotine replacement therapy, CHESS = Comprehensive Health Enhancement Support System 
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-132 

Reference List 

 1 An LC, Klatt C, Perry CL et al. The RealU online cessation intervention for college smokers: a randomized controlled trial. Prev M 2 Brendryen H, Drozd F, Kraft P. A digital smoking cessation program delivered through internet and cell phone without nicotine re

randomized controlled trial. J Med Internet Res 2008; 10(5):e51. 

 3 Curry SJ, McBride C, Grothaus LC, Louie D, Wagner EH. A randomized trial of self-help materials, personalized feedback, and te

nonvolunteer smokers. 1995; 63(6):1005-14. 

 4 Dijkstra A. Working mechanisms of computer-tailored health education: Evidence from smoking cessation. 2005; 20(5):527-39.  5 Haug S, Meyer C, Schorr G, Bauer S, John U. Continuous individual support of smoking cessation using text messaging: A pilot e

Nicotine Tob Res 2009. 

 6 Japuntich SJ, Zehner ME, Smith SS et al. Smoking cessation via the internet: a randomized clinical trial of an internet intervention 

smoking cessation intervention. Nicotine Tob Res 2006; 8 Suppl 1:S59-67. 

 7 Patten CA, Croghan IT, Meis TM et al. Randomized clinical trial of an Internet-based versus brief office intervention for adolescen

Patient Educ Couns 2006; 64(1-3):249-58. 

 8 Prochaska JO, DiClemente CC, Velicer WF, Rossi JS. Standardized, Individualized, Interactive, and Personalized Self-Help Progr

Cessation. 1993; 12(5):399-405. 

 9 Prokhorov AV, Kelder SH, Shegog R et al. Impact of A Smoking Prevention Interactive Experience (ASPIRE), an interactive, mu

prevention and cessation curriculum for culturally diverse high-school students. Nicotine Tob Res 2008; 10(9):1477-85. 

10 Shiffman S, Paty JA, Rohay JM, Di Marino ME, Gitchell J. The efficacy of computer-tailored smoking cessation material as a supp

polacrilex gum therapy. Arch Intern Med 2000; 160(11):1675-81. 

11 Schumann A, John U, Rumpf H-J, Hapke U, Meyer C. Changes in the "stages of change" as outcome measures of a smoking cessa

randomized controlled trial. 2006; 43(2):101-6. 

12 Schumann A, John U, Baumeister SE, Ulbricht S, Rumpf HJ, Meyer C. Computer-tailored smoking cessation intervention in a gen

Germany: outcome of a randomized controlled trial. Nicotine Tob Res 2008; 10(2):371-9. 

13 Severson HH, Gordon JS, Danaher BG, Akers L. ChewFree.com: evaluation of a Web-based cessation program for smokeless toba

Res 2008; 10(2):381-91. 

14 Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S, Hospers HJ, Skinner CS. The effects of computer-tailored smoking cessation me
Evidence Table 14. Description of RCTs addressing impact of CHI applications on intermediate outcomes in smoking (KQ1b) (continued)

G-133 

settings. 1994; 39(3):262-70. 

15 Strecher VJ, Marcus A, Bishop K et al. A randomized controlled trial of multiple tailored messages for smoking cessation among 

information service. J Health Commun 2005; 10 Suppl 1:105-18. 

16 Strecher VJ, Shiffman S, West R. Randomized controlled trial of a web-based computer-tailored smoking cessation program as a s

patch therapy. Addiction 2005; 100(5):682-8. 

17 Strecher VJ, Shiffman S, West R. Moderators and mediators of a web-based computer-tailored smoking cessation program among 

Nicotine Tob Res 2006; 8 Suppl 1:S95-101. 

18 Strecher VJ, McClure JB, Alexander GL et al. Web-based smoking-cessation programs: results of a randomized trial. Am J Prev M

19 Swartz LH, Noell JW, Schroeder SW, Ary DV. A randomised control study of a fully automated internet based smoking cessation 

2006; 15(1):7-12. 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking 

G-134 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

Smoking 

Control group Mean, 19.8 

SD, 1.6 

Black nonHispanic, 24(9.2) NS Yr in school: Freshman, 80 

(30.8) Sophomore, 64 (24.6) Junior, 67 (25.8) Senior, 49(18.9) 

NR F,196 (75.4) 

An, 20081

RealU intervention group 

Mean, 20.1 SD, 1.6 

Black nonHispanic, 

20(7.8) 

NS Yr in school: 

Freshman,67 (26.1) Sophomore, 63 (24.5) Junior, 68 (26.5) Senior, 59 (23.0) 

NR F,181 (70.4) 

Self-help booklet 

Mean, 39.5 SD, 11.0 

 Has college 

degree, 70(49) 

 F, 72(50) 

Brendryen, 2008 2

Happy Ending program (HE) 

Mean, 39.7 SD, 10.8 

 Has college 

degree, 76(52) 

 F, 73(50) 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-135 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

Control Mean, 41.2 

SD, 11.9 

White, 285(87) > 

$25,000, 230(70) 

Finished high school, 302(92) 

 F, 157(48) 

Curry, 1995 3

Booklet Mean, 41.3 

SD, 11.5 

White, 294(89) > 

$25,000, 251(76) 

Finished high school, 300(91) 

 F, 175(53) 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-136 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

Feedback Mean, 40.9 

SD, 11.1 

White, 283(86) > 

$25,000, 240(73) 

Finished high school, 303(92) 

 F, 174(53) 

Phone Mean, 40.8 

SD, 11.9 

White, 129(86) > 

$25,000, 112(75) 

Finished high school, 134(89) 

 F, 88(59) 

Standard information 

NR NR NR NR NR NR 

Personalization Adaptation 

Dijkstra, 2005 4

Feedback 

Hang, 2009 5

No Intervention Mean, 25.4 

SD, 4.9 

NR NR > 10 years, 63(98) NR F, 40(63) 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-137 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

1 SMS per week 

Mean, 25.2 SD, 4.8 

 > 10 years, 46(92) F, 28(56) 

3 SMS per week 

Mean, 24.3 SD, 3.8 

 > 10 years, 54(90) F, 31(52) 

Bupropion plus Counseling alone 

Mean, 41 SD, 11.8 

White nonHispanic, (82.6) NS <high school4 (2.8), High school/GED, 

40 (27.8) Some college/tech school, 68 (47.2) College/graduate school, 31(21.5) 

NR F, (54.9) 

Japuntich, 20066

With CHESS SCRP 

Mean, 40.6 SD, 12.4 

White nonHispanic, (75.4) NS <High school, 5 (3.6) 

High school/GED, 41 (29.5) Some college/tech school, 72 (51.8) College/graduate 

NR F, (55.0) 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-138 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

school, 21 (15.1) 

BOI Mean, 15.8 

SD, 1.4 

White nonHispanic, (86) NS 6th-8th grade, (9) 

9th-11th grade, (79) 

>12th grade, (13) 

NR F, (49) 

Pattens, 20067

SOS Mean, 15.7 

SD, 1.3 

White nonHispanic, (90) NS 6th-8th grade, (16) 

9th-11th grade, (71) 

>12th grade, (13) 

NR F, (50) 

Prochaska, 1993 8

Characteristics not reported by subgroup 

NR NR NR NR NR NR 

Clearing the Air self-help booklet 

NR NR NR NR NR NR 

Prokhorov, 2008 9

ASPIRE CDROM 

Schiffman, 200010

User Guide only 

Mean, 41.7 SD, 13 

NS Gross 

House Hold Income. USD mean, 38,000 

Mean, 13.5 yr SD, 2.1 

NR F, (54.9) 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-139 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

SD, 22,300 

CQP Mean, 41 

SD, 12.7 

NS Gross 

House Hold Income, USD mean, 39800 SD, 22300 

Mean, 13.6 SD, 2.2 

NR F, (53.4) 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-140 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

Committed Quitter Program + Call 

Mean, 41.7 SD, 12.9 

NS Gross 

House Hold Income, USD mean, 39,100 SD, 22,200 

Mean, 13.6 SD, 2.1 

NR F, (54.3) 

Schumann, 2006 11

Characteristics not reported by subgroup 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-141 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

Assessmentonly control group 

Mean, 44.2 SD, 13.2 

NS NS <10 yr, 81 (26.2) 

10 yr, 154 (49.8) >10 yr, 62 (20.1) 

NR 

Schumann, 200812

Computertailored TTMbased intervention group 

Mean, 44.8 SD, 14.6 

NS NS <10 yr, 77 (25.5) 

10 yr, 156 (51.7)  >10 yr, 54 (17.9) 

NR 

Severson, 200813

Text-based website (Basic Condition) 

Mean, 36.9 SD, 9.6 

White nonHispanic, 

1234(97.7) 

Black nonHispanic, 

15(1.2) 

Latino/Hispanic, 

14(1.1) 

API, 4(0.3) AIAN, 17(1.3) 

NS <High school, 

38(3.0) High school, 199(15.8) College, 548(43.4) >College 478(37.8) 

NR 

Strecher, Characteristics 

not reported by 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-142 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

1994 14

Study 1 

subgroup 

Strecher, 2005 15

Characteristics not reported by subgroup 

Strecher, Control 200516 Tailored 

intervention 

Baseline characteristics not reported 

Strecher, 200617

Non-tailored web-based cessation material 

Baseline characteristics not reported 

Low-tailored High tailored 

Baseline characteristics not reported 

Tailored Webbased intervention (Enhanced Condition) 

Mean, 36.7 SD, 9.7 

White nonHispanic, 

1228(97.5) 

Black nonHispanic, 12(1) Latino/Hispanic, 

17(1.3) 

API, 5(0.4) AIAN, 24(1.9) 

NS <High school, 

28(2.2) High school, 208(16.5) College, 542(43.0) >College, 482(38.3) 

NR 

Strecher, 200818

CQPLAN 

Swartz, 200619 90 day wait period for 

access to website 

Range, 18 to >70 

White nonHispanic, 

152(84.4) 

Black nonHispanic, 9(5) Latino/Hispanic, 

7(3.9) 

AIAN, 5(2.8) 

Other, (1.7 ) 

NS NS NR M, 88 (48.9) 

F, 92 (50.6) 

 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-143 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Marital Status 

 Video-based internet site 

Range, 18 to >70 

White nonHispanic, 

136(79.5) 

Black nonHispanic, 

14(8.2) 

Latino/Hispanic, 

8(4.7) 

AIAN, 2(1.2) Other, 9 (5.3 ) 

NS NS NR M, 80 (46.8) 

F, 91 (53.2) 

 

NR= Not Reported, NS= Not Significant, SD= Standard Deviation, SES= Socioeconomic Status, Yr= year, API = Asian Pacific Islander, AIAN = AmM= male, F = female, CQP = Committed Quitters Program, USD = United States Dollar 

Reference List 

 1 An LC, Klatt C, Perry CL et al. The RealU online cessation intervention for college smokers: a randomized controlled trial. Prev M 2 Brendryen H, Drozd F, Kraft P. A digital smoking cessation program delivered through internet and cell phone without nicotine re

randomized controlled trial. J Med Internet Res 2008; 10(5):e51. 

 3 Curry SJ, McBride C, Grothaus LC, Louie D, Wagner EH. A randomized trial of self-help materials, personalized feedback, and te

nonvolunteer smokers. 1995; 63(6):1005-14. 

 4 Dijkstra A. Working mechanisms of computer-tailored health education: Evidence from smoking cessation. 2005; 20(5):527-39.  5 Haug S, Meyer C, Schorr G, Bauer S, John U. Continuous individual support of smoking cessation using text messaging: A pilot e

Nicotine Tob Res 2009. 

 6 Japuntich SJ, Zehner ME, Smith SS et al. Smoking cessation via the internet: a randomized clinical trial of an internet intervention 

smoking cessation intervention. Nicotine Tob Res 2006; 8 Suppl 1:S59-67. 

 7 Patten CA, Croghan IT, Meis TM et al. Randomized clinical trial of an Internet-based versus brief office intervention for adolescen

Patient Educ Couns 2006; 64(1-3):249-58. 

 8 Prochaska JO, DiClemente CC, Velicer WF, Rossi JS. Standardized, Individualized, Interactive, and Personalized Self-Help Progr

Cessation. 1993; 12(5):399-405. 
Evidence Table 15. Description of consumer characteristics in addressing impact of CHI applications on intermediate outcomes in smoking (c

G-144 

 9 Prokhorov AV, Kelder SH, Shegog R et al. Impact of A Smoking Prevention Interactive Experience (ASPIRE), an interactive, mu

prevention and cessation curriculum for culturally diverse high-school students. Nicotine Tob Res 2008; 10(9):1477-85. 

10 Shiffman S, Paty JA, Rohay JM, Di Marino ME, Gitchell J. The efficacy of computer-tailored smoking cessation material as a supp

polacrilex gum therapy. Arch Intern Med 2000; 160(11):1675-81. 

11 Schumann A, John U, Rumpf H-J, Hapke U, Meyer C. Changes in the "stages of change" as outcome measures of a smoking cessa

randomized controlled trial. 2006; 43(2):101-6. 

12 Schumann A, John U, Baumeister SE, Ulbricht S, Rumpf HJ, Meyer C. Computer-tailored smoking cessation intervention in a gen

Germany: outcome of a randomized controlled trial. Nicotine Tob Res 2008; 10(2):371-9. 

13 Severson HH, Gordon JS, Danaher BG, Akers L. ChewFree.com: evaluation of a Web-based cessation program for smokeless toba

Res 2008; 10(2):381-91. 

14 Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S, Hospers HJ, Skinner CS. The effects of computer-tailored smoking cessation me

settings. 1994; 39(3):262-70. 

15 Strecher VJ, Marcus A, Bishop K et al. A randomized controlled trial of multiple tailored messages for smoking cessation among 

information service. J Health Commun 2005; 10 Suppl 1:105-18. 

16 Strecher VJ, Shiffman S, West R. Randomized controlled trial of a web-based computer-tailored smoking cessation program as a s

patch therapy. Addiction 2005; 100(5):682-8. 

17 Strecher VJ, Shiffman S, West R. Moderators and mediators of a web-based computer-tailored smoking cessation program among 

Nicotine Tob Res 2006; 8 Suppl 1:S95-101. 

18 Strecher VJ, McClure JB, Alexander GL et al. Web-based smoking-cessation programs: results of a randomized trial. Am J Prev M

19 Swartz LH, Noell JW, Schroeder SW, Ary DV. A randomised control study of a fully automated internet based smoking cessation 

2006; 15(1):7-12. 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b)

G‐145

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

Smoking 

Control 260 8 wks (%): 16.2 20 wks (%) 

19.6 

 30 weeks (%

23.1 

An, 20081 %abstinent for 

30 days 

RealU intervention 

257 8 wks (%): 16 

95 % CI: 0.64-1.66 

20 wks (%) 24.1 95% CI: 0.88-2.04 

 30 weeks (%

40.5 95% CI:1.58

Repeated Points of Abstinence (1 + 3 + 6 + 12 months) 

Self-help booklet 

146 10(7) 

Brendryen, 2008 2

 Happy Ending 

program (HE) 

144 29(20) 

Control 324 

(119, 133, 72) 

 Precontemp

group (13), fcontemplatiogroup (11), fpreparation g(18) 

Booklet 327 

(125, 131, 71) 

 Precontemp

group (10), fcontemplatiogroup (10), fpreparation g(15) 

Curry, 1995 3 7-day abstinence at 

21 months 

Feedback 323 

(128, 132, 

 Precontemp

group (5), focontemplatiogroup (12), fpreparation g
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐146

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

63) (16) 

Phone 150 

(64, 64, 22) 

 Precontemp

group (16), fcontemplatiogroup (11), fpreparation g(23) 

Control 324 

(119, 133, 72) 

 Precontemp

group (1), focontemplatiogroup (1), fopreparation g(6) 

Booklet 327 

(125, 131, 71) 

 Precontemp

group (0), focontemplatiogroup (0.5), preparation g(3) 

Feedback 323 

(128, 132, 63) 

 Precontemp

group (1), focontemplatiogroup (2), fopreparation g(3) 

Abstinent at 3, 12 and 21 months 

Phone 150 

(64, 64, 

 Precontemp

group (5), focontemplatiogroup (2), fo
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐147

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

22) preparation g

(9) 

Affective attitude 

Standard 51 NR At time of 

intervention Mean, 5.61 

 Personalization 50 NR At time of 

intervention Mean, 5.13 

 Adaptation 51 NR At time of 

intervention Mean, 5.48 

 Feedback 50 NR At time of 

intervention Mean, 5.55 

Cognitive attitude 

Standard 51 NR At time of 

intervention Mean, 2.62 

 Personalization 50 NR At time of 

intervention Mean, 2.52 

Dijkstra, 2005 4

 Adaptation 51 NR At time of 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐148

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point intervention Mean, 2.53 

 Feedback 50 NR At time of 

intervention Mean, 2.79 

Quitting attempts 

Standard NR NR 4 months, (2

 Personalization NR NR 4 months, (4 Adaptation NR NR 4 months, (2 Feedback NR NR 4 months, (4

Hang, 2009 5

No intervention 64 Mean 11.7 

SD 7.5 

 3 months, M

9.5, SD 5.5 

 SMS 1 per 

week 

50 Mean 12.4 

SD 7.3 

 3 months, M

10.2, SD 6.5 

Number of cigarettes smoked per day 

SMS 3 per week 

60 Mean 11.2 

SD 6.3 

 3 months, M

9.7, SD 6.4 

 No intervention 64 3 months, N 

(41) 

24 hour quit attempt 

SMS 1 per 50 3 months, N 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐149

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

week (42) 

 SMS 3 per 

week 

60 3 months, N 

(50) 

Control 144 3 months (%): 

(20.8) 

Japuntich, 6mos (%):15 20066

Abstinent 

CHESS SCRP 140 3 months (%): 

(22.9) OR:1.13 (.64-1.98) 

 6mos (%):11

OR:1.48 (.66

Control 69 8 wks 12 wks 24 wks (%): 

 95% CI:5-22 

Pattens, 20067

Smoking abstinence 

Internet based intervention 

70 8 wks 12 wks 24 wks (%): 

 95% CI:2-14 

ALA+ (self-help manual) 

 0 

TTT (individualized manual) 

 0 

ITT (interactive computer report) 

 0 

Point Prevalence Abstinence, Precontemplation stage 

PITT (personalized counselor +TTT+ITT) 

 0 

Prochaska, 1993 8

Point ALA+ 0 (11.1) 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐150

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

TTT 0 (5.0) ITT 0 (17.6) 

Prevalence Abstinence, Contemplation stage 

PITT 0 (5.3) ALA+ 0 (10.8) TTT 0 (15.4) ITT 0 (25.0) 

Point Prevalence Abstinence, Preparation stage 

PITT 0 (15.6) ALA+ 0 (11.6) TTT 0 (29.4) ITT 0 28.0) 

Point Prevalence Abstinence, Preparation stage 

PITT 0 (27.9) Control (Clearing the Air self-help booklet) 

516 (5.8) 

Smoking initiation rates at 18 months (nonsmokers at BL) 

ASPIRE CDROM 582 (1.9) 

Prokhorov, 2008 9

Smoking cessation rates at 18 months 

Control (Clearing the Air self-help 

34 (61.8) 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐151

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

(smokers at BL) booklet) 

Control 1203 28-day 

abstinence at 6 weeks (%): 28.6 95% CI:1.780 (1.342-2.360) 

 10-wks absti

at 12 weeks:95% CI:2.04(1.489-2.807

Schiffman, 200010

Abstinence rates 

Computer tailored smoking cessation materials (CQP) 

1217 28- day 

abstinence at 6 wks:41.6, 95% CI:1.780 (1.342-2.360) 

 10-wks absti

at 12 wks:3295% CI:2.04(1.489-2.807

Control (no intervention) 

Average 245 0.038 probability of progression (precontemplation and contemplation) 

Tailored letters 240 0.032 Control (no intervention) 

245 0.084 

Schumann, 2006 11

Average probability of regression (precontemplation and contemplation) 

Tailored letters 240 0.034 

Point- Control 309 6 mos: 46 12 mos: 55 18 mos:55 24 mos: 69 prevalence abstinence 

Computertailored smoking cessation intervention 

302 6 mos:46 12 mos :56 18 mos:55 24 mos:63 

Control 309 12 mos:38 18 ms:46 24 mos:46 

Schumann, 200812

Prolonged abstinence Computertailored 302 12 mos:35 18 mos:45 24 mos:46 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐152

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

smoking cessation intervention 

Tobacco Control 100 3 mos:26.9 6 mos:31 3 and 6 mosabstinence (complete case) Interactive, 

tailored web-based intervention 

159 3 mos:44.2 6 mos:46.2 3 and 6 mos

Tobacco Control 100 3 mos:13.9 6 mos:14.7 3 and 6 mosabstinence (intent-to-treat) Interactive, 

tailored webbased intervention 

159 3 mos:19.5 6 mos:19.3 3 and 6 mos

Smokeless Control 128 3 mos:32.4 6 mos:35.3 3 and 6 mostobacco use abstinence (complete case) 

Interactive, tailored webbased intervention 

189 3 mos:49.6 6 mos:51.3 3 and 6 mos

Control 128 3 mos:16.8 6 mos:16.8 3 and 6 mos

Severson, 200813

Smokeless tobacco use abstinence (intent-to-treat) 

Interactive, tailored webbased intervention 

189 3 mos:21.9 6 mos:21.4 3 and 6 mos

Control (generic letter) 

7-day (7.4) abstinence 

Tailored letter (20.8) 

Strecher, 1994 14Study 1 

7-day abstinence 

Control (generic letter) / light smoker 

 (7.1) 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐153

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

Control (generic letter) / heavy smoker 

 (7.7) 

Tailored letter / light smoker 

 (30. 7) 

Tailored letter / heavy smoker 

 (7.1) 

Control (no letter) / light smoker 

 (7.3) 

Control (no letter) / heavy smoker 

 (9.8) 

Tailored letter / light smoker 

 (19.1) 

Strecher, 1994 14Study 1 

7-day abstinence 

Tailored letter / heavy smoker 

 (3.9) 

Strecher, 2005 15

7-day abstinence at 12 months (intent to treat analysis) 

SU (single untailored booklet) 

 (8.1) 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐154

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

ST (single tailored booklet) 

 (7.2)

MT (multiple tailored materials) 

 (10.3)

MRT (multiple retailored materials) 

 (10.5)

SU (single untailored booklet) 

 (41.9) 

ST (single tailored booklet) 

 (37.4)

MT (multiple tailored materials) 

 (41.7)

7-day abstinence at 12 months of subjects who were abstinent at 5 months (intent to treat analysis) 

MRT (multiple retailored materials) 

 (53.6)

7-day abstinence at 12 months of 

SU (single untailored 

 (57.1) 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐155

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

booklet) ST (single tailored booklet) 

 (51.4)

MT (multiple tailored materials) 

 (63.3)

subjects who were abstinent at 5 months (per protocol analysis) 

MRT (multiple retailored materials) 

 (75.8)

28 day Control 588 6 wks (%): 46.8 12 wks abstinence rate CQ plan 640 6 wks (%): 54.4 

OR:1.36 95% CI: 1.08-1.70 

 12 wks 

Control 418 6 wks 12 wks (%): 

 Strecher, 200516

10 week continuous rates 

CQ Plan 446 6 wks 12 wks (%): 

OR: 1.63 95% CI:1.24

CQ-PLAN 1491 6 wks 12 wks:55.6 

95% CI: 

Tobacco related illness 

CONTROL 1491 6 wks 12 weeks:38

Non-smoking CQ PLAN 1491 6 wks 12 wks:57.7 children in household CONTROL 1491 6 wks 12 wks:38.5 

CQ-PLAN 1491 6 wks 12 wks:55.4 

Strecher , 200617

Frequency of alcohol CONTROL 1491 6 wks 12 wks:36.5 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐156

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

consumption 

High depth efficacy expectation 

466 6 mos follow 

up:32.4 

Depth of efficacy expectation of smoking cessation intervention 

Low depth efficacy expectation 

478 6 mos follow 

up:28.5 

High depth outcome expectation 

494 6 mos follow 

up:32.2 

Depth of outcome expectation of smoking cessation intervention 

Low depth outcome expectation 

450 6 mos follow 

up:28.7 

High depth success story 

488 6 mos follow 

up:34.3 

Depth of success stories of smoking cessation intervention 

Low depth success story 

456 6 mos follow 

up:26.8 

High personalization of message source 

481 6 mos follow 

up:33.6 

Personalization of message source 

Low personalization of message source 

463 6 mos follow 

up:27.4 

Multiple message exposure 

487 6 mos follow 

up:29.6 

Strecher, 200818

Timing of message exposure 

Ingle message exposure 

457 6 mos follow 

up:31.3 

Swartz, Control 9 90 day (%):8200619

Automated behavioral intervention for cessation of smoking at 90 day follow-up 

Those who received immediate access to the web site for automated 

21 90 day:24.1 

OR:3.57 95% CI:1.54
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐157

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at Time point 2 

Measure at Time point 3 

Measure at Time point 4 

Measure at Time point 

behavioral intervention for smoking Control (intent to treat model) 

9 90 day:5 

behavioral intervention for smoking (intent to treat model) 

21 90 day:12.3, 

OR:2.66 95% CI:1.18

CI = confidence interval, NR = not reported, NS = not specified, OR = odd ratio, wks = weeks, mos = months 

Reference List 

 1 An LC, Klatt C, Perry CL et al. The RealU online cessation intervention for college smokers: a randomized controlled trial. Prev M 2 Brendryen H, Drozd F, Kraft P. A digital smoking cessation program delivered through internet and cell phone without nicotine re

randomized controlled trial. J Med Internet Res 2008; 10(5):e51. 

 3 Curry SJ, McBride C, Grothaus LC, Louie D, Wagner EH. A randomized trial of self-help materials, personalized feedback, and te

nonvolunteer smokers. 1995; 63(6):1005-14. 

 4 Dijkstra A. Working mechanisms of computer-tailored health education: Evidence from smoking cessation. 2005; 20(5):527-39.  5 Haug S, Meyer C, Schorr G, Bauer S, John U. Continuous individual support of smoking cessation using text messaging: A pilot e

Nicotine Tob Res 2009. 

 6 Japuntich SJ, Zehner ME, Smith SS et al. Smoking cessation via the internet: a randomized clinical trial of an internet intervention 

smoking cessation intervention. Nicotine Tob Res 2006; 8 Suppl 1:S59-67. 

 7 Patten CA, Croghan IT, Meis TM et al. Randomized clinical trial of an Internet-based versus brief office intervention for adolescen

Patient Educ Couns 2006; 64(1-3):249-58. 
Evidence table 16. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in alcohol abuse (KQ1b) (continu

G‐158

 8 Prochaska JO, DiClemente CC, Velicer WF, Rossi JS. Standardized, Individualized, Interactive, and Personalized Self-Help Progr

Cessation. 1993; 12(5):399-405. 

 9 Prokhorov AV, Kelder SH, Shegog R et al. Impact of A Smoking Prevention Interactive Experience (ASPIRE), an interactive, mu

prevention and cessation curriculum for culturally diverse high-school students. Nicotine Tob Res 2008; 10(9):1477-85. 

10 Shiffman S, Paty JA, Rohay JM, Di Marino ME, Gitchell J. The efficacy of computer-tailored smoking cessation material as a supp

polacrilex gum therapy. Arch Intern Med 2000; 160(11):1675-81. 

11 Schumann A, John U, Rumpf H-J, Hapke U, Meyer C. Changes in the "stages of change" as outcome measures of a smoking cessa

randomized controlled trial. 2006; 43(2):101-6. 

12 Schumann A, John U, Baumeister SE, Ulbricht S, Rumpf HJ, Meyer C. Computer-tailored smoking cessation intervention in a gen

Germany: outcome of a randomized controlled trial. Nicotine Tob Res 2008; 10(2):371-9. 

13 Severson HH, Gordon JS, Danaher BG, Akers L. ChewFree.com: evaluation of a Web-based cessation program for smokeless toba

Res 2008; 10(2):381-91. 

14 Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S, Hospers HJ, Skinner CS. The effects of computer-tailored smoking cessation me

settings. 1994; 39(3):262-70. 

15 Strecher VJ, Marcus A, Bishop K et al. A randomized controlled trial of multiple tailored messages for smoking cessation among 

information service. J Health Commun 2005; 10 Suppl 1:105-18. 

16 Strecher VJ, Shiffman S, West R. Randomized controlled trial of a web-based computer-tailored smoking cessation program as a s

patch therapy. Addiction 2005; 100(5):682-8. 

17 Strecher VJ, Shiffman S, West R. Moderators and mediators of a web-based computer-tailored smoking cessation program among 

Nicotine Tob Res 2006; 8 Suppl 1:S95-101. 

18 Strecher VJ, McClure JB, Alexander GL et al. Web-based smoking-cessation programs: results of a randomized trial. Am J Prev M

19 Swartz LH, Noell JW, Schroeder SW, Ary DV. A randomised control study of a fully automated internet based smoking cessation 

2006; 15(1):7-12.
Evidence Table 17. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in obesity (KQ1b)

G-159 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Obesity Booth, 20081

Individuals interested in their own health care 

Personal monitoring device 

NS NS BMI was between 

24.5 and 37 kg/ m2, Internet access 

<18 yr, Pregnant or lactating or were currently receiving medications for Type 1 or Type 2 diabetes 

 

Burnett, 1985 2

Obesity 

Overweight females 

An interactive lap sized computer 

Home / Res Ns Consenting 30 – 50 yr 

old females to the newspaper advertisement 

Ns Paper and 

pencil method of providing feedback 

Cussler, 20083

Individuals interested in their own health care 

Interactive consumer website 

NS NS 40 - 55 yr, 

Women, have a BMI between 25.0 and 38.0 kg/m2, Nonsmoker and be free from major illnesses, Internet access 

 Self 

directed group 

Frenn, 2005 4

Obesity 

Students of 7th grade 

Computer based interactive web 

Computer labs in school 

NR 7th grade student 

who could read in English / Spanish and completed the consent form 

ns Regular 

Classroom assignmens 

Hunter, 20085

Individuals interested in their own health care 

Interactive consumer website 

NS 2006 18 - 65 yr, 

Weight within 5 pounds or above their maximum allowable weight for the USAF, Personal computer with Internet access, plans to remain in the local area for 1 year 

Lost more than 10 pounds in the previous 3 months, Used prescription or over-the-counter weight-loss medications in the previous 6 months, had any physical 

Usual care 
Evidence Table 17. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in obesity (KQ1b) (continued)

G-160 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

activity restrictions, had a history of myocardial infarction, stroke, or cancer in the last 5 years, reported diabetes, angina, or thyroid difficulties; or had orthopedic or joint problems, Women were excluded if they were currently pregnant or breast-feeding, or had plans to become pregnant in the next year 

Kroeze, 20086

Individuals interested in their own health care 

Interactive consumer website 

Worksites and 2 neighborhoods in the urban area of Rotterdam 

2003-2004 18-65 yr, 

Sufficient understanding of the Dutch language, No diet prescribed by a dietitian or physician, and no treatment for hypercholesterolemia 

 Generic 

condition 

McConnon, 20077

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

2003/ NS 18 - 65 yr, 

BMI 30 or more, able to access internet at least 1 time a week, able to read and write 

 Usual care 
Evidence Table 17. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in obesity (KQ1b) (continued)

G-161 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

English 

Taylor, 1991 9 Overweight women Pocket Computer 

Device 

Home / Res NS Overweight women 

BMi b/w 25 and 35 kg/m^3 

Bulemia, Depression, alcohol and drug dependence, psychosis, DSM III R 

Computer Assisted Therapy (CAT) 

Williamson, 200610

Individuals interested in their own health care 

Interactive consumer website 

Clinician office NS 11 - 15 yr, 

African-American, Female, BMI above the 85th percentile for age and gender based on 1999 National Health and Nutrition Examination Study normative data, at least one obese biological parent, as defined by  BMI > 30, one designated parent who was overweight (BMI > 27), adolescent’s family was willing to pay $300 out-of pocket expenses toward the purchase of the computer worth >$1000, the family home had electricity and at least one functional telephone line 

 Control and 

intervention adolescent

Womble , 200411

Individuals interested in their own 

Interactive consumer website 

NS 2001/ 

NS 

18-65 yr, BMI: 27-40 kg/m2, Daily access to the 

Type 1 or 2 diabetes, Uncontrolled 
Evidence Table 17. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in obesity (KQ1b) (continued)

G-162 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

health care internet HTN 

(BP>140/90), History of cerebrovascular, cardiovascular, kidney or liver disease, Use of medications known to affect body weight, pregnancy or lactation weight loss>=5% of initial weight, Use of anorectic agents in the previous 6 months, bulimia, major depression, or other psychiatric illness significantly disrupted daily functioning 

NS = not specified, yr = year, BMI = body mass index, kg/m2 = Kilograms per square meter, BP = blood pressure, HTN = hypertension 

Reference List 

 1 Booth AO, Nowson CA, Matters H. Evaluation of an interactive, Internet-based weight loss program: a pilot study. Health Educ R 2 Burnett KF, Taylor CB, Agras WS. Ambulatory computer-assisted therapy for obesity: A new frontier for behavior therapy. 1985;  3 Cussler EC, Teixeira PJ, Going SB et al. Maintenance of weight loss in overweight middle-aged women through the Internet. Obes

16(5):1052-60. 
Evidence Table 17. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in obesity (KQ1b) (continued)

G-163 

 4 Frenn M, Malin S, Brown RL et al. Changing the tide: An Internet/video exercise and low-fat diet intervention with middle-school 

21. 

 5 Hunter CM, Peterson AL, Alvarez LM et al. Weight management using the internet a randomized controlled trial. Am J Prev Med 

 6 Kroeze W, Oenema A, Campbell M, Brug J. The efficacy of Web-based and print-delivered computer-tailored interventions to red

randomized, controlled trial. J Nutr Educ Behav 2008; 40(4):226-36. 

 7 McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tr

2007; 7:206. 

 8 Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT Randomized Controlled Trial: Evaluation of an Intern

Program for Men. Obesity (Silver Spring) 2009. 

 9 Taylor CB, Agras WS, Losch M, Plante TG, Burnett K. Improving the effectiveness of computer-assisted weight loss. 1991; 22(2)

10 Williamson DA, Walden HM, White MA et al. Two-year internet-based randomized controlled trial for weight loss in African-Am

(Silver Spring) 2006; 14(7):1231-43. 

11 Womble LG, Wadden TA, McGuckin BG, Sargent SL, Rothman RA, Krauthamer-Ewing ES. A randomized controlled trial of a co

loss program. Obes Res 2004; 12(6):1011-8.
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

G-164 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

MaritaStatus 

Obesity 

 Comparison NS NS NS NS NR NS NS Online Diet advice and exercise program 

Mean, 46.4 SD, 12.5 

NS NS NS NR NS NS 

Booth, 20081

Online exercise program only 

Mean, 46.2 SD, 9.2 

NS NS NS NR NS NS 

Paper and Pencil method of providing feedback 

Burnett, 39.8 SD 5.5 Ns Ns Ns Ns All F 1985 2

Obesity 

A lap sized computer 

43.2 SD 8.8 All F 

Self directed group 

Mean, 48.2 SD, 4.2 

Cussler, 20083

Internet group Mean, 48.3 

SD, 4.4 

NS NS NS NR NR NR 

Frenn, 2005 4 Regular Classroom 12—14yrs Diet : Asians 2 Diet: Free Seventh grade Students NR Diet: M 22 (44.9) F 27 
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-165 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

MaritaStatus 

Assignments (4.1); 

Blacks 15 (30.6); Hispanics 17 (34.7); Native Americans 4 (8.2); Whites 4 (8.2); Others 7 (14.3) 

Activity: 

Asians 3 (5); Blacks 16 (26.7); Hispanics 24 (40); Native Americans 4 (6.5); Whites 4 (8.9); Others 9 (15) 

lunch 35(71.4); Reduced 6(12.2); No reduction 8(16.0) Activity: Free lunch 42(70.0); Reduced 8(13.3); No reduction 10(16.7) 

(55.1); Activity: M 30 (50) F 30 (50) 

Obesity 

8 sessions Internet based interactive model based on HP/TM 

 Diet: 

Asians 0 (0); Blacks 8 (20); Hispanics 22 (55); Native Americans 1 (2.5); Whites 5 

Diet: Free lunch 30(75.0); Reduced 5(12.5); No reduction 

 Diet group: 

M 12(30); F 28(70) Activity: M 14 (26.3) F 29 (73.7) 
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-166 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

MaritaStatus 

(12.5); Others 4 (10) 

Activity: 

Asians 0 (0); Blacks 9 (20.9); Hispanics 23 (53.3); Native Americans 0 (0); Whites 4 (9.3); Others 7 (16.3) 

5(12.5) Activity: Free lunch 31 (72.1); Reduced 6(14); No reduction 6 (14) 

Usual care Mean, 34.4 

SD, 7.2 

C, 222 C, 53.2 

Hunter, NS 12-16 years, 222(61.7) NR F, 222(50.5) 20085

Behavioral Internet treatment 

Mean, 33.5 SD, 7.4 

C, 224 C, 58.0 

NS 12-16 years, 224(63.9) NR F, 224(50.0) 

NR 

Generic condition 

Mean, 44.1 SD, 9.7 

Elementary,3(2) Lower secondary, 28(18.4) Higher secondary, 56(37.4) Tertiary, 63(42.2) 

F,150(56.0) 

Interactivetailored condition 

Mean, 44 SD, 10.56 

Elementary,4(2.6) Lower secondary,29(19.2) Higher secondary,51( 33.8) Tertiary, 67(44.4) 

F,151(53.6) 

Kroeze, 2008 6

Print-tailored condition 

Mean, 43.4 SD, 10.1 

NS NS 

Elementary,15(3.6) Lower secondary,26(18.6) Higher secondary,49(35.0) Tertiary,61(42.9) 

NR 

F,141(55.3) 

NR 

McConnon, 20077

Usual care Mean, 47.4 NS NS NS NR NR NR 
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-167 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

MaritaStatus 

Internet group Mean, 48.1 One information session + Program booklet 

34 SD 11.6 NS NS Student: 14 

Non Acad Staff: 13 Acad Staff: 4 

Measured by SEIFA score 1,2-0 3,4-5 5,6-9 7,8:11 9,10:3 

Morgan, All M 2009 8

Obesity 

SHED IT internet program w/ information session and program booklet (the program facilitates self monitoring and daily diary to which the researchers 

37.5 SD 10.4 

NS NS Student: 14 

Non Acad Staff: 14 Acad Staff: 6 

1,2-1 3,4-7 5,6-3 7,8:11 9,10:2 

All M 
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-168 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

MaritaStatus 

respond) Computer Assisted Therapy 

43.7. SD 11.1 

Taylor, NS NS NS NS All F 1991 9

Obesity 

1200 calorie diet (Frozen Food) followed by CAT 

Control and intervention adolescents 

Mean, 13.2 SD, 1.4 

Williamson, 200610

Control and intervention parents 

Mean, 43.2 SD, 6.2 

NS NS NS NR NR NR 

Womble, 200411

 Control Mean, 43.3 

SD, 11.1 

NS NS NS NR NR NR 
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-169 

Author, year 

Control Interventions Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

MaritaStatus 

ediets.com Mean, 44.2 

SD, 9.3 

C = Caucasian, NS = not specified, NR = not reported, F = female, kg = kilograms, BMI = body mass index, cm = centimeter, BP = blood pressure, 

kg/m2 = kilograms per square meter, SD = standard deviation, SES = Socio economic status 

Reference List 

 1. Booth AO, Nowson CA, Matters H. Evaluation of an interactive, Internet-based weight loss program: a pilot study. Health Educ  2. Burnett KF, Taylor CB, Agras WS. Ambulatory computer-assisted therapy for obesity: A new frontier for behavior therapy. 198 3. Cussler EC, Teixeira PJ, Going SB et al. Maintenance of weight loss in overweight middle-aged women through the Internet. Ob

2008; 16(5):1052-60. 
Evidence Table 18. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-170 

 4. Frenn M, Malin S, Brown RL et al. Changing the tide: An Internet/video exercise and low-fat diet intervention with middle-scho

18(1):13-21. 

 5. Hunter CM, Peterson AL, Alvarez LM et al. Weight management using the internet a randomized controlled trial. Am J Prev Me

 6. Kroeze W, Oenema A, Campbell M, Brug J. The efficacy of Web-based and print-delivered computer-tailored interventions to r

a randomized, controlled trial. J Nutr Educ Behav 2008; 40(4):226-36. 

 7. McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled 

Res 2007; 7:206. 

 8. Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT Randomized Controlled Trial: Evaluation of an Inte

Program for Men. Obesity (Silver Spring) 2009. 

 9. Taylor CB, Agras WS, Losch M, Plante TG, Burnett K. Improving the effectiveness of computer-assisted weight loss. 1991; 22(

 10. Williamson DA, Walden HM, White MA et al. Two-year internet-based randomized controlled trial for weight loss in African-A

(Silver Spring) 2006; 14(7):1231-43. 

 11. Womble LG, Wadden TA, McGuckin BG, Sargent SL, Rothman RA, Krauthamer-Ewing ES. A randomized controlled trial of a 

weight loss program. Obes Res 2004; 12(6):1011-8. 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b)

G‐171

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

Obesity 

EX 26 12weeks: 

mean, 2.1 range, -2.9-14.2 SD, 3.4 

Weight change (%) 

ED 27 12weeks: 

mean, 0.9 range, 3.0-8.6 SD, 2.5 

EX 26 12weeks 

mean, -4.5 range,  -12.5-4.7 SD, 4.5 

Waist circumference change (cm) 

ED 27 12weeks 

mean, -3.2 range, -8.7-2.2 SD, 2.9 

EX 26 Mean, 9151.5 

range, 3559,16623 SD, 3289.9 

 12weeks 

mean, 12299.6 range, 6214,19246 SD, 3514.7 

Physical activity (daily steps) 

ED 27 Mean, 8673.3 

range, 2784,15202 SD, 3567.3 

 12weeks 

mean, 12198.8 range, 6650,22572 SD, 4121.8 

Booth, 20081

Energy intake EX 26 12weeks 

mean, 131.1 SD, 759.9 

 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐172

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

ED 27 12weeks 

mean, -1812.6 SD, 729.9 

Paper and pencil method of providing feedback 

6 Gain of weight: 

0.67lbs SD 2.66lbs 

Short term weight change: Baseline 2 wk period 

Computer Assisted method of providing feedback 

6 Gain of weight: 

0.17lbs SD 0.41lbs 

Paper and pencil method of providing feedback 

6 Loss of weight: 

3.3 lbs SD 3.2 lbs 

Short term weight change: Post- baseline 8 wk period 

Computer Assisted method of providing feedback 

6 Loss of weight: 

8.1 lbs SD 2.7 lbs 

Burnett, 1985 2

Obesity

Long term weight changes (24 wks) 

Paper and pencil method of providing feedback 

6 Loss of Weight: 

4.17 lbs SD 4.83 lbs 

 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐173

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

Computer Assisted method of providing feedback 

6 Loss of Weight: 

15.67lbs SD 10.46 lbs 

Paper and pencil method of providing feedback 

6 Loss of Weight: 

2.34 lbs SD 7.31 

Long term weight changes (40 wks) 

Computer Assisted method of providing feedback 

6 Loss of Weight: 

17.67 lbs SD 13.82 lbs 

Self-reported Caloric intake 

Paper and pencil method of providing feedback 

6 2076 cal SD 165 

cal 

 1462 cal SD 

324 cal 

 Computer 

Assisted method of providing feedback 

6 1942 cal SD 334 

cal 

 1142 cal SD 

323 cal 

Self-reported physical 

Paper and pencil method of 

6 77 PA units SD 

128 

 206 PA units 

SD 108 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐174

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

activity providing 

feedback 

 Computer 

Assisted method of providing feedback 

6 172 PA units SD 

188 

 372 PA units 

SD 158 

How beneficial will this treatment be in promoting weight loss for you? 

Paper and pencil method of providing feedback 

6 2.72 SD 

0.58 

 Computer 

Assisted method of providing feedback 

6 2.79 SD 

0.94 

How beneficial will this treatment be in promoting weight loss for overwt indi.? 

Paper and pencil method of providing feedback 

6 2.84 SD 

0.54 

 Computer 

Assisted method of providing 

6 2.74 SD 

0.84 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐175

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

feedback Control 59 Mean, -5.2 

SD, 3.8 

 4-16months: 

mean, 1 SD, 4.6 

Weight change (kg) 

Internet group 

52 Mean, -5.3 

SD, 3.6 

 4-16months: 

mean, 0.7 SD, 5.4 

Control 59 Mean, -1.9 

SD, 1.4 

BL-4 months 

 4-16months 

mean, 1.9 SD, 1.5 

BMI 

Internet 52 Mean, -1.9 

SD, 1.4 

BL-4 months 

 4-16months 

mean, -2.1 SD, 1.4 

Control 59 mean, 144 

SD, 151 

BL-4 months 

 4-16months 

mean, 164 SD, 268 

Exercise energy expenditure (kcal/day) Internet 52 Mean, 151 

SD, 196 

BL-4 months 

 4-16months 

mean, 123 SD, 265 

Control 59 Mean, -370 

SD, 471 

BL-4 months 

 4-16months 

mean, 91 SD, 330 

Cussler, 20083

Energy intake (kcal/day) 

Internet 52 BL4months 4-16months mean, 74 

SD, 371 

 

Physical Activity 

Regular Classroom assignments 

60 Reduction in 

moderate/vigorous PA by 46 min measured by the log 

Frenn, 2005 4

Obesity

 8 sessions 

Internet based 

43 Increase in the PA 

by 22 min for those completing 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐176

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

interactive model based on HP/TM 

2/3 modules and 33 min for those completing all 3 modules (p=0,05) 

Regular Classroom assignments 

49 Fat intake went 

from 31.5 

Æ

31.6% (Not significant) 

Diet 

8 sessions Internet based interactive model based on HP/TM 

40 Reduced fat 

intake from 30.7 to 29.9% (p=0,008) 

Control 222 Mean, 86.6 

SD, 14.7 

 6months: 

mean, 87.4 SD, 14.7 

Body weight (kg) 

BIT 224 Mean, 87.4 

SD, 15.6 

 6months: 

mean, 85.5 SD, 15.8 

Control 222 Mean, 29.3 

SD, 3 

 6months 

mean, 29.4 SD, 3 

BMI (kg/m2) 

BIT 224 mean, 29.4 

SD, 3 

 6months 

mean, 28.8 SD, 3.3 

Control 222 Mean, 94.2 

 SD, 10.9 

 6months 

mean, 93.4 SD, 12.8 

Hunter, 20085

Waist circumference (cm) 

BIT 224 Mean, 94.5 

 SD, 11 

 6months 

mean, 92.2 SD, 11.6 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐177

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

Control 222 Mean, 34.2 

SD, 6.9 

 6months 

mean, 34.7 SD, 7 

Body fat percentage (%) 

BIT 224 6months 

mean, 33.9 SD, 7.3 

Control 133 g 1month 

mean, 88.4 SD, 39.9 

 6months: 

mean, 83.0 SD, 34.2 

Interactivetailored condition 

126 g 1 month 

mean, 77.4 SD, 30.9 

 6months: 

mean, 77.9 SD, 30.4 

Total fat intake 

Print-tailored condition 

124 g 1 month 

mean, 80.5 SD, 25.7 

 6months: 

mean, 76.1 SD, 26.9 

Control 133 g 1 month 

mean, 31.4 SD, 15 

 6months 

mean, 29.5 SD, 13.7 

Interactive Condition 

126 g 1 month 

mean, 28.3 SD, 12.9 

 6months 

mean, 28.5 SD, 10 

Saturated fat intake 

Print condition 

124 g 1 month 

mean, 28.9 SD, 9.8 

 6months 

mean, 27.0 SD, 10 

Control 133 mega joules 1 month 

mean, 9.4 SD, 3.1 

 6months 

mean, 8.9 SD, 3 

Interactive Condition 

126 mega joules 1 month 

mean, 8.6 SD, 2.5 

 6months 

mean, 8.4 SD, 2.5 

Kroeze, 2008 6

Energy intake 

Print 124 mega joules 1 month 6months 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐178

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

condition mean, 8.3 

SD, 2.7 

mean, 8.2 SD, 2.4 

Control 77 kg / m2 12 months 

after baseline: range,  -8.1 to +3.5; 

BMI change at 12 months (kg/m2) 

Internet group 

54 kg / m2 12 months 

after baseline: range, - 5.9 to 3.8; 

Control 77 6 months 12 months 

(%):(18) 

Loss of 5% or more body weight (12 months) 

Internet group 

54 6 months 12 months 

(%): (22) 

Using website at 6 months, at 12 months 

Internet group 

54 mean, 53 6 months 12 months 

(%): (29) 

Never used website 

Internet group 

54 6 months 12 months 

(%): (47) 

Of those who used website, found it easy / very easy 

Internet group 

54 mean, 63 6 months 12 months 

(%): (85 ) 

McConnon, 20077

Of those who us website, found it clear / very clear 

Internet group 

54 mean, 78 6 months 12 months 

(%): (76) 

Prog info + Booklet gp 

31 Loss of Weight: −3.0 

(−4.5, −1.4) KG

Morgan, 2009 8

Obesity

Change in body wt. 3m 

SHED IT group 

34 Loss of Weight: −4.8 

(−6.4, −3.3) KG
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐179

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

Prog info + Booklet gp 

31 Loss of Weight: −3.5 

(−5.5, −1.4) 

Change in body wt. 6m 

SHED IT group 

34 Loss of Weight: −5.3 

(−7.3, −3.3) 

Prog info + Booklet gp 

31 LOSS: −4.4 (−6.3, 

−2.5) CM 

Waist circumference (cm) 3m

SHED IT group 

34 LOSS: −5.2 (−7.1, 

−3.4) CM 

Prog info + Booklet gp 

Waist 31 −5.6 (−7.7, −3.5) CM circumference (cm) 6m 

SHED IT group 

34 −7.0 (−9.1, −4.9) CM 

Prog info + Booklet gp 

31 −0.9 (−1.4, −0.5) 

KG/M^2 

BMI (kg/m2) 3m 

SHED IT group 

34 −1.5 (−2.0, −1.0) 

KG/M^2 

Prog info + Booklet gp 

BMI (kg/m2) 6m 31 −1.1 (1.7, −0.5) 

SHED IT group 

34 −1.6 (−2.2, −1.0) 

Systolic blood pressure 3m 

Prog info + Booklet gp 

31 −8 (−12, −3) MM HG 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐180

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

SHED IT group 

34 −6 (−10, −1) MM HG 

Prog info + Booklet gp 

Systolic blood 31 −10 (−14, −6) pressure 6m 

SHED IT group 

34 −10 (−14, −7) 

Prog info + Booklet gp 

Diastolic blood 31 −6 (−10, −2) MM HG pressure 3m 

SHED IT group 

34 −4 (−8, −1) MM HG 

Prog info + Booklet gp 

Diastolic blood 31 −5 (−10, −2) pressure 6m 

SHED IT group 

34 −6 (−11, −1) 

Prog info + Booklet gp 

Resting heart 31 −7 (−11, −3) BPM rate 3m 

SHED IT group 

34 −9 (−12, −5) BPM 

Prog info + Booklet gp 

Resting heart 31 −7 (−12, −3) BPM rate 6m 

SHED IT group 

34 −6 (−11, −2) BPM 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐181

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

Prog info + Booklet gp 

31 Went Up by: 976 

(−12, 1,965) STEPS/DAY 

Physical activity (mean steps/day) 3m 

SHED IT group 

34 Went Up by: 1,184 

(234, 2,133) STEP/DAY 

Prog info + Booklet gp 

31 Went Up by: 1,302 

(241, 2,363) 

Physical activity (mean steps/day) 6m SHED IT 

group 

34 Went Up by: 938 

(−90, 1,966) 

Prog info + Booklet gp 

31 Went down by: 

−2,068 (−3,089, 

−1,047) KJ/DAY 

Energy intake (kJ/day) 3m 

SHED IT group 

34 Went down by: 

−3,195 (−4,159, 

−2,230) KJ/DAY 

Prog info + Booklet gp 

31 Went down by: 

−1,881 (−3,087, 

−676) KJ/DAY 

Energy intake (kJ/day) 6m 

SHED IT group 

34 Went down by: 

−3,642 (−4,764, 

−2,521) KJ/DAY 

Taylor, 1991 9

Obesity

Weight Loss (Posttreatment 12w – PreComputer Assisted Therapy (fu 12 wks) 28 M 3.1 SD 2.2 (Loss to fu 4, therefore 24 subjects analyzed) 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐182

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

treatment) 1200 calorie 

diet (Frozen Food) followed by CAT (fu 12 wks) 

27 M = 5.3 SD 2.2 

(Loss to fu 1, therefore 27 subject analyzed) 

Computer Assisted Therapy 

Weight Loss 21 M 0.9 SD 3.6 (F/u @ 6m – Pre-treatment) 

1200 calorie diet (Frozen Food) followed by CAT 

25 M 3.8 SD 2.7 

Control 50 24 month: 

mean A: 6.3 P: 0.06 SD A: 1.6 P: 0.89 

Williamson, 200610 Body weight (kg) 

Interactive nutrition education program and Internet counseling behavioral therapy for the intervention group 

47 Mean, 

A:93.3 P:101 SD, A: 22.5 P: 18.4 

 24 month: 

mean, A: 4.4 P: 1.1 SD, A: 1.7 P: 0.91 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐183

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

Control 50 BMI 6 month 12 month 18 month 24 month 

mean, A: 1.2 P: 0.04 SD A: .65 P: .34 

Body composition 

Interactive bNutrition education program and internet counseling behavioral therapy for the intervention group 

47 Mean, 

A:36.4 P:38.4 SD, A: 7.9 P:7.2 

6 month 12 month 18 month 24 month 

mean, A: 0.73 P: 0.55 SD, A: .66, P: 0.34 

 Control 50 6 month 12 month 18 month 24 month 

mean, A:0.84 P:0.51 SD, A:0.72 P:0.46 

Weight loss behavior (body fat %) 

Interactive bNutrition education program and internet counseling behavioral therapy for the intervention group 

47 Mean, 

A: 45.9 P: 48.4 SD, A: 7.5 P: 6.3 

6 month 12 month 18 month 24 month 

mean, A:-0.08 P:0.36 SD, A:0.71 P:0.46 

BMI (BMI Control 50 6 month 12 month 18 month 24 month 
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐184

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

raat timpo

mean, A: -0.001 SD, A: 0.003 

percentile) 

Interactive bNutrition education program and internet counseling behavioral therapy for the intervention group 

47 6 month 12 month 18 month 

mean, A: -0.004 SD, A: 0.003 

24 month 

 Control 16 16weeks 

mean, 3.6 SD, 4 

 52 weeks: 

mean, 4 SD, 5.1 

Weight change percent (%) 

ediets.com 15 16weeks 

mean, 0.9 SD, 3.2 

 52 weeks: 

mean, 1.1 SD, 4 

 Control 16 16weeks 

mean, 3 SD, 3.1 

 52 weeks 

mean, 3.3 SD, 4.1 

Womble, 200411

Weight change (kg) 

ediets.com 15 16weeks 

mean, 0.7 SD, 2.7 

 52 weeks 

mean, 0.8 SD, 3.6 

BMI = body mass index, BL = baseline, g = gram, kg = kilogram, SD = standard deviation, cm = centimeter, kg/m2 = Kilograms per square meter, 

kcal/day = kilocalories per day, A = Adolescents, P = parents 

Reference List 

 1 Booth AO, Nowson CA, Matters H. Evaluation of an interactive, Internet-based weight loss program: a pilot study. Health Educ R
Evidence table 19. Outcomes in studies addressing the impact of CHI application intermediate outcomes in obesity (KQ1b) (continued)

G‐185

 2 Burnett KF, Taylor CB, Agras WS. Ambulatory computer-assisted therapy for obesity: A new frontier for behavior therapy. 1985;  3 Cussler EC, Teixeira PJ, Going SB et al. Maintenance of weight loss in overweight middle-aged women through the Internet. Obes

16(5):1052-60. 

 4 Frenn M, Malin S, Brown RL et al. Changing the tide: An Internet/video exercise and low-fat diet intervention with middle-school 

21. 

 5 Hunter CM, Peterson AL, Alvarez LM et al. Weight management using the internet a randomized controlled trial. Am J Prev Med  6 Kroeze W, Oenema A, Campbell M, Brug J. The efficacy of Web-based and print-delivered computer-tailored interventions to red

randomized, controlled trial. J Nutr Educ Behav 2008; 40(4):226-36. 

 7 McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tr

2007; 7:206. 

 8 Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT Randomized Controlled Trial: Evaluation of an Intern

Program for Men. Obesity (Silver Spring) 2009. 

 9 Taylor CB, Agras WS, Losch M, Plante TG, Burnett K. Improving the effectiveness of computer-assisted weight loss. 1991; 22(2)

10 Williamson DA, Walden HM, White MA et al. Two-year internet-based randomized controlled trial for weight loss in African-Am

(Silver Spring) 2006; 14(7):1231-43. 

11 Womble LG, Wadden TA, McGuckin BG, Sargent SL, Rothman RA, Krauthamer-Ewing ES. A randomized controlled trial of a co

loss program. Obes Res 2004; 12(6):1011-8. 
Evidence Table 2. Description of RCTs addressing the impact of CHI applications on health care processes (KQ1a) 

G-14 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Asthma Bartholomew, 2000 1 Inner-city elementary 

and middle school–age 6-17 children with moderate to severe asthma 

Watch, Discover, Think and Act (An Interactive multimedia application on CDROM) 

Physician office 

4 to 15.6 Age 6–17 years, 

Moderate-tosevere asthma, English speaking parents, No chronic disease other than asthma 

Not speaking English, co-existing disease, inadequate reading level, parent inability to understand the study 

Participant assigned to usual-care 

Guendelman, 2002 2 Inner-city children 

as having asthma by a physician. 

Personal and interactive communication device (Health Buddy 

Home and in an outpatient hospital clinic. 

April 8, 1999, and July 5, 2000 

Children age 8- 16 years, had an Englishspeaking caregiver, had a telephone at home, and were diagnosed as having persistent asthma, Patient with 2 or more emergency department (ED) visits and/or at least 1 inpatient admission during the year before the study 

Patients involved in other asthma or drug efficacy studies, Involved in research that required behavior modification, Mental or physical challenges that made difficult to use Health Buddy. Children with comorbid conditions that could affect their quality of life. 

Participants using asthma diary 

Jan, 20073

Individuals interested in their own health care Caregiver, childhood asthma 

Personal monitoring device 

Home/ residence 

2004/ January to December 

6 - 12 yr, Caregivers have Internet access, persistent asthma diagnosis (GINA clinical practice guidelines) 

Diagnosed with Bronchopulmonary dysplasia, Diagnosed with other chronic co morbid conditions that could affect quality of life 

Verbal information and booklet for asthma education with written asthma diary. 
Evidence Table 2. Description of RCTs addressing the impact of CHI applications on health care processes (KQ1a) (continued) 

G-15 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Krishna, 20034

Individuals interested in their own health Parents/ caregivers 

Personal monitoring device 

Home/ residence 

1999/ NS < 18 yr, 

Confirmed asthma 

Cystic fibrosis, Bronchopulmonary dysplasia, Other chronic lung disease 

Traditional care 

Use of Contraception Chewining, 19995

Individuals interested in their own health care 

ComputerBased Decision Aid 

Clinician office 

NS < 20 yr, 

Female, ability to read and understand English, Expressed interest in getting a contraceptive 

 Standard 

information 

Yr = Year, NS = not specified, PEFR = Peak expiratory flow rate 
Evidence Table 2. Description of RCTs addressing the impact of CHI applications on health care processes (KQ1a) (continued) 

G-16 

Reference List 

 1 Bartholomew LK, Gold RS, Parcel GS et al. Watch, Discover, Think, and Act: Evaluation of computer-assisted instruction to impr

management in inner-city children. 2000; 39(2-3):269-80. 

 2 Guendelman S, Meade K, Benson M, Chen YQ, Samuels S. Improving asthma outcomes and self-management behaviors of inner-

randomized trial of the Health Buddy interactive device and an asthma diary. 2002; 156(2):114-20. 

 3 Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 4 Krishna S, Francisco BD, Balas EA, Konig P, Graff GR, Madsen RW. Internet-enabled interactive multimedia asthma education p

trial. Pediatrics 2003; 111(3):503-10. 

 5 Chewning B, Mosena P, Wilson D et al. Evaluation of a computerized contraceptive decision aid for adolescent patients. Patient E

38(3):227-39. 
Evidence Table 20. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in diabetes (KQ1b)

G-186 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Diabetes mellitus Glasgow, 20031

Individuals interested in their own health care 

Personal monitoring device 

NS NS Type 2 diabetes for 

more than 1 year, Planning to stay in area for one year, Meet Wellborn criteria for type 2 diabetes 

 Basic 

information 

Homko, 20072

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Duration, Sep 2004 to May 2006 

18-45 yr, documented GDM on 3-h oral glucose tolerance test, using the criteria of Carpenter and Coustan, 33 weeks gestation or less 

Prior history of glucose intolerance outside of pregnancy, multiple gestations 

Usual care, paper logbooks 

McKay, 20013

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS ≥ 40 or > 39 yr, 

Type 2 diabetes, physical activity level below the current minimum recommendation 

Contraindication to moderate physical activity as assessed by the Physical Activity Readiness Questionnaire 

Internet information only 

Richardson, 2007 4

Diabetes 

Type 2 diabetics 

Pedometer hooked onto Interactive computer based feedback mechanism 

Home/res NS 18 y/o Type 2 DM, 

email users w/ Window XP/2000 and self reported moderate PA less than 150 min/week. English speaking. Interested in starting a walking program (cleared by a physician) 

Pregnant women and folks who have used pedometer in last 30 days 

Employing lifestyle goals for overall steps recorded from the pedometer 

Wangberg, 20065

Individuals interested in their own health care 

Interactive consumer website 

NS NS 17-67 yr, 

Type I or II diabetes, Access to the internet 

Low selfefficacy 
Evidence Table 20. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in diabetes (KQ1b) (continued)

G-187 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Wise, 1986 6 Diabetes 

Diabetic individuals both NIDDM and IDDM 

Interactive computerized machine 

Home / Res 

Ns Diabetics attending 

Charing Cross hospitaland having DM > 2 yrs 

None specified 3 controls 

Used: a. No intervention (used for Glucose control assessment) No KAP b. Just the assessment of the KAP c. Takeaway corrective feedback 

Diabetes, heart disease or chronic lung disease Lorig, 20067 Individuals 

interested in their own health care 

Interactive consumer website 

NS NS >18 yr, 

Heart disease or chronic lung disease or Type 2 diabetes, Access to a computer with Internet and email capabilities, Agreed to 1–2 h/week of log on time spread over at least 3 sessions/wk for 6 wk, Able to complete the online questionnaire 

No cancer treatment in past year, Participated in the small-group Chronic Disease Self-Management Program 

Usual care 

h = hours, NS = not specified, yr = year, GDM = Gestational Diabetes Mellitus, wk = week 
Evidence Table 20. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in diabetes (KQ1b) (continued)

G-188 

Reference List 

 1 Glasgow RE, Boles SM, McKay HG, Feil EG, Barrera M Jr. The D-Net diabetes self-management program: long-term implementa

generalization results. Prev Med 2003; 36(4):410-9. 

 2 Homko CJ, Santamore WP, Whiteman V et al. Use of an internet-based telemedicine system to manage underserved women with g

mellitus. Diabetes Technol Ther 2007; 9(3):297-306. 

 3 McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes network internet-based physical activity intervention: a rand

Diabetes Care 2001; 24(8):1328-34. 

 4 Richardson CR, Mehari KS, McIntyre LG et al. A randomized trial comparing structured and lifestyle goals in an internet-mediate

people with type 2 diabetes. Int J Behav Nutr Phys Act 2007; 4:59. 

 5 Wangberg SC. An Internet-based diabetes self-care intervention tailored to self-efficacy. Health Educ Res 2008; 23(1):170-9.

 6 Wise PH, Dowlatshahi DC, Farrant S. Effect of computer-based learning on diabetes knowledge and control. 1986; 9(5):504-8.  7 Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care 2006; 44
Evidence Table 21. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

G-189 

Author, year 

Control Interventions Age Race, n (%) Income 

Education, n(%) SES 

Gender n (%) 

MSt

Diabetes mellitus 

Basic information 

Glasgow, 20031

Tailored selfmanagement intervention 

Baseline characteristics not reported 

Usual care, Paper logbooks 

Mean, 29.2 SD, 6.7 

White nonHispanic, 6(24) Black nonHispanic, 12(48) Latino/Hispanic, 

4(16) 

API, 3(12) 

USD  <15,000, 10(40)  15,000-$34,999, 

3(12) 

 35,000-$54,999, 

3(12) 

 >55,000, 3(12)  missing, 6(24) 

< 8 yr, 2(8) 8-12 yr, 12(48) 12-16 yr, 10(40) 

NR 

Homko, 20072

Telemedicine (website to document glucose levels and to communicate with healthcare team) 

Mean, 29.8 SD, 6.6 

White nonHispanic, 8(25) Black nonHispanic, 14(44) Latino/Hispanic, 

7(22) 

API, 3(9) 

USD  <15,000, 8(25)  15,000-$34,999, 

8(25) 

 35,000-$54,999, 

3(9) 

 >55,000, 6(19)  missing, 7(22) 

< 8 yr, 4(12.5), 8-12 yr, 12(37.5) 12-16 yr,15(47) 

NR 

McKay, 20013

Internet information only 

Mean, 52.3 White nonHispanic, (82) NS 12-16 yr, (50 ) NR 
Evidence Table 21. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued)

G-190 

Author, year 

Control Interventions Age Race, n (%) Income 

Education, n(%) SES 

Gender n (%) 

MSt

 Internet-based physical activity intervention 

NS NS NS NS NR 

Employing lifestyle goals for overall steps recorded from the pedometer 

52 +- 12 W (76) B (18) O 

(6) 

<30K-18 30-70K-18 >70K-65 (percent) 

HS DIP/GED: 6 Some Coll:47 Coll Degree: 18 Grad Degree: 29 

Richardson NS M (29) , 2007 4

Diabetes 

Employing structured goals that emphasize PA using computerized feedback mechanisms 

53 +-9 W (77) B (8) O 

(15) 

<30K-8 30-70K-31 >70K-62 (percent) 

HS DIP/GED: 8 Some Coll:15 Coll Degree:46 Grad Degree:31 

NS M (38) 

Low selfefficacy Mean, 37.3 range, 

33.2

–41.4 

NS NS 8-12 yr, (11) NR F, (63) 

Wangberg, 20065

High selfefficacy Mean, 42.9 range, 

38.0

–47.9 

NS NS 8-12 yr, (8) NR F, (50) 

IDDM 42 +/- 16 NS NS NS Sex ratio 

varied from 0.42 to 0.60. The study does not specify any other detail 

Control Group (AGE +/- SE) 

Wise, 1986 6 Diabetes 

Assessment on 44 +/- 17 
Evidence Table 21. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued)

G-191 

Author, year 

Control Interventions Age Race, n (%) Income 

Education, n(%) SES 

Gender n (%) 

MSt

KAP KAP –Feedback – KAP 

45 +/- 16 

KAP –Interactive computer –KAP 

41 +/- 18 

NIDDM 55 +/- 21 NS NS NS Sex ratio 

varied from 0.42 to 0.60. The study does not specify any other detail 

Control Group (AGE +/- SE) Assessment on KAP 

57 +/- 23 

KAP –Feedback – KAP 

58 +/- 17 

KAP –Interactive computer –KAP 

56 +/- 16 

Diabetes, heart disease or chronic lung disease 

Usual care Mean, 57.6 

SD, 11.3 

White nonHispanic, (88.7) Lorig , NS NS NR F, (71.6) 20067

Online intervention 

Mean, 57.4 SD, 10.5 

White nonHispanic, (87.3) NS NS NR F, (71.2) 

F = female, M = male, NS = Not specified, NR = Not reported, SES = Socio economic status, API = Asian/Pacific Islander, mg/dl = milligrams/decilit

HbA1c = hemoglobin A1c, yr = year, USD = united states dollar 
Evidence Table 21. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued)

G-192 

Reference List 

 1 Glasgow RE, Boles SM, McKay HG, Feil EG, Barrera M Jr. The D-Net diabetes self-management program: long-term implementa

generalization results. Prev Med 2003; 36(4):410-9. 

 2 Homko CJ, Santamore WP, Whiteman V et al. Use of an internet-based telemedicine system to manage underserved women with g

mellitus. Diabetes Technol Ther 2007; 9(3):297-306. 

 3 McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes network internet-based physical activity intervention: a rand

Diabetes Care 2001; 24(8):1328-34. 

 4 Richardson CR, Mehari KS, McIntyre LG et al. A randomized trial comparing structured and lifestyle goals in an internet-mediate

people with type 2 diabetes. Int J Behav Nutr Phys Act 2007; 4:59. 

 5 Wangberg SC. An Internet-based diabetes self-care intervention tailored to self-efficacy. Health Educ Res 2008; 23(1):170-9. 6 Wise PH, Dowlatshahi DC, Farrant S. Effect of computer-based learning on diabetes knowledge and control. 1986; 9(5):504-8. 

 7 Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care 2006; 44
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes

G‐193

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

Diabetes mellitus 

Control Mean, 2.22 

SD, 0.45 

10 months: mean, 2.03 SD, 0.38 

Kristal total (Dietary behavior) 

Tailored selfmanagement intervention 

 Mean, 2.19 

SD, 0.46 

10 months: mean, 1.93 SD, 0.38 

Control Mean, 44.4 

SD, 33.8 

10 months mean, 29.8 SD, 14.3 

Estimated grams of daily fat (grams) 

Tailored selfmanagement Mean, 40.8 SD, 23.8 10 months mean, 27.9 

SD, 14.3 

Control Mean, 26.8 

 SD, 20.4 

10 months mean, 32.1 SD, 22.9 

Minutes activity per day (minutes/day) 

Tailored selfmanagement Mean, 33.4 SD, 25.4 10 months mean, 30.9 

SD, 23 

Control Mean, 66.68 

 SD, 20.66 

10 months mean, 79.97 SD, 14.81 

Minutes activity per day 

Tailored selfmanagement Mean, 63.32 SD, 19.69 10 months mean, 78.4 

SD, 14.81 

Control Mean, 7.43 

SD, 1.71 

10 months mean, 7.67 SD, 1.1 

Guidelines met (% guidelines met) 

Tailored selfmanagement mean, 1.53 10 months mean, 7.42 

SD, 1.1 

Control Mean, 5.18 

 SD, 1.44 

10 months: mean, 5.02  SD, 1.17 

Hemoglobin A1C 

Tailored selfmanagement Mean, 5.7 SD, 1.89 10 months: mean, 5.13 

 SD, 1.16 

Glasgow, 20031

Lipid ratio Control Mean, 17.9 

SD, 10.56 

10 months mean, 12.93 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐194

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

SD, 9.11 

Tailored selfmanagement Mean, 18 SD, 10.02 10 months mean, 13.72 

SD, 9.12 

Control Mean, 4.14 

SD, 1.32 

10 months mean, 4.96 SD, 1.12 

CES-D total 

Tailored selfmanagement Mean, 4.14 SD, 1.2 10 months mean, 4.97 

SD, 1.12 

Control 25 Score on DES 37 weeks gestation: 

mean, 4 SD, 0.5 

Self-efficacy (DES) 

Telemedicine 32 Score on DES 37 weeks gestation: 

mean, 4.4 SD, 0.5 

Control 25 Frequency of monitoring 

(sets of data reported) 

37 weeks gestation mean, 73.7 SD, 56.7 

System use (# of sets of information sent on telemedicine system) 

Telemedicine 28 37 weeks gestation 

mean, 94.8 SD, 60 

Control 25 FBS (mg/dl) 37 weeks gestation 

mean, 88.6 SD, 9.5 

FBS 

Telemedicine 32 37 weeks gestation 

mean, 90.8 SD, 11.8 

Control 25 A1c at delivery (%) 37 weeks gestation 

mean, 6.2 SD, 2.2 

Homko, 20072

A1c at time of delivery 

Telemedicine 32 37 weeks gestation 

mean, 6.1 SD, 0.8 

Control 33 Mean, 7.3 

SD, 6.2 

8 weeks mean, 18 SD, 17.3 

McKay, 20013

Moderate-to-vigorous exercise Unadjusted (minutes/day) 

Internet-based physical 

35 Mean, 5.6 

SD, 6.2 

8 weeks mean, 17.6 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐195

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

activity intervention 

SD, 15.3 

Control 33 Mean, 8.4 

SD, 8.4 

8 weeks mean, 16.8 SD, 22.8 

Walking Unadjusted (minutes/day) 

Internet-based physical activity intervention 

35 Mean, 6.4 

SD, 6.2 

8 weeks mean, 12.5 SD, 9.5 

Control 33 Mean, 17.6 

 SD, 10.4 

8 weeks mean, 19.9 SD, 14.2 

Depressive symptoms 

Internet-based physical activity intervention 

35 Mean, 16.9 

 SD, 11.6 

8 weeks mean, 14.9 SD, 12.5 

Total Step Employing 

lifestyle goals for overall steps recorded from the pedometer 

17 4,157 ± 1,737 stps 6,279 ± 3,306 Diff: 

2,122 ± 3,179 

 Employing 

structured goals that emphasize PA using computerized feedback mechanisms 

13 5,171 ± 1,769 6,868 ± 3,751 Diff: 

1,697 ± 3,564 

Richardson, 2007 4

Diabetes

Bout Steps Employing 

lifestyle goals for overall steps 

17 286 ± 599 2,070 ± 2,814 1,783 ± 

2,741 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐196

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

recorded from the pedometer 

 Employing 

lifestyle goals for overall steps recorded from the pedometer 

13 516 ± 801 (NS from above) 2,616 ± 2,706 (NS diff 

from above) 

2,101 ± 2,815 (NS Diff from above) 

Satisfaction Employing 

lifestyle goals for overall steps recorded from the pedometer 

17 100% 

 Employing 

lifestyle goals for overall steps recorded from the pedometer 

13 62% 

Usefulness Employing 

lifestyle goals for overall steps recorded from the pedometer 

17 71% 

 Employing 

lifestyle goals for overall steps 

13 31% 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐197

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

recorded from the pedometer 

Adherence (Likelihood of wearing a pedometer) 

Employing lifestyle goals for overall steps recorded from the pedometer 

17 (3) 

 Employing 

lifestyle goals for overall steps recorded from the pedometer 

13 (15) 

Adherence (Mean hours of wearing a pedometer) 

Employing lifestyle goals for overall steps recorded from the pedometer 

17 16.5h 

 Employing 

lifestyle goals for overall steps recorded from the pedometer 

13 14.5h 

Low selfefficacy 15 Mean, 29.47 SD, 9.49 1 month - analyzed: mean, 30.60 

SD, 8.92 

Wangberg, 20065

Summary of Diabetes Self Care Activities 

High self- 14 Mean, 27.64 1 month - analyzed: 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐198

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

efficacy SD, 8.55 mean, 32.07 

SD, 7.5 

Low selfefficacy 15 Mean, 52.20 SD, 13.19 1 month - analyzed mean, 49.73 

SD, 14.18 

Perceived competence scale 

High low selfefficacy 14 Mean, 52.07 SD, 10.66 1 month - analyzed mean, 49.93 

SD, 10.83 

IDDM Patients Knowledge Index (KAP Questionnaire) 4—6mo 

Assessment of KAP only 

24 Knowledge Score: 79 SE 2 82 SE 2 

 Assessment + 

Feedback 

22 78 SE 2 83 SE 3 

 Assessment + 

Interactive computer 

20 77 SE 2 83 SE 2 

NIDDM Patients Knowledge Index (KAP Questionnaire) 4—6mo 

Assessment of KAP only 

22 Knowledge UNS UNS 

 Assessment + 

Feedback 

24 64 SE 2 73 SE 2 

 Assessment + 

Interactive computer 

21 60 SE 3 70 SE 2 

Wise, 1986 6

Diabetes

IDDM Patients 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐199

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

Knowledge Index (KAP Questionnaire) 4—6mo 

Control 20 HBA1c: 8.9% 8.8% 

 Assessment of 

KAP only 

24 9.1 SE 0.2 8.4 SE 0.1 

 Assessment + 

Feedback 

22 9.3 SE 0.5 8.1 SE 0.4 

 Assessment + 

Interactive computer 

20 9.3 SE 0.2 8.6 SE 0.3 

NIDDM Patients Knowledge Index (KAP Questionnaire) 4—6mo 

Control 21 HBA1c: 8.7% 8.5% 

 Assessment of 

KAP only 

22 9.6 SE 0.4 8.8 SE 0.3 

 Assessment + 

Feedback 

24 9.2 SE 0.4 7.9 SE 0.4 

 Assessment + 

Interactive computer 

21 8.7 SE 0.7 7.9 SE 0.6 

Diabetes, heart disease or chronic lung disease

Control 426 CHANGE in score on 

Health Distress Scale 

12 months: mean, -0.193 SD, 1.07 

Lorig, 20067 Change in health 

distress (0-5) 

Online intervention 

354 CHANGE in score on 

Health Distress Scale 

12 months: mean, -0.377 

 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐200

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

SD, 1.11 

Control 426 0-5 scale 12 months 

mean, -0.068 SD, 0.645 

Change in self reported global health(0-5) 

Online intervention 

354 12 months 

mean, -0.102 SD, 0.768 

Control 426 1-7scale 12 months 

mean, -0.064 SD, 0.926 

Change in illness intrusiveness 

Online intervention 

354 12 months 

mean, -0.150 SD, 1.023 

Control 426 0-3 Scale 12 months 

mean, -0.142 SD, 0.32 

Change in disability 

Online intervention 

354 12 months 

mean, -0.166 SD, 0.345 

Control 426 0-10scale 12 months: 

mean, -0.358  SD, 2.09 

Change in fatigue 

Online intervention 

354 12 months: 

mean, -0.720  SD, 2.14 

Control 426 0-10 scale 12 months 

mean, -0.047 SD, 2.46 

Change in pain 

Online intervention 

354 12 months 

mean, -0.367 SD, 2.72 

Control 426 0-10 scale 12 months 

mean, -0.216 SD, 2.4 

Change in shortness of breath 

Online intervention 

354 12 months 

mean, -0.537 SD, 2.41 

Change in self-efficacy Control 426 1-10 Scale 12 months: 

mean, 0.200 
Evidence table 22. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in diabetes (continued)

G‐201

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at final time point 

ratios at time points 

SD, 1.82 

Online intervention 

354 1-10 Scale 12 months: 

mean, 0.406 SD, 1.98 

 FBS = fasting blood sugar, DES = Diabetes Empowerment Scale, BL = baseline, NS = not significant, mg/dl = milligrams/deciliter

Reference List 

 1 Glasgow RE, Boles SM, McKay HG, Feil EG, Barrera M Jr. The D-Net diabetes self-management program: long-term implementa

generalization results. Prev Med 2003; 36(4):410-9. 

 2 Homko CJ, Santamore WP, Whiteman V et al. Use of an internet-based telemedicine system to manage underserved women with g

mellitus. Diabetes Technol Ther 2007; 9(3):297-306. 

 3 McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes network internet-based physical activity intervention: a rand

Diabetes Care 2001; 24(8):1328-34. 

 4 Richardson CR, Mehari KS, McIntyre LG et al. A randomized trial comparing structured and lifestyle goals in an internet-mediate

people with type 2 diabetes. Int J Behav Nutr Phys Act 2007; 4:59. 

 5 Wangberg SC. An Internet-based diabetes self-care intervention tailored to self-efficacy. Health Educ Res 2008; 23(1):170-9.

 6 Wise PH, Dowlatshahi DC, Farrant S. Effect of computer-based learning on diabetes knowledge and control. 1986; 9(5):504-8. 

 7 Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care 2006; 44
Evidence table 23. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in mental health 

G-202 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Depression/anxiety Christensen, 20041

Individual interested in their own health care 

Interactive consumer website 

NS NS ≥18 yr, 

Internet access, 22 or higher on the Kessler psychological distress scale 

>52 yr, receiving clinical care from either a psychologist or psychiatrist 

Control 

Neil, 2009 2

Depressed/Anxious youth 

Interactive consumer website 

School – classroom / community 

2006-07 Adolescents 13 – 17 

yrs completing the YouthMood project 

NS Use of 

website (open access) in community 

Proudfoot, 20043

Individual interested in their own health care 

Computerized cognitive behavioral therapy 

Clinician office 

NS 18-75 yr, 

Depression, Anxiety and depression, Anxiety, Not currently receiving any form of psychological treatment or counseling, Score of 4 or more on the 12 item general health questionnaire, 12 or more on the computer version of the Clinical Interview Schedule-Revised 

Active suicidal ideas, Diagnosis of psychosis or organic mental disorder, alcohol and/or drug dependence, Medication for anxiety and/or depression continuously for 6 months or more immediately prior to entry, Unable to attend 8 sessions at the surgery, Unable to read or write English 

Treatment as usual 

Warmerdam,2008 4

Depressed / Anxious 

Interactive Consumer website 

Home / res 08-09/06 – 

01-02/07 

>18 yrs, Score of >=16 on CES-D, knew Dutch, internet and email access 

CES-D scores greater than 32 

Wait-listed controls 

Phobia 
Evidence table 23. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G-203 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Schneider, 20055

Individual interested in their own health care 

Web based tailored self help information 

Home/ residence, Remote location: preferred site of patient 

NS Fulfill ICD-10 criteria 

for agoraphobia with/without panic disorder, social phobia or specific phobia, 4≥ for global phobia, Main goal negotiated and set with clinician, phobia for more than one year, Men: alcohol <21 units/week, Women: alcohol <14 units/week, No reading disorder hindering net use 

Current psychotic illness, suicide plans, no severe depression, disabling cardiac or respiratory disease, On benzodiazepine or diazepam equivalent dose of >5 mg/day, began or changed dose or type of antidepressant within the last 4 weeks, Substance abuse, Failed past exposure therapy of >4 sessions 

 

Stress Chiauzzi, 20086

 Interactive 

consumer website, 

University 2005 ≥18 and ≤24 yr, 

college students, scoring above 14 on the 

 A control 

website (CW) 

Hasson, 20057

Individual interested in their own health care 

Personal monitoring device 

NS NS Employment at a 

company insured by Alecta (occupational pension plan company) 

those who quit employment prior to completion of study, "communication related problem" 

Access to web-based tool 

Stress Management Zetterqvist, For stress Interactive Home / res 04/2000 – No specified inclusion or exclusion criteria Control 
Evidence table 23. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G-204 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

2003 8 manageme nt for gen 

population 

consumer web 

06-07/2000 unless the participant expressed a 

condition that would prevent him / her from completing the study 

NS = not specified, Yr = year 

Reference List 

 1. Christensen H, Griffiths KM, Jorm AF. Delivering interventions for depression by using the internet: randomised controlled trial. B 2. Neil AL, Batterham P, Christensen H, Bennett K, Griffiths KM. Predictors of adherence by adolescents to a cognitive behavior the

community-based settings. J Med Internet Res 2009; 11(1):e6. 

 3. Proudfoot J, Ryden C, Everitt B et al. Clinical efficacy of computerised cognitive-behavioural therapy for anxiety and depression i

randomised controlled trial. Br J Psychiatry 2004; 185:46-54. 

 4. Warmerdam L, van Straten A, Twisk J, Riper H, Cuijpers P. Internet-based treatment for adults with depressive symptoms: random

Med Internet Res 2008; 10(4):e44. 

 5. Schneider AJ, Mataix-Cols D, Marks IM, Bachofen M. Internet-guided self-help with or without exposure therapy for phobic and p

Psychother Psychosom 2005; 74(3):154-64. 

 6. Chiauzzi E, Brevard J, Thurn C, Decembrele S, Lord S. MyStudentBody-Stress: an online stress management intervention for colle

Commun 2008; 13(6):555-72. 

 7. Hasson D, Anderberg UM, Theorell T, Arnetz BB. Psychophysiological effects of a web-based stress management system: a prosp

controlled intervention study of IT and media workers. BMC Public Health 2005; 5:78. 

 8. Zetterqvist K, Maanmies J, Str+¦m L, Andersson G. Randomized controlled trial of internet-based stress management. 2003; 32(3)
Evidence Table 24. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

G-205 

Author, year 

Control Interventions Age 

Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) 

Depression/anxiety

Usual care Mean, 

43.4 SD, 13.7 

Bangladeshi, 1(1) Black Caribbean, 4(4) Indian, 3(3) Pakistani, 1(1) White,100(87) 

 0-10 yr, 17(14) 

11-12 yr, 28(23) 13-15 yr, 30(25)  >15 yr, 46(38) 

 M, 32(25) 

F, 96(75) 

Single, 33(26) Married, 54(43) Cohabiting, 11(9) Separated, 7(6) Divorced,15(12) Widowed, 5(4) 

Proudfoot, 20041

Internet therapy 

Mean, 43.6 SD, 14.3 

Black African, 1(1) Black Caribbean, 2(2) Black other, 3(2) White, 120(90) 

 <5 yr, 1(1) 

11-12 yr, 34(24) 13-15 yr, 31(22) >15 yr, 58(41) 

 M, 40(27) 

F,106(73) 

Single, 35(25) Married, 60(43) Cohabiting, 16(11) Separated, 4(3) Divorced,18(13) Widowed, 8(6) 

Control Mean, 

36.29 SD, 9.3 

Mean, 14.4 SD, 2.3 

F,124(70) M, 54(30) 

Married Cohabiting, 100(56) Divorced/ separated, 24(14) Never married, 53(36) 

Mood gym Mean, 

35.85 SD, 9.5 

Mean, 14.6 SD, 2.4 

F,136(75) M, 46(25) 

Married/ cohabiting, 98(54) Divorced/ separated, 26(14) Never married, 57(31) 

Christensen, 20042

Blue Pages Mean, 

37.25 SD, 9.4 

Mean, 15 SD, 2.4 

 F,115(69) 

M, 50(31) 

Married/ cohabiting, 100(61) Divorced/ separated, 24(15) Never Married, 
Evidence Table 24. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

G-206 

Author, year 

Control Interventions Age 

Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) 53(30) 

Use of website (open access) in community 

NS 5223/720

7 F 72% 

 

Neil, 2009 3

Use of website (open access) in classroom 

13 – 17 yrs 

 597/1000 

F (59.7) 

 

Wait-listed controls (87) 

44.1 NS Paid 

Jobs w/: 49 (58.3) 

Lower: 9 (10.3) Middle: 28 

(32.2) 

Higher: 50 

(57.5) 

NR 69 (79.3) 

Interactive computer tool based on Cog. Beh. Theory (88) 

45.7 43 (52.4) Lower: 9 (10.2) 

Middle: 26 

(29.5) 

Higher: 53 

(60.2) 

61 (69.3) 

Warmerdam,2008 4

Interactive computer tool based on Prob. Sol. Theory (88) 

45.1 43 (50.6) Lower: 5 (5.7) 

Middle: 18 

(20.5) 

Higher: 65 

(73.9) 

57 (64.8) 

PhobiaSchneider, Control NS NS NS NS NR 20055 Computer 

aided cognitive behavior therapy with self-help exposure 

NS NS NS NS NR 

StressChiauzzi, 20086

A control website(CW), 

Range, 18-24 

White nonHispanic, 48 black nonHispanic, 12 

NS Yr in School(n), 

First, 29 Second, 18 Third, 19 

NR M, 40 

F, 43 
Evidence Table 24. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

G-207 

Author, year 

Control Interventions Age 

Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) 

Latino/Hispanic, 9 API,(16) Other 7 

Fourth,17 

MyStudent Body–Stress website 

Range, 18-24 

White nonHispanic, 44 Black nonHispanic, 13 Latino/Hispanic, 5 API, 14 Other, 7 

NS Yr in School(n), 

First, 30 Second, 16 Third, 19 Fourth, 13 

NR M, 34 

F, 44 

NTX Range, 

18-24 

White nonHispanic, 50 Black nonHispanic, 7 Latino/Hispanic, 8 API, 13 Other, 8 

NS Yr in school (n), 

First, 23 Second, 19 Third, 12 Fourth, 24 

NR M, 42 

F, 36 

Access to web-based tool including monitoring tool for stress and health; diary connected to monitoring tool, and scientific info on stress and health 

NS NS USD 

 <25,000, 39(22)  25,000-40,000, 106(61)  >40,000, 27(16) 

8-12 yr, 89(51) 12-16yr, 83(48) 

NR M, 

112(64) F, 62(36) 

Married, 134(77Single, 38(22) 

Hasson, 20057

 NS NS USD 

 <25,000, 24(18)  25,000-40,000, 76(59)  >40,000, 27(21) 

8-12yr, 54(42) 12-16yr,73(57) 

NR M, 75(58) 

F, 54(42) 

Married, 102(79Single, 25(19) 

Stress ManagementZetterqvist, Control Group 38.7 (26— NS Work Student: 5 (12) NS M: 14/40 
Evidence Table 24. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

G-208 

Author, year 

Control Interventions Age 

Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) 

60) hours per 

week: 36.3 (0—60) 

Work: 33 (83) Unemployed: 2 

(5) 

(35%) 

2003 8

Self Help for stress management via internet 

40.0 (24—56) 

 Mean: 

29.6 (0—60) 

Student: 5 (22) Work: 15 (65) Unemployed:3 

(13) 

 M: 10/23 

(43%) 

 

NR = Not reported, M = male, F = female, AIAN = American Indian/Alaska Native, API = American/Pacific Islander, SD = standard deviation, 

SES= Socioeconomic Status, USD = United States Dollar 

Reference List 

 1. Proudfoot J, Ryden C, Everitt B et al. Clinical efficacy of computerised cognitive-behavioural therapy for anxiety and depression i

randomised controlled trial. Br J Psychiatry 2004; 185:46-54. 

 2. Christensen H, Griffiths KM, Jorm AF. Delivering interventions for depression by using the internet: randomised controlled trial. B 3. Neil AL, Batterham P, Christensen H, Bennett K, Griffiths KM. Predictors of adherence by adolescents to a cognitive behavior the

community-based settings. J Med Internet Res 2009; 11(1):e6. 

 4. Warmerdam L, van Straten A, Twisk J, Riper H, Cuijpers P. Internet-based treatment for adults with depressive symptoms: random

Med Internet Res 2008; 10(4):e44. 

 5. Schneider AJ, Mataix-Cols D, Marks IM, Bachofen M. Internet-guided self-help with or without exposure therapy for phobic and p

Psychother Psychosom 2005; 74(3):154-64. 
Evidence Table 24. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

G-209 

 6. Chiauzzi E, Brevard J, Thurn C, Decembrele S, Lord S. MyStudentBody-Stress: an online stress management intervention for colle

Commun 2008; 13(6):555-72. 

 7. Hasson D, Anderberg UM, Theorell T, Arnetz BB. Psychophysiological effects of a web-based stress management system: a prosp

controlled intervention study of IT and media workers. BMC Public Health 2005; 5:78. 

 8. Zetterqvist K, Maanmies J, Str+¦m L, Andersson G. Randomized controlled trial of internet-based stress management. 2003; 32(3)
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health 

G‐210

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Depression/anxiety 

 Control 109 BDI 6 Month: 

mean,16.2 SD, 10.1 

Depression (BDI) 

Computerized Therapy 

112 BDI 6 Month: 

mean, 11.6 SD, 9.6 

 Control 110 BAI 6 Month 

mean, 12.8 SD, 9.1 

Anxiety (BAI) 

Computerized Therapy 

115 BAI 6 Month 

mean, 10.6 SD, 8.4 

 Control 110 6 Month 

mean, 13.4 SD, 8.6 

Work and Social Adjustment scale computerized 

therapy 

115 6 Month 

mean, 10 SD, 7.8 

 Control 106 6 Month 

mean, 84.1 SD, 13.6 

ASQ,CoNeg 

Computerized therapy 

106 6 Month 

mean, 73.7 SD, 15.3 

 Control 106 6 Month: 

mean, 82.8  SD, 12.5 

Proudfoot, 20041

ASQ,CoPos 

Computerized therapy 

108 6 Month: 

mean, 87.6 SD, 13.5 

Use of website (open access) in community 

Neil, 2009 7207 5.46 SD 2.42 2 Depression 

score (Pretest) 

Use of website (open 

1000 2.62 SD 2.42 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐211

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

access) in classroom Use of website (open access) in community 

Anxiety (Pre- 7207 5.50 SD 2.59 test) 

Use of website (open access) in classroom 

1000 2.51 SD 2.44 

Use of website (open access) in community 

Warpy 7207 3.16 SD 0.71 thoughts score 

Use of website (open access) in classroom 

1000 2.58 SD 0.65 

Use of website (open access) in community 

No. of 7207 3.10 SD 3.85 exercises completed (0—28) 

Use of website (open access) in classroom 

1000 9.38 SD 6.84 

Warmerda, Depression Wait-listed 87 32.1 (9.3) 25.6 (9.9) 25.2 (9.9) 25.8 (10.4) 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐212

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

controls (87) Interactive computer tool based on Cog. Beh. Theory (88) 

88 31.2 (9.3) 22.9 (10.6) 19.4 

(11.3) 

 17.9 (11.7) 

(CES – D) 

Interactive computer tool based on Prob. Sol. Theory (88) 

88 31.9 (9.3) 20.6 (11.2) 20.6 

(11.3) 

 18.4 (12.1) 

Wait-listed controls (87) 

87 11.3 (3.6) 8.9 (3.9) 9.0 (3.8) 8.9 (4.0) 

Interactive computer tool based on Cog. Beh. Theory (88) 

88 10.6 (3.6) 7.8 (4.1) 6.7 (4.4) 6.6 (4.5) 

Anxiety using HADS 

Interactive computer tool based on Prob. Sol. Theory (88) 

88 10.2 (3.6) 7.1 (4.3) 6.9 (4.4) 6.6 (4.7) 

Wait-listed controls (87) 

87 0.59 (0.18) 0.69 (0.27) 0.65 

(0.27) 

 0.66 (0.27) 

2008 3

QoL using EQ5D 

Interactive computer tool based on 

88 0.64 (0.18) 0.68 (0.27) 0.73 

(0.27) 

 0.76 (0.27) 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐213

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Cog. Beh. Theory (88) Interactive computer tool based on Prob. Sol. Theory (88) 

88 0.59 (0.18) 0.73 (0.27) 0.73 

(0.27) 

 0.76 (0.27) 

Wait-listed controls (87) 

87 E: 0 (0.0) 

O: 10 (14.1) 

E:0 (0.0) O: 15 (21.1) 

 E: 0 (0.0) 

O: 9 (14.3) 

Interactive computer tool based on Cog. Beh. Theory (88) 

88 E: 0 (0.0) 

O: 11 (18.0) 

E: 26 (29.5) O: 21 (41.2) 

 E: 34 (38.6) 

O: 18 (39.1) 

Depression (CES – D) Proportion reaching clinically significant change 

Interactive computer tool based on Prob. Sol. Theory (88) 

88 E: 18 (20.5) 

O: 19 (36.5) 

E: 18 (20.5) O: 20 (39.2) 

 E: 30 (34.1) 

O: 17 (40.5) 

 Control 159 Mean score 

point improvement over baseline mean, 21.6 SD, 11.1 

 6 weeks: 

mean, 1.1 SD, 8.4 

Christensen, 20044

Center for Epidemiologic depression scale 

Blue Pages: Computer based psycho education 

136 Mean score 

point improvement over baseline 

 6 weeks: 

mean, 3.9 SD, 9.1 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐214

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

website offering information about depression 

mean, 21.1 SD, 10.4 

Mood GYM: Computer based Cognitive Behavior therapy 

136 Mean score 

point improvement over baseline mean, 21.8 SD, 10.5 

 6 weeks: 

mean, 4.2 SD, 9.1 

 Control 159 Mean score 

point improvement over baseline 

 6 weeks 

mean, 3.1 SD, 15.8 

Blue Pages: Computer based psycho education website offering information about depression 

136 Mean score 

point improvement over baseline 

 6 weeks 

mean, 6.4 SD, 18.1 

Automatic thoughts 

Mood GYM: Computer based Cognitive Behavior therapy 

136 Mean score 

point improvement over baseline 

 6 weeks 

mean, 9.3 SD, 16.9 

 Control 159 Mean score 

point improvement over baseline 

 6 weeks 

mean, -0.1 SD, 0.5 

Medical literacy 

Blue Pages: Computer based psycho education website 

136 Mean score 

point improvement over baseline 

 6 weeks 

mean, -0.6 SD, 0.7 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐215

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Offering information about depression Mood GYM: Computer based cognitive behavior therapy 

136 Mean score 

point improvement over baseline 

 6 weeks 

mean, -0.1 SD, 0.5 

Control 159 Mean score 

point improvement over baseline 

 6 weeks 

mean, -0 SD, 0.9 

Blue Pages: Computer based psycho education website offering information about depression 

136 6 weeks 

mean, -0.7 SD, 1.1 

Psychological literacy 

Mood GYM: Computer based cognitive behavior therapy 

136 6 weeks 

mean, -0.5 SD, 1 

Control 159 Mean score 

point improvement over baseline 

 6 weeks: 

mean, 0.1  SD, 1.6 

Lifestyle literacy 

Blue Pages: Computer based psycho education website offering 

136 Mean score 

point improvement over baseline 

 6 weeks: 

mean, -1.1  SD, 2 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐216

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

information about Depression Mood GYM: Computer based cognitive behavior therapy 

136 Mean score 

point improvement over baseline 

 6 weeks: 

mean, -0  SD, 0.5 

Control 159 Mean score 

point improvement over baseline 

 6 weeks 

mean, 0.1 SD, 1.6 

Blue Pages: Computer based psycho education website offering information about depression 

136 Mean score 

point improvement over baseline 

 6 weeks 

mean, -1.1 SD, 2 

Cognitive behavior therapy literacy 

Mood GYM: Computer based cognitive behavior therapy 

136 Mean score 

point improvement over baseline 

 6 weeks 

mean, -2 SD, 2.4 

Phobia 

Control 13 Mean, 7.2 

SD, 1.4 

Week 10 mean, 4.9 SD, 2 

 Week 14: 

mean, 4.9 SD, 1.7 

Schneider, 20055

Main problem(selfrating) 

Computer aided cognitive behavior therapy with self-help exposure 

31 Mean, 7 

SD, 1.2 

Week 10 mean, 4.7 SD, 2 

 Week 14: 

mean, 4.1 SD, 2.1 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐217

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Control 13 Mean, 7.3 

SD, 1.6 

Week 10 mean, 4.8 SD, 2 

 Week 14 

mean, 5 SD, 1.9 

Main goal(selfrating) 

Computer aided cognitive behavior therapy with self-help exposure 

31 Mean, 7 

SD, 1.2 

Week 10 Mean, 4.5 SD, 2.4 

 Week 14 

mean, 4.2 SD, 2.2 

Stress 

Control 78 7 months MyStudent Body–Stress website 

77 7 months 

Chiauzzi, 20086

Perceived Stress Scale 

No treatment control (NTX) 

80 7 months 

Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up: mean, SD, 

Self rated stress management 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up: mean, SD, 

Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Hasson, 20057

Self rated sleep quality 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers 

 6 month 

follow-up 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐218

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

covariate for baseline scores 

Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Self rated mental energy 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Self rated concentration ability 

Web-based stress management system 

121 6 month 

follow-up 

Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Self rated social support 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Biological marker: dehydroeoiand

Control 156 Changes in self 

rated measures and biological 

 6 month 

follow-up 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐219

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

markers covariate for baseline scores 

osterone sulphate 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

 Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Nero peptide 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

 Control 156 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Chromogranin 

Web-based stress management system 

121 Changes in self 

rated measures and biological markers covariate for baseline scores 

 6 month 

follow-up 

Stress 

Perceived Stress Scale 

Control 40 M 33.17 SD 

3.76 

 M 28.88 SD 

7.02 

Zetterqvist, 2003 8

 Interactive 23 32.91 SD 6.08 24.48 SD 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐220

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

self help stress management program 

7.17 

Hospital Anxiety and Depression Scale HADS 

Control 40 23.23 SD 5.85 18.70 

SD7.64 

 Interactive 

self help stress management program 

23 23.61 SD 5.96 14.13 SD 

7.09 

Anxiety Control 40 13.85 SD 4.12 11.10 SD 

5.05 

 Interactive 

self help stress management program 

23 13.43 SD 4.00 8.39 SD 

4.50 

Depression Control 40 9.38 SD 3.07 7.60 SD 

3.13 

 Interactive 

self help stress management program 

23 10.17 SD 2.90 5.74 SD 

3.14 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐221

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

LE (Life Events) (Holmes and Rahes Scale) 

Control 40 1.55 SD 1.22 1.60 SD 

1.28 

 Interactive 

self help stress management program 

23 1.52 SD 1.38 1.48 SD 

1.38 

Perceived Social Support PS-family 

Control 40 9.80 SD 3.45 9.62 SD 

3.62 

 Interactive 

self help stress management program 

23 8.48 SD 3.46 8.61 SD 

3.63 

Perceived Social Support PS-friends 

Control 40 9.40 SD 3.26 9.82 SD 

3.99 

 Interactive 

self help stress management program 

23 9.78 SD 3.66 10.09 SD 

4.01 

BL = baseline, SD = standard deviation, BDI = Beck Depression Inventory, BAI = Beck Anxiety Inventory, ASQ C0Neg/CoPos = Attribution Style Q

index for negative/positive situations 
Evidence Table 25. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in mental health (continued)

G‐222

Reference List 

 1. Proudfoot J, Ryden C, Everitt B et al. Clinical efficacy of computerised cognitive-behavioural therapy for anxiety and depression i

randomised controlled trial. Br J Psychiatry 2004; 185:46-54. 

 2. Neil AL, Batterham P, Christensen H, Bennett K, Griffiths KM. Predictors of adherence by adolescents to a cognitive behavior the

community-based settings. J Med Internet Res 2009; 11(1):e6. 

 3. Warmerdam L, van Straten A, Twisk J, Riper H, Cuijpers P. Internet-based treatment for adults with depressive symptoms: random

Med Internet Res 2008; 10(4):e44. 

 4. Christensen H, Griffiths KM, Jorm AF. Delivering interventions for depression by using the internet: randomised controlled trial. B 5. Schneider AJ, Mataix-Cols D, Marks IM, Bachofen M. Internet-guided self-help with or without exposure therapy for phobic and p

Psychother Psychosom 2005; 74(3):154-64. 

 6. Chiauzzi E, Brevard J, Thurn C, Decembrele S, Lord S. MyStudentBody-Stress: an online stress management intervention for colle

Commun 2008; 13(6):555-72. 

 7. Hasson D, Anderberg UM, Theorell T, Arnetz BB. Psychophysiological effects of a web-based stress management system: a prosp

controlled intervention study of IT and media workers. BMC Public Health 2005; 5:78. 

 8. Zetterqvist K, Maanmies J, Str+¦m L, Andersson G. Randomized controlled trial of internet-based stress management. 2003; 32(3)
Evidence Table 26. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in asthma and COPD (KQ1b)

G-223 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control In

Asthma Jan , 20071

Individuals interested in their own health care Caregiver, childhood asthma 

Personal monitoring device 

Home/ residence 

2004/ January to December 

6 - 12 yr, Caregivers have Internet access, Persistent asthma diagnosis (GINA clinical practice guidelines) 

Diagnosed with bronchopulmonary dysplasia, Diagnosed with other chronic co morbid conditions that could affect quality of life 

Verbal information and booklet for asthma education with written asthma diary. 

BlAsAn bareexraSysuinfan suteon foanth

Joseph, 20072

Individuals interested in their own health care 

Interactive consumer website 

Remote location: school 

NS 9-11 grade, 

Current asthma 

 Generic 

asthma website 

Ta

Krishna, 20033

Individuals interested in their own health Caregiver: Parents/ caregivers 

Personal monitoring device 

Home/ residence 

1999/ NS <18 yr, 

Confirmed asthma 

Cystic fibrosis, Bronchopulmonary dysplasia, Other chronic lung disease 

Traditional care 

Inintmasedpr

COPD Nguyen, 20084

Individuals interested in their own health care 

Interactive consumer website 

Academic medical centers 

2005 Diagnosis of COPD 

and being clinically stable for at least 1 month, Spirometry results showing at least mild obstructive 

Any active symptomatic illness, Participated in a pulmonary rehabilitation program in the last 12 months, 

Face-to-face (fDSMP) 

In(e
Evidence Table 26. Description of RCTs addressing the impact of CHI applications on intermediate outcomes in asthma and COPD (KQ1b) (

G-224 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control In

disease, ADL limited by dyspnea, Use of the Internet and/or checking email at least once per week with a windows operating system, Oxygen saturation > 85% on room air or ¡Ü 6 L/min of nasal oxygen at the end of a 6-minute walk test 

Were currently participating in > 2 days of supervised maintenance exercise 

NS = not specified, yr = year, PEFR = Peak expiratory flow rate, COPD = Chronic obstructive pulmonary disease, ADL = Activities of daily living, eD

based dyspnea self-management programs, fDSMP = face-to-face dyspnea self-management programs, min = minutes 

Reference List 

 1. Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 2. Joseph CL, Peterson E, Havstad S et al. A web-based, tailored asthma management program for urban African-American high scho

Crit Care Med 2007; 175(9):888-95. Notes: CORPORATE NAME: Asthma in Adolescents Research Team 

 3. Krishna S, Francisco BD, Balas EA, Konig P, Graff GR, Madsen RW. Internet-enabled interactive multimedia asthma education p

trial. Pediatrics 2003; 111(3):503-10. Notes: CORPORATE NAME: Randomized trial 

 4. Nguyen HQ, Donesky-Cuenco D, Wolpin S et al. Randomized controlled trial of an internet-based versus face-to-face dyspnea sel

for patients with chronic obstructive pulmonary disease: pilot study. J Med Internet Res 2008; 10(2):e9.
Evidence Table 27. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

G-225 

Author, year 

Control Interventions Age Race, n (%) Income Education, n (%) SES 

Gendern (%) 

Asthma 

Verbal information and booklet for asthma education with written asthma diary 

Mean, 9.9 SD, 3.2 

Education of primary caregiver: HS diploma or below, 43 (56.6) College or above, 33 (43.4) 

M, 28(3F, 48(63

Jan, 20071

Participants received asthma education and with interactive asthma monitoring system 

Mean, 10.9 SD, 2.5 

NS NS 

Education of primary caregiver: HS diploma or below, 58(66.0) College or above, 30 (34.0) 

NR 

M, 35(3F, 53(60

Generic asthma website 

Joseph, 2007*2

Tailored website 

Mean, 15.3 SD, 1 

NS USD 

mean, 12,049 SD, 2,442 

NS NR F, 199 (

Traditional care 

Range, 0-17 yr 

White nonHispanic, 102(84.3) Black nonHispanic, 9(7.4) AIAN, 7(5.8) Other, 3 

Preschool/none: 58 (47.9) Kindergarten: 6(5) Elementary: 27(22.3) Jr High 24 (19.8) High School 6 (5) 

M, 76 (6F, 45 (3

Krishna, 2003†3

Internetenabled interactive multimedia asthma education program 

Range, 0-17 yr 

White nonHispanic, 93(86.9) Black nonHispanic, 10(9.3) AIAN, 2(1.9) Other, 2(1.9) 

NS 

Preschool/none: 48 (44.9) Kindergarten: 12(11.2) Elementary: 23 (21.5) Jr High 19 (17.6) High school 5 (4.1) 

NR 

M, 72 (6F, 35 (3

COPD Nguyen, 20084

Face-to-face (fDSMP), 

Mean, 70.9 SD, 8.6 

White nonHispanic, 20(100) NS 12-16 yr, 8(40) >16yr, 12(60) Not currently employed or 

currently disabled or 

F, 9 (45
Evidence Table 27. Description of consumer characteristics in studies addressing the impact of CHI applications on intermediate outcomes in 

(continued) 

G-226 

Author, year 

Control Interventions Age Race, n (%) Income Education, n (%) SES 

Gendern (%) 

retired: 15 (75) 

eDSMP Mean, 68 

SD, 8.3 

White nonHispanic, 18 (95) 12-16 yr, 10(50) >16yr, 9(50) Not currently employed or 

currently disabled or retired: 13 (72) 

F, 8(39) 

* Consumer characteristics were not stratified by intervention † Education of caregiver was not reported NS = not specified, SES = Socioeconomic Status, F = female, M = male, AIAN = American Indian/Alaska Native, Yr = year, SD = standard deviation

USD = United States dollar, eDSMP = Internet-based dyspnea self-management programs, fDSMP = face to face dyspnea self management programs 

Reference List 

 1. Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 2. Joseph CL, Peterson E, Havstad S et al. A web-based, tailored asthma management program for urban African-American high scho

Crit Care Med 2007; 175(9):888-95. 

 3. Krishna S, Francisco BD, Balas EA, Konig P, Graff GR, Madsen RW. Internet-enabled interactive multimedia asthma education p

trial. Pediatrics 2003; 111(3):503-10. 

 4. Nguyen HQ, Donesky-Cuenco D, Wolpin S et al. Randomized controlled trial of an internet-based versus face-to-face dyspnea sel

for patients with chronic obstructive pulmonary disease: pilot study. J Med Internet Res 2008; 10(2):e9. 
Evidence Table 28. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes of asthma and COPD (KQ1b)

G-227 

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at final time point 

ratitimpoi

Asthma 

Monitoring adherence Control 71 85.6 12 week, 93.5 (peak flow meter technique score (%)) 

Education and interactive asthma monitoring system 

82 83.5 12 week, 99.7 

Control 71 Mean, 21 

SD, 4.5 

12 weeks, mean, 15 SD, 5.3 

Monitoring adherence (asthma diary entries per month) 

Education and interactive asthma monitoring system 

82 Mean, 27 

SD, 3.2 

12 weeks, mean, 23 SD, 4.3 

Monitoring adherence Control 71 93.2 12 weeks, 53.4 (adherence to asthma diary (%)) 

Education and interactive asthma monitoring system 

82 96.0 12 weeks, 82.5 

Therapeutic adherence Control 71 80.3 12 week, 93.1 (DPI or MDI plus spacer technique score (%)) 

Education and interactive asthma monitoring system 

82 82.1 12 week, 96.5 

 Control 71 82.3 12 week, 42.1 

Jan , 20071

Therapeutic adherence (adherence to inhaled corticosteroid, (%)) 

Education and interactive asthma monitoring system 

82 83.5 

NA 

12 week, 63.2 

NA 

Control 143 12 months, n(%) 

18 (12.6) 

Controller medication adherence: positive behavior change 

Puff City internet intervention 

152 12 months, n(%) 

31 (20.4) 

Control 143 12 months, n(%) 

91 (63.6) 

Controller medication adherence: no change in negative behavior 

Puff City internet intervention 

152 12 months, n(%) 

95 (62.5) 

Control 143 12 months, n(%) 

34 (23.8) 

Controller medication adherence: negative change in behavior Puff City internet 

intervention 

152 12 months, n(%) 

26 (17.1) 

Control 143 12 months, n(%) 

46 (32.2) 

Rescue inhaler availability: positive behavior change Puff City internet 

intervention 

152 12 months, n(%) 

59 (38.8) 

Joseph , 20072

Rescue inhaler availability: 

Control 143 

 NR NA 

12 months, n (%) 62 (43.3) 

NA 
Evidence Table 28. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes of asthma and COPD (KQ1b) (c

G-228 

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at final time point 

ratitimpoi

no change in negative behavior 

Puff City internet intervention 

152 12 months, n (%) 

74 (48.7) 

Control 143 12 months, n (%) 

35 (24.5) 

Rescue inhaler availability: negative change in behavior 

Puff City internet intervention 

152 12 months, n (%) 

19 (12.5) 

 Control 23 Mean, 48.41 

SD, 6.64 

12 months mean, 52.3 SD, 5.55 

Asthma Knowledge score (caregivers of children 0-6yr) 

Interactive asthma education 

24 Mean, 47.94 

SD, 5.24 

12 months mean, 55.68 SD, 4.28 

 Control 28 Mean, 49.57 

SD, 4.75 

12 months mean, 55.38 SD, 4.16 

Asthma knowledge score (caregivers of children 7-17yr) 

Interactive asthma education 

26 Mean, 49.95 

SD, 5.59 

12 months mean, 55.68 SD, 4.28 

 Control 28 Mean, 43.44 

SD, 4.75 

12 months mean, 47.51 SD, 5.95 

Asthma knowledge score (children 7-17yr) 

Interactive asthma education 

25 Mean, 49.95 

SD, 6.10 

12 months mean, 53.12 SD, 5.56 

 Control 23 mean, 2.52 

SD, 6.71 median, 5 95% CI, -0.38 to 5.42 

Change in knowledge(caregivers of children 0-6yr) 

Interactive asthma education 

24 mean, 7.97 

SD, 4.57 median, 7 95% CI, 5 to 11 

 Control 28 mean, 2.38 

SD, 4.38 median, 2.55 95% CI, 0 to 4 

Krishna, 20033

Change in knowledge (caregivers of children 7-17yr) 

Interactive asthma education 

26 

NA 

mean, 4.62 SD, 4.48 median, 3 95% CI, 2 to 7 

 NA 
Evidence Table 28. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes of asthma and COPD (KQ1b) (c

G-229 

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at final time point 

ratitimpoi

 Control 27 mean, 4.44 

SD, 5.49 median, 4 95% CI, 2 to 7 

Change in knowledge (children 7-17yr) 

Interactive asthma education 

25 mean, 10 

SD, 6.99 median, 8 95% CI, 7 to 11 

COPD 

Face-to-face dyspnea self-management program 

20 Mean, 12.5 

SD, 2.3 

3 months mean,13.3 SD, 1.6 

6 months mean,13.8 SD, 1.5 

Dyspnea knowledge score (range 0-15) 

Internet-based dyspnea selfmanagement program 

19 Mean, 12.6 

SD, 1.8 

3 months mean, 13.8 SD,1.0 

6 months mean, 14.1 SD, 1.0 

Face-to-face dyspnea self-management program 

20 Mean, 4.6 

SD, 2.4 

3 months mean, 5.5 SD,3.3 

6 months mean, 5.0 SD, 3.6 

Nguyen, 20084

Self-efficacy score for managing dyspnea (range 0-10) 

Internet-based dyspnea selfmanagement program 

19 Mean, 4.7 

SD, 2.3 

3 months mean, 6.8 SD,2.3 

6 months mean, 6.7 SD, 2.6 

NA 

NS = not specified, NA = not applicable, yr = year, SD = standard deviation, BL = baseline, NR = not reported 

Reference List 

 1. Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 2. Joseph CL, Peterson E, Havstad S et al. A web-based, tailored asthma management program for urban African-American high scho

Crit Care Med 2007; 175(9):888-95. 

 3. Krishna S, Francisco BD, Balas EA, Konig P, Graff GR, Madsen RW. Internet-enabled interactive multimedia asthma education p

trial. Pediatrics 2003; 111(3):503-10. 

 4. Nguyen HQ, Donesky-Cuenco D, Wolpin S et al. Randomized controlled trial of an internet-based versus face-to-face dyspnea sel

for patients with chronic obstructive pulmonary disease: pilot study. J Med Internet Res 2008; 10(2):e9.
Evidence table 29. Description of RCTs addressing the impact of CHI applications on intermediate outcomes on miscellaneous topic

G-230 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

CVD Kukafka, 20021

Individuals interested in their own health care 

Interactive consumer website 

NS NS Unspecified AMI risk 

criteria 

 

Simkins, 19862

Individuals interested in their own health care 

Electronic medication reminder 

Primary care or specialty clinics at an university health care 

Duration, 3 months 

64-67yr Group1 

Arthritis Lorig, 20083

Individuals interested in their own health care 

Interactive consumer website

NS 2004/NS 18 and older, 

a diagnosis of OA, rheumatoid arthritis (RA), or fibromyalgia, could have other chronic conditions Internet and email access agreed to 1–2 hours per week of log-on time spread over at least 3 sessions/week for 6 weeks 

Active treatment for cancer for 1 year, participated in the small-group ASMP or the Chronic Disease Self-Management Program 

Usual care 

Back pain Buhrman,20044

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS 18-65 years old, 

Internet access, been in contact with a physician, have back pain, have chronic pain (>3 months) 

Suffer of pain that can increase as a consequence of activity, wheelchair bound, have planned any surgical treatment, suffer from heart 

Wait-list 
Evidence table 29. Description of RCTs addressing the impact of CHI applications on intermediate outcomes on miscellaneous topic (continue

G-231 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

and vascular disease 

Behavioral risk factors Oenema, 20085

Individuals interested in their own health care 

Personalized health risk assessment tool 

NS 2004/ NS 30 years or older, 

Dutch adults, Internet skills, sufficient understanding of the Dutch language 

Insufficient understanding of the Dutch language, poor Internet skills 

Control group 

Breast cervical prostate and laryngeal cancer Jones, 19996

Individuals interested in their own health care 

Interactive consumer website 

Clinician office 

1996/ NS Breast, laryngeal, 

prostate, cervical cancer patients receiving care at oncology center, 

Receiving palliative treatment, no knowledge of diagnosis, visual or mental handicap, severe pain 

Booklet information 

Cervical cancer Campbell, 19977

Individuals interested in their own health care 

Personalized health risk assessment tool 

Clinician office 

1995/ NS Between 18 and 70 

years, can speak and read English well enough to use computer 

Survey without computer generated printed feed back 

Cancer, Prostate Forsch, 20088

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

2005 >50 yr, 

Men 

 Control 

Caregiver decision making Brennan, 19959 Caregivers of persons 

with Alzheimer's Disease 

Interactive consumer website 

Home/ residence 

NS Primary 

responsibility as a family caregiver for a person with Alzheimer's disease living at home, has a local 

 Comparison 

group 
Evidence table 29. Description of RCTs addressing the impact of CHI applications on intermediate outcomes on miscellaneous topic (continue

G-232 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

telephone exchange, the ability to read and write English 

Change in health behavior Harari, 200810

Individuals interested in their own health care 

Personalized health risk assessment tool 

NS 2001/ NS 65 and older Nursing home 

resident, needing help in basic activities of daily living, dementia, terminal disease, non-English speaking 

Usual care control group 

 Paperny, 199011

Adolescent with high risk behavior 

Personalized health risk assessment tool 

Clinician office 

Duration 3 years 

Voluntary participation, both male and female, Teen agers 

Participants unwillingness 

Group Q: 251 participants those who has given a written questionnaire before physical exam and printout shared with the clinician 

Headache Devineni, 200512

Individuals interested in their own health care 

Personal monitoring device 

Home/ residence 

Chronic tension or migraine HA for at least one year 

New headache onset within the past year, head injury or major illness in temporal proximity 

Delayed 
Evidence table 29. Description of RCTs addressing the impact of CHI applications on intermediate outcomes on miscellaneous topic (continue

G-233 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

to headache onset, Secondary headache diagnosis, Concurrent chronic pain disorder other than primary migraine or tension headache 

HIV/AIDS FlatleyBrennan, 199813

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS HIV infected, 

ability to read and type English, home telephone line 

 Received 

brochure 

Menopause HRT Rostom, 200214

Individuals interested in their own health care 

Computerized decision aid 

Home/ residence 

NS 40-70, 

women, pre and post menopausal, fully fluent in spoken and written English, no evidence of cognitive impairment or psychiatric illness 

 Audio 

booklet 

Schapira, 200715

Individuals interested in their own health care 

Personalized health risk assessment tool 

Clinician office 

May 2002-Oct 2003 

45-74 yr, female, post menopausal, VA clinic patient 

Non English speaking, MMSE < 23 

Printed pamphlet 

Preventing falls in the elderly Yardley, 200716

Individuals interested in their own health care 

Interactive consumer website, 

NS July-Dec 

2005 

<65 yr, used site more than once 

Use of contraception Chewning, 19917

Individuals interested in their own 

Computer based decision Aid 

Clinician office 

NS < 20 years, 

Female, ability to read and 

 Standard 

information 
Evidence table 29. Description of RCTs addressing the impact of CHI applications on intermediate outcomes on miscellaneous topic (continue

G-234 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

health care understand English, 

expressed interest in getting a contraceptive 

NS = Not specified, OA = Osteoarthritis, RA = rheumatoid arthritis, CHESS = Comprehensive Health Enhancement Support System, Yr = year 

Reference List 

 1 Kukafka R, Lussier YA, Eng P, Patel VL, Cimino JJ. Web-based tailoring and its effect on self-efficacy: results from the MI-HEA

trial. Proc AMIA Symp 2002; 410-4. 

 2 Simkins CV, Wenzloff NJ. Evaluation of a computerized reminder system in the enhancement of patient medication refill complian

Pharm 1986; 20(10):799-802. 

 3 Lorig KR, Ritter PL, Laurent DD, Plant K. The internet-based arthritis self-management program: a one-year randomized trial for 

fibromyalgia. Arthritis Rheum 2008; 59(7):1009-17. 

 4 Buhrman M, Faltenhag S, Strom L, Andersson G. Controlled trial of Internet-based treatment with telephone support for chronic b

111(3):368-77. 

 5 Oenema A, Brug J, Dijkstra A, de Weerdt I, de Vries H. Efficacy and use of an internet-delivered computer-tailored lifestyle interv

fat intake, physical activity and smoking cessation: a randomized controlled trial. Ann Behav Med 2008; 35(2):125-35. 

 6 Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 1999 7 Campbell E, Peterkin D, Abbott R, Rogers J. Encouraging underscreened women to have cervical cancer screening: the effectivene

Prev Med 1997; 26(6):801-7. 

 8 Frosch DL, Bhatnagar V, Tally S, Hamori CJ, Kaplan RM. Internet patient decision support: a randomized controlled trial compari

for men considering prostate cancer screening. Arch Intern Med 2008; 168(4):363-9. 

 9 Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. 

44(3):166-72. 

10 Harari D, Iliffe S, Kharicha K et al. Promotion of health in older people: a randomised controlled trial of health risk appraisal in Br

Ageing 2008; 37(5):565-71. 
Evidence table 29. Description of RCTs addressing the impact of CHI applications on intermediate outcomes on miscellaneous topic (continue

G-235 

11 Paperny DM, Aono JY, Lehman RM, Hammar SL, Risser J. Computer-assisted detection and intervention in adolescent high-risk h

116(3):456-62. 

12 Devineni T, Blanchard EB. A randomized controlled trial of an internet-based treatment for chronic headache. Behav Res Ther 200

13 Flatley-Brennan P. Computer network home care demonstration: a randomized trial in persons living with AIDS. Comput Biol Me14 Rostom A, O'Connor A, Tugwell P, Wells G. A randomized trial of a computerized versus an audio-booklet decision aid for wome

menopausal hormone replacement therapy. Patient Educ Couns 2002; 46(1):67-74. 

15 Schapira MM, Gilligan MA, McAuliffe T, Garmon G, Carnes M, Nattinger AB. Decision-making at menopause: a randomized con

computer-based hormone therapy decision-aid. Patient Educ Couns 2007; 67(1-2):100-7. 

16 Yardley L, Nyman SR. Internet provision of tailored advice on falls prevention activities for older people: a randomized controlled 

Promot Int 2007; 22(2):122-8. 

17 Chewning B, Mosena P, Wilson D et al. Evaluation of a computerized contraceptive decision aid for adolescent patients. Patient E

38(3):227-39. 
Evidence Table 3. Description of consumer characteristics in RCTs addressing the impact of CHI applications on health care processes (KQ1

G-17 

Author, Year 

Control Intervention Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Asthma 

Control n=63 usual care 

Range, 6-17 

Hispanic,28(44.1) African American,32(50.8) White ,3(4.8) Other, 

NS Parent education: 

None, 2(3.2) Elementary, 15(23.8) High school, 28(44.4) College, 18(27.0) 

NS Male, 

44(69.8) Female, 19(30.2) 

Bartholomew, 2000 1

Intervention n,70 computer intervention (watch, discover, think and act) 

Range,6-17 

Hispanic,33(47.1) African American,34(48.6) White ,2(2.9) Other,1(1.4) 

NS None,3(4.3) 

Elementary, 20(29.0) High school, 34(49.3) College, 12(17.3) 

NS Male, 

42(60.0) Female, 28(40.0) 
Evidence Table 3. Description of consumer characteristics in RCTs addressing the impact of CHI applications on health care processes (KQ1

G-18 

Author, Year 

Control Intervention Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Guendelman, 20022

Control, 68 participants used an asthma diary. 

12.2 (2.9) 

Black, 50 (74) White,8 (12) Others,10 (15) 

NS NS NS Male, 37 (54 
Evidence Table 3. Description of consumer characteristics in RCTs addressing the impact of CHI applications on health care processes (KQ1

G-19 

Author, Year 

Control Intervention Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Intervention,66 Health Buddy(is a personal and interactive communication device) 

12.0 (2.3 Black, 52 (79) 

White,5 (8) Others,9 (14) 

NS NS NS Male, 40 

(61) 

Verbal information and booklet for asthma education with written asthma diary 

Mean, 9.9 SD, 3.2 

Education of primary 

caregiver 

HS diploma or below, 

43(56.6) 

College or above, 33(43.4) 

M, 28(36.8) F, 48(63.2) 

Jan, 20073

 Intervention Mean, 

10.9 SD, 2.5 

NR NR 

Education of primary caregiver HS diploma or below, 58(66.0) College or above, 30(34.0) 

NR 

M, 35(39.7) F, 53(60.2) 
Evidence Table 3. Description of consumer characteristics in RCTs addressing the impact of CHI applications on health care processes (KQ1

G-20 

Author, Year 

Control Intervention Age Race, n(%) Income Education, n(%) SES 

Gender, n(%) 

Traditional care Range, 

0-17 

White non-Hispanic, 

102(84.3) 

Black non-Hispanic, 

9(7.4) 

AIAN, 7(5.8) Not specified, 3 

NR Preschool/none, 

58(47.9) Kindergarten, 6(5) Elementary, 27(22.3) Jr High, 24(19.8)  High School, 6(5) 

NR M, 76(62.8) 

F, 45(37.2) 

Krishna, 20034

Internet-enabled interactive Multimedia asthma education program 

Range, 0-17 

White non-Hispanic, 

93(86.9) 

Black non-Hispanic, 

10(9.3) 

AIAN, 2(1.9) Not specified, 2(1.9) 

NR Preschool/none, 

48(44.9) Kindergarten, 

12(11.2) 

Elementary, 23(21.5) Jr High, 19(17.6) High school, 5(4.1) 

NR M, 72(67.3) 

F, 35(32.7) 

Use of contraception 

Standard information 

Chewning, F(100) 19995

Computerized decision aid 

NR NR NR NR NR 

F(100) 

NR= Not Reported, SD= Standard Deviation, SES= Socioeconomic Status, Yr= year, CBT= Cognitive Behavioral Therapy, WL= Wait List, AIAN= A

Native, M = Male, F = Female 

Reference List 

 1. Bartholomew LK, Gold RS, Parcel GS et al. Watch, Discover, Think, and Act: Evaluation of computer-assisted instruction to impr

management in inner-city children. 2000; 39(2-3):269-80. 

 2. Guendelman S, Meade K, Benson M, Chen YQ, Samuels S. Improving asthma outcomes and self-management behaviors of inner-

randomized trial of the Health Buddy interactive device and an asthma diary. 2002; 156(2):114-20. 

 3. Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 4. Krishna S, Francisco BD, Balas EA, Konig P, Graff GR, Madsen RW. Internet-enabled interactive multimedia asthma education p

trial. Pediatrics 2003; 111(3):503-10. 

 5. Chewning B, Mosena P, Wilson D et al. Evaluation of a computerized contraceptive decision aid for adolescent patients. Patient E

38(3):227-39.
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

G-236 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

CVD 

Control NS NS NS NS NR Ntailored Webbased NS NS NS NS NR N

Kukafka, 20021

Non-tailored Web-based 

NS NS NS NS NR N

Group1 Mean, 64 NS NS NS NR C

mm

Group 2 Mean, 66 NS NS NS NR C

pm

Simkins, 19862

Group 3 Mean, 67 NS NS NS NR C

mm

Arthritis 

Usual care Mean, 

52.5 range, 22–89 SD, 12.2 

White nonHispanic, 425(93.7) 

NS Mean, 15.7 

SD, 3.11 

NR F, 

425(90.5) 

Married: 425(71.1) 

HvmS

Lorig, 20083

Online intervention 

Mean, 52.2 SD, 10.9 

White nonHispanic, 441(90.9) 

NS Mean, 15.6 

SD, 3.09 

NR F, 

441(89.8) 

Married: 441(65.5) 

HvmS

Back pain Buhrman, 20044

Wait-list Mean, 45 

SD, 10.7 

NS NS <8 yr, 7(24.1) 

8-12 yr, 6(21) 12-14 yr, 2 (6.9) 14-16 yr, 14 (48.3) 

NR M, 

11 (37.9) F, 18 (62.1) 

 S

YNPBBaPPCNP
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-237 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

P

Internet-based pain management program 

Mean, 43.5 SD, 10.3 

NS NS <8 yr, 2 (9.1) 

8-12 yr, 6 (27) 12-14 yr, 3 (13.6) 14-16 yr, 11 (50) 

NR M, 

8 (36.4) F, 14 (63.6) 

 S

Y5 N1PB7 B1PP1C8 N4 P3 P1 

Behavioral risk factors 

Control group Mean, 

44.1 SD, 10.4 

NS NS Educational 

level: High 453 (42) Medium 324 (30) Low 302 (28) 

NR M, 

507 (47) F, 572 (53) 

Oenema, 20085

Internet group Mean, 

43.1 SD, 10.4 

NS NS Educational 

level: High 432 (40) Medium 3 67 (34) Low 281 (26) 

NR M, 

497 (46) F, 583 (54) 

Breast cervical prostate and laryngeal cancer

Booklet information NS NS NS NS NS NS NS N

Jones, 19996

NS NS NS NS NS NS NS N

Cervical cancer 
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-238 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

Survey without computer generated printed feed back 

 < 50 yr, (78) 

Australian born, (94) 

NS 8-12 yr,(55) NR Married or 

living with partner, (71) 

FN

Campbell, 19977

 intervention NS NS NS NS NR N

Cancer, Prostate 

Control Mean, 59 

SD, 5.1 

White nonHispanic, 133(880) Black nonHispanic , 4(2.6) Latino/Hispanic, 6(4) API, 6(4) Not specified, 2 (1.3) 

NS High school or 

less 6(4) Some college44(29.1) College 42(27.8) Some graduate school 10(6.6) 

NR NS Married 

123(81.5) Other 28(18.5) 

Ihw

Traditional decision aid 

Mean, 58.5 SD, 5.5 

White nonHispanic, 133(85.8) Black nonHispanic, 6(3.9) Latino/ Hispanic, 7(4.5) API, 4(2.6) Not specified, 5 (3.2), 

NS High school or 

less 8(5.2) Some college 39(25.2) College44(28.4) Some graduate school13(8.4) 

NR NS Married 

119(76.8) Other 36(23.2) 

Ihw

Forsch, 20088

Chronic disease trajectory model 

Mean, 58.4 median, range, SD, 5.6 

White nonHispanic, 127(83) Black nonHispanic, 2(1.3) Latino/ 

NS High school or 

less 6(3.9) Some college40(26.1) College 35(22.9) Some graduate 

NR NS Married 

114(74.5) Other 39(25.5) 

Ihw
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-239 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

Hispanic, 15(9.8) API, 7(4.6) 

school 12(7.8) 

Caregiver decision making 

Comparison group 

Mean, 64 White nonHispanic, 

(72) 

Brennan, NS 12-16 yr, (86) NR F,(67) 19959

 Experimental NS NS NS NS NR N

Change in Health behavior 

Usual care control group 

Mean, 74.2 SD, 6 

NS NS NS NR F, 

564(52.9) 

 F

h(I1d

Harari, 200810

HRA-O intervention group 

Mean, 74.7 SD, 6.3 

NS NS NS NR F, 

526(56.0) 

 F

h(I1d

Paperny, 199011

Control: Group Q: 251 participants those who has given a written questionnaire before physical exam and printout shared with the clinician 

mean,15.1 SD, 1.46 

White,(33) Hawaiian, (12) Oriental, (32) Pacific/mixture, (12) Other (11) 

Financial assistance (10) 

NR NR M, 

131(52) 

 N

 Intervention 

Group (1): 265 participants those who was given computer 

mean,14.9 SD, 1.44 

White (33) Hawaiian (13) Oriental (30) Pacific/mixture (13) Other (11) 

(10) NR M, 

154(58) 
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-240 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

questionnaire after the physical exam and printout remain private Group (2): 294 participants those who was given computer questionnaire before the physical exam and printout shared with clinician 

mean, 15.0 SD, 1.37 

White (34) Hawaiian (14) Oriental (32) Pacific/mixture, (11) Other, (9) 

(11) NR NR M, 176 

(60) 

 N

Headache 

Delayed Mean, 

43.6 SD, 11.8 

NS NS NS NR M,10 (21) 

F, 37 (79) 

 H

MSMMSYMS

Devineni, 200512

Treatment Mean, 

43.6 SD, 12 

NS NS NS NR M,5 (12) 

F,34 (88) 

 H

MSMMSYMS

HIV/AIDS FlatleyBrennan, 199813

Received brochure 

Mean, 34 SD, 10.8 

White nonHispanic, (58) 

NS Mean, 14 

SD, 2.7 

NR Living Alone: 

mean, 27 
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-241 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

Received computer intervention 

Mean, 33 SD, 7.3 

White nonHispanic, (64) 

NS Mean, 13 

SD, 2.6 

NR Living Alone: 

mean, 29 

Gustafson, Control NR NR NR NR NR NR H199414 CHESS NR NR NR NR NR NR HMenopause HRT 

Audio booklet Mean, 

53.8 SD, 8.13 

NS NS 8-12 yr,7 (26.9) 

12-16y r, 19(73.1 ) 

NR C

1M7Cd6S1

Rostom, 200215

Interactive computerized DA 

Mean, 50.6 SD, 7.67 

NS NS 8-12yr, 6 (24) 

12-16 yr, 19 (76 ) 

NR C

1M1Cd8 (

Schapira, 200716

Printed pamphlet 

Mean, 57.8 SD, 7.5 

White nonHispanic, 64 (73) Black nonHispanic, 22(25)  AIAN,2 (2) 

USD  <19,999, 25 (28)  20,000-34,999, 32 (36)  35,000-49999, 17 (19)  50,000-74,999, 

NS NR N
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-242 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

11 (13)  75,000+, 3 (3) 

Computerbased decision aid 

Mean, 57.8 SD, 7.2 

White nonHispanic, 64 (72) Black nonHispanic, 24 (27)  AIAN, 1(1) 

USD  <19,999, 31 (35)  20,000-34,999, 22 (25)  35,000-49999, 19 (21)  50,000-74,999, 11 (12)  75,000+,  6 (7) 

NS NR N

Preventing falls in the elderly 

 Control NS NS NS NS NR M, 42 (31) 

F, 94 (69) 

 S

gqh(hmuptt

Yardley, 200717

Tailored NS NS NS NS NR M, 54 (37) 

F, 90 (63) 

 S

gqh(h(uptt
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-243 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Marital Status, n(%) O

Use of contraception 

Standard information 

Chewning, NS NS NS NS NR N19918

Computerized decision 

NS NS NS NS NR N

NR= Not Reported, NS= Not Significant SD= Standard Deviation, SES= Socioeconomic Status, Yr= year, API = Asian, Pacific Islander, AIAN = American Indian / Alaska Native, CVD = Cardiovascular Disease, F = female, M = Male 

Reference List 

 1 Kukafka R, Lussier YA, Eng P, Patel VL, Cimino JJ. Web-based tailoring and its effect on self-efficacy: results from the MI-HEA

trial. Proc AMIA Symp 2002; 410-4. 

 2 Simkins CV, Wenzloff NJ. Evaluation of a computerized reminder system in the enhancement of patient medication refill complian

Pharm 1986; 20(10):799-802. 

 3 Lorig KR, Ritter PL, Laurent DD, Plant K. The internet-based arthritis self-management program: a one-year randomized trial for 

fibromyalgia. Arthritis Rheum 2008; 59(7):1009-17. 

 4 Buhrman M, Faltenhag S, Strom L, Andersson G. Controlled trial of Internet-based treatment with telephone support for chronic b

111(3):368-77. 

 5 Oenema A, Brug J, Dijkstra A, de Weerdt I, de Vries H. Efficacy and use of an internet-delivered computer-tailored lifestyle interv

fat intake, physical activity and smoking cessation: a randomized controlled trial. Ann Behav Med 2008; 35(2):125-35. 

 6 Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 1999

 7 Campbell E, Peterkin D, Abbott R, Rogers J. Encouraging underscreened women to have cervical cancer screening: the effectivene

Prev Med 1997; 26(6):801-7. 

 8 Frosch DL, Bhatnagar V, Tally S, Hamori CJ, Kaplan RM. Internet patient decision support: a randomized controlled trial compari

for men considering prostate cancer screening. Arch Intern Med 2008; 168(4):363-9. 

 9 Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. 

44(3):166-72. 
Evidence Table 30. Description of consumer characteristics studies addressing the impact of CHI applications on intermediate outcomes in m

(continued) 

G-244 

10 Harari D, Iliffe S, Kharicha K et al. Promotion of health in older people: a randomised controlled trial of health risk appraisal in Br

Ageing 2008; 37(5):565-71. 

11 Paperny DM, Aono JY, Lehman RM, Hammar SL, Risser J. Computer-assisted detection and intervention in adolescent high-risk h

116(3):456-62. 

12 Devineni T, Blanchard EB. A randomized controlled trial of an internet-based treatment for chronic headache. Behav Res Ther 200

13 Flatley-Brennan P. Computer network home care demonstration: a randomized trial in persons living with AIDS. Comput Biol Me14 Gustafson DH, Hawkins RP, Boberg EW, Bricker E, Pingree S, Chan CL. The use and impact of a computer-based support system 

AIDS and HIV infection. 1994; 604-8. 

15 Rostom A, O'Connor A, Tugwell P, Wells G. A randomized trial of a computerized versus an audio-booklet decision aid for wome

menopausal hormone replacement therapy. Patient Educ Couns 2002; 46(1):67-74. 

16 Schapira MM, Gilligan MA, McAuliffe T, Garmon G, Carnes M, Nattinger AB. Decision-making at menopause: a randomized con

computer-based hormone therapy decision-aid. Patient Educ Couns 2007; 67(1-2):100-7. 

17 Yardley L, Nyman SR. Internet provision of tailored advice on falls prevention activities for older people: a randomized controlled 

Promot Int 2007; 22(2):122-8. 

18 Chewning B, Mosena P, Wilson D et al. Evaluation of a computerized contraceptive decision aid for adolescent patients. Patient E

38(3):227-39. 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐245

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

CVD 

Control 17 Self-efficacy 

scores 

 

Non-Tailored 13 Self-efficacy 

scores 

Symptoms 

Tailored 17 Self-efficacy 

scores 

 

Control 32 Self-efficacy 

scores 

1 month 3 month 

Non-tailored 31 self-efficacy 

scores 

1 month 3 month 

Action 

Tailored 31 self-efficacy 

scores 

1 month 3 month 

Control Self-efficacy 

scores 

1 month 3 month 

Non-Tailored Self-efficacy 

scores 

1 month 3 month 

Kukafka, 20021

Cognitive 

Tailored Self-efficacy 

scores 

1 month 3 month 

Control 104 Compliant 

months 

 Month 3: 

mean, 0.58 SD, 0.5 

Simkins, 19862

Medication refill compliance 

Group 2 received 

101 Compliant 

months 

 Month 3: 

mean, 0.65 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐246

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

reminder by postcard 

SD, 0.52 

Group 3 received reminder by calling 

69 Compliant 

months 

 Month 3 

mean, 0.64 SD, 0.46 

 Control 104 non-compliant 

months 

Month 1 Month 3 

mean, 0.43 SD, 0.5 

Group 2 received reminder by postcard 

101 Non-compliant 

months 

Month 1 Month 3 

mean, 0.35 SD, 0.52 

Medication refill noncompliance 

Group 3 received reminder by calling 

69 Non-compliant 

months 

Month 1 Month 3 

mean, 0.36 SD, 0.46 

Arthritis 

Control 344 Mean, 2.37 

SD, 1.19 

6months 1year: 

mean, 2.25 SD, 1.19 

Health distress 

Online intervention 

307 Mean, 2.41 

SD, 1.2 

6months 1year: 

mean, 2 SD, 1.18 

Control 344 Mean, 3.22 

SD, 0.903 

6months 1year 

mean, 3.29 SD, 0.885 

Activity limitation 

Online intervention 

307 Mean, 3.17 

SD, 0.973 

6months 1year 

mean, 3.09 SD, 0.962 

Control 344 Mean, 0.569 

 SD, 0.446 

6months 1year 

mean, 0.573 SD, 0.457 

Self reported global health 

Online intervention 

307 Mean, 0.547 

 SD, 0.401 

6months 1year 

mean, 0.514 SD, 0.445 

Control 344 Mean, 6.37 

SD, 2.22 

6months 1year 

mean, 6.1 SD, 2.35 

Lorig, 20083

Pain 

Online 307 6months 1year 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐247

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

intervention mean, 5.77 

SD, 2.53 

 Control 344 Mean, 4.96 

 SD, 1.98 

6months 1year: 

mean, 5.34  SD, 2.06 

Self efficacy 

Online intervention 

307 Mean, 5.08 

 SD, 2.13 

6months 1year: 

mean, 5.89  SD, 2.09 

Back pain 

 Control 29 Mean, 12.3 

SD, 7.4 

 2 months: 

mean, 11.9 SD, 6.9 

CSQDiverting attention 

Cognitive behavior intervention 

22 Mean, 11.6 

SD, 5.7 

 2 months: 

mean, 12.3 SD, 5.2 

 Control 29 Mean, 5.4 

SD, 6.5 

 2 months 

mean, 4.6 SD, 5.9 

CSQReinterpret pain sensations Cognitive 

behavior intervention 

22 Mean, 3.6 

SD, 3.5 

 2 months 

mean, 4.4 SD, 3.6 

Control Mean, 18.3 

 SD, 6.6 

 2 months 

mean, 17.3 SD, 6.7 

CSQ-Coping self-statement 

Cognitive behavior intervention 

 Mean, 18.4 

 SD, 6.5 

 2 months 

mean, 19.1 SD, 5.8 

Control 29 Mean, 13.5 

SD, 6.6 

 2 months 

mean, 12.9 SD, 6.5 

CSQ-Ignore pain sensations 

Cognitive behavior intervention 

22 2 months 

mean, 13.7 SD, 7 

Control 29 Mean, 10.4 

 SD, 6.7 

 2 months: 

mean, 8.5  SD, 6 

Buhrman,20044

CSQ-Praying or hoping 

Cognitive behavior intervention 

22 Mean, 12 

 SD, 6.9 

 2 months: 

mean, 9.8  SD, 5.1 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐248

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Control 29 Mean, 13.7 

SD, 6.9 

 2 months 

mean, 12.3 SD, 7.2 

CSQCatastrophizing 

Cognitive behavior intervention 

22 Mean, 13.6 

SD, 7.7 

 2 months 

mean, 8.6 SD, 5.2 

 Control 22 Mean, 17.3 

SD, 6.1 

 2 months 

mean, 16.9 SD, 6.3 

CSQIncrease activity level 

Cognitive behavior intervention 

22 Mean, 14.4 

SD, 5 

 2 months 

mean, 14.8 SD, 5.6 

Control 29 Mean, 2.9 

SD, 1.1 

 2 months: 

mean, 2.9 SD, 1 

CSQ-Control over pain 

Cognitive behavior intervention 

22 Mean, 2.8 

SD, 1 

 2 months: 

mean, 3.9 SD, 0.7 

Behavioral risk factors 

Control 930 Mean, -0.16 

SD, 0.82 

 one month: 

mean, -0.19 SD, 0.82 

Self-rated saturated fat intake 

Internet group 887 Mean, -0.19 

SD, 0.78 

 One month: 

mean, -0.18 SD, 0.79 

Control 890 Mean, -0.29 

SD, 0.92 

 One month 

mean, -0.3 SD, 0.93 

Oenema, 20085

Self rated PA level 

Internet group 827 Mean, -0.31 

SD, 0.91 

 One month 

mean, -0.29 SD, 0.85 

Breast cervical prostate and laryngeal cancer 

Booklet (control) 

Satisfaction 150 58(40) Score >2 (n (%)) Personal 

computer information 

156 68(46) 

Jones, 19996

Satisfaction Score 

Booklet (control) 

150 32 to 48 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐249

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

>2(95% CI of percentage 

Personal computer information 

156 38 to 54 

Booklet (control) 

Prefer 150 12/122(10) computer to 10 minute consultation with professional 

Personal computer information 

156 38/131(29) 

Cervical cancer 

Control 32 6 months 

(24.6), 95% CI 

Pap smear within 6 months in women who were under screened by Path report 18-49 years 

Experimental 56 6 months 

(37.8), 95% CI 

 

Pap smear Control 44 6 months within 6 months in women who were under screened by Self report 18-49 years 

Intervention 52 6 months 

Pap smear Control 41 6 months within 6 months in women who were under screened by Path report 50-70 years 

Intervention 38 6 months  Control 21 6 months 

Campbell, 19977

Pap smear within 6 Intervention 22 6 months 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐250

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

months in women who were under screened by Self report 50-70 years 

Cancer, Prostate 

Traditional decision aid 

155 Posttest: 

mean, 8.65 SD, 0.18 

Chronic disease trajectory model 

153 Posttest: 

mean, 8.03 SD, 0.18 

Forsch, 20088

Total knowledge scores(completer cases only), mean (SE) 

Combined 152 Posttest: 

mean, 8.03 SD, 0.18 

Caregiver decision making 

 Control 49 Likert scale, 14 

items, 5 choices mean, 54.65 SD, 7.3 

 12 months: 

mean, 54.7 SD, 6.1 

Decision confidence 

Experimental 47 Likert scale 14 

items, 5 choices mean, 51.9 SD, 6 

 12 months: 

mean, 56.8 SD, 7 

 

 Control 49 Number of 

alternatives caregiver considers to solve a problem: mean, 2.51 SD, 0.91 

 12 months 

mean, 2.37 SD, 78 

Brennan, 19959

Improved decision making skill 

Experimental 47 Number of 

alternatives 

 12 months 

mean, 2.4 

 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐251

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

caregiver considers to solve a problem: mean, 2.53 SD, 0.78 

SD, 0.61 

 Control 49 Score on 

Instrumental and Expressive Support Scale (IESS) mean, 62.7  SD, 15.5 

 12 months 

mean, 62.6 SD, 16 

Isolation 

Experimental 47 Mean, 63.4 

 SD, 16.6 

 12 months 

mean, 65 SD, 17.4 

 

Change in health behavior 

Control 1066 12 

months:(84) 

Self-reported health behavior HRA-O 

intervention group 

940 12 months 

(76) 

 Control 1066 12 months 

(84) 

Harari, 200810

Preventative care uptake 

HRA-O intervention group 

940 12 months 

(76) 

High cigarette Control 10 use 

Intervention group 1 

25 

Paperny, 199011

 Intervention 

group 2 

25 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐252

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Control 8 Intervention group 1 

19 

Frequent marijuana use (weekly) 

Intervention group 2 

22 

Control 13 Intervention group 1 

28 

High alcohol use (weekly) 

Intervention group 2 

28 

Control 24 Intervention group 1 

70 

Problems at home with parents ,family 

Intervention group 2 

72 

Control 34 Intervention group 1 

69 

Often sad, upset or unhappy 

Intervention group 2 

66 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐253

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Control 45 Intervention group 1 

67 

Feeling sad or down lately 

Intervention group 2 

63 

Control 27 Intervention group 1 

74 

Would like contraceptive information 

Intervention group 2 

66 

Control 56 Intervention group 1 

82 

Has a lover now 

Intervention group 2 

82 

Control 56 Intervention group 1 

75 

Had sexual intercourse 

Intervention group 2 

75 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐254

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Control 50 Intervention group 1 

61 

Taking medications 

Intervention group 2 

62 

Headache

 Control 47 Mean, 35.5 

SD, 20.9 

 2 months 

mean, 31.7 SD, 22.4 

Headache symptom questionnaire 

 Intervention 39 Mean, 33.8 

SD, 19.3 

 2 months 

mean, 20.3 SD, 15.9 

 Control 39 Mean, 54.2 

SD, 20.5 

 2 months 

mean, 49.6 SD, 23.1 

Headache disability inventory 

 Intervention 39 Mean, 52.9 

SD, 18.8 

 2 months 

mean, 38 SD, 19.5 

CES-D (depression scale) 

 47 Mean, 13.9 

 SD, 9.5 

 2 months 

mean, 14.3 SD, 12.1 

 Control 39 

Mean, 25.6 SD, 15.9 

 2 months 

mean, 20.8 SD, 17.2 

Devineni, 200512

Trait-anxiety scale 

 Intervention 39 2 months 

mean, 18.4 SD, 15.7 

HIV/AIDS 

Control 26 Mean score 

mean, 52.8 SD, 6 

 Postintervention: 

mean, 56.47 SD, 4.2 

 

FlatleyBrennan, 199813

Improved decision making confidence 

Computer link 31 Mean score 

mean, 54.35 SD, 5.9 

 Postintervention: 

mean, 51.45 SD, 6.9 

 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐255

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Control 26 Mean score: 

mean, 4.73 SD, 1.4 

 Postintervention 

mean, 5.47 SD, 1.3 

 

Improved decision making skill 

Computer link 31 Mean Score: 

mean, 4.58 SD, 5.4 

 Postintervention 

mean, 5.4 SD, 1.5 

 

Control 26 Mean score 

mean, 67.05  SD, 17 

 Postintervention 

mean, 68 SD, 16.8 

 

Reduced social isolation 

Computer Link 31 Mean score 

mean, 63.5  SD, 14.4 

 Postintervention 

mean, 66.08 SD, 13.68 

 

 Control 26 Mean score 

mean, 13.8 SD, 4.93 

 Postintervention 

mean, 13.65 SD, 1.3 

 

Differential decline in health status 

Computer link 31 Postintervention 

mean, 13 SD, 1.7 

RR OR timpo0.0

 Average Control 28 (65) Quality of life (%) 

CHESS 30 (68) Control 97 Cost went 

up $457 

Gustafson, 1994 14

Hospital cost ($/person/Month) CHESS 107 Cost went 

down $148 

Menopause HRT Rostom, Realistic 

expectations 

Control 26 Final score: 

difference in 

 Difference in 

posttest 

 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐256

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Mean scores; baseline and point 4 score: mean score mean, 37.2 SD, 25.5 

scorebaseline score: mean, 52.7 SD, 37.5 

Computerized decision aid 

25 Final score: 

difference in Mean scores; baseline and point 4 score: mean score mean, 32 SD, 30.4 

 Difference in 

posttest scorebaseline score: mean, 52.7 SD, 37.5 

 

 Control 26 Final score: 

difference in Mean scores; baseline and point 4 score: mean score: mean, 78.7 SD, 16.7 

 Postinterventi

on questionnaire mean, 87.1 SD, 11.8 

Difference in posttest scorebaseline score mean, 8.4 SD, 13.3 

 

200215

Knowledge 

Computerized decision aid 

25 Final score: 

difference in Mean scores; baseline and point 4 score: mean score: mean, 76.4 SD, 14.9 

 Postinterventi

on questionnaire mean, 93.8 SD, 9 

Difference in posttest scorebaseline score mean, 17.5 SD, 13.4 

 

 Control 86 3 months 

mean, 15.5 ; median, ; range, 14.9, 16.0; 

Schapira, 200716 Menopause- related 

knowledge and healthrisk expectations Computerbased decision 

aid 

85 3 months 

mean, 15.1 ; median, ; range, 14.5, 15.7;, 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐257

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

 Control 85 3 months 

prior decision 

 3 months 

mean, 4.37 median, range, 4.26, 4.48 

Satisfaction with decision 

Computerbased decision aid 

85 3 months 

prior decision 

 3 months 

mean, 4.37 median, range, 4.26, 4.47 

Control 85 3 months 

prior decision 

 3 months 

mean, 1.78 median, range, 1.67, 1.90 

Decisional conflict 

computerbased decision aid 

85 3 months 

prior decision 

 3 months 

mean, 1.74 median, range, 1.62, 1.85 

 Control 85 3 months 

prior decision 

 3 months 

mean, 1.9 median, range, 1.75, 2.05 

Decisional conflict Decisional uncertainty subscale 

Computerbased decision aid 

85 3 months 

prior decision 

 3 months 

mean, 1.88 range, 1.73, 2.03 

 Control 85 3 months 

prior decision 

 3 months: 

mean, 1.78 : range, 1.66, 1.91: 

Decisional conflict Factors of uncertainty subscale Computerbased decision 

aid 

85 3 months 

prior decision 

 3 months: 

mean, 1.73  SD, 1.61, 1.86 

Decisional conflict 

 85 3 months 

prior 

 3 months 

mean, 1.70 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐258

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

Effective decisionmaking subscale 

decision median, 

range, 1.58, 1.82 SD, 

Decision to use hormone therapy 

Control 86 3 months - 

prior decision 

 3 months 

Decision to use hormone therapy 

Computerbased decision aid 

85 3 months - 

prior decision 

 3 months 

preventing falls in the elderly 

 Control 136 6 point scale Adter 

reviewing the intervention or control web site: mean, 4.65 SD, 0.79 

Intention to carry out the recommended activities 

Tailored 144 6 point scale Adter 

reviewing the intervention or control web site: mean, 4.86 SD, 0.61 

 Control 136 Adter 

reviewing the intervention or control web site mean, 4.6 SD, 0.77 

Yardley, 200717

Personal relevance 

Tailored 144 Adter 

reviewing the intervention or control 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐259

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

web site mean, 4.83 SD, 0.65 

 Control 136 Adter 

reviewing the intervention or control web site mean, 5.08 SD, 0.64 

Interest 

Tailored 144 Adter 

reviewing the intervention or control web site mean, 5.03 SD, 0.61 

 Control 136 Adter 

reviewing the intervention or control web site mean, 4.8 SD, 0.79 

Suitability of the activities 

Tailored 144 Adter 

reviewing the intervention or control web site mean, 4.95 SD, 0.6 

 

Self-efficacy Control 136 Adter 

reviewing the intervention or control 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐260

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

web site: mean, 4.35  SD, 0.95 

Tailored 144 Adter 

reviewing the intervention or control web site: mean, 4.61  SD, 0.7 

 Control 136 Adter 

reviewing the intervention or control web site mean, 4.79 SD, 0.74 

Outcome expectancy 

Tailored 144 Adter 

reviewing the intervention or control web site mean, 4.78 SD, 0.67 

use of contraception 

 Control NA Mean, 1.95 

SD, 1.13 

Initial visit mean, 2.29 SD, 1.03 

 1 year: 

mean, 3.05 SD, 1.24 

 

OC knowledge Chicago 

Computerized decision aid 

NA Mean, 1.89 

SD, 1.07 

Initial visit mean, 3.28 SD, 1.17 

 1 year: 

mean, 3.23 SD, 1.27 

 

Chewning, 199918

OC knowledge Madison 

 Control NA Mean, 2.48 

SD, 1.21 

Initial visit mean, 3.58 SD, 1.06 

 1 year 

mean, 3.76 SD, 1.02 

 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐261

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Ratimpo

 Computerized 

decision aid 

NA Mean, 2.46 

SD, 1.3 

Initial visit mean, 4.49 SD, 0.78 

 1 year 

mean, 3.95 SD, 0.91 

 

 Control NA Initial visit 

mean, 11.26 SD, 15.93 

 1 year 

mean, 6.38 SD, 13.45 

 

OC efficacy Chicago 

Computerized decision aid 

NA Initial visit 

mean, 4.59 SD, 9.2 

 1 year 

mean, 5.66 SD, 8.45 

 

 Control NA Initial visit 

mean, 4.8 SD, 5.58 

 1 year 

mean, 4.83 SD, 9.15 

 

OC efficacy Madison 

Computerized decision aid 

NA Mean, 2.09 

SD, 2.2 

Initial visit 1 year 

mean, 4 SD, 8.26 

 

BL = baseline, SD = standard deviation, CI = confidence interval, OC = oral contraceptive, CES-D = Center for Epidemiologic Studies Depression Sc

Reference List 

 1 Kukafka R, Lussier YA, Eng P, Patel VL, Cimino JJ. Web-based tailoring and its effect on self-efficacy: results from the MI-HEA

trial. Proc AMIA Symp 2002; 410-4. 

 2 Simkins CV, Wenzloff NJ. Evaluation of a computerized reminder system in the enhancement of patient medication refill complian

Pharm 1986; 20(10):799-802. 

 3 Lorig KR, Ritter PL, Laurent DD, Plant K. The internet-based arthritis self-management program: a one-year randomized trial for 

fibromyalgia. Arthritis Rheum 2008; 59(7):1009-17. 

 4 Buhrman M, Faltenhag S, Strom L, Andersson G. Controlled trial of Internet-based treatment with telephone support for chronic b

111(3):368-77. 

 5 Oenema A, Brug J, Dijkstra A, de Weerdt I, de Vries H. Efficacy and use of an internet-delivered computer-tailored lifestyle interv
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐262

fat intake, physical activity and smoking cessation: a randomized controlled trial. Ann Behav Med 2008; 35(2):125-35. 

 6 Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 1999

 7 Campbell E, Peterkin D, Abbott R, Rogers J. Encouraging underscreened women to have cervical cancer screening: the effectivene

Prev Med 1997; 26(6):801-7. 

 8 Frosch DL, Bhatnagar V, Tally S, Hamori CJ, Kaplan RM. Internet patient decision support: a randomized controlled trial compari

for men considering prostate cancer screening. Arch Intern Med 2008; 168(4):363-9. 

 9 Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. 

44(3):166-72. 

10 Harari D, Iliffe S, Kharicha K et al. Promotion of health in older people: a randomised controlled trial of health risk appraisal in Br

Ageing 2008; 37(5):565-71. 

11 Paperny DM, Aono JY, Lehman RM, Hammar SL, Risser J. Computer-assisted detection and intervention in adolescent high-risk h

116(3):456-62. 

12 Devineni T, Blanchard EB. A randomized controlled trial of an internet-based treatment for chronic headache. Behav Res Ther 200

13 Flatley-Brennan P. Computer network home care demonstration: a randomized trial in persons living with AIDS. Comput Biol Me14 Gustafson DH, Hawkins RP, Boberg EW, Bricker E, Pingree S, Chan CL. The use and impact of a computer-based support system 

AIDS and HIV infection. 1994; 604-8. 

15 Rostom A, O'Connor A, Tugwell P, Wells G. A randomized trial of a computerized versus an audio-booklet decision aid for wome

menopausal hormone replacement therapy. Patient Educ Couns 2002; 46(1):67-74. 

16 Schapira MM, Gilligan MA, McAuliffe T, Garmon G, Carnes M, Nattinger AB. Decision-making at menopause: a randomized con

computer-based hormone therapy decision-aid. Patient Educ Couns 2007; 67(1-2):100-7. 

17 Yardley L, Nyman SR. Internet provision of tailored advice on falls prevention activities for older people: a randomized controlled 

Promot Int 2007; 22(2):122-8. 
Evidence Table 31. Outcomes in studies addressing the impact of CHI applications on intermediate outcomes in miscellaneous conditions of in

G‐263

18 Chewning B, Mosena P, Wilson D et al. Evaluation of a computerized contraceptive decision aid for adolescent patients. Patient E

38(3):227-39. 
Evidence table 32. Description of RCTs addressing the impact of CHI applications on relationship-centered outcomes

G-264 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Breast cancer Green, 20051 Individuals 

interested in their own health care 

Personalized health risk assessment tool 

Clinician office 

Between May 2000 and September 2002 

≥18 yr, Female, Read, write and speak English, Scheduled a genetic counseling appt to evaluate personal and/or family history of breast cancer, Able to give informed consent 

Previously underwent genetic counseling, Testing for inherited breast cancer susceptibility 

Counseling without computer intervention 

Gustafson, 20082

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Between April 1995 and May 1997 

<60 yr, Breast cancer patients, Within 6 months of diagnosis, Not homeless, Not active illegal drug users 

Allocated standard intervention 

Gustafson, 20013

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Between April 1995 and May 1997 

<60 yr, Breast cancer patients, Within 6 months of diagnosis, Not homeless, Not active illegal drug users 

 Allocated 

standard intervention 

Maslin, 19984 Individuals 

interested in their own health care 

Interactive computerized video system 

Clinician office 

NS Non metastatic 

breast cancer 

Advanced breast cancer, Metastatic disease, Sensory impairment, do not understand English 

Standard care 

Caregiver decision making Brennan, 19955 Caregivers of persons Interactive consumer Home/ residence NS Primary responsibility as a Comparison group 
Evidence table 32. Description of RCTs addressing the impact of CHI applications on relationship-centered outcomes (continued)

G-265 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

with Alzheimer's Disease 

website family caregiver for a 

person with Alzheimer's disease living at home, Has a local telephone exchange, The ability to read and write English 

HIV/AIDS FlatleyBrennan, 19986

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS HIV infected, 

Ability to read and type English, Home telephone line 

 Received 

brochure 

Arthritis Sciamanna, 20057

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS Had knee joint 

symptoms for at least the past three months, Saw a doctor for knee symptoms, Has the diagnosis of osteoarthritis 

Patient did not report having knee joint symptoms for at least the past three months, Patient did not report having been seen by a doctor for the knee symptoms, Does not have the diagnosis of osteoarthritis 

Completed questionnaire before intervention 

Vaginal or c-section delivery Montgomery, 20078

Individuals interested in their own health care 

Decision analysis tool re caesarean delivery after having had a caesarean delivery 

Home/ residence Clinician office 

2004 (may) to 2006 (august) 

Pregnant, one previous lower segment caesarean section, no current obstetric problems, delivery expected at >= 37 weeks 

Limited ability to speak or understand English 

Usual care 

 NS = Not specified, Yr = year, CHESS = Comprehensive Health Enhancement Support System 
Evidence table 32. Description of RCTs addressing the impact of CHI applications on relationship-centered outcomes (continued)

G-266 

Reference List 

 1. Green MJ, Peterson SK, Baker MW et al. Use of an educational computer program before genetic counseling for breast cancer sus

duration and content of counseling sessions. Genet Med 2005; 7(4):221-9. 

 2. Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 3. Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 4. Maslin AM, Baum M, Walker JS, A'Hern R, Prouse A. Using an interactive video disk in breast cancer patient support. Nurs Time 5. Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. 

44(3):166-72. 

 6. Flatley-Brennan P. Computer network home care demonstration: a randomized trial in persons living with AIDS. Comput Biol Me 7. Sciamanna CN, Harrold LR, Manocchia M, Walker NJ, Mui S. The effect of web-based, personalized, osteoarthritis quality improv

patient satisfaction with osteoarthritis care. Am J Med Qual 2005; 20(3):127-37. 

 8. Montgomery AA, Emmett CL, Fahey T et al. Two decision aids for mode of delivery among women with previous caesarean secti

trial. BMJ 2007; 334(7607):1305. 
Evidence Table 33. Description of consumer characteristics in RCTs addressing KQ 1c (impact of CHI applications on relationship-centered 

G-267 

Author, year 

Control Intervention Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) Marital Status 

Breast cancer

Counseling without computer intervention 

Mean, 44 range, 24-71 

White nonHispanic, 100(95) 

NS College grad, 

53 (50) 

NR 

Green, 20051

Counseling & 

Interactive computer program 

Mean, 45 range, 23-77 

White nonHispanic, 100(95) 

NS College grad, 

65 (62) 

NR 

Usual Care with books 

Gustafson, NS NS NS NR 20082

CHESS NS NS NS NR 

Allocated standard intervention 

Mean, 44.4 SD, 7.1 

White nonHispanic, (72) USD >40,000, 

(50.8) 

Bachelor’s degree, (40.2) 

NR Living with 

Partner, (72.6) 

Gustafson, 20013

Received CHESS intervention, a home based computer 

Mean, 44.3 SD, 6.6 

White nonHispanic, (76) USD 40,000, 

(58.1) 

Bachelor’s degree, (45.8) 

NR Living with 

Partner, (71.9) 
Evidence Table 33. Description of consumer characteristics in RCTs addressing KQ 1c (impact of CHI applications on relationship-centered 

G-268 

Author, year 

Control Intervention Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) Marital Status 

system 

Maslin, Standard care Mean, 52.1 NS NS NS NR 19984 Interactive 

Video Disk for shared decision making 

 NS NS NS NR 

Caregiver decision making

Comparison group 

Mean, 64 White nonHispanic, (72) Brennan, NS (86) NR F, (67) 19955

Computer Link 

 NS NS NS NR 

HIV/AIDS 

Received brochure 

Mean, 34 SD, 10.8 

White nonHispanic, (58) NS mean, 14 SD, 2.7 Flatley- NR Living Alone Brennan, 

19986 Received 

Computer Link 

mean, 33 SD, 7.3 

White nonHispanic, (64) NS mean, 13 SD, 2.6 NR Living Alone 

Arthritis

Completed questionnaire before intervention 

Mean, 49.3 White nonHispanic, 50 

(87.7) black nonHispanic, 4 (7) Latino/ Hispanic, 1 (1.8) API, 0, (0) AIAN, 1, (1.8) 

NS 14 (24.6) NR F, 

41 (71.9) M, 16 (28.9) 

Sciamanna, 20057

Patient satisfaction survey administered after participating in the webbased intervention 

Mean, 46.6 White nonHispanic, 55, 

(85.9) Black nonHispanic, 4 (6.3) Latino/Hispanic, 3 (4.7) AIAN, 2 (3.1) 

NS 17 (26.6) NR F, 

52 (81.3) M, 12 (18.7) 

Vaginal or c-section deliveryMontgomery, 20078

Usual Care Mean, 32.4 

SD, 4.6 

NS Pound 

<20, 42 

Highest Educational 

NR 
Evidence Table 33. Description of consumer characteristics in RCTs addressing KQ 1c (impact of CHI applications on relationship-centered 

G-269 

Author, year 

Control Intervention Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) Marital Status 

(18) 20-30, 53 (23) 30-40, 51 (22) 40-50, 43 (18) >50, 46 (20) 

Qualification None, 12 (5) GCSE, 99 (40) A level, 42 (17) Degree,92 (38) 

Computerized Educational Information 

Mean, 32.8 SD, 4.7 

NS Pounds 

<20, 44 (19) 20-30, 57 (24) 30-40, 46 (19)  40-50, 37 (16) >50, 52 (22) 

Highest Educational Qualification None, 10(4) GCSE, 92(37) A level, 47(19) Degree, 97(39) 

NR 

Decision analysis program 

Mean, 32.5 SD, 4.8 

NS Pounds 

<20, 48 (20) 20-30, 49 (21) 30-40, 44 (19) 40-50, 46 (19) >50, 50 (21) 

Highest Educational Qualification None, 7(3) GCSE, 97(40) A level (36(15) Degree, 103(42) 

NR 

NR= Not Reported, NS= Not Significant, SD= Standard Deviation, SES= Socioeconomic Status, Yr= year, AIAN = American Indian/Alaska Native, A

GCSE= General Certificate of Secondary Education 
Evidence Table 33. Description of consumer characteristics in RCTs addressing KQ 1c (impact of CHI applications on relationship-centered 

G-270 

Reference List 

 1. Green MJ, Peterson SK, Baker MW et al. Use of an educational computer program before genetic counseling for breast cancer sus

duration and content of counseling sessions. Genet Med 2005; 7(4):221-9. 

 2. Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 3. Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 4. Maslin AM, Baum M, Walker JS, A'Hern R, Prouse A. Using an interactive video disk in breast cancer patient support. Nurs Time 5. Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. 

44(3):166-72. 

 6. Flatley-Brennan P. Computer network home care demonstration: a randomized trial in persons living with AIDS. Comput Biol Me 7. Sciamanna CN, Harrold LR, Manocchia M, Walker NJ, Mui S. The effect of web-based, personalized, osteoarthritis quality improv

patient satisfaction with osteoarthritis care. Am J Med Qual 2005; 20(3):127-37. 

 8. Montgomery AA, Emmett CL, Fahey T et al. Two decision aids for mode of delivery among women with previous caesarean secti

trial. BMJ 2007; 334(7607):1305. 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes

G‐271

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Breast Cancer 

Control 105 After 

counseling session with genetic counselor 

Alter content of discussions 

Counseling & 

Interactive computer program 

106 After 

counseling session with genetic counselor:100 

 Control 105 After 

counseling session with genetic counselor 

Change the way they used their time 

Counseling & 

Interactive computer program 

106 After 

counseling session with genetic counselor:100 

 Control 105 After 

counseling session with genetic counselor 

Used time more efficiently 

Counseling & 

Interactive computer program 

106 After 

counseling session with genetic counselor:100 

 Control 105 After 

counseling session with genetic counselor 

Green, 20051

Skip material typically present 

Counseling & 

Interactive computer 

106 After 

counseling session with 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐272

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

program genetic 

counselor:100 

 Control 105 After 

counseling session with genetic counselor 

Effectiveness of counseling session 

Counseling & 

Interactive computer program 

106 After 

counseling session with genetic counselor:100 

 Control 105 After 

counseling session with genetic counselor 

Shorter counseling sessions 

Counseling & 

Interactive computer program 

106 After 

counseling session with genetic counselor 

 Control 80 Quality of life 

mean, 0.18 SD, 0.53 

 9 month: 

mean, 0.11 SD, 0.45 

Gustafson, 20082

Social support 

Internet 75 Quality of life 

mean, -0.02 SD, 0.56 

 9 month: 

mean, 0.07 SD, 0.45 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐273

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

CHESS 80 Quality of life 

mean, 0.02 SD, 0.54 

 9 month: 

mean, 0.18 SD, 0.54 

 Control 80 Social 

support: mean, 0.23 SD, 0.49 

4 month 9 month 

mean, 0.13 SD, 0.54 

Internet 75 social 

support: mean, -0.08 SD, 0.56 

4 month 9 month 

mean, 0.06 SD, 0.58 

Quality of life 

CHESS 80 Social 

support: mean, 0.16 SD, 0.49 

4 month 9 month 

mean, 0.21 SD, 0.55 

 Control 80 Health and 

information competence mean, 0.17  SD, 0.39 

4 month 9 month 

mean, 0.12 SD, 0.37 

Health competence 

Internet 75 Health and 

information competence mean, -0.03  SD, 0.48 

4 month 9 month 

mean, 0.06 SD, 0.49 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐274

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

CHESS 80 Health and 

information competence mean, 0.12  SD, 0.47 

4 month 9 month 

mean, 0.18 SD, 0.48 

 Control 125 2 month 

mean, 65.6 

 5 month: 

mean, 65.8 

Information competence 

Chess 121 2 month 

mean, 70.4 

 5 month: 

mean, 69.3 

 Control 125 2 month 

mean, 74.3 

 5 month 

CHESS 121 2 month 

mean, 80.7 

 5 month 

 Control 125 2 month 

mean, 74.3 

 5 month 

Participation 

CHESS 121 2 month 

mean, 80.7 

 5 month 

 Control 125 2 month 

mean, 77.3 

 5 month 

Gustafson, 20013

Confidence in doctors 

CHESS 121 mean, 83 2 month RR or 

OR time point 3, 0.05 

 5 month 

Interactive Video Disk for shared decision making 

51 Score on 

HAD 

Anxiety and 9 months later depression 

 51 9 months later 

Control 51 9 months after 

diagnosis 

Maslin, 19984

Satisfaction with treatment decision 

Interactive Video Disk for shared 

51 9 months after 

diagnosis 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐275

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

decision making 

Caregiver decision making 

 Control 49 Likert scale, 

14 items, 5 choices mean, 54.65 SD, 7.3 

 12 months: 

mean, 54.7 SD, 6.1 

Decision confidence 

ComputerLink 47 Likert scale 

14 items, 5 choices mean, 51.9 SD, 6 

 12 months: 

mean, 56.8 SD, 7 

 Control 49 Number of 

alternatives caregiver considers to solve a problem: mean, 2.51 SD, 0.91 

 12 months 

mean, 2.37 SD, 78 

Improved decision making skill 

ComputerLink 47 Number of 

alternatives caregiver considers to solve a problem: mean, 2.53 SD, 0.78 

 12 months 

mean, 2.4 SD, 0.61 

 Control 49 Score on 

Instrumental and Expressive Support Scale (IESS) mean, 62.7  SD, 15.5 

 12 months 

mean, 62.6 SD, 16 

Brennan, 19955

Isolation 

ComputerLink 47 mean, 63.4 

 SD, 16.6 

 12 months 

mean, 65 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐276

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

SD, 17.4 

HIV/AIDS 

 Control 26 Mean score 

mean, 52.8 SD, 6 

 Postintervention: 

mean, 56.47 SD, 4.2 

Improved decision making confidence 

Computer Link 

31 Mean score 

mean, 54.35 SD, 5.9 

 Postintervention: 

mean, 51.45 SD, 6.9 

 Control 26 Mean score: 

mean, 4.73 SD, 1.4 

 Postintervention 

mean, 5.47 SD, 1.3 

Computer Link 

31 Mean Score: 

mean, 4.58 SD, 5.4 

 Postintervention 

mean, 5.4 SD, 1.5 

Improved decision making skill Reduced social isolation 

 Control 26 Mean score 

mean, 67.05  SD, 17 

 Postintervention 

mean, 68 SD, 16.8 

 Computer 

Link 

31 Mean score 

mean, 63.5  SD, 14.4 

 Postintervention 

mean, 66.08 SD, 13.68 

 26 Mean score 

mean, 13.8 SD, 4.93 

 Postintervention 

mean, 13.65 SD, 1.3 

FlatleyBrennan, 19986

Differential decline in health status 

Computer Link 

31 RR or OR 

time point 2, 0.05 

 Postintervention 

mean, 13 SD, 1.7 

Arthritis Sciamanna, 20057

Patient overall satisfaction 

 Control 57 One 

measurement only, survey 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐277

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

before or after viewing the web-based module 

score with the osteoarthritis care they are receiving www.myexpert

doctor.comtailored feedback on quality of received care

64 One 

measurement only, survey before or after viewing the web-based module: 

Vaginal or c-section delivery 

 Usual Care 201 Total score 

on DCS 

 37 weeks 

gestation (DCS): mean, 27.8 SD, 14.6 

Computerized Educational Information 

201 Total score 

on DCS 

 37 weeks 

gestation (DCS): mean, 22.5 SD, 13.2 

mean (SD) on DCS at follow up 

Decision analysis program 

198 Total score 

on DCS 

 37 weeks 

gestation (DCS): mean, 23.6 SD, 15.1 

Difference between groups in total score on DCS (decision v usual care) 

 Usual Care 201 Difference 

between groups on total score on DCS(adjusted figure 

2 weeks post delivery (satisfaction with decision) 

 time point 4, OR:1.42(0.94 to 2.14)

37 weeks gestation (DCS) mean, -4 range, -6.5 to -1.5 

Montgomery, 20078

Odds ratio for caesarean (elective and emergency) 

Computerized Educational Information

201 Odds ratio for 

vaginal v c section (elective and emergency) 

2 weeks post delivery (satisfaction with decision) 

37 weeks gestation (DCS) 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐278

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

decision analysis v usual care 

Decision analysis program 

 2 weeks 

post delivery (satisfaction with decision) 

37 weeks gestation (DCS) 

Usual Care 2 weeks 

post delivery (satisfaction with decision) 

37 weeks gestation (DCS) 

v vaginal, decision v usual care 

Computerized Educational Information 

 2 weeks 

post delivery (satisfaction with decision) 

37 weeks gestation (DCS) 

Satisfaction with decision (decision analysis v usual care) 

Decision analysis program 

201 Satisfaction 

with decision as odds ratio (decision analysis v usual care) 

2 weeks post delivery (satisfaction with decision) 

 37 weeks 

gestation (DCS) mean, 0.14 range, 0.02 to 0.27 

Usual Care 201 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:50 

Computerized Educational Information 

201 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:49 

Mode of delivery - elective caesarean 

Decision analysis program 

198 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:41 

Usual Care 238 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:20 

Computerized Educational Information 

240 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:22 

Delivery - emergency caesarean 

Decision 235 6 weeks questionnaire Hospital 
Evidence Table 34. Outcomes in studies addressing the impact of CHI applications on relationship-centered outcomes (continued)

G‐279

Author, year Outcomes 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

analysis program 

post delivery at 36 weeks 

gestation 

records: type of delivery:21 

Usual Care 238 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:30 

Computerized Educational Information 

240 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:29 

Delivery - vaginal birth 

Decision analysis program 

235 6 weeks 

post delivery 

 questionnaire 

at 36 weeks gestation 

Hospital records: type of delivery:37 

BL = baseline, SD = standard deviation, OR = odd ratio, RR = relative ratio, DCS = decisional conflict scale 

Reference List 

 1. Green MJ, Peterson SK, Baker MW et al. Use of an educational computer program before genetic counseling for breast cancer sus

duration and content of counseling sessions. Genet Med 2005; 7(4):221-9. 

 2. Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 3. Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 4. Maslin AM, Baum M, Walker JS, A'Hern R, Prouse A. Using an interactive video disk in breast cancer patient support. Nurs Time 5. Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. 

44(3):166-72. 

 6. Flatley-Brennan P. Computer network home care demonstration: a randomized trial in persons living with AIDS. Comput Biol Me 7. Sciamanna CN, Harrold LR, Manocchia M, Walker NJ, Mui S. The effect of web-based, personalized, osteoarthritis quality improv

patient satisfaction with osteoarthritis care. Am J Med Qual 2005; 20(3):127-37. 

 8. Montgomery AA, Emmett CL, Fahey T et al. Two decision aids for mode of delivery among women with previous caesarean secti

trial. BMJ 2007; 334(7607):1305.
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes)

G-280 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Alzheimer’s Tarraga, 20061

Individuals interested in their own health care 

Interactive consumer website 

Clinician office 

NS > 65 yr, 

treated with CHEI x 1 yr, > 3 yrs education, MMSE 18-24, a Global Deterioration Scale (GDS) score of 3 or 4, absence of uncontrolled disruptive behaviors, absence of major depression, absence of structural lesions in the computed tomogram, absence of history of alcohol or the substance abuse 

Uncontrolled disruptive behaviors (e.g., aggression, delusions, hallucinations and agitation) that could interfere with program administration and/or neuropsychological assessments, major depression, current or partial remission, structural lesions in the computed tomogram or magnetic resonance image, history of alcohol or other substance abuse, severe auditory, visual or motor deficits that may interfere with cognitive testing 

ChEI contro

Arthritis Lorig, 20082

Individuals interested in their own health care 

Interactive consumer website

NS 2004/ 

NS 

18 and older, a diagnosis of OA, rheumatoid arthritis (RA), or fibromyalgia, could have other chronic conditions Internet and email access 

Active treatment for cancer for 1 yr, participated in the small-group ASMP or the Chronic Disease Self-Management Program 

Usual care 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-281 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

agreed to 1–2 hours per week of log-on time spread over at least 3 sessions/week for 6 weeks 

Asthma Jan, 20073

Individuals interested in their own health care Caregiver, childhood asthma 

Personal monitoring device 

Home/ residence 

2004/ January to December 

6 - 12 yr, caregivers have Internet access, persistent asthma diagnosis (GINA clinical practice guidelines) 

Diagnosed with bronchopulmonary dysplasia, diagnosed with other chronic co morbid conditions that could affect quality of life 

Verbal information and booklet for asthma education with written asthma diary. 

Back pain Buhrman, 20044

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS, 18-65 yr, 

Internet access, Been in contact with a physician, Have back pain, Have chronic pain (>3 months), 

Suffer of pain that can increase as a consequence of activity, wheelchair bound, have planned any surgical treatment, suffer from heart 

Wait-list 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-282 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

and vascular disease 

Breast cancer Gustafson, 20015

Individuals interested in their own health care 

Interactive consumer website, 

Home/ residence 

Duration, Between April 1995 and May 1997 

<60 years, Breast cancer patients, Within 6 months of diagnosis, not homeless, not active illegal drug users, 

 Allocated 

standard intervention 

Gustafson, 20086

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Between April 1995 and May 1997 

<60 years, Breast cancer patients, Within 6 months of diagnosis, not homeless, not active illegal drug users 

Allocated standard intervention 

Maslin, 19987

Individuals interested in their own health care 

Interactive computerized video system 

Clinician office 

NS Non metastatic 

breast cancer, 

Advanced breast cancer, metastatic disease, sensory impairment, do not understand English, 

Standard care 

Chronic adult aphasia Katz, 19978

Individuals interested in their own health care 

Interactive consumer website 

Clinician office 

NS <85 years, 

aphasia subsequent to a single left hemisphere, thromboembolic infarct, completed at least 8th grade, pre-morbidly literate in English, living in nonVisual acuity better than 20/100 corrected in the better eye, auditory acuity better than 40 dB speech reception threshold, language treatment in the three months prior No treatmen
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-283 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

institutionalized environment, at least one year post-onset of aphasia, performs between 15th and 90th overall percentile on the Porch Index of communicative Ability, Pre-morbidly righthanded 

to entry into the study 

COPD Nguyen, 20089

Individuals interested in their own health care 

Interactive consumer website, 

Academic medical centers 

2005/ NS Diagnosis of COPD 

and being clinically stable for at least 1 month, spirometry results showing at least mild obstructive disease, ADL limited by dyspnea, use of the Internet and/or checking email at least once per week with a windows operating system, oxygen saturation > 85% on room air or ¡Ü 6 L/min of nasal oxygen at the end of a 6-minute walk test, 

Any active symptomatic illness, participated in a pulmonary rehabilitation program in the last 12 months, were currently participating in > 2 days of supervised maintenance exercise 

Face-to-face (fDSMP) 

Headache Trautman, 200810

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS 10-18 yr, 

At least two headache attacks per month 

 EDU (First 

training session of CBT on 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-284 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Parents/caregivers 

headache information plus chat communicaon) 

Mental health/Depression Christensen, 200411

Individuals interested in their own health care 

Interactive consumer website 

NS NS >18 years, 

internet access, 22 or higher on the Kessler psychological distress scale 

> 52 years, receiving clinical care from either a psychologist or psychiatrist 

Control 

Hasson, 200512

Individuals interested in their own health care 

Personal monitoring device 

NS NS Employment at a 

company insured by Alecta (occupational pension plan company) 

Those who quit employment prior to completion of study, "communication related problem" 

Access to web-based tool including monitoring tool for stress and health; diary connected tmonitoring tool, and scientific infon stress and health 

Kerr, 200813

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS 18 - 25 years, 

BMI 25-39 

 Enhanced 

standard care 

March, 200814

Individuals interested in their own health care: children parents 

 NS NS 7 - 12 yr, 

primary diagnosis of an anxiety disorder with severity level of 4 or more on 8 point scale ( I-e moderate severity), a minimum reading level of 8 years access to internet at 

Developmental disorders, learning disability, depressive disorder, other psychological treatment, primary behavioral disorder, 

Wait list (WL) 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-285 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

home failure to complete 

screening assessment 

Orbach, 200715

 Interactive 

consumer website, 

Remote location: university campus 

NS Both, 

students in Kings College London, London University, access to a computer connected to the Internet 

Receiving treatment for anxiety, 

Control 

Proudfoot, 200316

Individuals interested in their own health care 

Personal monitoring device 

Clinician office 

NS 18-75 years old, 

depression, anxiety and depression, Anxiety, >=4 on General Health Questionnaire-12, >=12 on Clinical Interview ScheduleRevised 

Psychological treatment or counseling, current Suicidal ideation, psychotic disorder organic mental disorder, alcohol and/or drug dependence, on medication for anxiety and/or depression for >=6 months immediately prior to entry, unable to read or write, unable to attend 8 session at surgery 

Usual treatment 

Spek, 200817

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Duration, 12months 

Age between 50 and 75 years, an Edinburgh Depression Scale (EDS) score of 12 or more, no DSM-IV diagnosis of depression, access to the internet and the 

Suffering from any other psychiatric disorder in immediate need of treatment and suicidal ideation 

Waiting list control 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-286 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

ability to use the internet 

Diabetes Homko, 200718

Individuals interested in their own health care 

Interactive consumer website, 

Home/ residence 

Duration, Sep 2004 to May 2006 

18-45 years, documented GDM on 3-h oral glucose tolerance test, using the criteria of Carpenter and Coustan, 33 weeks gestation or less 

Prior history of glucose intolerance outside of pregnancy, multiple gestations 

Usual care, paper logbooks 

Tjam, 200619 Individuals 

interested in their own health care 

Interactive consumer website 

Clinician office 

2years duration 

65 yr, Both male and female, Internet proficient have access, have access to internet 

Below40 and above65 years, blindness, little or no dexterity, education level below grade 5, ESRD ,  gestational diabetes 

Individuals with Diabetes Education Centers program n, 20 

Wise, 1986 20Diabetes 

Diabetic individuals both NIDDM and IDDM 

Interactive computerized machine 

Home / Res 

Ns Diabetics attending 

Charing Cross hospitaland having DM > 2 yrs 

None specified 3 controls 

Used: a. No intervention (used for Glucose control assessmentNo KAP b. Just the assessment of the KAP c. Takeaway corrective feedback 

Diet/exercise/physical activity NOT Obesity Adachi, Individuals Tailored Home/ 2002/ 20-65 yr, Untailored 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-287 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

200721 interested 

in their own health care 

advice based on answers to a questionnaire 

residence January to 

September 

BMI ≥24, BMI r≥ 23 with mild 

hypertension Hyperlipidemia, or DM 

self-help booklet with 7-month selmonitoring 

Hunter, 200822

Individuals interested in their own health care 

Interactive consumer website, 

NS Year, 2006 18 - 65 yr, 

weight within 5 pounds or above their maximum allowable weight (MAW) for the USAF, personal computer with internet access, plans to remain in the local area for 1 year, 

Lost more than 10 pounds in the previous 3 months, used prescription or over-thecounter weightloss medications in the previous 6 months, had any physical activity restrictions, had a history of myocardial infarction, stroke, or cancer in the last 5 years, reported diabetes, angina, or thyroid difficulties; or had orthopedic or joint problems, women were excluded if they were currently pregnant or breast-feeding, or had plans to become pregnant in the next year 

Usual care 

McConnon, 200723

Individuals interested 

Interactive consumer 

Home/ residence 

2003/ NS 18 - 65 yr, 

BMI 30 or more, 

 Usual care 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-288 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

 in their own 

health care 

website, able to access 

internet at least 1 time a week, able to read and write English 

Tate, 200624

Individuals interested in their own health care 

Interactive consumer website 

Clinician office 

NS, 20 to 65, 

body mass index of 27 to 40, willingness to use meal replacements as part of the dietary regimen, availability of a computer with internet access 

Heart attack, stroke, or cancer in the past 5 years, diabetes, angina or orthopedic or joint problems that would prohibit exercise, major psychiatric disorder involving hospitalization during the past year, current, planned, or previous (within 6 months) pregnancy 

No counseling 

Williamson, 200625

Individuals interested in their own health care 

Interactive consumer website 

Clinician office 

NS 11 - 15 yr, 

African-American, female, BMI above the 85th percentile for age and gender based on 1999 National Health and Nutrition Examination Study normative data, at least one obese biological parent, as defined by BMI > 30, one designated 

 Control and 

intervention adolescents 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-289 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

parent who was overweight (BMI > 27), adolescent’s family was willing to pay $300 out-of pocket expenses toward the purchase of the computer worth >$1000, the family home had electricity and at least one functional telephone line 

HIV AIDS Gustafson, 199926

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS Dementia, 

Control subject with room mate in experimental group 

No intervention 

Pain Tolerance Borckardt, 200727

Individuals interested in their own health care 

Computer assisted imagery system 

Remote; Sound proof lab at the university 

NS Distraction 

group 

Obesity Morgan, 2009 28

Obesity 

Overweight and obese males 

Interactive website 

Home / Res 

Sept to Dec 2007 

Consenting Male individuals from U of Newcastle responding to adv who were obese / overwt (BMI 25—37), 18—60 y/o. 

H/o major medical problem like heart disease in past 5 years, DM, orthopedics or joint problem that would be a barrier to PA, recent weight loss of 4.5 kg or consuming meds affecting body wt. 

One information session + Program booklet 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-290 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

NS = not specified, yr = year, CHESS = Comprehensive Health Enhancement Support System, CBT = computer based training, eDSMP = Internet ba

programs, fDSMP = face-to-face dyspnea self-management programs, BMI = body mass index 

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G-291 

13 Kerr J, Patrick K, Norman G et al. Randomized control trial of a behavioral intervention for overweight women: impact on depress

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17 Spek V, Cuijpers P, Nyklicek I et al. One-year follow-up results of a randomized controlled clinical trial on internet-based cognitiv

subthreshold depression in people over 50 years. Psychol Med 2008; 38(5):635-9. 

18 Homko CJ, Santamore WP, Whiteman V et al. Use of an internet-based telemedicine system to manage underserved women with g

mellitus. Diabetes Technol Ther 2007; 9(3):297-306. 

19 Tjam EY, Sherifali D, Steinacher N, Hett S. Physiological outcomes of an internet disease management program vs. in-person coun

controlled trial. 2006; 30(4):397-405. 

20 Wise PH, Dowlatshahi DC, Farrant S. Effect of computer-based learning on diabetes knowledge and control. 1986; 9(5):504-8. 

21 Adachi Y, Sato C, Yamatsu K, Ito S, Adachi K, Yamagami T. A randomized controlled trial on the long-term effects of a 1-month 

program assisted by computer tailored advice. Behav Res Ther 2007; 45(3):459-70. 

22 Hunter CM, Peterson AL, Alvarez LM et al. Weight management using the internet a randomized controlled trial. Am J Prev Med 

23 McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tri

2007; 7:206. 

24 Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored counselin

an Internet weight loss program. Arch Intern Med 2006; 166(15):1620-5. 

25 Williamson DA, Walden HM, White MA et al. Two-year internet-based randomized controlled trial for weight loss in African-Am

(Silver Spring) 2006; 14(7):1231-43. 

26 Gustafson DH, Hawkins R, Boberg E et al. Impact of a patient-centered, computer-based health information/support system. Am J 

9. 
Evidence Table 35. Description of RCTs addressing KQ1d (impact of CHI applications on clinical outcomes) (continued)

G-292 

27 Borckardt JJ, Younger J, Winkel J, Nash MR, Shaw D. The computer-assisted cognitive/imagery system for use in the managemen

2004; 9(3):157-62. 

28 Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT Randomized Controlled Trial: Evaluation of an Intern

Program for Men. Obesity (Silver Spring) 2009. 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes)

G‐293

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Alzheimer’s

ChEI control Mean, 76.9 

SD, 4.5 

NS NS NS NR F,12 

Integrated psycho stimulation program (PPI) 

Mean, 77.4 SD, 4.7 

NS NS NS NR F,14 

Tarraga, 20061

Interactive multimedia internet-based system (IMIS) 

Mean, 75.8 SD, 5.9 

NS NS NS NR F,13 

Arthritis

Usual care Mean, 52.5 

range 22–89 SD, 12.2 

White nonHispanic, 425(93.7) 

NS Mean, 15.7 

SD, 3.11 

NR F, 

425(90.5) 

Marrie425(7

Lorig, 20082

Online intervention Mean, 52.2 

SD, 10.9 

White nonHispanic, 441(90.9) 

NS Mean, 15.6 

SD, 3.09 

NR F, 

441(89.8) 

Marrie441(6

AsthmaJan, 20073

Verbal information and booklet for asthma education with written asthma diary. 

Mean, 9.9 SD, 3.2 

NS NS NS NR M,28(36.8) 

F, 48(63.2) 

 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐294

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Participant received asthma education and with interactive asthma monitoring system 

Mean, 10.9 SD, 2.5 

NS NS NS NR M,35(39.7) 

F, 53(60.2) 

 

Back pain Buhrman, 20044

Wait-list Mean, 45 

SD, 10.7 

NS NS <8 yr, 7(24.1) 

8-12 yr, 6(21) 12-14 yr, 2 (6.9) 14-16 yr, 14 (48.3) 

NR M,11(37.9) 

F, 18(62.1) 

 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐295

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Internet-based pain management program 

Mean, 43.5 SD, 10.3 

NS NS <8 yr, 2 (9.1) 

8-12 yr, 6 (27) 12-14 yr, 3 (13.6) 14-16 yr, 11 (50) 

NR M, 8 (36.4) 

F,14 (63.6) 

 

Breast cancer

Allocated standard intervention 

Mean, 44.4 median, range, SD, 7.1 

White nonHispanic, 72 USD >40,000, 

50.8% 

12-16 yr (40.2) 

NR Living 

StatuLiving Partn(72.6) 

Gustafson, 20015

Received CHESS intervention, a home based computer system 

Mean, 44.3 median, range, SD, 6.6 

White nonHispanic, 76 USD >40,000, 

(58.1) 

12-16 yr (45.8) 

NR Living 

StatuLiving Partn(71.9 

Usual Care with books 

Gustafson, NS NS NS NS NR 20086

 Internet NS NS NS NS NR 

Maslin, Standard care Mean, 52.1 NS NS NS NR 19987

 IVD shared NS NS NS NS NR 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐296

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

decision program 

Chronic adult aphasia

No treatment Mean, 62.8 

range, 53-70 SD, 5.1 

NS NS Mean, 13.6 yr 

SD, 2.2 

NR 

Computer stimulation 

Mean, 66.4 range, 53-76 SD, 6 

NS NS mean, 15 

SD, 2.8 

NR 

Katz, 19978

Computer reading treatment 

Mean, 61.6 range, 48-83 SD, 10 

NS NS Mean, 14.4 

SD, 3.3 

NR 

COPD

Face-to-face (fDSMP), 

Mean, 70.9 SD, 8.6 

White nonHispanic, 20(100) 

NS 12-16 yr, 8(40) 

>16 yr, 12(60) 

NR F, 9 (45) 

Nguyen, 20089

eDSMP Mean, 68 

SD, 8.3 

White nonHispanic, 18 (95) 

NS 12-16 yr, 

10(50) >16 yr, 9(50) 

NR F, 8(39) 

Headache 

EDU (First training session of CBT on headache information plus chat communication) 

Trautman, NS NS NS NS NR NS NS 200810

 Mean, 13.4 

range, 10-18 SD, 2.6 

NS NS NS NR NS NS 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐297

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

 Mean, 13.4 

range, 10-18 SD, 2.6 

NS NS NS NR NS NS 

Mental health/ Depression

Control Mean, 36.29 

SD, 9.3 

NS NS Mean, 14.4 

SD, 2.3 

NR F, 124 (70) 

M, 54 (30) 

Marriecohab100 (5Divorparate(14) Nevemarrie53 (36

Mood gym Mean, 35.85 

SD, 9.5 

NS NS Mean, 14.6 

SD, 2.4 

NR F, 136 (75) 

M, 46 (25) 

Marriecohab98 (54Divorparate26 (14Nevemarrie57 (3

Christensen, 200411

Blue Pages Mean, 37.25 

SD, 9.4 

NS NS Mean, 15 

SD, 2.4 

NR F, 115 (69) 

M, 50 (31) 

Marrieabitin(61%) Divorparate24(15NeveMarrie53 (30

Hasson, 200512

Access to webbased tool including monitoring tool for stress and health; diary connected to monitoring tool, and scientific info on stress and 

NS NS US D 

 <25,000, 39 (22)  25,000-40,000, 106 (61)  >40,000, 27 (16) 

8-12 yr, 89 (51) 12-16 yr, 83 (48) 

NR M, 

112 (64) F, 62 (36%) 

MaritastatusMarrie134 (7Single38 (22 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐298

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

health  NS NS USD 

 <25,000, 24 (18)  25,000-40,000, 76 (59)  >40,000, 27 (21) 

 8-12 yr, 54 (42), 12-16 yr, 73 (57) 

NR M, 

75 (58) F, 54 (42) 

MaritastatusMarrie102 (7Single25 (19

Enhanced standard care. Standard care participants received usual advice from their provider concerning overweight; to change their physical activity and eating habits. They also received a standard set of materials summarizing recommendations for diet and exercise. 

Mean, 41.6(8.9) 

NS NS 81(14.3) NR Marrie

living partneyes, 128(6

Kerr, 200813

PACEi Mean, 40.8 

SD, 8.4 

NS NS 105(51.2) NR Marrie

living partneyes, 140(6
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐299

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Wait list (WL) Mean, 9.09 

SD, 1.44 

NS Australian 

dollar  <40,000, 33(12.5)  41,000–60,000, 33(34.4)  61,000–80,000, 33(15.5)  81,000–100,000, 33(6.3)  >100,000, 33(31.3) 

NS NR F, 33(57.6) 

M, 33(42. 

March, 200814

Internet-based CBT (NET) 

Mean, 9.75 SD, 1.24 

NS Australian 

dollar  <40,000 40(21.1)  41,000–60,000 40(26.2)  61,000–80,000, 40(15.8)  81,000–100,000,40(15.8)  >100,000, 40(21.1) 

NS NR F, 40(52.5) 

M,40(47.5) 

Orbach, 200715

Control Mean, 22.54 

range, 20.07–24.97 SD, 5.71 

NS NS Yr at 

university: mean, 3.02 median, SD, 2.81 

NR F, 24 (86) 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐300

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

CBT Mean, 24.72 

range, 22.36–27.09 SD, 6.89 

NS NS NS NR F, 18 (60) 

Usual treatment Mean, 45.7 

SD, 14.1 

Black Caribbean 2 (3) Indian 3 (5) Pakistani 1 (2) White 57 (88) 

NS <5 yr, 1(1) 

5-10yr, 10 (14) 11-12 yr, 17(24) 13-15yr,  15 (21) >15, 28(39) 

NR F, 

57 (73.1) M, 21(26.9) 

Single 20 (2Marrie 34 (4Coha7 (9) Sepa 2 (3) Divor 8 (11Widow 4 (5) 

Proudfoot, 200316

Beating the Blues intervention 

Mean, 43.7 SD, 14.7 

Black African: 1(1) Black Caribbean 2(3) Black other 2 (3) White 68 (88) 

NS <5yr, 0 

5-10yr, 8 (10) 11-12 yr,  22 (26) 13-15yr,  15 (18) >15yr, 39 (46) 

NR F, 

 66 (74.2) M,  23 (25.8) 

Single25 (29Marrie 32 (3Coha9 (11) Sepa2(2) Divor 13 (1Widow 5 (6) 

Waiting list control Mean, 55 

SD, 4.6 

NS NS NS NR M, 110 

F, 191 

Spek, 200817

 NS NS NS NS NR 

DiabetesHomko, 200718

Usual care, paper logbooks 

Mean, 29.2 SD, 6.7 

White nonHispanic, 6(24) USD <15,000, < 8 yr, 2(8) 8-12 yr, 12(48) NR 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐301

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Black nonHispanic, 12(48) Latino/Hispanic, 4(16) API, 3(12) 

10(40) 15,000-34,999, 3(12) 35,000-54,999, 3(12) >55,000, 3(12) missing, 6(24) 

12-16 yr, 10(40) 

Telemedicine (website to document glucose levels and to communicate with health-care team) 

Mean, 29.8 SD, 6.6 

White nonHispanic, 8(25) Black nonHispanic, 14(44) Latino/Hispanic, 7(22) API, 3(9) 

USD  <$15,000, 8(25) $15,000-$34,999, 8(25) $35,000-$54,999, 3(9)  >$55,000, 6(19) missing, 7(22) 

<8 yr, 4(12.5) 8-12 yr, 12(37.5) 12-16 yr, 15(47) 

NR 

Individual with Diabetes Education Centers (DEC) program n,20 

Range, 65 NS NS <8 yr, 

 8 (40) 8-12 yr,  3 (15) 12-16 yr, 9 (45) 

Tjam, 2006 NR NS NS 

19

Individual with interactive internet program 

Range, 65 NS NS <8 yr, 

8 (21.6) 8-12 yr, 5 (13.5) 

NR NS NS 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐302

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

12-16 yr, 24 (64.9) 

IDDM 42 +/- 16 NS NS NS Sex ratio 

varied from 0.42 to 0.60. The study does not specify any other detail 

Control Group (AGE +/- SE) Assessment on KAP 

44 +/- 17 

KAP –Feedback – KAP 

45 +/- 16 

KAP –Interactive computer –KAP 

41 +/- 18 

NIDDM 55 +/- 21 NS NS NS Sex ratio 

varied from 0.42 to 0.60. The study does not specify any other detail 

Wise, 1986 20 Diabetes 

Control Group (AGE +/- SE) 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐303

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Assessment on KAP 

57 +/- 23 

KAP –Feedback – KAP 

58 +/- 17 

KAP –Interactive computer –KAP 

56 +/- 16 

Diet/exercise/physical activity not obesity

 Control Mean, 46.3 

SD, 8.6 

NS NS NS NR 

Behavioral weight control program with 6-month weight and targeted 

Mean, 46.6 SD, 10.1 

NS NS NS NR 

Adachi, 200721

Untailored selfhelp booklet with 7-month self monitoring 

Mean, 46.6 SD, 9 

NS NS NS NR 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐304

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Usual care Mean, 34.4 

SD, 7.2 

White nonHispanic, 222 (53.2) 

NS 12-16 yr, 

222(61.7) 

NR F, 

222(50.5) 

Hunter, 200822

Behavioral Internet treatment 

Mean, 33.5 SD, 7.4 

White nonHispanic, 224 (58.0) 

NS 12-16 yr, 

224(63.9), 

NR F, 

224(50.0) 

"usual care". Participants randomized to the usual care group were advised to continue with their usual approach to weight loss and were given a small amount of printed information at baseline, reflecting the type of information available within primary care. 

Mean, 47.4 NS NS NS NR 

McConnon, 200723

Internet group Mean, 48.1 NS NS NS NR 

Tate, 200624 No counseling Mean, 49.9 

SD, 8.3 

Minority, 6 (9) NS >16 yr, (49) NR F, 55 (82 ) 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐305

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

Human email counseling 

Mean, 49.7 SD, 11.4 

Minority, 8(13 ) NS >16 yr, (56) NR F, (53, 87) 

Automated feedback 

Mean, 47.9 SD, 9.8 

 Minority, 6(10) NS >16 yr, (59) NR F, 54 (84) 

Control and intervention adolescents 

Mean, 13.2 SD, 1.4 

NS NS NS NR 

Williamson, 200625

Control and intervention parents 

Mean, 43.2 SD, 6.2 

NS NS NS NR 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐306

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

HIV 

Control Mean, 34.5 White, nonHispanic, (86.7) Mean, 14.7yr Living 

mean(31.9) 

Gustafson, 199926

CHESS Mean 34.8 White, nonHispanic, (81.2) Mean, 14.3 yr Living 

mean

Pain 

Distraction group Mean, 20.29 

SD, 2.38 

NS NS NS NR M, 26 

F, 38 

Borckardt, NS200727

Computerized Pain Management 

Mean, 20.52 SD, 2.86 

NS NS NS NR M, 26 

 F, 30 

NS

Obesity Morgan, 2009 28

Obesity 

One information session + Program booklet 

34 SD 11.6 NS NS Student: 14 

Non Acad 

Staff: 13 

Acad Staff: 4 

Measured by SEIFA score 1,2-0 3,4-5 5,6-9 

All M 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐307

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

MaritStatun(%) 

7,8:11 9,10:3 

 SHED IT internet 

program w/ information session and program booklet (the program facilitates self monitoring and daily diary to which the researchers respond) 

37.5 SD 10.4 NS NS Student: 14 

Non Acad 

Staff: 14 

Acad Staff: 6 

1,2-1 3,4-7 5,6-3 7,8:11 9,10:2 

All M 

NR= Not Reported, NS= Not Significant, SD= Standard Deviation, SES= Socioeconomic Status, Yr = year, M = male, F = female, API = Asian/Pacifcomputer-based training, kg = kilogram, BMI= Body Mass Index, QOL= Quality of Life, CHESS = Comprehensive Health Enhancement Support Sys

Reference List 

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Alzheimer's disease. J Neurol Neurosurg Psychiatry 2006; 77(10):1116-21. 

 2 Lorig KR, Ritter PL, Laurent DD, Plant K. The internet-based arthritis self-management program: a one-year randomized trial for 

fibromyalgia. Arthritis Rheum 2008; 59(7):1009-17. 

 3 Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 4 Buhrman M, Faltenhag S, Strom L, Andersson G. Controlled trial of Internet-based treatment with telephone support for chronic b

111(3):368-77. 
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G‐308

 5 Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 6 Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 7 Maslin AM, Baum M, Walker JS, A'Hern R, Prouse A. Using an interactive video disk in breast cancer patient support. Nurs Time

 8 Katz RC, Wertz RT. The efficacy of computer-provided reading treatment for chronic aphasic adults. J Speech Lang Hear Res 199

 9 Nguyen HQ, Donesky-Cuenco D, Wolpin S et al. Randomized controlled trial of an internet-based versus face-to-face dyspnea sel

for patients with chronic obstructive pulmonary disease: pilot study. J Med Internet Res 2008; 10(2):e9. 

10 Trautmann E, Kro?ner-Herwig B. Internet-based self-help training for children and adolescents with recurrent headache: A pilot st

11 Christensen H, Griffiths KM, Jorm AF. Delivering interventions for depression by using the internet: randomised controlled trial. B12 Hasson D, Anderberg UM, Theorell T, Arnetz BB. Psychophysiological effects of a web-based stress management system: a prosp

controlled intervention study of IT and media workers. BMC Public Health 2005; 5:78. 

13 Kerr J, Patrick K, Norman G et al. Randomized control trial of a behavioral intervention for overweight women: impact on depress

Anxiety 2008; 25(7):555-8. 

14 March S, Spence SH, Donovan CL. The Efficacy of an Internet-Based Cognitive-Behavioral Therapy Intervention for Child Anxie

Psychol 2008. 

15 Orbach G, Lindsay S, Grey S. A randomised placebo-controlled trial of a self-help Internet-based intervention for test anxiety. Beh

45(3):483-96. 

16 Proudfoot J, Goldberg D, Mann A, Everitt B, Marks I, Gray JA. Computerized, interactive, multimedia cognitive-behavioural prog

depression in general practice. Psychol Med 2003; 33(2):217-27. 

17 Spek V, Cuijpers P, Nyklicek I et al. One-year follow-up results of a randomized controlled clinical trial on internet-based cognitiv

subthreshold depression in people over 50 years. Psychol Med 2008; 38(5):635-9. 

18 Homko CJ, Santamore WP, Whiteman V et al. Use of an internet-based telemedicine system to manage underserved women with g

mellitus. Diabetes Technol Ther 2007; 9(3):297-306. 
Evidence Table 36. Description of consumer characteristics in RCTs addressing KQ 1d (impact of CHI applications on clinical outcomes) (co

G‐309

19 Tjam EY, Sherifali D, Steinacher N, Hett S. Physiological outcomes of an internet disease management program vs. in-person coun

controlled trial. 2006; 30(4):397-405. 

20 Wise PH, Dowlatshahi DC, Farrant S. Effect of computer-based learning on diabetes knowledge and control. 1986; 9(5):504-8. 

21 Adachi Y, Sato C, Yamatsu K, Ito S, Adachi K, Yamagami T. A randomized controlled trial on the long-term effects of a 1-month 

program assisted by computer tailored advice. Behav Res Ther 2007; 45(3):459-70. 

22 Hunter CM, Peterson AL, Alvarez LM et al. Weight management using the internet a randomized controlled trial. Am J Prev Med 

23 McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tri

2007; 7:206. 

24 Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored counselin

an Internet weight loss program. Arch Intern Med 2006; 166(15):1620-5. 

25 Williamson DA, Walden HM, White MA et al. Two-year internet-based randomized controlled trial for weight loss in African-Am

(Silver Spring) 2006; 14(7):1231-43. 

26 Gustafson DH, Hawkins R, Boberg E et al. Impact of a patient-centered, computer-based health information/support system. Am J 

9. 

27 Borckardt JJ, Younger J, Winkel J, Nash MR, Shaw D. The computer-assisted cognitive/imagery system for use in the managemen

2004; 9(3):157-62. 

28 Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT Randomized Controlled Trial: Evaluation of an Intern

Program for Men. Obesity (Silver Spring) 2009. 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes

G-310 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Alzheimer’s 

 Control 12 Mean, 20 

SD, 4.35 

 24wks: 

mean, 21.83 SD, 4.48 

IMIS,IPP, ChEIs 

15 Mean, 22.4 

SD, 5.7 

 24wks: 

mean, 21.33 SD, 5.74 

Tarraga, 20061

Alzheimer’s Disease Assessment Scale-Cognitive 

IPP, ChEIs 16 Mean, 

21.19 SD, 5.73 

 24wks: 

mean, 22.31 SD, 6.81 

Arthritis 

 Control 344 Mean, 2.37 

SD, 1.19 

6mos 1yr: 

mean, 2.25 SD, 1.19 

Health distress 

Online intervention 

307 Mean, 2.41 

SD, 1.2 

6mos 1yr: 

mean, 2 SD, 1.18 

 Control 344 Mean, 3.22 

SD, 0.903 

6mos 1yr 

mean, 3.29 SD, 0.885 

Activity limitation 

Online intervention 

307 Mean, 3.17 

SD, 0.973 

6mos 1yr 

mean, 3.09 SD, 0.962 

 Control 344 Mean, 

0.569  SD, 0.446 

6mos 1yr 

mean, 0.573 SD, 0.457 

Self reported global health 

Online intervention 

307 Mean, 

0.547  SD, 0.401 

6mos 1yr 

mean, 0.514 SD, 0.445 

 Control 344 Mean, 6.37 

SD, 2.22 

6mos 1yr 

mean, 6.1 SD, 2.35 

Pain 

Online intervention 

307 6mos 1yr 

mean, 5.77 SD, 2.53 

 Control 344 Mean, 4.96 

SD, 1.98 

6mos 1yr: 

mean, 5.34  SD, 2.06 

Lorig, 20082

Self efficacy 

Online intervention 

307 Mean, 5.08 

SD, 2.13 

6mos 1yr: 

mean, 5.89 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-311 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

 SD, 2.09 

Asthma 

 Control 71 Mean, 0.05 

median, 0 SD, 0.13 

 Week 12: 

mean, 0.05; median, 0; SD, 0.19 

Symptom score at nighttime 

Participants received asthma education and with interactive asthma monitoring system 

82 Mean, 0.11 

median, 0 range, 0.00-0.58 SD, 0.28 

 Week 12 

mean, 0.04 median, 0 range, 0.00-1027 SD, 0.17 

 Control 71 Mean, 0.03 

median, 0  range, 0.00-0.58 SD, 0.11 

 Week 12 

mean, 0.05 median, 0 range, 0.00-0.91 SD, 0.07 

Symptom score at daytime 

Participants received asthma education and with interactive asthma monitoring system 

82 Mean, 0.14 

median, 0 range, 0.00-1.17 SD, 0.32 

 Week 12 

 Control 71 Mean, 

219.2 median, 212.7 range, 125.0-361.9 SD, 58.0 

 Week12: 

mean, 230.0 median, 229.6 range, 147.5-374.2 SD, 57.9 

Jan, 20073

Morning PEF 

Participants received asthma education and with interactive asthma monitoring system 

82 Mean,223.1 

median, 214.6 range, 128.2-385.0 SD, 55.5 

 Week, 12 

mean, 241.9 median 220.0 range, 126.7-594.3 SD, 81.4 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-312 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

 Control 71 Mean, 

224.7 median, 213.8 range, 107.5-356.6 SD, 57.6 

 Week, 12 

mean, 235.9 median, 232.1 range, 142.5-428.4 SD, 61.6 

Night PEF 

Participants received asthma education and with interactive asthma monitoring system 

82 Mean, 

232.5 median 223.3 range, 141.4-389.4 SD, 55.3 

 Week, 12 

mean, 255.6 median, 244.1 range, 123.3-655.5 SD, 86.7 

Back pain 

Control 29 Mean, 13.7 

SD, 6.9 

 2 mos 

mean, 12.3 SD, 7.2 

CSQ- Catastrophizing 

Cognitive Behavior Intervention 

22 Mean, 13.6 

SD, 7.7 

 2 mos 

mean, 8.6 SD, 5.2 

Control 29 Mean, 2.6 

SD, 1.0 

 2 mos 

mean, 2.9 SD, .1.0 

CSQ-Ability to decrease pain 

Cognitive Behavior Intervention 

22 Mean, 3.0 

SD, 0.8 

 2 mos 

mean, 3.9 SD, 0.9 

Control 29 Mean, 2.9 

SD, 1.1 

 2 mos: 

mean, 2.9 SD, 1 

Buhrman, 20044

CSQ-Control over pain 

Cognitive Behavior Intervention 

22 Mean, 2.8 

SD, 1 

 2 mos: 

mean, 3.9 SD, 0.7 

Breast cancer 

Control 125 2 mos 

mean, 78.2 

 5 mos: 

mean, 74.7 

Gustafson, 20015 Social/family well being 

(quality of life) 

Chess 121 2 mos 

mean, 79.3 

 5 mos: 

mean, 75.8 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-313 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Control 125 2 mos 

mean, 72.8 SD, 

 5 mos 

mean, 75.3 

Emotional wellbeing (quality of life) 

CHESS 121 2 mos 

mean, 73.9 

 5 mos 

mean, 76.3 

Control 125 2 mos 

mean, 63.0 

 5 mos 

mean, 69.9 

Functional wellbeing (quality of life) 

CHESS 121 2 mos 

mean, 62.2 

 5 mos 

mean, 70.4 

Control 125 2 mos 

mean, 63.3 

 5 mos 

mean, 64.7 

Breast cancer concerns (quality of life) 

CHESS 121 2 mos 

mean, 65.1 

 5 mos 

mean, 67.6 

Control 80 Mean, 0.18 

SD, 0.53 

 9 mos: 

mean, 0.11 SD, 0.45 

Gustafson, 20086 Quality of life 

Internet 75 Mean, -0.02 

SD, 0.56 

 9 mos: 

mean, 0.07 SD, 0.45 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-314 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

CHESS 80 Mean, 0.02 

SD, 0.54 

 9 mos: 

mean, 0.18 SD, 0.54 

Control 48 Score on 

HAD 

Maslin, 9 mos later 19987 Anxiety and depression 

 IVD shared decision program 

51 9 mos later 

Chronic adult aphasia 

Control 15 Mean, 59.5 

SD, 16.2 

 Week, 26 

mean, 61.3 SD, 17.4 

Computer reading treatment 

21 Mean, 57.3 

SD, 17.9 

 Mean, 66.4 

SD, 19.4 

Porch Index of Communicative Ability (percentiles) Overall 

Computer stimulation 

19 Mean, 51.9 

SD, 20.3 

 Mean, 56.3 

SD, 20.9 

Control 15 Mean, 55.6 

SD, 16.0 

 Week, 26 

mean, 58.1 SD, 19.1 

Computer reading treatment 

21 Mean, 54.4 

SD,17.8 

 Mean, 62.3 

SD, 22.3 

Porch Index of Communicative Ability (percentiles) Verbal 

Computer stimulation 

19 Mean, 49.3 

SD, 24.6 

 Mean, 50.6 

SD, 24.5 

Control 15 Mean, 72.2 

SD, 24.8 

 Week, 26 

mean, 72.2 SD, 23.7 

Katz, 19978

Western Aphasia Battery Aphasia "Quotient" Computer 21 Mean, 68.9 Mean, 73.6 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-315 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

reading treatment 

SD, 24.3 SD, 22.6 

Computer stimulation 

19 Mean, 61.9 

SD, 29.5 

 Mean, 63.4 

SD, 28.5 

Control 15 Mean, 7.0 

SD, 3.2 

 Week, 26 

mean, 6.7 SD, 3.4 

Computer reading treatment 

21 Mean, 6.7 

SD, 3.0 

 Mean, 7.3 

SD, 2.9 

Western Aphasia Battery Aphasia "Repetition" 

Computer stimulation 

19 Mean, 6.0 

SD, 3.5 

 Mean, 6.1 

SD, 3.4 

COPD 

Control 20 Score o n 

CRQ dyspnea subscale (score range from 5-35) rating 5 activities ion a Likert scale of 1-7 points. mean, 15.9 SD, 5.4 

3 mos mean, 19.2 SD, 5.8 

 6 mos: 

mean, 19.9 SD, 6.2 

Nguyen, 20089

Score on CRQ subscale for dyspnea with ADLs 

Electronic dyspnea self management program 

19 Mean, 18.8 

SD, 6.2 

3 mos mean, 22.3 SD, 4.6 

 6 mos: 

mean, 21.3 SD, 6 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-316 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Headache 

 Control 8 Number of 

headaches mean, 13.8 SD, 10.1 

Posttreatment mean, 12.3 SD, 8.6 

 6 mos followup Frequency 

CBT 8 Number of 

headaches mean, 15.2 SD, 10.9 

Posttreatment mean, 8.1 SD, 8 

 6 mos followup: 

mean, 8 SD, 7.8 

 Control 8 Duration of 

headaches: mean, 6 SD, 5-24 

posttreatment mean, 5.1 SD, 2-23 

 6 mos followup Duration 

CBT 8 Duration of 

headaches: mean, 3.8 SD, 2-24 

Posttreatment mean, 3.5 SD, 2.24 

 6 mos followup 

mean, 3.3 SD, 1.23 

 8 Intensity of 

Headaches mean, 5.8  SD, 1.5 

Posttreatment mean, 5 SD, 1.3 

 6 mos followup Intensity 

CBT 8 Intensity of 

Headaches mean, 4.7  SD, 0.8 

Posttreatment mean, 4.7 SD, 1.3 

 6 mos followup 

mean, 4.2 SD, 1.9 

 8 PCS-C 

mean, 36.4 SD, 9.7 

Posttreatment mean, 37.3 SD, 7.9 

 6 mos followup 

Trautman, 200810

Pain catastrophizing 

CBT 8 Mean, 30 

SD, 5.9 

Posttreatment 6 mos follow- up 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-317 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

mean, 28.3 SD, 5.8 

Mental Health (Depression/Anxiety)

 159 Mean score 

point improvement over baseline mean, 21.6 SD, 11.1 

 6 wks: 

mean, 1.1 SD, 8.4 

Blue Pages: Computer based psycho education website offering information about depression 

136 Mean score 

point improvement over baseline mean, 21.1 SD, 10.4 

 6 wks 

mean, 3.9 SD, 9.1 

Christensen, 200411

Center for Epidemiologic depression scale 

Mood GYM: Computer based Cognitive Behavior therapy 

136 Mean score 

point improvement over baseline mean, 21.8 SD, 10.5 

 6 wks 

mean, 4.2 SD, 9.1 

 Control 156 Changes in 

self rated measures and biological markers covariated for baseline scores 

 6 mos follow 

up 

Hasson, 200512 Biological marker: 

dehydroeoiandosterone sulphate 

Web-based stress Management system 

121 Changes in 

self rated measures and biological markers covariated 

 6 mos followup 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-318 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

for baseline scores 

 Control 156 Changes in 

self rated measures and biological markers covariated for baseline scores 

 6 mos 

Follow- up 

Nero peptide 

Web-based stress management system 

121 Changes in 

self rated measures and biological markers covariated for baseline scores 

 6 mos 

follow up 

 Control 156 Changes in 

self rated measures and biological markers covariated for baseline scores 

 6 mos 

follow up 

Chromogranin 

Web-based stress management system 

121 Changes in 

self rated measures and biological markers covariated for baseline scores 

 6 mos follow 

up 

Kerr, 200813

CES-D score Control 146 CES-D 

score (10 or 

6 mos 12 mos 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-319 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

greater probable depression)mean, 7.5 SD, 4.43 

PACEi 146 CESD 

score (10 or greater probable depression)mean, 7.38 SD, 4.96 

6 mos 12 mos 

 Control Mean, 5.83 

SD, 0.6 

Post treatment at 10 wks mean, 5.14 SD, 1.43 

Clinical severity at 6 mos rating 

 Web based intervention 

 Mean, 6.07 

SD, 0.58 

Post treatment at 10 wks mean, 4.3 SD, 1.58 

at 6 mos mean, 2.32 SD, 1.78 

 

 Control Mean, 

51.72 SD, 5.24 

Post treatment at 10 wks mean, 54.93 SD, 8.91 

at 6 mos 

March, 200814

Children global assessment score 

 Web based intervention 

 Mean, 

50.87 SD, 3.95 

Post treatment at 10 wks 

at 6 mos mean, 73.67 

 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-320 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

mean, 61.73 SD, 8.71 

SD, 9.14 

 Control 1 10 wks: 

3.4 

6 mos 

Does not meet criteria for any anxiety disorder 

 Web based intervention 

17 10 wks: 

16.7 

6 mos: 60.7 

Control 28 Mean, 

59.18 SD, 12.20 

 Post 

treatment: mean, 54.25 SD, 11.31 

Test Anxiety Inventory 

Cognitive Behavior Therapy group (CBT) 

30 Mean, 

58.14 SD, 8.43 

 Post 

treatment: mean, 47.31 SD, 9.49 

 Control 28 Mean, 

12.73 SD, 1.66 

 Post 

treatment mean, 12.62 SD, 2.04 

Anxiety Hierarchy Questionnaire 

Cognitive Behavior Therapy group (CBT) 

30 Mean, 

12.64 SD, 1.67 

 Post 

treatment mean, 10.38 SD, 3.45 

 Control 28 AH test 

mean, 48.74 SD, 8.18 

 Post 

treatment mean, 51.58 SD, 9.82 

AH tests (perceptual and numerical) 

Cognitive Behavior Therapy group (CBT) 

30 Mean, 

44.55 SD, 13.32 

 Post 

treatment mean, 46.6 SD, 13.3 

Orbach, 200715

General SelfEfficacy Scale Control 28 Mean, 53.46 

 SD, 13.09 

 Post 

treatment: mean, 44.69 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-321 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

 SD, 7.67 

Cognitive Behavior Therapy group (CBT) 

30 Mean, 

57.97  SD, 7.84 

 Post 

treatment: mean, 60.24  SD, 7.67 

 Control 42 Mean, 

24.08 SD, 9.78 

 6mos 

mean, 16.07 SD, 13.06 

BDI(beck depression inventory) 

beating the blues 44 Mean, 

25.38 SD, 11.05 

 6mos 

mean, 9.61 SD, 10.06 

 Control 38 Mean, 

19.39 SD, 9.72 

 6mos 

mean, 11.32 SD, 9.61 

BAI (beck anxiety inventory) 

beating the blues 40 Mean, 

18.33 SD, 9.61 

 6mos 

mean, 8.73 SD, 7.66 

Control 42 Mean, 

18.46  SD, 8.25 

 6mos 

mean, 12.1 SD, 10.11 

Proudfoot, 200316

WSA (work and social adjustment scale) Beating the blues 45 Mean, 

19.89  SD, 9.29 

 6mos 

mean, 9.11 SD, 8.97 

Control 58 Mean, 

18.31 SD, 7.88 

 12mos: 

mean, 12.88 SD, 10.1 

Group CBT 66 Mean, 

17.99 SD, 9.39 

 12mos: 

mean, 12.14 SD, 8.76 

Spek, 200817

Treatment response after 1 yr treatment 

Internet based intervention 

58 Mean, 

19.07 SD, 7.04 

 12mos: 

mean, 10.45 SD, 8.05 

Diabetes

Insulin therapy Control 25 4(n,1) 

Telemedicine 28 31 

(n,10) 

Homko, 200718

FBS Control 25 FBS mg/dl 37 wks 

gestation mean, 88.6 SD, 9.5 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-322 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Telemedicine 32 37 wks 

gestation mean, 90.8 SD, 11.8 

Control 25 A1c at 

delivery (%) 

 37 wks 

gestation mean, 6.2 SD, 2.2 

A1c at time of delivery 

Telemedicine 32 37 wks 

gestation mean, 6.1 SD, 0.8 

 Control 19 Mean, 6.8 

SD, 1.0 

3 mos mean, 6.8 SD, 1 

 12 mos: 

mean, SD, 

A1C (%) 

Individuals with interactive internet program 

34 Mean, 6.7 

SD, 1 

3 mos mean, 6.5 SD, 1 

 12 mos: 

mean, SD, 

 Control 8 Mean, 7.98 

SD, 2.07 

3 mos 6 mos 12 mos 

mean, 7.71 SD, 2.14 

FBG (MMOL/L) 

Individuals with interactive internet program 

17 Mean, 8.51 

SD, 2.46 

3 mos 6 mos 12 mos 

mean, 8.02 SD, 2.17 

 Control 9 mean, 5.38 

 SD, 1.13 

3 mos 6 mos 12 mos 

mean, 4.6 SD, 0.9 

TC (MMOL/L 

Individuals with interactive internet program 

16 Mean, 4.98 

 SD, 1.11 

3 mos 6 mos 12 mos 

mean, 5.15 SD, 1.42 

 Control 14 Mean, -0.09 

SD, 0.12 

3 mos 6 mos 

mean, 2.1 SD, 0.76 

 12 mos 

Tjam, 200619

TG (MMOL/L) 

Individuals with interactive internet program 

24 3 mos 

mean, 1.9 SD, 1.1 

6 mos 12 mos 

Wise, 1986 IDDM Patients 20Diabetes Knowledge Index 

(KAP Questionnaire) 

Assessment of KAP only 

24 Knowledge 

Score: 79 SE 2 

82 SE 2 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-323 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Assessment + Feedback 

4—6mo 22 78 SE 2 83 SE 3 

Assessment + Interactive computer 

20 77 SE 2 83 SE 2 

NIDDM Patients 

Assessment of KAP only 

22 Knowledge 

UNS 

UNS 

Assessment + Feedback 

24 64 SE 2 73 SE 2 

Knowledge Index (KAP Questionnaire) 4—6mo 

Assessment + Interactive computer 

21 60 SE 3 70 SE 2 

IDDM Patients 

Control 20 HBA1c: 8.9% 8.8% Assessment of KAP only 

24 9.1 SE 0.2 8.4 SE 0.1 

Assessment + Feedback 

22 9.3 SE 0.5 8.1 SE 0.4 

Knowledge Index (KAP Questionnaire) 4—6mo 

Assessment + Interactive computer 

20 9.3 SE 0.2 8.6 SE 0.3 

NIDDM Patients 

Control 21 HBA1c: 8.7% 8.5% Assessment of KAP only 

22 9.6 SE 0.4 8.8 SE 0.3 

Assessment + Feedback 

24 9.2 SE 0.4 7.9 SE 0.4 

Knowledge Index (KAP Questionnaire) 4—6mo 

Assessment + Interactive computer 

21 8.7 SE 0.7 7.9 SE 0.6 

Diet/exercise/physical activity not obesityAdachi, 200721 % weight loss Control (Group B) 50 1 mos mean, -0.05 

SD, 1.4 

3 mos mean, -1.6 SD, 2.3 

 7 mos 

mean, -2.2 SD, 3.5 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-324 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Computer tailored program with 6-mos weight and targeted behavior’s selfmonitoring, (Group KM) 

36 1 mos 

mean, -1.8 SD, 1.9 

3 mos mean, -3.6 SD, 3.3 

 7 mos 

mean, 4.7 SD, 4.5 

Computer tailored program only, (Group K) 

44 1 mos 

mean, -1.5 SD, 1.6 

3 mos mean, -2.6 SD, 2.8 

 7 mos 

mean, -3.3 SD, 4.3 

untailored selfhelp booklet with 7-mos selfmonitoring of weight and walking, (Group BM) 

53 1 mos 

mean, -0.08 SD, 1.3 

3 mos mean, -2 SD, 2.5 

 7 mos 

mean, -2.6 SD, 3.4 

 Control 222 Mean, 86.6 

SD, 14.7 

 6mos: 

mean, 87.4 SD, 14.7 

Hunter, 200822 Body weight (kg) 

BIT 224 Mean, 87.4 

SD, 15.6 

 6mos: 

mean, 85.5 SD, 15.8 

Control 77 6 mos 12 mos: 

18 

McConnon, 200723

Loss of 5% or more body weight (12 mos) 

internet group 54 6 mos 12 mos: 

22 

Control 59 3mos 

mean, -2.8 SD, 3.5 

 6 mos 

mean, -2.6 SD, 5.7 

Tate, 200624 Weight loss 

Tailored Computer44 3mos mean, -5.3 6mos mean, -4.9 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-325 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Automated Feedback 

SD, 4.2 SD, 5.9 

Human Email Counseling (HC) 

52 3mos 

mean, -6.1 SD, 3.9 

 6mos 

mean, -7.3 SD, 6.2 

Control 50 24 mos: 

mean A,6.3 P,-0.06 SD, A,1.6 P,0.89 

Body weight (kg) 

Interactive Nutrition education program and internet counseling behavioral therapy for the intervention group 

47 24 mos: 

mean A: 4.4 P: -1.1 SD A: 1.7 P: 0.91 

 Control 50 6 mos 12 mos 18 mos 24 mos 

mean A:1.2, P:0.04 SD A:.65, P;.34 

BMI 

Interactive Nutrition education program and internet counseling behavioral therapy for the intervention group 

47 Mean, 

A:36.4, P;38.4 SD A:7.9, P:7.2 

6 mos 12 mos 18 mos 24 mos 

mean  A:0.73, P:-0.55 SD A:.66, P:0.34 

Williamson, 200625

Weight loss Control 50 6 mos 12 mos 18 mos 24 mos 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-326 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

mean A:0.84, P:0.51 SD, A:0.72, P:0.46 

behavior (body fat %) 

Interactive Nutrition education program and internet counseling behavioral therapy for the intervention group 

47 Mean 

A:45.9, P:48.4 SD A:7.5 P:6.3 

6 mos 12 mos 18 mos 24 mos 

mean, A:-.08, P:0.36 SD A:0.71, P:0.46 

 Control 50 BMI 6 mos 12 mos 18 mos 24 mos 

mean, A:-0.001 SD, A:0.003 

BMI (percentile) 

Interactive Nutrition education program and internet counseling behavioral therapy for the intervention group 

47 6 mos 12 mos 18 mos 

mean A:-0.004 SD A:0.003 

24 mos 

HIV 

Active life Control 97 1.37(22) 

CHESS 107 1.66(27) 

Social support Control 97 4.24(24) 

CHESS 107 4.47(27) 

Control 97 3.64(23) 

Gustafson, 199926

Participation in health care CHESS 107 4.15(24) 

PainBorckardt, Cold Pressor Control 64 Seconds Immediate 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-327 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Tolerance post: 

mean, 73.25 

200727

 CACIS 56 Seconds Immediate 

post: mean, 86.25 

Obesity 

Change in body wt. 3m 

Prog info + Booklet gp 

31 Loss of 

Weight: −3.0 (−4.5, −1.4) 

KG

 SHED IT group 34 Loss of 

Weight: −4.8 (−6.4, −3.3) 

KG

Change in body wt. 6m 

Prog info + Booklet gp 

31 Loss of 

Weight: −3.5 (−5.5, −1.4) 

 SHED IT group 34 Loss of 

Weight: −5.3 (−7.3, −3.3) 

Waist circumference (cm) 3m

Prog info + Booklet gp 

31 LOSS: −4.4 

(−6.3, −2.5) 

CM 

SHED IT group 34 LOSS: −5.2 

(−7.1, −3.4) 

CM 

Waist circumference (cm) 6m 

Prog info + Booklet gp 

31 −5.6 (−7.7, 

−3.5) CM 

SHED IT group 34 −7.0 (−9.1, 

−4.9) CM 

BMI (kg/m2) 3m Prog info + 

Booklet gp 

31 −0.9 (−1.4, 

−0.5) KG/M^2 

SHED IT group 34 −1.5 (−2.0, 

−1.0) KG/M^2 

BMI (kg/m2) 6m Prog info + 

Booklet gp 

31 −1.1 (1.7, 

−0.5) 

SHED IT group 34 −1.6 (−2.2, 

−1.0) 

Systolic blood pressure 3m 

Prog info + Booklet gp 

31 −8 (−12, −3) 

MM HG 

Morgan, 2009 28 Obesity

SHED IT group 34 −6 (−10, −1) 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-328 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

MM HG 

Systolic blood pressure 6m 

Prog info + Booklet gp 

31 −10 (−14, −6) 

SHED IT group 34 −10 (−14, −7) 

Diastolic blood pressure 3m 

Prog info + Booklet gp 

31 −6 (−10, −2) 

MM HG 

SHED IT group 34 −4 (−8, −1) 

MM HG 

Diastolic blood pressure 6m 

Prog info + Booklet gp 

31 −5 (−10, −2) 

SHED IT group 34 −6 (−11, −1) 

Resting heart rate 3m 

Prog info + Booklet gp 

31 −7 (−11, −3) 

BPM 

SHED IT group 34 −9 (−12, −5) 

BPM 

Resting heart rate 6m 

Prog info + Booklet gp 

31 −7 (−12, −3) 

BPM 

SHED IT group 34 −6 (−11, −2) 

BPM 

Physical activity (mean steps/day) 3m 

Prog info + Booklet gp 

31 Went Up by: 

976 (−12, 1,965) STEPS/DAY 

SHED IT group 34 Went Up by: 

1,184 (234, 2,133) STEP/DAY 

Physical activity (mean steps/day) 6m 

Prog info + Booklet gp 

31 Went Up by: 

1,302 (241, 2,363) 

SHED IT group 34 Went Up by: 

938 (−90, 1,966) 

Energy intake (kJ/day) 3m 

Prog info + Booklet gp 

31 Went down 

by: −2,068 (−3,089, −1,047) KJ/DAY 

SHED IT group 34 Went down 

by: −3,195 (−4,159, −2,230) KJ/DAY 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-329 

Author, year Outcome 

Control Intervention n 

Measure at BL 

Measure at time point 2 

Measure at time point 3 

Measure at time point 4 

Measure at final time point 

Energy intake (kJ/day) 6m 

Prog info + Booklet gp 

31 Went down 

by: −1,881 (−3,087, −676) KJ/DAY 

SHED IT group 34 Went down 

by: −3,642 (−4,764, −2,521) KJ/DAY 

BL = baseline, SD = standard deviation, , mos = months, wks = weeks, CHESS = Comprehensive Health Enhancement Support System, CES-D = Ce

Studies Depression Scale, CBT = cognitive behavior therapy, CACIS = Computer-Assisted Cognitive/Imagery System, FBG = Fasting blood glucose, 

TC = total cholesterol, TG = triglycerides, A1c = glycosylat ed hemoglobin 

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13 Kerr J, Patrick K, Norman G et al. Randomized control trial of a behavioral intervention for overweight women: impact on depress

Anxiety 2008; 25(7):555-8. 

14 March S, Spence SH, Donovan CL. The Efficacy of an Internet-Based Cognitive-Behavioral Therapy Intervention for Child Anxie

Psychol 2008. 

15 Orbach G, Lindsay S, Grey S. A randomised placebo-controlled trial of a self-help Internet-based intervention for test anxiety. Beh

45(3):483-96. 

16 Proudfoot J, Goldberg D, Mann A, Everitt B, Marks I, Gray JA. Computerized, interactive, multimedia cognitive-behavioural prog

depression in general practice. Psychol Med 2003; 33(2):217-27. 

17 Spek V, Cuijpers P, Nyklicek I et al. One-year follow-up results of a randomized controlled clinical trial on internet-based cognitiv

subthreshold depression in people over 50 years. Psychol Med 2008; 38(5):635-9. 

18 Homko CJ, Santamore WP, Whiteman V et al. Use of an internet-based telemedicine system to manage underserved women with g

mellitus. Diabetes Technol Ther 2007; 9(3):297-306. 

19 Tjam EY, Sherifali D, Steinacher N, Hett S. Physiological outcomes of an internet disease management program vs. in-person coun

controlled trial. 2006; 30(4):397-405. 

20 Wise PH, Dowlatshahi DC, Farrant S. Effect of computer-based learning on diabetes knowledge and control. 1986; 9(5):504-8. 

21 Adachi Y, Sato C, Yamatsu K, Ito S, Adachi K, Yamagami T. A randomized controlled trial on the long-term effects of a 1-month 

program assisted by computer tailored advice. Behav Res Ther 2007; 45(3):459-70. 

22 Hunter CM, Peterson AL, Alvarez LM et al. Weight management using the internet a randomized controlled trial. Am J Prev Med 23 McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tri

2007; 7:206. 
Evidence Table 37. All outcomes KQ 1d, impact of CHI applications on clinical outcomes (continued)

G-331 

24 Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored counselin

an Internet weight loss program. Arch Intern Med 2006; 166(15):1620-5. 

25 Williamson DA, Walden HM, White MA et al. Two-year internet-based randomized controlled trial for weight loss in African-Am

(Silver Spring) 2006; 14(7):1231-43. 

26 Gustafson DH, Hawkins R, Boberg E et al. Impact of a patient-centered, computer-based health information/support system. Am J 

9. 

27 Borckardt JJ, Younger J, Winkel J, Nash MR, Shaw D. The computer-assisted cognitive/imagery system for use in the managemen

2004; 9(3):157-62. 

28 Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT Randomized Controlled Trial: Evaluation of an Intern

Program for Men. Obesity (Silver Spring) 2009. 
Evidence table 38. Description of RCTs addressing the impact of CHI applications on economic outcomes (KQ1e)

G-332 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control 

Asthma Joseph, 20071

Individual interested in their own health 

Interactive consumer website 

Remote location: school 

NS 9-11 grade, 

Current asthma 

NS Generic 

asthma website 

T

Breast cervical prostate and laryngeal cancer Jones, 19992

Individual interested in their own health care 

Interactive consumer website 

Clinician office 

December 1996-December 1997/NS 

Breast, laryngeal, prostate, Cervical cancer patients receiving care at oncology center 

Receiving palliative treatment, no knowledge of diagnosis, visual or mental handicap , severe pain 

Booklet information 

Gcc

Obesity McConnon, 20073

Individual interested in their own health care 

Interactive consumer website 

Home/ residence 

NS 18 - 65 yr, 

BMI 30 or more, able to access internet at least 1 time a week, able to read and write English 

NS Usual care 

NS = Not specified, BMI = Body mass index, Yr = year 

Reference List 

 1. Joseph CL, Peterson E, Havstad S et al. A web-based, tailored asthma management program for urban African-American high scho

Crit Care Med 2007; 175(9):888-95. 

 2. Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 199 3. McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tri

2007; 7:206. 
Evidence table 39. Description of consumer characteristics in studies addressing the impact of CHI applications on economic outcomes (KQ1e

G-333 

Author, year 

Control Intervention Age 

Race, n(%) Income 

Education, n(%) SES Gender, n (%) 

Asthma Joseph, Generic asthma website 20071 Tailored website 

Mean, 15.3 SD, 1 

NS NA High school 

students 

NS F, 199 (63.4) 

Cancer: breast, lung, prostate and esophageal 

Booklet information General computer information 

Jones, 19992

Personal computer information 

No baseline participant characteristics were provided 

Obesity 

"usual care". Participants randomized to the usual care group were advised to continue with their usual approach to weight loss and were given a small amount of printed information at baseline, reflecting the type of information available within primary care. 

Mean, 47.4 

McConnon, 20073

Internet group Mean, 48.1 

NS NS NS NS NS 

* Only “all participants” data was provided in this paper with a notation that there were no differences between the treatment and control groups 

NS = not specified, SD = standard deviation, BMI = body mass index, kg = kilogram, SES= Socioeconomic Status, NA = Not Applicable, QoL = qual

Reference List 

 1. Joseph CL, Peterson E, Havstad S et al. A web-based, tailored asthma management program for urban African-American high scho

Crit Care Med 2007; 175(9):888-95. 

 2. Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 199

 3. McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tri

2007; 7:206.
Evidence table 4. Outcomes in studies addressing Key Question 1a, impact of CHI applications on health care processes 

G-21 

Author, Year Outcome 

Control Intervention n Measure at BL 

Measure at final time point 

ratios at time poin

AsthmaBartholomew, Control: Usual care 63 20001 Intervention 

Watch, Discover, Think and Act (An Interactive multimedia application on CDROM) 

70 

Control: participants used an asthma diary 

66 Limitation in activity 

No:19 (28) Yes: 49 (72) Peak flow measurement ever: No 12 (18) Yes 20 (29) Missing data: 36 (53) Coughing No7(10) Yes 61(90) Trouble sleeping No Yes 

Limitation in activity No:32 (53) Yes: 28(47) Peak flow measurement No 27(40) Yes 26 (38) Missing data 36 (53) Coughing No21(35) Yes39(65) Trouble sleeping No Yes 

Guendelman, 20022

Health and quality of life and process evaluation 

Intervention: Health Buddy(is a personal and interactive communication device) 

68 Limitation in activity 

No: 22 (33) Yes:44 (67) Peak flow measurement ever No 14 (21) Yes 22(33) Missing data 30 (45) Coughing No10(15) Yes56(85) Trouble sleeping No Yes 

Limitation in activity No: 42 (68) Yes:20(32) Peak flow measurement ever No 38 (58) Yes 19(29) Missing data 09 (14) Coughing No23(37) Yes39(63) Trouble sleeping No Yes 

0.52 

Jan, 20073 Monitoring adherence Control 71 Mean, 85.6 12 week mean, 93.5 
Evidence table 4. Outcomes in studies addressing Key Question 1a, impact of CHI applications on health care processes (continued) 

G-22 

Author, Year Outcome 

Control Intervention n Measure at BL 

Measure at final time point 

ratios at time poin

Asthma education and an interactive asthma monitoring system 

82 Mean, 83.5 12 week 

mean, 99.7 

Therapeutic Control 15 adherence Asthma education 

and an interactive asthma monitoring system 

23 

 Control 71 Mean, 93.2 12 week 

mean, 53.4 

Adherence to daily diary entry Asthma education 

and an interactive asthma monitoring system 

82 Mean, 96 12 week 

mean, 82.5 

 Control 71 Mean, 80.3 12 week 

mean, 93.4 

Therapeutic adherence: DPI or MDI plus spacer technique score 

Asthma education and an interactive asthma monitoring system 

82 Mean, 82.1 12 week 

mean, 96.5 

 Control 97 Mean, 82.3 12 week 

mean, 42.1 

Peak flow meter technique score 

Asthma education and an interactive asthma monitoring system 

99 Mean, 83.5 12 week 

mean, 63.2 

 Control 44 Mean, 90.7 

SD, 114.8 

12 months mean, 41 SD, 82 

Days of quick relief medicine 

Internet-enabled asthma education program 

42 12 months 

mean, 26.3 SD, 56.6 

 Control 44 Mean, 6.4 

 SD, 10.5 

12 months mean, 1.3  SD, 2.2 

Krishna, 20034

Urgent physician visit 

Internet-enabled asthma education program 

42 Mean, 6.6 

 SD, 10.5 

12 months mean, 0.8  SD, 1.5 
Evidence table 4. Outcomes in studies addressing Key Question 1a, impact of CHI applications on health care processes (continued) 

G-23 

Author, Year Outcome 

Control Intervention n Measure at BL 

Measure at final time point 

ratios at time poin

 Control 44 Mean, 1.2 

SD, 2.8 

12 months mean, 0.6 SD, 1.1 

Emergency room visit 

Internet-enabled asthma education program 

42 Mean, 2 

SD, 4.2 

12 months mean, 0.1 SD, 0.4 

Control 44 Mean, 350.53 

SD, 649.61 

12 months mean, 753.88 SD, 706.94 

Daily dose of inhaled corticosteroids 

Internet-enabled asthma education program 

42 Mean, 353.09 

SD, 615.83 

12 months mean, 433.51 SD, 569.13 

Use of contraception

 Control NA Initial visit 

mean, 11.26 SD, 15.93 

1 yr mean, 6.38 SD, 13.45 

Oral contraceptive efficacy Chicago Computerized 

decision aid 

NA Initial visit 

mean, 4.59 SD, 9.2 

1 yr mean, 5.66 SD, 8.45 

 Control NA Initial visit 

mean, 4.8 SD, 5.58 

1 yr mean, 4.83 SD, 9.15 

Chewning, 19995

Oral contraceptive efficacy Madison Computerized 

decision aid 

NA Initial visit 

mean, 2.09 SD, 2.2 

1 yr mean, 4 SD, 8.26 

NA= Not applicable, NS= Not Significant, Yr = year, BL = baseline, SD = standard deviation 
Evidence table 4. Outcomes in studies addressing Key Question 1a, impact of CHI applications on health care processes (continued) 

G-24 

Reference List 

 1. Bartholomew LK, Gold RS, Parcel GS et al. Watch, Discover, Think, and Act: Evaluation of computer-assisted instruction to impr

management in inner-city children. 2000; 39(2-3):269-80. 

 2. Guendelman S, Meade K, Benson M, Chen YQ, Samuels S. Improving asthma outcomes and self-management behaviors of inner-

randomized trial of the Health Buddy interactive device and an asthma diary. 2002; 156(2):114-20. 

 3. Jan RL, Wang JY, Huang MC, Tseng SM, Su HJ, Liu LF. An internet-based interactive telemonitoring system for improving child

Taiwan. Telemed J E Health 2007; 13(3):257-68. 

 4. Krishna S, Francisco BD, Balas EA, Konig P, Graff GR, Madsen RW. Internet-enabled interactive multimedia asthma education p

trial. Pediatrics 2003; 111(3):503-10. 

 5. Chewning B, Mosena P, Wilson D et al. Evaluation of a computerized contraceptive decision aid for adolescent patients. Patient E

38(3):227-39. 
Evidence table 40. All outcomes in studies addressing the impact of CHI applications on economic outcomes (KQ1e) 

G‐334

Author, year Outcome 

Control Intervention n Measure at BL Measure at fina

Asthma 

 Control 152 12 months: 

no cost estimate 

Joseph, 20071 Cost of program delivery 

Treatment 162 

No baseline measure of cost 

12 months: cost of referral c$6.66/per treatm

Cancer: breast cervical prostate and laryngeal cancer 

Control: books alone 162 General computer information 143 

Cost of the computer information system—Manual extraction of Patient data 

Tailored computer information 162 9x the cost of a Control: books alone 162 General computer information 143 

Cost of the computer information system—use of electronic patient record Tailored computer information 162 No difference in 

tailored systems 

Control: books alone 162 £7/patient General computer information 143 £2.8/patient 

Jones, 19992

Materials cost 

Tailored computer information 162 

No baseline measure of cost 

NS 

Obesity 

Total costs Control 110 £276.12 

Website (internet group) 111 £992.40* Control 110 NS 

McConnon, 20073

Incremental costeffectiveness Website (internet group) 111 

No baseline costs reported 

£39,248/QALY 

*Difference in cost is due to the cost of developing the website; when this fixed cost was removes; total costs were lower in the intervention group (ac

BL = baseline, NS = not specified, QALY = quality-adjusted life year, $ = United States Dollars 

Reference List 

 1. Joseph CL, Peterson E, Havstad S et al. A web-based, tailored asthma management program for urban African-American high scho

Crit Care Med 2007; 175(9):888-95. 

 2. Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 199

 3. McConnon A, Kirk SF, Cockroft JE et al. The Internet for weight control in an obese sample: results of a randomised controlled tri

2007; 7:206.
Evidence Table 41. Description of all study designs addressing barriers KQ 2

G-335 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

Simon, 20081 Survey/ interview User level Breast cancer Individuals interested in their 

own health care , women getting mammogram 

Interactive electronic tool 

Clinician office 

2007/  NS 

Female schedula mamm

Cimino, 20012 Survey (crosssectional) System level 

User level 

Usage study of general med group 

Individuals interested in their own health care 

Access to patient records with tailored feedback 

NS 1999/ 

NS 

Keselman, 20073 Survey System level 

User level 

Multiple condition 

Individuals interested in their own health care 

Interactive consumer website 

NS 2006 Who vie

paper oelectronrecords the past 

Shaw, 20084 Applicability of a CSHIP 

model to discern why people with cancer seek online information to cope with disease 

User level Breast 

cancer 

Individuals interested in their own health care 

Interactive consumer website 

NS May2001 Women 

participaat or beof the fepoverty resided one of 5Wisconscounties defined Office oManageBudget within oof diagnbreast chad metbreast cnot homable to runderstainformed letter 

Nijland, 20085 System level 

User Level 

Design of the Internetbased 

Individuals interested in their own health care, non-medical 

Interactive consumer website 

Home/ residence workplace 

NS At least 

Dutch shad expwith usin
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-336 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

applications for self-care 

caregiver the Inte

based applicat

Morak, 20086 Pilot User level Obesity Individuals interested in their 

own health care 

Interactive consumer website 

Clinician office 

NS 

Steele, 20077 RCT System level Diet/exercis e/ physical 

activity NOT obesity 

Individuals interested in their own health care 

Interactive consumer website 

Local community of Rock Hampton, Queensland 

12 weeks More th

years olBoth mafemale Functionmobile m10 min Inactive Access internet Singed consentLess thayears Functionimmobilthan 10 Active No acceinternet Did not informed 

Wangberg, 20088

RCT User level Diabetes Individuals 

interested in their own health care 

Interactive consumer website 

NS NS 17-67 y

Type I odiabetes access internet 

Lober, 20069 Survey User level Computer literacy 

Computer anxiety Cognitive impairment Health literacy 

Individuals interested in their own health care 

Patient kiosk Home/ 

residence Remote location, common computer area 

8 months Residen

publicly subsidizhousing 
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-337 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

Physical impairment 

Stock, 200610

Withinsubject design 

System level 

Usability Individuals 

interested in their own health care 

Palmtop computer 

NS NS 18-54 y

both mafemale intellectdisabilithearing motor sinteract palmtop 

Mangunkusumo, 200711 RCT System level Diet/exercis e/ physical 

activity NOT obesity 

Individuals interested in their own health care, student--with parental consent 

Internet site Remote 

location, at a secondary school 

NS 

Ferney, 200612

Qualitative; semistructured interview 

System level 

Diet/exercise/ physical activity NOT obesity 

Individuals interested in their own health care 

Interactive consumer website, 

Unspecified location of a study group 

NS 

Temesgen, 200613 Survey System level 

User level 

HIV/AIDS Individuals 

interested in their own health care 

Interactive consumer website, 

Home/ residence 

6 months HIV pos

Owen, 200414 Survey User level Breast cancer Individuals interested in their 

own health care 

Interactive consumer website, 

Clinician office 

1999/ NS Appoint

Dept of Hematoology at UniversAlabamBirmingCompreCancer Histologconfirmecancer 
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-338 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

Lahdenpera, 200015

Interviewed before use of IT application 

User level Hypertensi

on 

Individuals interested in their own health care 

Personal monitoring device 

Clinician office 

Between summer 1997 and Autumn 1998 

16-64 yHyperteone yeaHypertemedicatone yeaor none Three sblood prreadings exceedemm Hg 

Weber, 199816

 User level DSM-III-R 

psychiatric disorders 

Individuals interested in their own health care 

Interactive self assessment 

NS NS Mixed D

psychiadisorderhealthy volunteehospitalpsych d

Jenkinson, 199817

Qualitative study 

User level Prostate 

cancer 

Individuals interested in their own health care 

Interactive consumer website, 

Clinician office 

NS Newly d

(1-12 mEnglish localized cancer 

Paperny, 199718 Survey after use User level HIV/AIDS and 

substance abuse 

Individuals interested in their own health care, Youth at risk 

Interactive consumer website, 

Clinician office Public Health Fairs, school, detention facility, runaway shelter and a youth corrections facility 

NS "Teens" 
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-339 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

McTavish, 199419

Qualitative User level Breast 

cancer 

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

1993 and 1994 

Stage 1 breast c

Cavan, 200320

Pilot trial User level Diabetes Individuals 

interested in their own health care 

Interactive consumer website 

Home/ residence 

NS 29-61 y

Feil, 200021 A study in a primary 

care setting 

User level Diabetes Individuals 

interested in their own health care 

Interactive consumer website 

Home/ residence 

1 yr 40-75 y

Female Have cuinternet Type 2 dHealthy Diagnosleast 1 yNot movstaying area Can reaEnglish Under 475 years Male Current access No type diabetes Incapactoo ill Diagnosthan 1 yMoving area Can’t rewrite En
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-340 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

Zeman, 200622 A study on PDA 

implementation problem 

System level User level 

Mental health 

NS Disease 

specific sensor 

Clinician office 

NS 

Bryce, 200823

Combination of qualitative methods and quantitative methods 

User level System level 

Diabetes Individuals 

interested in their own health care 

Web-Based Portal for Management of Diabetes 

 Between 

August 2004 and January 2005 

participaover the 21yearsWere Enspeakinreceived diagnos1 or type 2 diabetmellitusAgreed a focus session completsurvey 

Leslie, 200524

RCT User level Physical 

Activity/ Diet/ Obesity 

Individuals interested in their own health care 

Interactive consumer website 

NS 

Ferrer-Roca, 200425

mobile phone text messaging (short message service; SMS) for diabetes management 

User level System level 

Diabetes Individuals 

interested in their own health care 

mobile phone text messaging 

Home/ residence 

8 months All patie

diagnosdiabetes were agyears. Patients have thepersonaphone, or have to one bto a rela

Lenert, 200326

Pilot User level Smoking 

Cessation 

Individuals interested in their own health care 

Interactive consumer website 

NS 
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-341 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusi

Kressig, 200227

To determine if older adults are capable and willing to interact with a computerized exercise promotion interface 

User level Physical 

Activity/ Diet/ Obesity 

Individuals interested in their own health care 

Interactive consumer website 

participthey weyears of older anwithout contrainfor exer

Brug, 199828 RCT User level Physical Activity/ 

Diet/ Obesity 

Individuals interested in their own health care 

computergenerated tailored poster 

 

Boberg, 199529 Survey User level HIV/AIDS Individuals interested in their 

own health care 

Interactive consumer website (CHESS) 

Home/ residence 

 people l

AIDS/Hinfection 

Shaw, 200130 RCT System level All cancer Individuals interested in their 

own health care 

 Physician 

office 

January 1, 1996 to August 1, 1997 

Eligible were 18 years olEnglish, never hacolonoswere noschedulreceive. 

Strecher, 199431 RCT User level Smoking cessation Individuals interested in their 

own health care 

computergenerated tailored letter 

Home/ residence 

During March and April 1990 

Eligible were 40 years olHad seefamily pin the prmore thmonths being interviewhad tele
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-342 

Author, year Design 

Barrier type 

TARGET Condition Consumer Application Location 

Year/ duration 

InclusioExclusiwith avaand wornumber

NS = not specified, CHESS = Comprehensive Health Enhancement Support System, yr = year, RCT = randomized controlled trial, CHESS = Compre

Support System 

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 3. Keselman A, Slaughter L, Smith CA et al. Towards consumer-friendly PHRs: patients' experience with reviewing their health re

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 4. Shaw BR, Dubenske LL, Han JY et al. Antecedent characteristics of online cancer information seeking among rural breast cance

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Couns 2007; 67(1-2):127-36. 

 8. Wangberg SC. An Internet-based diabetes self-care intervention tailored to self-efficacy. Health Educ Res 2008; 23(1):170-9. 

 9. Lober WB, Zierler B, Herbaugh A et al. Barriers to the use of a personal health record by an elderly population. AMIA Annu Sy

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disabilities. J Intellect Dev Disabil 2006; 31(1):39-46. 

 11. Mangunkusumo R, Brug J, Duisterhout J, de Koning H, Raat H. Feasibility, acceptability, and quality of Internet-administered a

promotion in a preventive-care setting. Health Educ Res 2007; 22(1):1-13. 

 12. Ferney SL, Marshall AL. Website physical activity interventions: preferences of potential users. Health Educ Res 2006; 21(4):56
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-343 

 13. Temesgen Z, Knappe-Langworthy JE, St Marie MM, Smith BA, Dierkhising RA. Comprehensive Health Enhancement Support 

people with HIV infection. AIDS Behav 2006; 10(1):35-40. 

 14. Owen JE, Klapow JC, Roth DL, Nabell L, Tucker DC. Improving the effectiveness of adjuvant psychological treatment for wom

feasibility of providing online support. Psychooncology 2004; 13(4):281-92. 

 15. Lahdenpera TS, Kyngas HA. Patients' views about information technology in the treatment of hypertension. J Telemed Telecare 

 16. Weber B, Fritze J, Schneider B, Simminger D, Maurer K. Computerized self-assessment in psychiatric in-patients: acceptability, 

of computer attitude. Acta Psychiatr Scand 1998; 98(2):140-5. 

 17. Jenkinson J, Wilson-Pauwels L, Jewett MA, Woolridge N. Development of a hypermedia program designed to assist patients wi

cancer in making treatment decisions. J Biocommun 1998; 25(2):2-11. 

 18. Paperny DM. Computerized health assessment and education for adolescent HIV and STD prevention in health care settings and 

Behav 1997; 24(1):54-70. 

 19. McTavish FM, Gustafson DH, Owens BH et al. CHESS: An interactive computer system for women with breast cancer piloted w

population. Proc Annu Symp Comput Appl Med Care 1994; 599-603. 

 20. Cavan DA, Everett J, Plougmann S, Hejlesen OK. Use of the Internet to optimize self-management of type 1 diabetes: prelimina

DiasNet. 2003; 9 Suppl 1. 

 21. Feil EG, Glasgow RE, Boles S, McKay HG. Who participates in internet-based self-management programs? A study among nov

primary care setting. 2000; 26(5):806-11. 

 22. Zeman L, Johnson D, Arfken C, Smith T, Opoku P. Lessons learned: challenges implementing a personal digital assistant (PDA) 

health in primary care. Families, Systems & Health: The Journal of Collaborative Family HealthCare 2006; 24(3):286-9

 23. Bryce CL, Zickmund S, Hess R et al. Value versus user fees: perspectives of patients before and after using a web-based portal f

diabetes. Telemed J E Health 2008; 14(10):1035-43. 

 24. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. 2005; 40(1

 25. Ferrer-Roca O, C+írdenas A, Diaz-Cardama A, Pulido P. Mobile phone text messaging in the management of diabetes. 2004; 10 26. Lenert L, Mu+_oz RF, Stoddard J et al. Design and pilot evaluation of an internet smoking cessation program. 2003; 10(1):16-20

 27. Kressig RW, Echt KV. Exercise prescribing: Computer application in older adults. 2002; 42(2):273-7. 
Evidence Table 41. Description of all study designs addressing barriers KQ 2 (continued)

G-344 

 28. Brug J, Glanz K, Van Assema P, Kok G, Van Breukelen GJP. The Impact of Computer-Tailored Feedback and Iterative Feedbac

Vegetable Intake. 1998; 25(4):517-31. 

 29. Boberg EW, Gustafson DH, Hawkins RP et al. Development, acceptance, and use patterns of a computer-based education and so

people living with AIDS/HIV infection. 1995; 11(2):289-311. 

 30. Shaw MJ, Beebe TJ, Tomshine PA, Adlis SA, Cass OW. A randomized, controlled trial of interactive, multimedia software for p

education. 2001; 32(2):142-7. 

 31. Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S, Hospers HJ, Skinner CS. The effects of computer-tailored smoking cessation m

practice settings. 1994; 39(3):262-70. 
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications 

G-345 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Maritstatu

Simon, 20081

 Mean, 56.68 

range, 36-89 SD, 11.22 

AA: 26 (40) Caucasian: 39 (60) 

USD <20,000, 20 (31) 20,000-50,000, 11 (17) >50,000, 29 (45) 

<8 yr, 22 (34) 8-12 yr, 27 (41) 12-16 yr,16 (25) 

NS NS 

Cimino, 20012

No control group 

NS NS NS NS 

Keselman, 20073

No control group 

 White nonHispanic, 95 

API, 2 Other, 5 

 8-12 yr, 9 

12-16 yr, 48 >16 yr, 39 

 M, 14 

F, 89 

Shaw, 20084

CHESS users Mean, 51.81 

SD, 12.11 

White nonHispanic, 144(100) 

NS Some junior 

high, 1 (0.7) Some high school, 12 (8.3) High school degree, 48 (33.3) Some college, 39 (27.1) 

NS NS 

Nijland, 20085

No control group 

NS NS NS NS NS NS 

Morak, 20086 Obese patient with mobile 

phone 

Mean, 48 range, 24-71 

NS NS NS NS NS 

Steele, 20077

Face to face n,52 

Mean, 38.3 SD, 12.6 

NS NS NS NS NS 
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications (continued)

G-346 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Maritstatu

Steele, 20077

 Intervention Mean, 39.3 

SD, 14.4 

NS NS NS M, 

11 (21.6) F, 40 (78.4) 

NS 

Wangberg, 20088

No control group, 

Mean, 37.3 range, 33.2–41.4 

NS NS NS NS F, (63) NS 

Wangberg, 20088 High self- efficacy Mean, 42.9 range, 

38.0–47.9 

NS NS 8-12 yr, (8) F,(50) NS 

Lober, 20069 All participants Mean, 69 range, 49-92 NS NS NS NS F,(82) NS Stock, 200610 All subjects n,32 Mean, 30.8 range, 18-54 

SD, 12.1 

NS NS NS NS NS 
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications (continued)

G-347 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Maritstatu

Mangunkusumo, 200711

 Intervention Mean, 15 

range, 13-17 

Dutch, (76.5) Other, (23.5) 

 Lower 

secondary/ Vocational, (59.1) International secondary, (18.6) Upper secondary, (22.3) 

NS M,(43.9) NS 

Ferney, 200612

Define, low self-efficacy 

NS NS NS NS NS NS 

Ferney, 200612 Study group 18-44 yr, 16 45-65 yr, 24 NS NS 12-16 yr, 23 Employed Marrie

partne31 

Temesgen, 200613 Range, 30- 59 White non- Hispanic, 

7(87.5) NS, 1, (12.5) 

 All finished 

high school 

 NS 

Owen, 200414

 Intervention Mean, 53.9 White nonHispanic, (84) 

Black nonHispanic, (16) 

USD median, 45,000 

Yr mean, 14 

 NS 

Lahdenpera,200015

 Intervention Mean, 46 

range, 32-63 

NS NS NS Low, 13 

higher income, 14 

M, 9 (42.9) F, 12 (57.1) 

NS 

Weber, 199816 Comparison Mean, 30.5 SD, 7.8 NS NS NS NS NS Weber, 199816

Patients Mean, 50.7 

SD, 19.4 

NS NS NS NS NS 

Jenkinson, 199817

No control group 

NS NS NS NS NS NS 

Paperny, 199718

Public School Mean, 15.5 

range, 15- 

NS NS NS NS F, (51) NS 

Paperny, 199718

Medical clinics/ health fairs 

Range, 13-19 

NS NS NS NS 

Paperny, Detainees and Mean, 15.4 NS NS NS NS NS 
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications (continued)

G-348 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Maritstatu

199718 runaways range 13- McTavish, 199419 Intervention Range, 36- 66 NS NS NS NS NS Cavan, 200320

Patients with type 1 diabetes n,6 

Mean, 36 range, 29-61 

NS NS NS NS NS 

Feil, 200021 Define, healthy group NS NS NS NS NS NS Feil, 200021 Participants Mean, 59.2 SD, 6.9 NS NS NS NS NS Zeman, 200622 Participants NS Black non- Hispanic, (83) USD <30,000, ( 40) 12-16 yr, (65) NS NS 

Preportal group 

mean, 53 SD, 13 

Nonwhite, 7(33) High school 

graduate 6 (29) Some college 7 (33) College graduate 2 (10) Postgraduate degree 6 (29)

Bryce, 200823

portal-user group 

mean, 55  SD, 11 

Nonwhite, 4(22) High school 

graduate 1 (6) Some college 5 (28) College graduate 4(22) Postgraduate degree 8 (44)

Leslie, 200524

Print Websitedelivered intervention 

mean age of 43 years 

 72% had 

completed secondary school or higher 

Ferrer-Roca, 200425 Participants range 18-75 
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications (continued)

G-349 

Author, year 

Control Interventions Age Race, n(%) Income 

Education, n(%) SES 

Gender, n(%) 

Maritstatu

Lenert, 200326 Participants mean, 46yrs Caucasian, (84) some college 

education (75) 

 F (78) 

Kressig, 200227 Participants mean, 70.4 SD, 6.9 

range, 60 -87years 

 some 

college education or more, 33 

 M 17 

F 17 

Brug, 199828

Control Intervention 

mean, 44 SD, 14 

 college 

degree (42) 

 F (82) 

Boberg, 199529 CHESS (the Comprehensiv

e Health Enhancement Support System) 

mean, 34.9yrs 

White, (78.1) Non-White, (21.9) 

Average $15,010 

Average 13.9 years 

No (47.8) Yes (52.2) 

M (82.8) F 17.2 

Living Status Alone8) Not a(75.2) 

Shaw, 200130

Interactive Computerassisted Instruction Program 

mean, 53.9yrs SD, 13.83yrs 

 College 

degree, (58) 

 F (56) 

Strecher, 199431

Control Intervention 

mean, 49.5yrs 

 F (67.7) 

AA = African-American, Yr = year, NS = Not specified, SD = standard deviation, SES = Socioeconomic Status, M = Male, F = Female, USD = UniteC = Caucasian, BMI = body mass index, API = Asian/Pacific Islander 

Reference List 

 1. Simon C, Acheson L, Burant C et al. Patient interest in recording family histories of cancer via the Internet. Genet Med 2008; 10 2. Cimino JJ, Patel VL, Kushniruk AW. What do patients do with access to their medical records? Stud Health Technol Inform 200

 3. Keselman A, Slaughter L, Smith CA et al. Towards consumer-friendly PHRs: patients' experience with reviewing their health re

Proc 2007; 399-403. 

 4. Shaw BR, Dubenske LL, Han JY et al. Antecedent characteristics of online cancer information seeking among rural breast cance
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications (continued)

G-350 

of the Cognitive-Social Health Information Processing (C-SHIP) model. J Health Commun 2008; 13(4):389-408. 

 5. Nijland N, van Gemert-Pijnen J, Boer H, Steehouder MF, Seydel ER. Evaluation of internet-based technology for supporting sel

encountered by patients and caregivers when using self-care applications. J Med Internet Res 2008; 10(2):e13. 

 6. Morak J, Schindler K, Goerzer E et al. A pilot study of mobile phone-based therapy for obese patients. J Telemed Telecare 2008 7. Steele R, Mummery KW, Dwyer T. Development and process evaluation of an internet-based physical activity behaviour change 

Couns 2007; 67(1-2):127-36. 

 8. Wangberg SC. An Internet-based diabetes self-care intervention tailored to self-efficacy. Health Educ Res 2008; 23(1):170-9. 

 9. Lober WB, Zierler B, Herbaugh A et al. Barriers to the use of a personal health record by an elderly population. AMIA Annu Sy

 10. Stock SE, Davies DK, Davies KR, Wehmeyer ML. Evaluation of an application for making palmtop computers accessible to ind

disabilities. J Intellect Dev Disabil 2006; 31(1):39-46. 

 11. Mangunkusumo R, Brug J, Duisterhout J, de Koning H, Raat H. Feasibility, acceptability, and quality of Internet-administered a

promotion in a preventive-care setting. Health Educ Res 2007; 22(1):1-13. 

 12. Ferney SL, Marshall AL. Website physical activity interventions: preferences of potential users. Health Educ Res 2006; 21(4):56

 13. Temesgen Z, Knappe-Langworthy JE, St Marie MM, Smith BA, Dierkhising RA. Comprehensive Health Enhancement Support 

people with HIV infection. AIDS Behav 2006; 10(1):35-40. 

 14. Owen JE, Klapow JC, Roth DL, Nabell L, Tucker DC. Improving the effectiveness of adjuvant psychological treatment for wom

feasibility of providing online support. Psychooncology 2004; 13(4):281-92. 

 15. Lahdenpera TS, Kyngas HA. Patients' views about information technology in the treatment of hypertension. J Telemed Telecare 

 16. Weber B, Fritze J, Schneider B, Simminger D, Maurer K. Computerized self-assessment in psychiatric in-patients: acceptability, 

of computer attitude. Acta Psychiatr Scand 1998; 98(2):140-5. 

 17. Jenkinson J, Wilson-Pauwels L, Jewett MA, Woolridge N. Development of a hypermedia program designed to assist patients wi

cancer in making treatment decisions. J Biocommun 1998; 25(2):2-11. 

 18. Paperny DM. Computerized health assessment and education for adolescent HIV and STD prevention in health care settings and 

Behav 1997; 24(1):54-70. 

 19. McTavish FM, Gustafson DH, Owens BH et al. CHESS: An interactive computer system for women with breast cancer piloted w
Evidence table 42. Characteristics of consumers in studies addressing barriers to CHI applications (continued)

G-351 

population. Proc Annu Symp Comput Appl Med Care 1994; 599-603. 

 20. Cavan DA, Everett J, Plougmann S, Hejlesen OK. Use of the Internet to optimize self-management of type 1 diabetes: prelimina

DiasNet. 2003; 9 Suppl 1. 

 21. Feil EG, Glasgow RE, Boles S, McKay HG. Who participates in internet-based self-management programs? A study among nov

primary care setting. 2000; 26(5):806-11. 

 22. Zeman L, Johnson D, Arfken C, Smith T, Opoku P. Lessons learned: challenges implementing a personal digital assistant (PDA) 

health in primary care. Families, Systems & Health: The Journal of Collaborative Family HealthCare 2006; 24(3):286-9

 23. Bryce CL, Zickmund S, Hess R et al. Value versus user fees: perspectives of patients before and after using a web-based portal f

diabetes. Telemed J E Health 2008; 14(10):1035-43. 

 24. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. 2005; 40(1

 25. Ferrer-Roca O, C+írdenas A, Diaz-Cardama A, Pulido P. Mobile phone text messaging in the management of diabetes. 2004; 10 26. Lenert L, Mu+_oz RF, Stoddard J et al. Design and pilot evaluation of an internet smoking cessation program. 2003; 10(1):16-20

 27. Kressig RW, Echt KV. Exercise prescribing: Computer application in older adults. 2002; 42(2):273-7.  28. Brug J, Glanz K, Van Assema P, Kok G, Van Breukelen GJP. The Impact of Computer-Tailored Feedback and Iterative Feedbac

Vegetable Intake. 1998; 25(4):517-31. 

 29. Boberg EW, Gustafson DH, Hawkins RP et al. Development, acceptance, and use patterns of a computer-based education and so

people living with AIDS/HIV infection. 1995; 11(2):289-311. 

 30. Shaw MJ, Beebe TJ, Tomshine PA, Adlis SA, Cass OW. A randomized, controlled trial of interactive, multimedia software for p

education. 2001; 32(2):142-7. 

 31. Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S, Hospers HJ, Skinner CS. The effects of computer-tailored smoking cessation m

practice settings. 1994; 39(3):262-70. 
Evidence table 43. All barriers identified to the use of CHI applications

G-352 

Author, year Barriers and target conditions 

DATA collection method Comment Con

Simon, 20081 Barrier type: user-level barriers Other: privacy 

Barrier under study: CHI application use too time consuming; confidentiality/privacy; control of information; lack of technical infrastructure. Patient preferences: not comfortable giving history to provider in person either; did not need a risk assessment; using an electronic tool to record data as opposed to paper and pen Other important barriers identified during study: knowledge literacy. Target condition: breast cancer; any form of familial cancer. Other: family history of cancer 

 Non-validated survey Respondents also cited 

incentives to use an electronic tool for recording family h/o cancer: save time, help researchers and help speed research for people "in their culture", to pass on to family, something new. 

This this pliterase mimpomamare nonlinrecorchallclinicfor recharawhich regarnonusecuinformwherintegresea

Cimino, 20012 Barrier type: user-level barriers; systems-level barriers. 

Barrier under study: application usability; incompatibility with current care; knowledge literacy; lack of technical infrastructure. Target condition: personal health record 

Non-validated survey Qualitative 

 use o

patiecondphys

Keselman, 20073 Barrier type: user-level barriers; systems-level barriers. 

Barrier under study: application usability; confidentiality/privacy; control of information; 

knowledge literacy; language. Target condition: multiple -patients' information and comprehension needs related to their medical records. 

 Non-validated survey the p

work transfriendpreseconcreprepromrecorlay re

Shaw, 20084 Barrier type: user-level barriers Barrier under study: cultural; knowledge 

literacy. Patient preferences: beliefs affect goals & values; well being (functional and emotional) 

Validated survey C-SHIP Model Factors Cancerrelevant encodings and selfconstrual’s functional well-being 

Cancer-relevant beliefs and expectancies. Health selfThe v

the Chave relatias co
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-353 

Author, year Barriers and target conditions 

DATA collection method Comment Con

Target condition: breast cancer. efficacy. Cancer-relevant 

affects. Emotional well-being Negative emotion Cancerrelevant goals and values Need for information Cancer-relevant self-regulatory competencies and skills Participation in health care Health information competence Barriers to information Social support 

inform

Nijland, 20085 Barrier type: user-level barriers; systems-level barriers. 

Barrier under study: application usability; incompatibility with current care. Target condition: various health complaints - symptom review 

Qualitative "caregiver" means physician in 

this article 

QualPatiefriendNavigsearcadeqUnclehypeuselelayoufeatuTechbugs back care compinform= 146relevInformdigitawas tInformvirtuabe usinsufpersocompinformmismusers medifeatu
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-354 

Author, year Barriers and target conditions 

DATA collection method Comment Con

in thecare Self-ProbCareprescanswImpletrainieducof apof edregulfor se

Morak, 20086 Barrier type: user-level barriers Barrier under study: application usability ; CHI 

application use too time consuming; Barrier under study: lack of technical infrastructure ; Patient preferences: opinion of using own mobile phone or PC Target condition: obesity 

 Non-validated survey Abou

were acqustudyany afew ctechnyounpatieskills helpdtuitio

Steele, 20077 Barrier type: systems-level barriers. Barrier under study: application usability; 

knowledge literacy; lack of technical infrastructure. Target condition: physical activity/diet 

Non-validated survey Qualitative 

 Prefe

Face IO=6(stronagreeaskesatisfundeconteinformNo sratinggroupPartimeancredi
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-355 

Author, year Barriers and target conditions 

DATA collection method Comment Con

0.05] 154) Resprelevthe pFAC(4.3 asseaccegroupof IMparticthe wrest oaccework using friendparticInternenvirbeing perceuniveterms (userpreserelevthe win TasignifIM an

Wangberg, 20088 Barrier type: User level Barrier under study:

Patient preferences: perceived usefulness; lack of viral marketing; number of accesses. Target condition: diabetes 

Non-validated survey, scale was not reported 

Perceived usefulness might also be seen as a user lever barrier 

The usefu3.1–4a sligTherpercethe twP = .users site twas 
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-356 

Author, year Barriers and target conditions 

DATA collection method Comment Con

daystherespen(CI95mean (CI95checself-cacceonly downwas betwsite aselfcr = .1time perceP = .

Lober, 20069 Barrier type: user-level barriers Barrier under study: knowledge literacy; 

physical limitations; cognitive impairment; health literacy; computer anxiety. Target condition: PHR use by elderly population. 

Empirical based on trial data. Report by the nurse helping the patient to use the system. 

 Elder

of the and mthe mindepanxieliteraimpalitera

Stock, 200610 Barrier type: systems-level barriers. Barrier under study: application usability. 

Target condition: intellectual disabilities. 

Empirical based on trial data 

The average number of prompts for participant to complete the navigation using the pocket voyager interface was 1.41, while the average need for assistance when using the mainstream windows CE operating system was 5.34. Similarly participant made an average of only .78 errors when using the PVP. Compared to an average of 3.22 errors made when using the mainstream interface. One 

see c
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-357 

Author, year Barriers and target conditions 

DATA collection method Comment Con

of the major barriers to access in the mainstream windows CE operating system is it complexity(several different methods for accomplishing the same task), button icons by themselves did not provide enough information to participants (non-readers) to enable independent program identification 

Mangunkusumo, 200711 Barrier type: systems-level barriers Barrier under study: application usability. 

Patient preferences: acceptability; usability; credibility Target condition: physical activity/diet (specify) 

Validated survey Non-validated survey 

 Using 

adolecare posit

Ferney, 200612 Barrier type: systems-level barriers. Barrier under study: application usability. 

Patient preferences: no published studies on user-centered website design and development Target condition: physical activity website. 

Qualitative Four 

relati‘interconteRecoand sparticthese in in colum

Temesgen, 200613 Barrier type: user-level barriers; systems-level barriers 

Barrier under study: application usability ; CHI 

application use too time consuming; incompatibility with current care; knowledge literacy; lack of technical infrastructure Patient preferences: goals and expectations; finances Target condition: HIV/AIDS 

 Non-validated survey They gave patients laptops and 

internet access so did not assess the barrier. 

our pmaingeogrelaticompthe mcomminfecinterewhetcompand tinfecCondgreat
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-358 

Author, year Barriers and target conditions 

DATA collection method Comment Con

for sy

Owen, 200414 Barrier type: user-level barriers Barrier under study: application usability; lack 

of technical infrastructure. Patient preferences: expect internet based therapy to work. Target condition: breast cancer. 

Non-validated survey( did examine internal consistency) 

 An in

womnearlsampinternthat iequain immenttherathe ain intaskea smpatieInternparticconsbeing 

Lahdenpera, 200015 Barrier type: user-level barriers Barrier under study: confidentiality/privacy; 

incompatibility with current care; knowledge literacy; lack of technical infrastructure. Patient preferences: lack of personal contact with provider; patients attitude to IT Target condition: hypertension 

Qualitative Even 

undeof hythemsomeabouexpeor nohome decisintervedocat the be no when nurseand tuse." 

Weber, 199816 Barrier type: user-level barriers Barrier under study: CHI application use too 

time consuming; knowledge literacy. Patient preferences: 

Validated survey Non-validated survey 

 Mixe

relatiexpeopini
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-359 

Author, year Barriers and target conditions 

DATA collection method Comment Con

Target condition: mental health: psychiatric inpatients 

Jenkinson, 199817 Barrier type: user-level barriers Barrier under study: application usability 

Patient preferences: visual preferences and information needs of patients. Target condition: cancer: prostate 

Qualitative Patie

for mdiagnavaileffeccompfor coVisuaabouinterf

Paperny, 199718 Barrier type: user-level barriers Barrier under study: confidentiality/privacy; 

control of information; incompatibility with current care. Patient preferences: automated health education. Target condition: HIV/AIDS Other: STD 

Qualitative Interactive, computer-assisted 

identification of high-risk behaviors and health needs is thorough, accurate, painless, and easy and saves interviewer time. 

Avoidand bsharialmoautomand h

McTavish, 199419 Barrier type: user-level barriers Barrier under study: application usability ; 

knowledge literacy Target condition: breast cancer. 

Empirical based on trial data Non-validated survey 

The geographic barrier (location, cost, child care) were easily over some by their inhome use of the CHESS computer 

CHEextrelack not a 

Cavan, 200320 Barrier type: User barriers Barrier under study: application usability ; 

internet access Target condition: diabetes 

Pilot study Internet access is becoming 

more reliable, a rapid and preferably automated method of data entry would minimize the risk of data loss. 

No d

Feil, 200021 Barrier type: user-level barriers Barrier under study: application usability; CHI 

application use too time consuming; knowledge literacy; lack of technical infrastructure; access. Target condition: diabetes 

 Non-validated survey The home based intervention 

was free of charge, convenient (the participant were loaned a specialized computer) and designed to mitigate frequent participation barriers such as cost, transportation, child care, travel costs and work schedules 

The intervwide regarsevefamilgeneintern

Zeman, 200622 Barrier type: user-level barriers; systems-level barriers 

Barrier under study: application usability ; CHI 

Empirical based on trial data Non-validated survey; 

Lack of physician interest and motivation is a critical barrier even if technology offers a lowLack 

motiveven 
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-360 

Author, year Barriers and target conditions 

DATA collection method Comment Con

application use: too time consuming; confidentiality/privacy; control of information ; cultural; Knowledge literacy; utility; Target condition: mental health : range of psychiatric conditions 

research assistant recorded this 

cost alternative, requires few additional resources, is easy to use, and provides evidencebased diagnostic and treatment information. 

cost addituse, baseinform

Bryce, 200823 Barrier type: user-level barriers, systems-level barriers. 

Barrier under study: Comparison of preportal participants to portal users about interest in a number of features of a portal. Also, participants were queried about the acceptability of fees. 

The substantial investment to develop a portal was discussed as a system level barrier. Target condition: diabetes

Qualitative In general, preportal 

participants anticipated features to be more useful than portal users actually found them to be, with the exception of electronic communication with healthcare practices. Most participants did not find fees acceptable. 

Potediabecapaself mand cof acthan Most oppo

Leslie, 200524 Barrier type: User-level barriers Barrier under study: application usability

Target condition: Physical Activity/ Diet/ Obesity 

Non-validated survey The 

healtprogoppoWeb‘passbeen may websupdaregulappepotenchallprograpprorecrumainthe p

Ferrer-Roca, 200425 Barrier type: User-level barriers, systems-level barriers. 

Barrier under study: application usability, User satisfaction Target condition: Diabetes 

Non-validated survey the tr

SMS fast, adjunthe mdiabeour c
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-361 

Author, year Barriers and target conditions 

DATA collection method Comment Con

usefuand tare kdifficdiabe

Lenert, 200326 Barrier type: User-level barriers Barrier under study: application usability, 

complex design Target condition: Smoking Cessation

Non-validated survey this p

desigthat min hediffer desiginform

Kressig, 200227 Barrier type: user-level barriers Barrier under study: application usability, user 

friendlinessTarget condition: Physical Activity/ Diet/ Obesity 

Non-validated survey the d

suppinterapromamonpopu

Brug, 199828 Barrier type: user-level barriers Barrier under study: application usability

Target condition: Physical Activity/ Diet/ Obesity 

Non-validated survey comp

indiveffecrecomand tincreimpanutritreduc

Boberg, 199529 Barrier type: user-level barriers Barrier under study: application usability

Target condition: HIV/AIDS 

Non-validated survey This 

compcharainformonly, used provianalyfacinHIV i

Shaw, 200130 Barrier type: System-level barriers Barrier under study: application usability

Target condition: all cancer 

Non-validated survey The 

demoof a mprogpatie
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-362 

Author, year Barriers and target conditions 

DATA collection method Comment Con

affecthe drequi

Strecher, 199431 Barrier type: User-level barriers Barrier under study: application usability

Target condition: Smoking Cessation

Non-validated survey Resu

indiccompmesslight 

Reference List 

 1. Simon C, Acheson L, Burant C et al. Patient interest in recording family histories of cancer via the Internet. Genet Med 2008; 10(1

 2. Cimino JJ, Patel VL, Kushniruk AW. What do patients do with access to their medical records? Stud Health Technol Inform 2001;  3. Keselman A, Slaughter L, Smith CA et al. Towards consumer-friendly PHRs: patients' experience with reviewing their health reco

Proc 2007; 399-403. 

 4. Shaw BR, Dubenske LL, Han JY et al. Antecedent characteristics of online cancer information seeking among rural breast cancer 

the Cognitive-Social Health Information Processing (C-SHIP) model. J Health Commun 2008; 13(4):389-408. 

 5. Nijland N, van Gemert-Pijnen J, Boer H, Steehouder MF, Seydel ER. Evaluation of internet-based technology for supporting self-c

encountered by patients and caregivers when using self-care applications. J Med Internet Res 2008; 10(2):e13. 

 6. Morak J, Schindler K, Goerzer E et al. A pilot study of mobile phone-based therapy for obese patients. J Telemed Telecare 2008; 

 7. Steele R, Mummery KW, Dwyer T. Development and process evaluation of an internet-based physical activity behaviour change p

Couns 2007; 67(1-2):127-36. 

 8. Wangberg SC. An Internet-based diabetes self-care intervention tailored to self-efficacy. Health Educ Res 2008; 23(1):170-9. 

 9. Lober WB, Zierler B, Herbaugh A et al. Barriers to the use of a personal health record by an elderly population. AMIA Annu Symp 

 10. Stock SE, Davies DK, Davies KR, Wehmeyer ML. Evaluation of an application for making palmtop computers accessible to individ

disabilities. J Intellect Dev Disabil 2006; 31(1):39-46. 

 11. Mangunkusumo R, Brug J, Duisterhout J, de Koning H, Raat H. Feasibility, acceptability, and quality of Internet-administered ado

a preventive-care setting. Health Educ Res 2007; 22(1):1-13. 

 12. Ferney SL, Marshall AL. Website physical activity interventions: preferences of potential users. Health Educ Res 2006; 21(4):560-
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-363 

 13. Temesgen Z, Knappe-Langworthy JE, St Marie MM, Smith BA, Dierkhising RA. Comprehensive Health Enhancement Support Sy

with HIV infection. AIDS Behav 2006; 10(1):35-40. 

 14. Owen JE, Klapow JC, Roth DL, Nabell L, Tucker DC. Improving the effectiveness of adjuvant psychological treatment for women 

feasibility of providing online support. Psychooncology 2004; 13(4):281-92. 

 15. Lahdenpera TS, Kyngas HA. Patients' views about information technology in the treatment of hypertension. J Telemed Telecare 2 16. Weber B, Fritze J, Schneider B, Simminger D, Maurer K. Computerized self-assessment in psychiatric in-patients: acceptability, fe

computer attitude. Acta Psychiatr Scand 1998; 98(2):140-5. 

 17. Jenkinson J, Wilson-Pauwels L, Jewett MA, Woolridge N. Development of a hypermedia program designed to assist patients with 

in making treatment decisions. J Biocommun 1998; 25(2):2-11. 

 18. Paperny DM. Computerized health assessment and education for adolescent HIV and STD prevention in health care settings and 

Behav 1997; 24(1):54-70. 

 19. McTavish FM, Gustafson DH, Owens BH et al. CHESS: An interactive computer system for women with breast cancer piloted with 

population. Proc Annu Symp Comput Appl Med Care 1994; 599-603. 

 20. Cavan DA, Everett J, Plougmann S, Hejlesen OK. Use of the Internet to optimize self-management of type 1 diabetes: preliminary 

2003; 9 Suppl 1. 

 21. Feil EG, Glasgow RE, Boles S, McKay HG. Who participates in internet-based self-management programs? A study among novic

primary care setting. 2000; 26(5):806-11. 

 22. Zeman L, Johnson D, Arfken C, Smith T, Opoku P. Lessons learned: challenges implementing a personal digital assistant (PDA) t

health in primary care. Families, Systems & Health: The Journal of Collaborative Family HealthCare 2006; 24(3):286-98. 

 23. Bryce CL, Zickmund S, Hess R et al. Value versus user fees: perspectives of patients before and after using a web-based portal f

diabetes. Telemed J E Health 2008; 14(10):1035-43. 

 24. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. 2005; 40(1):54 25. Ferrer-Roca O, C+írdenas A, Diaz-Cardama A, Pulido P. Mobile phone text messaging in the management of diabetes. 2004; 10(

 26. Lenert L, Mu+_oz RF, Stoddard J et al. Design and pilot evaluation of an internet smoking cessation program. 2003; 10(1):16-20.  27. Kressig RW, Echt KV. Exercise prescribing: Computer application in older adults. 2002; 42(2):273-7. 

 28. Brug J, Glanz K, Van Assema P, Kok G, Van Breukelen GJP. The Impact of Computer-Tailored Feedback and Iterative Feedback 

Vegetable Intake. 1998; 25(4):517-31. 
Evidence table 43. All barriers identified to the use of CHI applications (continued)

G-364 

 29. Boberg EW, Gustafson DH, Hawkins RP et al. Development, acceptance, and use patterns of a computer-based education and s

people living with AIDS/HIV infection. 1995; 11(2):289-311. 

 30. Shaw MJ, Beebe TJ, Tomshine PA, Adlis SA, Cass OW. A randomized, controlled trial of interactive, multimedia software for patie

education. 2001; 32(2):142-7. 

 31. Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S, Hospers HJ, Skinner CS. The effects of computer-tailored smoking cessation me

settings. 1994; 39(3):262-70. 
Evidence Table 5. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) 

G-25 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria 

Exclusion criteria Control In

Breast cancer Gustafson, 20011

Individuals interested in their own health care 

Interactive consumer computerbased program 

Home/ residence 

Accrued between April 1995 and May 1997 

<60 yr, Women within 6 months of diagnosis of breast cancer, Not homeless, able to give informed consent, Understand and answer sample questions from the pretest Not active illegal drug users 

 Given copy 

of Dr. Susan Love’s Breast Book 

Cinhococe

Gustafson, 20082

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS Women within 61 

days of breast cancer diagnosis. Not homeless, able to give informed consent, understand and answer sample questions from the pretest 

 1.Choice of 

several books on breast cancer or set of audiotape OR 2. Access to the Internet 

CinwGan

Breast cervical prostate and laryngeal cancer Jones, 19993

Individual interested in their own health care 

Interactive consumer website 

Clinician office 

1996-1997 patients identified 

Existing breast, cervical, prostate, or laryngeal, cancer patients receiving radiotherapy at one oncology center 

Receiving palliative treatment, No knowledge of diagnosis, visual or mental handicap , severe pain or symptoms 

1. Booklet information2. General information about cancer, organized on computer as hypertext document 

Pinsuthrehyto Agem

Yr = year, CHESS = Comprehensive Health Enhancement Support System, NS = Not specified 
Evidence Table 5. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-26 

Reference List 

 1 Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 2 Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 3 Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 1999
Evidence Table 6. Description of consumer characteristics in RCTs addressing the impact of CHI applications on intermediate outcomes (KQ

G-27 

Author, Year 

Control Intervention Age Race, n(%) Income 

Education, n 

(%) SES 

Gender, n (%) 

Marital Status 

Breast cancer 

Allocated standard intervention 

Mean, 44.4 SD, 7.1 

White nonHispanic (72) USD 40,000, 

(50.8) 

12-16yr,( 40.2) NR Living with 

partner, (72.6) 

Gustafson, 20011

Received CHESS intervention, a home based computer system 

Mean, 44.3 SD, 6.6 

White nonHispanic (76) USD 40,000, 

(58.1) 

12-16 yr, (45.8) NR Living with 

partner, (71.9) 

Usual Care with 

Gustafson, books NS NS NS NS NS NS NS 20082 NS NS NS NS NS NS NS Breast cervical prostate and laryngeal cancer 

Booklet 

Jones, information NS NS NS NS NS NS NS 19993 NS NS NS NS NS NS NS 

NS= Not Specified, SD= Standard Deviation, SES= Socioeconomic Status, Yr= year, USD = United States Dollar 

Reference List 

 1 Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 2 Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 3 Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 1999
Evidence table 7: Outcomes in studies addressing KQ1b, impact of CHI application on intermediate outcomes 

G-28 

Author, year 

Outcomes Control 

Intervention 

n Measure 

at BL 

Measure at time point 2 

Measure at final time point 

Mean difference (95% CI) 

Si

 Control 125 2 month 

mean,78.4 

5 month mean, 79.3 

Social Support 

CHESS 121 2 month 

mean,80.2 

5 month mean, 84.2 

2 mos: 2.4 (-1.2-5.9) 5 mos: 4.9 (1.4-8.4) 

2 5 

Control 125 2 month 

mean, 65.6 

5 month mean, 65.8 

Information competence 

CHESS 121 2 month 

mean, 70.4 

5 month mean, 69.3 

2 mos: 4.8 (1.5-8.1) 5 mos: 3,5 (0.0-6.9) 

2 5 

Control 125 2 month 

mean, 67.2 

5 month mean, 69.6 

Unmet information needs CHESS 121 2 month 

mean, 70.0 

5 month mean, 67.0 

2 mos: 2.8 (-2.7-8.4 5 mos: -2.6 (-8.2-2.9) 

2 5 

Control 125 2 month 

mean, 73.1 

5 month mean, 72.8 

Participation, behavioral involvement CHESS 121 2 month 

mean, 75.6 

5 month mean, 74.5 

2 mos: 2.5 (-1.1-6.1) 5 mos: 1.7 (-2.3-5.6) 

2 5 

Control 125 2 month 

mean, 74.3 

5 month mean, 76.5 

Participation, level of comfort 

CHESS 121 2 month 

mean, 80.7 SD, 

5 month mean, 79.1 

2 mos: 6.4 (2.1-10.7) 5 mos: 2.6 (-1.4-6.7) 

2 5 

Control 125 2 month 

mean, 77.3 

5 month mean, 79.0 

Gustafson,20011

Confidence in doctors 

CHESS 121 2 month 

mean, 83.0 

5 month mean, 82.8 

2 mos: 5.7 (1.0-11.3) 5 mos: 3.8 (-2.2-9.8) 

2 5 
Evidence table 7: Outcomes in studies addressing KQ1b, impact of CHI application on intermediate outcomes (continued) 

G‐29

 2 month Effect size 

(p value) 

4 month Effect size 

(p value) 

9 month 

 Control 83 CHESS 

minus control: mean, 0.16 SD, 0.49 

0.32 (0.039) 78 CHESS 

minus control: mean, 0.25 SD, 0.53 

0.46 (0.004)** 

75 CHE

minucontmeaSD, 

Internet 79 Internet 

minus control: mean, -0.08 SD, 0.56 

-0.14 (.39) 80 Internet 

minus control: mean, -0.03 SD, 0.60 

0.05 (0.77) 75 Inter

minucontmeaSD, 

Social support 

CHESS 90 CHESS 

minus Internet: mean, 0.16 SD, 0.49 

0.47 (0.003)** 

85 CHESS 

minus Internet: mean, 0.20 SD, 0.56 

0.35 (0.027) 80 CHE

minuIntermeaSD, 

Control 83 CHESS 

minus control: mean, 0.12 SD, 0.47 

0.25 (0.126) 78 CHESS 

minus control: mean, 0.07 SD, 0.40 

0.17 (0.32) 75 CHE

minucontmeaSD, 

Internet 79 Internet 

minus control: mean, -0.03 SD, 0.48 

-0.06 (0.69) 80 Internet 

minus control: mean, -0.05 SD, 0.45 

-0.10 (.53) 75 Inter

minucontmeaSD, 

Gustafson,20082

Health & information competence 

CHESS 90 CHESS 

minus Internet: mean, 0.17 SD, 0.39 

0.44 (0.007)** 

85 CHESS 

minus Internet: mean, 0.19 SD, 0.40 

0.23 (0.15) 80 CHE

minuIntermeaSD, 

* “p<0.05. CHESS vs. control and Internet vs. control comparisons share alpha, thus p<0.025 for significance” ** “p<0.01. CHESS vs. control and Internet vs. control comparisons share alpha, thus p<0.025 for significance” NS = not significant, CHESS = Comprehensive Health Enhancement Support System, SD = standard deviation, mos = months, BL = baseline, CI = co
Evidence table 7: Outcomes in studies addressing KQ1b, impact of CHI application on intermediate outcomes (continued) 

G‐30

Author, year Outcomes 

Control intervention n 

No. (%) 95% CI on % 

P value of difference Personal vs. general information on computer 

P value of difference Computer vs. booklet 

Booklet 150 58 (40) 

32 to 48 

0.04 (personal better) 

0.77 

ComputerPersonal Information via computer 

156 68 (46) 

38 to 54 

Satisfaction Score >2  No. (%) a few days after information given 

Computer - General information about cancer 

128 41 (34) 

26 to 42 

Booklet 150 12/122(10) ComputerPersonal Information via computer 

156 38/131(29) 

Jones, 19993

Prefer computer to 10 minute consultation with professional (at 3 months of follow up) 

Computer - General information about cancer 

128 22/110 (20) 0.12 <0.001 

(computer more likely) 
Evidence table 7: Outcomes in studies addressing KQ1b, impact of CHI application on intermediate outcomes (continued) 

G‐31

Reference List 

 1. Gustafson DH, Hawkins R, Pingree S et al. Effect of computer support on younger women with breast cancer. J Gen Intern Med 20 2. Gustafson DH, Hawkins R, Mctavish F et al. Internet-based interactive support for cancer patients: Are integrated systems better? 

 3. Jones R, Pearson J, McGregor S et al. Randomised trial of personalised computer based information for cancer patients. BMJ 199
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes)

G-32 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Diet/exercise/physical activity NOT obesity Adachi, 20071

Individuals interested in their own health care 

Tailored advice based on answers to a Computerized questionnaire 

Home/ residence 

2002/ January to September 

20-65 yr, BMI greater>24, 

BMI greater> 23 with mild Hypertension, Hyperlipidemia, or DM 

BMI>30, history of 

major medical or psychiatric problems or orthopedic problems that prohibited exercise, received a diet and/or exercise program within 6 months, currently/previously/planned to be pregnant within 6 months 

Untailored self-help booklet with 7-month self monitoring of weight and walking; Self-help booklet only 

Anderson, 20012

Consumers interested in their own health 

Interactive computer based program 

Kiosk based computers located in supermark-ets 

NS NS NS No 

interventioncontrol condition 

Brug, 19983

General public interested in their own health 

Computergenerated feedback letters 

Home based 

NS NS NS General 

Information 

Brug, 19994

Individuals interested in their own health 

Computertailored nutrition education 

Computer based; otherwise nonspecified 

NS NS NS First 

intervention (comparison group) provided subjects with personal letters with tailored dietary feedback about fat, fruit and 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-33 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

vegetables only 

Campbell 19945

Adult patients from four North Carolina family practices 

individually computertailored nutrition messages 

Home based 

Between September and November 1991 

Office staff recruited participants as they checked in for any type of medical appointment. 

patients who were too ill or mentally unable to complete the baseline survey 

Messages were mailed to participants 

Campbell, 19996

Low income women enrolled in the Food Stamp program 

Interactive computer based program 

Facility based (food stamp office) 

January through April, 1995 

18 years of age or older, spoke English and either had children under 18 living at home or were pregnant 

NS No 

Intervention 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-34 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Campbell, 20047 Participant s in the 

Special Supplemental Nutrition Program for Women, Infants and Children (WIC) 

CDROM program 

Clinicbased NS Being at least 18 years of age, 

receiving WIC benefits for self or child(ren), and speaking and understanding English. For those women who were pregnant or breast-feeding, it was required that they have at least one prior nutrition session with a WIC nutritionist before being referred to the computer program 

Women deemed as high risk by the nutritionist (eg, owing to pregnancy complications) were excluded from the study because of the probable need for more intensive counseling and follow-up. 

Control group completed the surveys but did not receive the intervention until after followup 

Haerens, 20058

Middle school adolescents) 

Computertailored feedback 

School based 

Measures were assessed at the beginning (September 2003) and repeated at 

NS NS No 

intervention 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-35 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control the end of the school year (June 2004). 

Haerens, 20079

Individuals interested in their own health care (students in randomly selected 7th grade class) 

Interactive computertailored intervention 

Remote location: school 

Year, 2005 (November)/ 50 minute intervention with 3 month follow up 

7th grade students, parental consent 

 No 

intervention 

Haerens, 200910

Adolescent population 

Web-based computer tailored intervention 

Home Based 

February–March 2007 

NS NS Generic 

feedback letter 

Hurling, 200611

Individuals interested in their own health 

Internet-basedexercise motivation and action support system (Test system) 

Computer based; otherwise nonspecified 

NS NS taking of 

prescription medication, known heart conditions or related symptoms and receipt of advice from a health professional not to engage in physical activity or exercise 

Noninteractive Internetbased physical activity system) 

Hurling, 200712

Individuals interested in their own health care 

Internet and mobile phone for self reported physical activity 

Home/ residence 

Duration, 3 month, September to December, 2005. 

30-55 yr, Body mass index 19-30, Not vigorously active, Not taking regular prescription medication, Internet and email access, 

Employee of Unilever, 1 or more items on the PAR-Q, 1 or more items on the Rose Angina Questionnaire 

No intervention 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-36 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Mobile phone user 

King, 200613 people with type 2 

diabetes 

Interactive CDROM 

Facility based 

NS At least 25 years 

old; diagnosed with type 2 diabetes for 6 months or more; able to read and write in English; and able to perform moderate level PA 

NS generic 

health risk appraisal CD-ROM 

Kristal, 200014

Enrollees of a large health maintenance organization 

Computergenerated personalized letter and computer generated behavioral feedback 

Homebased NS GHC enrollment, age (18–69) and 

an ability to complete the baseline survey in English. 

Living outside of area or no longer enrolled in GHC 

Usual Care Group (no intervention) 

Lewis, 200815 Sedentary adults 

interested in their own health

Web-based computertailored Feedback 

Computer/Home based 

January 2003 through May 2006

NS NS Standard 

Internet

Low, 200616

Individuals interested in their own health care 

Interactive consumer website (Student Bodies,

NS 2001 (F) first or second 

year college, northeast private, liberal arts college 

Women with previous diagnosis of eating disorders or who were currently purging, 

Control 

Mangunkusumo, 200717

Individuals interested in their own health care: 

Internet site Remote 

location (e.g. library internet 

NS Secondary school 

students of the same grade 

 Preprinted 

generic advice on fruit consumption 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-37 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

student--with parental consent 

cafe); at a secondary school 

and a mailed referral where applicable after baseline assessment 

Marcus, 200718

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

15 Jan 2003 through 6 June 2006 

≥18 yr, 

sedentary (<90 minutes of physical activity each week) 

History of coronary or valvular heart disease, Hypertension, Diabetes mellitus, chronic obstructive pulmonary disease, stroke, osteoarthritis, orthopedic problems that would limit treadmill testing, or any other serious medical condition that would make physical activity unsafe or unwise, consuming 3 or more alcoholic drinks per day on 5 or more days of the week, Current or planned pregnancy, planning to move from the area within the next year, current suicidal ideation or psychosis, current clinical depression and/or 

 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-38 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

hospitalization because of a psychiatric disorder in the past 6 months, current clinical depression and/or hospitalization because of a psychiatric disorder in the past 6 months, taking medication that may impair physical activity tolerance or performance , and/or previous participation in exercise trials of authors 

Napolitano, 200319

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence, Remote location: work place 

12 weeks 18-65 years old, 

120 minutes or less of moderate intensity physical activity per week, 60 minutes or less of vigorous intensity physical activity per week 

Coronary artery disease, Stroke, Alcoholism or substance abuse, Hospitalization for a psychiatric disorder in the last 3 years, Currently suicidal or psychotic, Orthopedic problems that could limit exercise, and current or planned pregnancy 

Wait list control group 

Oenema, 200120

Individuals interested in their own health care 

Interactive consumer website, 

Classroom or office of adult education institutes 

NS Insufficient 

understanding of Dutch 

Non-tailored nutrition information letter 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-39 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

(sites of recruitment) 

Richardson, 2007 21 Sedentary adults with 

type II Diabetes 

Enhanced pedometers with embedded USB ports, uploaded detailed, timestamped stepcount data to a website called Stepping Up to Health; and received automated step-count feedback, automatically calculated goals, and tailored motivational messages 

Home/ residence 

NS At least 18 years 

of age and had type 2 diabetes. Eligible participants also reported regular email use, and had access to an Internetconnected computer with a Windows 2000 or XP operating system and an available USB. Participants also had to be able to communicate in English, provide written consent, and obtain medical clearance to start a walking program from a primary care physician, endocrinologist, or cardiologist. 

If they had used a pedometer in the past 30 days or were pregnant. 

Participants randomized to receive LG were instructed to focus on total accumulated steps. 

Smeets, 200722

Individuals interested in their own health care 

Tailored newsletter computer generated based on information about the Individuals 

Home/ residence 

15 months 18-65 yr, Control 

group receiving one general information letter 

Spittaels, Healthy Web based Home NS 20 and 55 years of NS No 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-40 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

200723 adults 

interested in their own health 

based age, 

had no history of cardiovascular diseases, and had access to the Internet 

intervention 

Spittaels, 200724

Individuals interested in their own health care 

Interactive consumer website 

 Work NS 25-55 yr, 

internet access at home or work 

History of cardiovascular disease 

Online nontailored standard physical activity advice 

Tan, 200525 Individuals 

interested in their own health care 

Interactive consumer website 

Home/ residence, Remote location: work place, 

NS 18-65 yr, 

Command of Dutch language, Access to computer with a CD-ROM 

 No 

information 

Vandelanotte, 200526

Individuals interested in their own health care 

Interactive computertailored intervention 

University computer lab 

NS 20-60 yr Complaints related 

to physical activity, Complaints related to fat intake (cardiovascular disease, diabetes, anorexia, problems with stomach, liver, gallbladder or intestine) 

Tailored physical activity and fat intake Interventions after 6 month FU 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-41 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Verheijden, 200427

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

Duration, Baseline collected between September 2002 to December 2002 with 8 month follow up 

Greater than or equal to 40 years old, Diabetes mellitus type 2, Hypertension, Dyslipidemia 

No internet access Usual care 

WylieRosett, 200128

Individuals (BMI > 25 kg/m2) in a freestanding health maintenance organization 

Computerized tailoring 

Kiosk based 

NS BMI > 25 kg/m2+ 

one cardiovascular risk factor 

Intention to move beyond commuting distance 

Work book only 

Eating disorder Winzelberg, 200029

Individuals interested in their own health care 

Interactive consumer website 

Home/ residence 

NS (F), West coast 

public university students Desire to improve body image satisfaction 

History of Bulimia or anorexia, currently engaged in purging activities, BMI below 18 

No intervention 

Nutrition intervention Bruge, 199630

Individuals interested in their own health care 

Interactive consumer website 

NS NS Employees of 

Royal Shell laboratory in Amsterdam, Netherlands 

Non-tailored group; general nutrition information 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-42 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

Silk, 200831 Individuals 

interested in their own health care Mothers or pregnant 

Interactive consumer website 

Home/ residence, Remote location: community agency or extension service office 

2 weeks 18-50 years, 

(F), one or more children or pregnant, poverty (yearly income less than or equal to 185% of the federal poverty index 

 Pamphlet 

Overweight and binge eating Jones, 200832

Individuals interested in their own health care 

Interactive consumer website 

NS 2005; 16 

weeks 

>85th percentile 

for age-adjusted BMI, binge eating or overeating behaviors at a frequency of >1 

times per week in the previous 3 months, access to a computer and the Internet, not currently enrolled in a formal binge eating or weight loss program (eg, Weight Watchers), absence of any medical condition in which the actual condition or treatment affects weight and/or appetite, absence of anorexia nervosa 

 Wait list 

control group 
Evidence Table 8. Description of RCTs addressing KQ1b (impact of CHI applications on intermediate outcomes) (continued) 

G-43 

Author, year 

Consumer under study 

CHI Application type Location 

Year data collected/ duration of intervention Inclusion criteria Exclusion criteria Control 

and bulimia nervosa 

NS = not specified, yr = year 

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 2 Anderson ES, Winett RA, Wojcik JR, Winett SG, Bowden T. A computerized social cognitive intervention for nutrition behavior: 

effects on fat, fiber, fruits, and vegetables, self-efficacy, and outcome expectations among food shoppers. 2001; 23(2):88-

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Vegetable Intake. 1998; 25(4):517-31. 

 4 Brug J, Steenhuis I, Van Assema P, Glanz K, De Vries H. Computer-tailored nutrition education: Differences between two interve

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primary care settings. 1994; 84(5):783-7. 

 6 Campbell MK, Honess-Morreale L, Farrell D, Carbone E, Brasure M. A tailored multimedia nutrition education pilot program for 

receiving food assistance. 1999; 14(2):257-67. 

 7 Campbell MK, Carbone E, Honess-Morreale L, Heisler-MacKinnon J, Demissie S, Farrell D. Randomized trial of a tailored nutriti

program for women receiving food assistance. 2004; 36(2):58-66. 

 8 Haerens L, De Bourdeaudhuij I, Maes L, Vereecken C, Brug J, Deforche B. The effects of a middle-school healthy eating interven

and fruit intake and soft drinks consumption. 2007; 10(5):443-9. 

 9 Haerens L, Deforche B, Maes L, Brug J, Vandelanotte C, De Bourdeaudhuij I. A computer-tailored dietary fat intake intervention f

a randomized controlled trial. Ann Behav Med 2007; 34(3):253-62. 

10 Haerens L, Maes L, Vereecken C, De Henauw S, Moreno L, De Bourdeaudhuij I. Effectiveness of a computer tailored physical act

adolescents compared to a generic advice. Patient Educ Couns 2009. 
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G-44 

11 Hurling R, Fairley BW, Dias MB. Internet-based exercise intervention systems: Are more interactive designs better? 2006; 21(6):7

12 Hurling R, Catt M, Boni MD et al. Using internet and mobile phone technology to deliver an automated physical activity program: 

trial. J Med Internet Res 2007; 9(2):e7. 

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management intervention. 2006; 31(2):128-37. 

14 Kristal AR, Curry SJ, Shattuck AL, Feng Z, Li S. A randomized trial of a tailored, self-help dietary intervention: The puget sound 

2000; 31(4):380-9. 

15 Lewis B, Williams D, Dunsiger S et al. User attitudes towards physical activity websites in a randomized controlled trial. Prev Me

16 Low KG, Charanasomboon S, Lesser J et al. Effectiveness of a computer-based interactive eating disorders prevention program at 

Disord 2006; 14(1):17-30. 

17 Mangunkusumo R, Brug J, Duisterhout J, de Koning H, Raat H. Feasibility, acceptability, and quality of Internet-administered ado

in a preventive-care setting. Health Educ Res 2007; 22(1):1-13. 

18 Marcus BH, Lewis BA, Williams DM et al. A comparison of Internet and print-based physical activity interventions. Arch Intern M

19 Napolitano MA, Fotheringham M, Tate D et al. Evaluation of an internet-based physical activity intervention: a preliminary invest

2003; 25(2):92-9. 

20 Oenema A, Brug J, Lechner L. Web-based tailored nutrition education: results of a randomized controlled trial. Health Educ Res 2

21 Richardson CR, Mehari KS, McIntyre LG et al. A randomized trial comparing structured and lifestyle goals in an internet-mediate

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22 Smeets T, Kremers SP, Brug J, de Vries H. Effects of tailored feedback on multiple health behaviors. Ann Behav Med 2007; 33(2)

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25 Oenema A, Tan F, Brug J. Short-term efficacy of a web-based computer-tailored nutrition intervention: main effects and mediators

29(1):54-63. 
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G-45 

26 Vandelanotte C, De Bourdeaudhuij I, Sallis JF, Spittaels H, Brug J. Efficacy of sequential or simultaneous interactive computer-tai

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Psychol 2000; 68(2):346-50. 

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32 Jones M, Luce KH, Osborne MI et al. Randomized, controlled trial of an internet-facilitated intervention for reducing binge eating 

adolescents. Pediatrics 2008; 121(3):453-62. 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes

G-46 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

 diet/exercise/physical activity NOT obesity

Control Mean, 46.3 

SD, 8.6 

NS NS NS NR 

Behavioral weight control program with 6-month weight and targeted behavior’s selfmonitoring 

Mean, 46.6 SD, 10.1 

NS NS NS NR 

Untailored selfhelp booklet with 7-month self monitoring 

Mean, 46.6 SD, 9 

NS NS NS NR 

Adachi, 20071

Behavioral weight control program 

Mean, 45.3 SD, 10.4 

NS NS NS NR 

Control NS NS NS NS NS NS Intervention NS NS NS NS NS NS 

Anderson, 20012

Sample statistics 

NS White (92) Median 

annual 

Mean years of education 

NS F (96) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-47 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

$35,000; $20,000 or less (12) 

14.78±2.11; 12 years or fewer (20) 

ControlGeneral Information 

NS NS NS NS NS NS 

Tailored Feedback; Tailored + Iterative Feedback 

NS NS NS NS NS NS 

Brug, 19983

Baseline Statistics 

44 (SD 14) years. 

NS NS College degree 

(42) 

NS F (82) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-48 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

Comparison 163 

Brug, M 41.3 NS NS NS NS NS 19994

Experimental 152 

M 38.6 NS NS NS NS NS 

No messages (124) 

NS NS NS NS NS NS 

Tailored messages (134) 

NS NS NS NS NS NS 

Non-tailored messages (136) 

NS NS NS NS NS NS 

Campbell, 19945

Baseline characteristics 

Average age of 40.8 years 

Minority enrollment (19.0) 

Median annual household level was $30 000 to $39 000, 

(mean 13.6 years), NS F (75.3) 

Control 212 28.9 (0.59) Caucasian 

10.8 AfricanAmerican 82.1 Hispanic 1.9 American Indian 1.9 other ethnicity 3.3 

NS Less than high 

school (33.0) high school graduate or GED ( 36.3) beyond high school (30.7) 

NS NS 

Campbell, 19996

Intervention 165 

30.2 (0.67) Caucasian 

7.3 AfricanAmerican 87.3 Hispanic 

NS Less than high 

school (33.9) High school graduate or GED (37.0) beyond high school 

NS NS 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-49 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

1.2 American Indian 0.6 other ethnicity 3.6 

(29.1) 

Control-No intervention (166) 

27.5 (8.6) African 

American 26.7; White nonHispanic 60.6; Other 12.7 

NS High school 17.1; 

High school or GED 67.1; Beyond high school (any trade/beauty school/college) 15.8 

NS F(97) 

Campbell, 20047

Computer tailored interactive nutrition education (141) 

27.3 (7.9) African 

American 39.7; White nonHispanic 48.9; Other 12.7 

NS High school 21.3; 

High school or GED 66.7; Beyond high school (any trade/beauty school/college) 12.0 

NS F(98) 

Control condition (n 5 schools, 759 pupils) 

12.85 (0.71) NS NS NS Lower 

SES (52.4) 

Girls (58.8) 

 

Intervention with parental support (n schools, 1226 pupils) 

13.04 (0.79) NS NS NS Lower 

SES (68.0) 

Girls (40.1) 

 

Haerens, 20058

Intervention alone (n 5 schools, 1006 pupils) 

13.24 (0.87) NS NS NS Lower 

SES (78.9) 

Girls (15.6) 

 

Haerens, 20079

No intervention Mean, 13.2 

SD, 0.5 

NS NS General, 84 (55.6) 

TechnicalNR F,111 (73.5) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-50 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

vocational, 67 (44.4) 

M,40 (26.5) 

 Intervention Mean, 13.3 

SD, 0.5 

NS NS General, 90 (58.8) 

Technicalvocational, 63 (41.2) 

NR F,103 

(67.3) M, 50 (32.7) 

 

ControlGeneric feedback information 

NS NS NS NS NS NS 

Computer tailored feed back 

NS NS NS NS NS NS 

Haerens, 200910

Baseline Characteristics 

14.6 _ (1.2) NS NS NS NS (526 

boys, 645 girls 

 

Hurling, Control 22 M 34.9 NS NS NS NS F 78 200611 Intervention 25 M 34.0 NS NS NS NS F72 Hurling, 200712

No intervention Mean, 40.1 

SD, 7.7 

White nonHispanic, (97) 

NS NS NR F, (70) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-51 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

 Internet and 

mobile phone intervention 

Mean, 40.5 SD, 7.1 

White nonHispanic, (100) 

NS NS NR F (64) 

King, 200613

Generic health risk appraisal CD-ROM 

61.0 (11.0) Hispanic 

(8.2) White (79.1) 

 Income Less than $10,000 5.3 $10,000 to $29,999 20.0 $30,000 to $49,999 35.3 $50,000 to 

Completed high school (27.4) Technical school (37.6) Completed college (22.9) Graduate degree (12.1) 

NS F(51.3) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-52 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

$69,999 18.7 $70,000 to $89,999 12.0 $90,000 or more 11.9 

Interactive CD-ROM 

61.9 (11.7) Hispanic 

(17.4) White (74.3) 

Income Less than $10,000 4.8 $10,000 to $29,999 24.8 $30,000 to $49,999 27.0 $50,000 to $69,999 20.0 $70,000 to $89,999 9.7 $90,000 or more 8.2 

Completed high school (27.4) Technical school (37.6) Completed college (22.9) Graduate degree (12.1) 

NS F(50.0) 

Usual Care (730) 

NS NS NS NS NS NS 

Intervention (729) 

NS NS NS NS NS NS 

Kristal, 200014

Base line characteristics 

44.9 ± 14.9 White 85.9; 

Black 4.5; Asian 5.8; 

(%, $1,000),<25 

NS NS M (50.9) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-53 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

Hispanic 3.0; Other 0.8 

(12.2); 25–34 16.9; 35–49 25.4; 50–69 23.7; 701 21.7 

Standard Internet 

NS NS NS NS NS NS 

MotivationallyTailored Internet 

NS NS NS NS NS NS 

Lewis, 200815

Baseline Statistics 

NS Caucasian 

(76.3) 

NS NS NS Women 

(82.7) 

 

Control NS Students of 

color (8.4) 

NS NS NR 

Student bodies with a moderated discussion 

NS NS NS NS NR F (100) 

Low, 200616

Student bodies with a unmoderated discussion 

NS NS NS NS NR F (100) 

Internet Mean, 15 

range, 13-17 

Dutch, (76.5) Turkish (5.0) Moroccan (3.3) Surinamese (2.4) Antillean/Arubans (0.4) Other (12.3) 

NS NS NR M, (43.9) 

Mangunkusumo, 200717

Control NS NS NS NS NR NS 

Marcus, Control 46.3 (9.4) NS NS NS NR 200718 Tailored print Mean, 445 White non- USD College graduate NR F, (83.7) Married, 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-54 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

SD, 9.6 Hispanic, 

(77.9) 

>50,000, (57.0) 

(or doing post graduate work), (72.1) 

 (69.8) 

Tailored internet 

Mean, 44.5 SD, 9 

White nonHispanic, (82.7) 

USD >50,000, (58.0) 

College graduate (or doing post graduate work), (64.2) 

NR F, (81.5) Married, 

(63.0) 

Control 46.3 (9.4 White nonHispanic, 

(84.1) 

USD >50,000, (53.7) 

College graduate (or doing post graduate work), (64.6) 

NR F, (82.9) Married, 

(55.6) 

Wait list control group 

Napolitano, NS NS NS NS NR 200319

 Internet NS NS NS NS NR Non-tailored nutrition information letter 

Oenema, NS NS NS NS NR 200120

Web based tailored nutrition education 

NS NS NS NS NR 

Richardson, 200721 Lifestyle Goals (LG) Group 

(17) 

52 ± 12 White (76), 

Black (18), Other (6) 

<30,000 (18), 30,000-70,000 (18), >70,000(65) 

HS diploma or GED (6), Some college (47), College degree (18), Graduate degree (29) 

NS M (29) 

F(71) 

 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-55 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

Structured Goals (SG) Group (13) 

53 ± 9 White (77), 

Black (8), Other (15) 

<30,000(8), 30,000-70,000 (31), >70,000 (62) 

HS diploma or GED (8), Some college (15), College degree (46), Graduate degree (31) 

NS M (38) 

F(62) 

 

Control group receiving one general information letter 

Range, 18-65 Mean, 47 SD, 11 

NS NS Primary or basic 

vocational school(10), Secondary vocational level or high school degree (42), Higher vocational school, college degree, or university degree(48) 

Smeets, NR F (57) 200722

Computer generated tailored newsletter 

NS NS NS NS NR NS 

Spittaels, 200723 No Intervention Age in years 40.7 (5.3) NS NS Higher education 72.7 NS F(66.7) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-56 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

Website with computer tailored feedback 

Age in years 43.3 (5.7) 

NS NS Higher education 

61.9 

NS F (65.3) 

Website without computer tailored feedback 

Age in years 39.6 (5.0) 

NS NS Higher education 

67.4 

NS F (66.7) 

Spittaels, 200724

Standard advice 

Range, 25-55 

NS NS College or 

university 

NR F (27) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-57 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

mean, 40.9 SD, 8 

degree(59.6) 

Tailored advice + email 

Range, 5-55 mean, 39.7 SD, 8.9 

NS NS College or 

university degree(63.4 ) 

NR F (38.8) 

Tailored advice Range, 25-

55 mean, 39.3 SD, 8.7 

NS NS College or 

university degree(68.9) 

NR F (32) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-58 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

Tan, 2005 No information NS NS NS NS NR 

25

Tailored Information 

NS NS NS NS NR 

No counseling Mean, 49.9 

SD, 8.3 

Minority 6(9) 

NS College 

graduate(49) 

NR F: 55, (82) 49 (73) 

Human email counseling 

Mean, 47.9 SD, 11.4 

Minority 8(13) 

NS College 

graduate(56) 

NR F: 54,( 84) 53(83) 

Tate, 200626

Automated feedback 

Mean, 47.9  SD, 9.8 

 Minority 6(10) 

NS College graduate 

(59) 

NR F: 53,( 87) 46(75) 

Vandelanotte Control NS NS NS NS NR NS , 200527 Sequential 

Interactive computer 

NS NS NS NS NR NS 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-59 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

tailored intervention 

 Simultaneous 

interactive computer tailored intervention 

NS NS NS NS NR NS 

Verheijden, 200428

Usual care Mean, 64 

SD, 10 

NS NS < High school13 

(18) , Intermediate22 (30), >B.Sc. level38 (52) 

NR M: 43 (59) 

F: 30 (41) 

 

 Web-Based 

Targeted Nutrition Counseling and Social Support 

Mean, 62 SD, 11 

NS NS < High school 15 

(21), Intermediate 31 (42), >B.Sc. level27 (37) 

NR M: 38 (52) 

F: 35 (48) 

 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-60 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

Work book only 

52.5± 11.50 White 

100(86.2) 

NS Education>1 yr in 

college 99 (85.3) 

NS F 88 

(75.7) 

 

Computer tailored feedback 

52.7± 11.27 White 195 

(82.6) 

NS Education>1 yr in 

college 193 (81.8) 

NS F 199 

(84.3) 

 

Wylie-Rosett, 200129

computer tailored feedback plus staff consultation 

51.6± 12.14 White 193 

(81.8) 

NS Education>1 yr in 

college 192 (85.6) 

NS F 197 

(83.5) 

 

Eating disorder

Non-tailored group 

Mean, 39 SD, 8 

NS NS 

University training (34) Technical degree (59) Less than high school (7) 

NR M (83) 

Bruge, 199630

Intervention group 

NS NS NS NS NR NS 

Silk, 200831 Video game Mean, 33, 

SD, 8.28 

European American (68) 

Yearly income less or 

Less than college (87) 

NR F (100) 
Evidence Table 9. Description of consumer characteristics in RCTs addressing KQ 1b (impact of CHI applications on intermediate outcomes) 

G-61 

Author, year 

Control Interventions Age 

Race, n(%) Income Education, n(%) SES 

Gender, n (%) 

Marital Status, n(%) 

African American (25) Latino (5) Asian (1) Other (1) 

equal to (185) of federal index: (100) 

High school or GED equivalent (44) 

Web site NS NS NS NS NR NS Define NS NS NS NS NR NS No intervention Mean, 20 

range, 18-33 SD, 2.8 

White nonHispanic, (53) Black nonHispanic, (3) Latino/Hispanic, (35) API, (5) Other, (3) 

Winzelberg, NS NS NR F (100) 200032

 Intervention NS NS NS NS NR NS 

Overweight and binge eating

Wait list control group 

Mean, 15.2 SD, 1.1 

White nonHispanic, 32 Black nonHispanic, 6 Latino/Hispanic, 10 API, Other, 5 

NS Grade in school, n: 

9th, 20 10th, 19 11th, 13 12th, 1 

NR F, 35 

M, 18 

 

Jones, 200833

SB2-BED Mean, 15 

SD, 1 

White nonHispanic, 35 Black nonHispanic, 2 Latino/Hispanic, 12 Other, 3 

NS Grade in school, n: 

9th 26 10th 16 11th 10 12th 0 

NR F, 38 

M, 14 

 

NR= Not Reported, NS= Not Specified, SD= Standard Deviation, SES= Socioeconomic Status, Yr= year, CBT= Cognitive Behavioral Th

BMI= Body Mass Index, QOL= Quality of Life, USD= United States Dollars, Female = F, Male = M, AIAN = American Indian/Alaska Nativ