Evidence Report/Technology Assessment 

Number 175 

Barriers and Drivers of Health Information Technology 

Use for the Elderly, Chronically Ill, and Underserved 

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, MD 20850 

www.ahrq.gov 

Contract No. 290-02-0024 

Prepared by: 

Oregon Evidence-based Practice Center, Portland, Oregon 

Investigators 

Holly Jimison, Ph.D. 

Principal Investigator 

Paul Gorman, M.D. 

Susan Woods, M.D. 

Peggy Nygren, M.A. 

Miranda Walker, M.A. 

Susan Norris, M.D., M.P.H., M.Sc. 

William Hersh, M.D. 

AHRQ Publication No. 09-E004

November 2008 
This report is based on research conducted by the Oregon Evidence-based Practice Center 

(EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), 

Rockville, MD (Contract No. 290-02-0024). The findings and conclusions in this 

document are those of the authors, who are responsible for its content, and do not 

necessarily represent the views of AHRQ. No statement in this report should be construed 

as an official position of AHRQ or of the U.S. Department of Health and Human Services.

The information in this report is intended to help clinicians, employers, policymakers, and 

others make informed decisions about the provision of health care services. This report is

intended as a reference and not as a substitute for clinical judgment. 

This report may be used, in whole or in part, as the basis for the development of clinical 

practice guidelines and other quality enhancement tools, or as a basis for reimbursement 

and coverage policies. AHRQ or U.S. Department of Health and Human Services 

endorsement of such derivative products may not be stated or implied. 
ii

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: 

Jimison H, Gorman P, Woods S, Nygren P, Walker M, Norris S, Hersh W. Barriers and Drivers 

of Health Information Technology Use for the Elderly, Chronically Ill, and Underserved. 

Evidence Report/Technology Assessment No. 175 (Prepared by the Oregon Evidence-based 

Practice Center under Contract No. 290-02-0024). AHRQ Publication No. 09-E004. Rockville, 

MD: Agency for Healthcare Research and Quality. November 2008. 

No investigators have any affilications 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. 
iii

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 reports and assessments provide organizations 

with comprehensive, science-based information on common, costly medical conditions and new 

health care technologies. 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. Jean Slutsky, P.A., M.S.P.H. 

Director Director, Center for Outcomes and Evidence 

Agency for Healthcare Research and Quality 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 

Agency for Healthcare Research and Quality 
iv

Acknowledgments 

The research team would like to express our regard and appreciation for the efforts of 

Andrew Hamilton, MLS, for database searching; Kimberly Peterson, M.S., and Lakshmi 

Harinath, M.D., MPH, for data abstraction assistance; Roger Chou, M.D., for oversight and 

guidance; Janna Kimel and Alia Broman for program support; and everyone else who shared 

support and ideas throughout the development of this report. We would also like to offer special 

thanks to Teresa Zayas Cabán, Ph.D., of the Agency for Healthcare Research and Quality for her 

contributions as our Task Order Officer. 
v

Structured Abstract 

Objectives: We reviewed the evidence on the barriers and drivers to the use of interactive 

consumer health information technology (IT) by specific populations, namely the elderly, those 

with chronic conditions or disabilities, and the underserved. 

Data Sources: We searched MEDLINE®, CINHAHL®, PsycINFO®, the Cochrane Controlled 

Trials Register and Database of Systematic Reviews, ERIC, and the American Association of 

Retired Persons (AARP) AgeLine® databases. We focused on literature 1990 to present. 

Methods: We included studies of all designs that described the direct use of interactive 

consumer health IT by at least one of the populations of interest. We then assessed the quality 

and abstracted and summarized data from these studies with regard to the level of use, the 

usefulness and usability, the barriers and drivers of use, and the effectiveness of the interactive 

consumer health IT applications. 

Results: We identified and reviewed 563 full-text articles and included 129 articles for 

abstraction. Few of the studies were specifically designed to compare the elderly, chronically ill, 

or underserved with the general population. We did find that several types of interactive 

consumer health IT were usable and effective in multiple settings and with all of our populations 

of interest. Of the studies that reported the impact of interactive consumer health IT on health 

outcomes, a consistent finding of our review was that these systems tended to have a positive 

effect when they provided a complete feedback loop that included (a) monitoring of current 

patient status, (b) interpretation of this data in light of established, often individualized, treatment 

goals, (c) adjustment of the management plan as needed, (d) communication back to the patient 

with tailored recommendations or advice, and (e) repetition of this cycle at appropriate intervals. 

Systems that provided only one or a subset of these functions were less consistently effective. 

The barriers and drivers to use were most often reported as secondary outcomes. Many 

studies were hampered by usability problems and unreliable technology, primarily due to the 

research being performed on early stage system prototypes. However, the most common factor 

influencing the successful use of the interactive technology by these specific populations was 

that the consumers’ perceived a benefit from using the system. Convenience was an important 

factor. It was critical that data entry not be cumbersome and that the intervention fit into the 

user’s daily routine. Usage was more successful if the intervention could be delivered on 

technology consumers used every day for other purposes. Finally, rapid and frequent 

interactions from a clinician improved use and user satisfaction. 

Conclusions: The systems described in the studies we examined depended on the active 

engagement of consumers and patients and the involvement of health professionals, supported by 

the specific technology interventions. Questions remain as to (a) the optimal frequency of use of 

the system by the patient, which is likely to be condition-specific; (b) the optimal frequency of 

use or degree of involvement by health professionals; and (c) whether the success depends on 

repeated modification of the patient’s treatment regimen or simply ongoing assistance with 

applying a static treatment plan. However, it is clear that the consumer’s perception of benefit, 
vi

convenience, and integration into daily activities will serve to facilitate the successful use of the 

interactive technologies for the elderly, chronically ill, and underserved. 
vii 

Contents 

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

Evidence Report.................................................................................................................7

Chapter 1. Introduction .......................................................................................................9 

1.a. Goal of the Report ..................................................................................................9 

1.b. Background ............................................................................................................9 

1.c. Burdens of Illness, Conditions, and the Digital Divide .......................................10 

Chapter 2. Methods ...........................................................................................................13 

2.a. Technical Expert Panel Input ...............................................................................13 

2.b. Key Questions and Analytic Framework..............................................................13

2.c. Population ............................................................................................................15 

2.d. Technology ..........................................................................................................15 

2.d.i. Further refinement of technologies ....................................................................16 

2.e. Outcomes ............................................................................................................. 17 

2.f. Study Design ........................................................................................................ 17 

2.g. Data Sources and Search Strategy .......................................................................18 

2.h. Inclusion and Exclusion of Literature...................................................................18 

2.i. Data Abstraction ....................................................................................................18 

2.j. Quality Rating of Studies ......................................................................................19 

2.k. Data Synthesis ......................................................................................................19 

2.l. Peer Review ..........................................................................................................19 

Chapter 3. Results .............................................................................................................21 

3.a. Findings for Key Question 1: Level of Use of Interactive Consumer Health IT 21 

3.a.i. Current level of use of specific forms of interactive consumer health IT 22 

3.a.ii. Primary uses of interactive consumer health IT ...............................................23 

3.a.iii. Variation of use according to type of interactive consumer health IT 

 application..............................................................................................................24 

3.a.iv. Variation of use according to settings and availability of the interactive 

 consumer health IT tools .......................................................................................24 

3.a.v. Level and type of use in populations of interest compared with the 

 general population .................................................................................................25 

3.a.vi. Summary of findings of the use of interactive consumer health IT..................25 

3.b. Findings for Key Question 2: Usability of Interactive Consumer Health IT.......25 

3.b.i. Usefulness of various types of interactive consumer health IT applications ....26 

3.b.ii. Usability factors associated with various types of interactive consumer 

 health IT ................................................................................................................27 

3.b.iii. Usability issues for special populations .......................................................... 28 

3.b.iv. Summary of usability and usefulness issues.....................................................29 
viii 

3.c. Findings for Key Question 3: Barriers to Use of Interactive Consumer 

 Health IT ...............................................................................................................29 

3.c.i Lack of perceived benefit ....................................................................................30 

3.c.ii. Technology intervention did not fit their lifestyle ............................................31 

3.c.iii. Lack of trust of the system ...............................................................................31 

3.c.iv. Technological malfunctions .............................................................................31 

3.c.v. Technology is too Cumbersome ........................................................................32 

3.c.vi. Confusion with the technology and content .....................................................32 

3.c.vii. Specific barriers with interactive reminding technologies ..............................32 

3.c.viii. Clinician factors as barriers ...........................................................................33 

3.c.ix. Cost as a barrier ................................................................................................33 

3.c.x. Age and disabilities as barriers .........................................................................33 

3.c.xi. Summary of barriers to the use of interactive consumer health IT...................34 

3.d. Findings for Key Question 4: Drivers and Facilitators of the Use of 

 Interactive Consumer Health IT ............................................................................34 

3.d.i. Perception of health benefit ...............................................................................35 

3.d.ii. Perception of convenience and accessibility benefits .......................................36 

3.d.iii. Rapid response from clinicians ........................................................................37 

3.d.iv. Benefit of anonymity for sensitive health topics .............................................37 

3.d.v. Benefit of understanding physicians and the health care system ......................37 

3.d.vi. Deliver information on devices they use every day .........................................38 

3.d.vii. Tailored to patients’ interest ...........................................................................38 

3.d.viii. Patients in poor health benefit more ..............................................................38 

3.d.ix. Drivers for older users – Gradual introduction of technology .........................39 

3.d.x. Summary of the drivers to the use of interactive consumer health IT...............39 

3.e. Findings for Key Question 5: Effectiveness of Interactive Consumer 

 Health IT ...............................................................................................................39 

3.e.i. Complete feedback loops ...................................................................................40 

3.e.ii. Reminders, alone and with monitoring .............................................................45 

3.e.iii. Monitoring alone ..............................................................................................46 

3.e.iv Peer interaction .................................................................................................46 

3.e.v. Education or information alone .........................................................................47 

3.e.vi. Access to EMR data and email system ............................................................47 

3.e.vii Web-based assessment with recommendations ..............................................48 

3.e.viii. Systems may not be for everyone ..................................................................48 

3.e.ix. Summary of the effectiveness of interactive consumer health IT.....................48 

3.f. Specific Findings for Populations of Interest .......................................................49 

3.f.i. African-American population..............................................................................49 

3.f.ii. Rural population.................................................................................................50 

3.f.iii Underserved population .....................................................................................50 

3.f.iv. Elderly population .............................................................................................51 

Chapter 4. Discussion .......................................................................................................53

4.a. Use of Interactive Consumer Health IT .............................................................. 54 

4.b. Usability of Interactive Consumer Health IT .......................................................55 
ix 

4.c. Barriers to the Use of Interactive Consumer Health IT .......................................56 

4.d. Drivers to the Use of Interactive Consumer Health IT ........................................57 

4.e. Effectiveness of Interactive Consumer Health IT ................................................57 

4.f. Use of Interactive Consumer Health IT by Populations of Interest .....................59 

4.g. Technical Difficulties Interfering with Evaluations ............................................59 

Chapter 5. Future Research ...............................................................................................61 

Chapter 6. Conclusions .....................................................................................................63 

References .........................................................................................................................65 

Figures 

Figure 1. Analytic Framework ..........................................................................................14 

Figure 2. Spectrum of Information Interventions .............................................................17 

Tables 

Table 1: A summary of populations included in this review............................................15 

Table 2: Health IT inclusions and exclusions ..................................................................16 

Table 3. Examples of complete feedback loop systems ...................................................58 

Summary Tables 

Summary Table 1. Summary of Usage Studies (KQ1) 

Summary Table 2. Usability (KQ2) 

Summary Table 3. Barriers (KQ3) 

Summary Table 4. Drivers (KQ4) 

Summary Table 5. Outcomes (KQ5) 

Summary Table 6. Populations of Interest 

Appendixes 

Appendix A. Technical Expert Panel Members 

Appendix B. Search Strategies 

Appendix C. Inclusion/Exclusion Criteria 

Appendix D. Search and Selection of Literature 

Appendix E. Excluded Studies List 

Appendix F. Quality Assessment Criteria 

Appendix G. Peer Reviewers 

Appendix H. Data Abstraction Table with References 

Appendix I: Study Quality Assessment Table with References 

Appendixes and Evidence Tables are available electronically at 

http://www.ahrq.gov/downloads/pub/evidence/pdf/hitbarriers/hitbar.pdf
1 

Executive Summary 

Background 

Interactive consumer health information technology (health IT) promises to engage 

consumers and promote their active participation in improving their health. Interactive 

technologies provide health information and tools that are patient-centered, offering care and 

support that integrates individual needs and preferences into clinical information systems. Such 

technologies, by tailoring health information and delivering targeted care, have significant 

potential to foster patient self-care monitoring and management. As the evidence on health IT 

interventions increases, it is important to understand how specific target groups such as the 

elderly, those with chronic conditions or disabilities, and underserved populations are able to 

access, use, and benefit from these applications. 

This report was prepared on behalf of the Agency for Healthcare Research and Quality 

(AHRQ), in response to a recommendation from the American Health Information Community. 

The systematic search conducted for this report focused on studies of interactive health IT used 

by consumers or patients, and targeted to our populations of interest. The report describes factors 

influencing the use, usefulness, and usability of interactive consumer health IT for elderly, 

chronically ill, and underserved populations. It also summarizes the scientific evidence on the 

effectiveness of interactive health IT applications and identifies gaps in research. By examining 

factors that influence the use and outcomes of interactive consumer health IT, the report provides 

a context for future inquiry in the study of applications designed for patients to participate more 

fully in their health care and health care decisions. 

Methods 

Our search for the published literature included structured searches of both standard and 

specialty bibliographic databases. We searched MEDLINE® (1950- Nov 2007), CINAHL® 

(1982-Feb 2008), PsycINFO® (1806-Feb 2008), the Cochrane Controlled Trials Register and 

Database of Systematic Reviews (1st quarter 2008), ERIC (1960- March 2008), and the 

American Association of Retired Persons (AARP) AgeLine® (1978-Dec 2007). We focused our 

review of citations from 1990 to present. We included studies of all designs that described the 

use of interactive consumer health IT by at least one of the populations of interest (elderly, 

chronically ill, or underserved). We then abstracted and summarized data from these studies with 

regard to key questions on: 

1. Level of technology usage 

2. Usefulness and usability of the technology 

3. Barriers to technology use 

4. Drivers and facilitators to technology use 

5. Effectiveness of the technology 
2 

Our analytic framework, developed in consultation with AHRQ and a Technical Expert 

Panel, helped guide the systematic review process. We specifically searched for articles having 

primary or subgroup analyses of the following health IT user groups: 

1. Elderly (over the age of 65 or functionally elderly) 

2. Chronically ill (condition longer than 1 year that would benefit from treatment) or 

disabled (impairments leading to activity limitations) 

3. Underserved (including underserved minorities, low-income populations, and medically 

underserved geographical regions) 

It was important to clearly define health IT that was both interactive and intended to be used 

by patients or consumers. Included papers were those that examined technology meeting the 

following criteria: 

1. A consumer interacts directly with the technology 

2. The computer processes the information in some way 

3. A consumer receives or has access to patient-specific information in return. 

Using this definition led us to include technologies such as self-management systems 

providing tailored advice, personal health records with data from a provider’s electronic medical 

record (EMR), tailored health education, and online support groups. We specifically excluded 

Web browsing of general health information databases, telemedicine systems which provide a 

simple conduit for communication between patient and health professional, and passive 

monitoring systems or electronic surveys without feedback to the patient. 

Results 

We identified a total of 8,522 abstracts, reviewed 563 full-text articles and included 129 

articles for abstraction. Few studies were specifically designed to compare the elderly, 

chronically ill, or underserved populations with the general population. But in general, we did 

find evidence that several types of interactive consumer health IT were usable and effective in 

multiple settings and with all of our populations of interest. 

The Level of Use of Interactive Consumer Health IT 

In our review of studies having some description on the level of usage of interactive 

consumer health IT, we found that the data were limited and presented challenges. Measures 

related to usage varied considerably and offered no apparent standardization of terminology. We 

also found that the frequency of health IT usage needed to be placed in the context of expected or 

intended use of the technology. 

Some studies reported on the frequency of participant use of technology over time, i.e., mean 

logins per subjects per month, or allowed an estimation of this measure. Collectively, such 

studies observed reductions in usage over time. Among studies of web-based interactive systems 

that offered usage frequency, findings ranged from less than 1 to as many as 27 mean logins per 

subject month. Empiric observation revealed that more highly used applications offered patient 

self-management tools and/or peer group support. Studies that compared the use of specific 
3 

components within an application found the most frequently used functions to be online peer 

group support, bulletin boards and self-management tools. These applications were tested across 

a variety of chronic conditions, including diabetes, tobacco use, chronic obstructive lung disease, 

and congestive heart failure. 

The Usefulness and Usability of Interactive Consumer Health IT 

Most studies providing information on the usefulness and usability of interactive consumer 

health IT systems addressed these issues as a secondary component of the study. However, the 

data indicated that usability played a key role in the use of these systems. Most researchers 

measured usefulness as part of their usability assessment, finding user perceptions generally 

rated high, especially for self-management systems. For each type of interactive consumer health 

information technology, the evidence offered examples that users believed were easy to use as 

well as those having usability issues. A small number of studies focused on usability testing 

early in design of the application. In these cases, the goal was to discover and fix usability issues 

early on in the process. Most issues had to do with graphics clarity and simplicity for older or 

impaired users. 

Barriers to the Use of Interactive Consumer Health IT 

The most frequent barrier to consumer use of interactive health IT across studies we 

examined was the lack of a perceived benefit. In several studies, lack of convenience was an 

important barrier. Specifically, patients were less likely to use systems requiring access to 

equipment or technology that did not fit seamlessly into their normal daily routines. Similarly, 

overly cumbersome data entry was perceived as a barrier. Several studies found that of the level 

and timeliness of clinician involvement with the system was directly associated with patient use; 

low clinician participation (when it was expected by the patient) led to low patient use. Technical 

issues often prevented consistent use of health IT systems, especially among studies conducted 

on early system prototypes. Finally, although subjects were often provided with the technology 

for a research trial, several studies identified that costs and access to the technology would serve 

as barriers to use of health IT beyond the study interval. 

Drivers of the Use of Interactive Consumer Health IT 

As a counterpoint to the findings above, the most frequent factor associated with increased 

use of the interactive health IT was the patient’s perception of a health benefit. Most of the 

systems we reviewed were designed for use by patients with a chronic condition, and it was 

important that patients believed the system was helping them manage their condition. 

Convenience of access to the interactive technology, and the amount of time required for the 

intervention, were both factors driving technology use. Interventions found to be beneficial were 

those that were delivered on devices familiar to patients and used routinely for other purposes. 

Clinician involvement was a driver in patient use of the technology; furthermore, rapid response 

from a clinician was shown to increase patient satisfaction. Also, providing content tailored to 

an individual consumer was shown to increase both system use and satisfaction. Finally, an 
4 

important driver of technology use relating to older users was the gradual introduction of 

technology. 

Effectiveness of Interactive Consumer Health IT 

An important and consistent finding in this review was that systems were effective when they 

provided a complete feedback loop that included (a) monitoring of patient status; (b) 

interpretation of this data by comparison to individual treatment goals; (c) adjustment of 

management according to patient status; (d) timely communication to the patient of tailored 

recommendations or advice; and (e) repetition of this cycle at appropriate intervals. These 

“complete loop” interventions were effective across a wide range of conditions, technologies, 

geographical areas, and patients’ socioeconomic status. Systems providing only one or a subset 

of these functions were less consistently effective. Systems that delivered reminders, alone or 

based on patient self-monitoring were not consistently effective. Systems offering online peer 

interaction, alone or as part of multi-component systems, were generally effective in improving 

intermediate outcomes but not health outcomes. Weak but consistent evidence showed that 

education or information alone had minimal effect on health outcomes. Online access to a 

patient view of the clinician’s EMR did not improve outcomes, except when combined with 

tailoring or bi-directional information flow. When asked, in some cases patients expressed a 

strong preference for human interaction, even with systems that were effective. Many studies 

showed these systems to be effective in our specific populations of interest: elders, those with 

chronic illness, members of underserved populations. However, no direct comparisons of 

interactive health IT between a target population and the general population were identified. 

Discussion 

There were several challenges to reviewing interventions based on interactive consumer 

health IT, primarily due to the fact that the interventions being studied varied so widely. First, 

the underlying technology varied significantly and included Web-based applications, hand-held 

PDAs or cell phones, touch-screen computers or kiosks. Second, the systems were designed for 

a wide variety of purposes, not a single intervention. Even though we focused the review on 

systems for the elderly, chronically ill, and underserved populations, we covered many 

application areas, such as systems for diabetes, asthma, cancer, mental disorders, smoking 

cessation, and medication reminding. Children with chronic conditions were included in our 

review, and we found that they have very different types of usage issues and systems designed 

for them. Third, the frequency of intended use of the various systems could be quite different. 

Some were single-use interactive educational systems or decision aids. Others were intended for 

daily use in the home. Fourth, these systems varied widely in complexity, from simple systems 

providing a single function to complex systems with a robust suite of diverse features. Last, the 

embedded care protocols and the degree to which clinicians participated differed dramatically 

from study to study. All of these issues are critical when interpreting the results of these studies, 

and the variability in approaches demands caution in generalizing these results too broadly. 

Few of the studies were specifically designed to compare the elderly, chronically ill, or 

underserved to the general population. But in general, we found that several types of interactive 

consumer health IT applications were usable and effective in many settings and with all of our 
5 

populations of interest. The most important and consistent finding regarding the effectiveness of 

these technology interventions was the benefit of systems that provided a complete feedback 

loop including assessment of current patient status, interpretation of this status information in 

light of established treatment goals or plans, and communication back to the patient with tailored 

recommendations or advice, repeated over time. Interactive consumer health IT applications that 

provided only one or a subset of these functions were less consistently effective. 

The barriers and drivers to use were most often reported as secondary outcomes in these 

studies. Many studies were hampered by usability problems and unreliable technology, 

primarily due to the nature of research typically being performed on early stage system 

prototypes. But beyond this, the most common factor influencing the successful use of the 

interactive technology by these special populations was that the consumers perceived a benefit 

from using the system. Convenience was also an important factor. It was critical that data entry 

not be cumbersome and that the intervention fit into the user’s daily routine. Usage was more 

successful if the intervention could be delivered on technology consumers used every day for 

other purposes. Finally, rapid and frequent interactions from a clinician improved use and user 

satisfaction. 

The systems described in the studies we examined depended on the active engagement of 

patients and the involvement of health professionals, supported by the specific technology 

interventions. Questions remain as to (a) the optimal frequency of use of the system by the 

patient, which is likely to be condition-specific; (b) the optimal frequency of use or degree of 

involvement by the health professionals; (c) whether their success depends on repeated 

modification of the patient’s treatment regimen or simply ongoing assistance with applying a 

static treatment plan. However, it is clear that the consumer’s perception of benefit, convenience 

and integration into daily activities will serve to facilitate the successful use of the interactive 

technologies for the elderly, chronically ill, and underserved. Perhaps most challenging, these 

systems shift the locus of care away from traditional physician office visits, and many of them 

involve the participation of a multidisciplinary health care team; these activities are difficult to 

support financially under current episode-based, fee-for-service health care reimbursement 

mechanisms. 
Evidence Report 
 

9 

Chapter 1. Introduction 

1. a. Goal of the Report 

In response to a recommendation given to the U.S. Secretary of Health and Human Services 

by the American Health Information Community’s Chronic Care Workgroup, the Agency for 

Healthcare Research and Quality (AHRQ) contracted with Oregon Health & Science 

University’s Evidence-based Practice Center to generate an evidence report on the barriers and 

drivers to the use of health information technology (health IT) by the elderly, those with chronic 

illness or disabilities, and the underserved. This report describes factors influencing the use and 

usability of interactive consumer health IT in these populations. It also summarizes the scientific 

evidence on outcomes of using these technologies, identifies gaps in the evidence, and provides a 

context for future inquiry. The report’s primary focus is to identify barriers and drivers to use in 

these populations and to characterize the features of the technology interventions that relate to 

improved outcomes for these specific populations. 

1. b. Background 

A promising aspect of interactive consumer health IT is the potential to engage and support 

consumers in their own care by integrating their health information needs and preferences into 

information systems. Such technologies provide targeted or tailored health information and/or 

self-care management tools or applications designed to support patients’ or consumers’ 

management of their health, health care, or health information. It is important to understand 

whether and how populations such as the elderly, chronically ill (including individuals with 

disabilities and individuals who need chronic care or end-of-life care), and underserved 

populations (e.g., inner-city/rural, low income, minority, etc.) will be able to access, use, and 

benefit from health IT. 

The health care needs of these populations are growing, and health information management 

tools have the potential to improve access to relevant health information, facilitate 

communication between patients and providers, and support chronic disease management. There 

is an opportunity to help bridge health disparities for these populations. In order to develop and 

provide appropriate and effective approaches, we need to understand the barriers and drivers of 

health IT use for these populations. Potential barriers and drivers of health IT use include 

motivation, cost, access to technology, literacy, education, language, culture, or 

telecommunication infrastructure. 

The American Medical Informatics Association (AMIA) Working Group on Consumer 

Health Informatics defines consumer health informatics as electronic information and 

communication technologies that patients/consumers use to improve their medical outcomes 

and/or participate in their health care decisionmaking process.1

 In addition, consumer health IT 

may assist individual consumers in gathering, understanding, and sharing health information 

with their providers.2

Interactive consumer health technology applications have had an increasingly important role 

to play in health care. Recent work based on the Institute of Medicine’s Crossing the Quality 

Chasm report3 has focused on fostering self-management support by encouraging providers to 

use education and other supportive interventions in order to systematically increase patients’ 
 

10 

skills and confidence in managing their health problems.4

 Two of the specific initiatives have to 

do with patient-centered care and informatics. Patient-centered care aims to inform and involve 

patients and their families in decisionmaking and self-management, coordinate and integrate 

care, provide physical comfort and emotional support, understand patients' concepts of illness 

and their cultural beliefs, and understand and apply principles of disease prevention and 

behavioral change appropriate for diverse populations. Informatics aims to communicate, 

manage knowledge, and support decisionmaking using IT.4

This evidence review focuses on interactive consumer health IT where patients or consumers 

interact directly with the technology and receive patient-specific information in return. Examples 

of these systems include home monitoring technology with interactive disease-management or 

self-management technology, educational or decision-aid software that is interactively tailored to 

the patient’s needs, online patient support groups, tailored interactive health reminder systems 

where interactions are linked with electronic medical records, and patient-physician electronic 

messaging. These types of systems may be implemented on a variety of platforms using 

Web/Internet technology, desktop computer applications, touch screen kiosks, personal digital 

assistants (PDAs), cell phones, or combinations of the above. 

This review includes care protocols that may or may not involve health care professionals as 

part of the system, and includes only computer-mediated interactional technology that does more 

than simply act as a conduit between humans, as in many telemedicine applications. We consider 

both the specific elements and functions of the technology, as well as the embedded care 

protocols and implementation features. Many factors relating to how the technology is deployed 

to the consumer or health system can influence access, usability, and effectiveness. 

1. c. Burdens of Illness, Conditions, and the Digital Divide 

Approximately 120 million Americans have one or more chronic illnesses, accounting for 70 

to 80 percent of health care costs. Twenty-five percent of Medicare recipients have four or more 

chronic conditions, accounting for two thirds of Medicare expenditures.5, 6 Most patients with 

chronic conditions such as hypertension, diabetes, hyperlipidemia, congestive heart failure, 

asthma, and depression are not treated adequately, and the burden of chronic illness is magnified 

by the fact that chronic conditions often occur as comorbidities.7, 8

One key element of systems-oriented chronic care models is support of patient selfmanagement in the home environment.9

 Such self-management support can reduce 

hospitalizations, emergency department use, and overall managed care costs.7, 8, 10-13 In this 

evidence review, we specifically examine the factors that influence the use and outcomes of 

technology designed to empower patients to participate more fully in their health care and health 

care decisions, with a specific emphasis on understanding the issues for the elderly, chronically 

ill, and underserved. 

A great many elderly persons receiving home care have functional limitations, such as 

reduced sensory, cognitive, or motor capabilities and may require disease management for 

multiple chronic conditions. Although health IT has the potential to empower patients to become 

more active in the care process, the elderly may be disadvantaged unless the designers of both 

software and hardware technology consider their needs explicitly.14 Usability and accessibility 

issues are important quality criteria for Web-based interventions, but often neglected by 

designers and evaluators.15
 

11 

Finally, the opportunities afforded by consumer health IT are not equally available to 

everyone. Both researchers and policy makers have attempted to understand and remedy the link 

between health disparities and Internet access. Those who most need health information often 

lack the means, knowledge, and skills necessary to benefit from Internet health resources.16 One 

significant barrier to the use of Internet health resources is consumer access. In 2006, the Pew 

Internet and American Life Project found that more than one quarter of U.S. adults had no online 

presence, and many Americans used a slow-speed connection.17 Non-users were more likely to 

be poor, less educated, over the age of 65, disabled, members of ethnic minorities, and nonnative English speakers.17
 

13 

Chapter 2. Methods 

2. a. Technical Expert Panel Input 

Eight individuals with relevant expertise served on the Technical Expert Panel to help define 

the scope of the project, provide feedback on our analytic framework and key questions, and 

weigh in on other critical issues throughout the project (Appendix A*

). We specifically elicited 

feedback on definitions of the populations of interest (e.g., who constitutes underserved and 

vulnerable populations), important chronic illnesses, examples and definitions for interactive 

consumer health information applications, and significant barriers, drivers, and outcomes. The 

experts also helped to identify sources of unpublished (gray) and difficult-to-find literature and 

relevant databases. 

2. b. Key Questions and Analytic Framework 

Our preliminary key questions were developed by AHRQ and edited based on feedback from 

our team, the Technical Expert Panel, and AHRQ. The final key questions listed below were 

used to guide the systematic review process. 

1. Among elderly, chronically ill, and underserved populations, what is the current level of 

use of specific forms of interactive consumer health IT? 

a. What are the primary uses of interactive consumer health IT? 

b. How does interactive consumer health IT use vary? 

c. Does use vary in settings where consumers have access to interactive health IT 

tools? 

d. How does the level and type of health IT use for the elderly, chronically ill, and 

underserved populations compare with that of the general population? 

2. In the elderly, chronically ill, and underserved populations, what type of interactive 

consumer health IT is most useful and easy for people to use? 

a. How useful are various types of interactive consumer health IT applications? 

b. What are the usability factors associated with various types of interactive 

consumer health IT? 

3. In the elderly, chronically ill, and underserved populations, what barriers hinder the use 

of consumer health IT? 

a. How do these barriers vary for these populations of interest? 

b. How do these barriers vary by type of interactive consumer health IT application? 

4. In the elderly, chronically ill and underserved populations, what drivers or facilitators 

may stimulate or enable the use of consumer health IT? 

a. How do these drivers and facilitators vary for these populations of interest? 

b. How do these drivers and facilitators vary by type of interactive consumer health 

IT application? 

 

*

Appendixes and Evidence Tables cited in this report are available electronically at http://www.ahrq.gov/clinic/tp/hitbartp.htm
 

14 

5. In the elderly, chronically ill, and underserved populations, is interactive consumer health 

IT effective in improving outcomes? 

a. How does the technology’s effectiveness vary for our populations of interest? 

b. How does the technology’s effectiveness in these populations of interest differ 

from the effectiveness in the general population? 

The analytic framework (Figure 1) is a graphic representation18 of the relationships among 

the populations, interventions, and outcomes examined in the key questions, along with potential 

barriers and drivers to health IT. We categorized barriers and drivers as: (1) patient 

characteristics, (2) attributes of the health IT, and (3) environmental factors. The Technical 

Expert Panel worked with us to refine the analytic framework, as well as to further clarify our 

definitions of patient populations, technologies, and settings of interest. 

Figure 1. Analytic Framework 

Note: Analytic framework for the use of health IT by the elderly, chronically ill, and underserved. Solid arrows 

represent relationships among variables in the framework where we systematically searched for evidence. Dashed 

arrows represent well-established relationships outside the scope of this review. The circled numbers indicate key 

questions. 

Urban / Rural,

Broadband or Wireless Access,

Home / Community / Facility,

Medical Services Access,

Reimbursement Policies

Outcomes

Quality of Life,

Self-report Health 

Satisfaction,

Health Care Costs

1,1a, 

1b, 2

Intermediate 

Outcomes

Knowledge,

Self-efficacy,

Health behaviors,

Treatment decisions,

Physiological 

measures,

Shared decision 

making,

Health care access

Use of

Health IT

Patient Characteristics

Age, Gender, Ethnicity

Education, Language, 

Chronic condition, 

Disability, Income

Literacy (numeracy included),

Technical Expertise

Health Belief , Preferences,

Health Insurance Status

Technology

Web, Email, Online

Support Group,

Telemedicine,

Home monitoring,

PDA, Cell Phone,

Video

Environment

5

1,1d,

3,4

3, 4

1,1c,

3, 4

5

* Chronically Ill, Elderly, Underserved

P 

o 

p 

u 

l 

a 

t 

i 

o 

n* 
 

15 

2. c. Population 

Populations examined in this review (Table 1) include the elderly, chronically ill, and 

underserved. Elderly was defined as mean age greater than 65 years, or functionally elderly as 

agreed upon by the reviewers. In considering populations with chronic illnesses, we included 

conditions that patients had for more than one year and that would benefit from ongoing 

monitoring or treatment. This included conditions such as diabetes, asthma, heart failure, 

chronic obstructive pulmonary disease, as well as mental illnesses. We also included disabilities 

where there was an impairment in body function or structure that interfered with a patient’s 

activities. We defined underserved populations as those with an inhibited ability to receive 

either medical or information services. This included underserved minorities, low-income 

populations, and medically underserved geographic regions. 

Table 1: A summary of populations included in this review 

Populations Potential 

Categories 

Description 

elderly elderly over 65 years of age 

functionally elderly by life experience or health concerns; need clarity/interobserver agreement 

chronic condition more than 1 year and benefits from ongoing monitoring or 

treatment 

impairment impairment in body function or structure 

activity limitation difficulty in executing activities 

chronically ill 

or disabled 

participation 

restriction 

problems experienced in participating in life situations 

medically 

underserved 

population or 

medically 

underserved area 

study uses Bureau of Health Professions / Health Resources 

and Services Administration designated medically 

underserved population or medically underserved area 

medically 

underserved area 

region where individuals are inhibited in their ability to 

receive services 

underserved 

underserved group population which is inhibited in its ability to receive medical 

or information services 

2. d. Technology 

Our review focused on highly interactive technologies, intended primarily for use by patients 

or consumers, that incorporated both patient interaction and patient-specific feedback (Table 2). 

Examples of included technologies are self-management and monitoring systems with tailored 

health advice; personal medical records or access to personal data in a provider EMR; health 

games that adapt to patient conditions; interactive online support or chat groups; and sites with 

storage, indexing, retrieval, display, or advanced features (summarization and decision support). 

Technology interventions were excluded if they had limited or no patient-driven 

involvement, if the interactions were without computer information support, or if they did not 
 

16 

provide patient-specific information to patients (e.g., Web browsing of general health 

information databases). Systems primarily or entirely for health professionals or caregivers were 

also excluded. Examples of excluded technologies include: passive monitoring without 

interactive feedback, information Web sites such as WebMD and MedlinePlus®, frequentlyasked-question Websites, simple video or phone conferencing, non-tailored educationinformation only programs, or electronic surveys without feedback. 

Table 2: Health IT inclusions and exclusions 

Include: Health IT program where: 

• Patient interacts with the technology, 

• Computer/technology processes the information in some way, and

• Patient receives information in return or has access to patient-specific information. 

o Examples: tailored health advice; clinic-linked personal health record/access; risk assessment

 tools, health calculators, predictive tools; interactive online support or chat groups; health 

games that adapt to the patient (more than simple “next page”); programs or sites with storage,

 indexing, retrieval, display, or advanced features (summarization and decision support).

Exclude: Health IT program where:

• Limited or no patient or consumer driven involvement or 

• Web browsing of general health information databases or 

• Patient-clinician communication without computer information support (simple 

videoconferencing or phone) or 

• Systems where the patient interacts but doesn’t receive patient-specific information 

back (e.g., electronic survey – no feedback) or 

• Systems primarily or entirely for health professionals or caregivers 

o Examples: passive monitoring; direct video or phone conferencing; general Web-based content

 (e.g., WebMD, MedlinePlus®, etc.); education, information-based only programs; electronic 

surveys without feedback; frequently asked question sites; tele-visit that replaces the face-toface interaction with a clinician; video interaction

2. d. i. Further Refinement of Technologies 

Some health IT interventions fit our inclusion criteria, but either had minimal tailoring of 

information, or minimal patient interaction with the system. Minimal tailoring included 

educational systems that primarily tailored the interaction based on the user’s knowledge or 

interest, and minimal interaction included high-technology tailored reminding systems with 

patient-specific information but where patients had not entered or requested information. This 

led us to develop a method of grouping the technologies based on how they clustered along two 

axes: (1) the degree to which the system assessed patient-specific information and (2) the degree 

to which the system conveyed patient-specific recommendations (Figure 2). 

We excluded health IT systems such as general health Websites and practice guidelines (at 

the far left of Figure 2) as they did not both assess and produce patient-specific information. We 

also excluded monitoring only systems (at the bottom of Figure 2) as these systems transmit 

information to providers but do not require patient interaction or provide the patient with 

information. The most relevant systems to this review are in the far upper right of this diagram 

and include self management systems where patients input information about themselves and 

receive tailored feedback in response. Studies examining these systems received full abstraction 

and review. Other types of interventions—such as high-technology reminding, education with 
 

17 

minimal tailoring, one-time-use decision aids, and online forums—were summarized 

qualitatively. 

Figure 2. Spectrum of Information Interventions 

Note: This diagram illustrates how various health information technologies differ to the degree in which they encode 

information about the patient (along the abscissa) and the degree to which they provide patient-specific 

recommendations back to the user (along the ordinate). Our technologies of interest did both (lighter colored 

ellipses). 

2. e. Outcomes 

We included studies reporting any of a broad spectrum of outcome measures, including 

technology use, health related behaviors, health service utilization, disease status, quality of life 

and functional outcomes 

2. f. Study Designs 

We included a broad spectrum study designs in this review, appropriate to the nature of our 

key questions. Whereas randomized controlled trials (RCTs) are considered optimal for the 

assessing the impact of specific interventions on outcomes, other study designs were more 

appropriate to answer other questions, such as epidemiologic studies to address rates of use of 
 

18 

interactive consumer health IT, and qualitative or usability methods to address questions 

regarding barriers and drivers, or usability. We included only reports of original studies and 

excluded letters, editorials, opinion pieces, news items, and commentary. 

We confined this review to studies where the full-text was available in English. We 

identified foreign language articles with an English abstract to determine if they contained 

relevant data but did not find any relevant studies that required translation. 

2. g. Data Sources and Search Strategy 

Our search for the published literature included structured searches of both standard and 

specialty bibliographic databases. We searched MEDLINE® (1950-Nov 2007), CINAHL® 

(1982-Feb 2008), PsycINFO® (1806- Feb 2008), the Cochrane Controlled Trials Register and 

Database of Systematic Reviews (1st quarter 2008), ERIC (1960-March 2008), and the American 

Association of Retired Persons (AARP) Ageline® (1978-Dec 2007). Although all database 

search coverage predated 1990, we focused our review on citations from 1990 to present. 

Detailed searches and time periods covered are found in Appendix B†

. 

We also examined the reference lists of key included studies and members of the TEP 

provided input on additional citations. We examined relevant systematic reviews retrieved from 

our searches for further citations. 

2. h. Inclusion and Exclusion of Literature 

In all, 8,522 English abstracts were examined and 563 full-text papers were examined for 

inclusion by two reviewers using refined inclusion and exclusion criteria (Appendix C). 

Disagreements between reviewers were resolved by discussion and consensus. Appendix D 

provides details on the number of captured titles and abstracts and the number of full text papers 

reviewed for each key question. A listing of the studies excluded after full text review are in 

Appendix E. 

2. i. Data Abstraction 

Based on the criteria in Appendix C, we included 129 unique publications for data 

abstraction. All included studies were abstracted into evidence tables. Data abstracted included 

study design, recruitment procedures, characteristics of the population, and details on the intent 

and implementation of the intervention. All data on outcomes including technology use, health 

related behaviors, health service utilization, disease status, quality-of-life and functional 

outcomes were abstracted. Many studies applied to more than one question. 

 

†

Appendixes and Evidence Tables cited in this report are available electronically at http://www.ahrq.gov/clinic/tp/hitbartp.htm
 

19 

2. j. Quality Rating of Studies 

Each study was assessed for internal validity using quality rating criteria developed by the 

United States Preventive Services Task Force,18 and the Common Drug Review Process.19 

Factors such as adequate randomization, allocation concealment, baseline comparability of 

participants, blinding, and low loss to follow-up were used to assess potential study bias.19, 20 For 

Key Question 5, which addressed the impact of interactive consumer health IT on outcomes, 

systematic reviews, randomized controlled trials, cohort studies, and case control studies were 

included and rated as good, fair, or poor.18 Other studies designs, often using qualitative 

methods, were considered for key questions on usage, usability, barriers, and drivers. Although 

our team reviewed criteria for rating qualitative study designs,21 these methods did not lend 

themselves to also addressing the qualitative secondary outcomes of the effectiveness studies. 

Thus, we did not formally rank each qualitative study (Appendix F) and incorporated evidence 

from all studies with information on usability, barriers, and drivers. 

2. k. Data Synthesis 

We focused our synthesis on interactive consumer health IT for which the patient interaction 

with the technology was significant and frequent and where subjects received patient-specific 

information in return. For these technologies, we fully abstracted the data and performed quality 

assessment. For educational systems with minimal tailoring, as well as for training and 

reminding systems, we abstracted only major results and provided a separate summary of those 

findings. 

We reviewed and summarized our findings based on suggested approaches for developing 

both frameworks (as recommended by Popay and colleagues)22 and field design propositions or 

guidelines for working with evidence from disparate technologies.23, 24 This approach provided 

an opportunity to obtain an inferred set of findings that transcended individual cases.23 Design 

propositions such as patient, environment, and technology characteristics; how and to what 

degree the patients or consumers used the technology; and intermediate and final health 

outcomes were included. 

After studies were abstracted and assessed for quality, the investigators rated the strength of 

the body of evidence for each key question, also according to the criteria outlined by the Guide 

to Community Preventive Services.25 In assessing quality at the key question level, investigators 

considered aggregate internal validity, aggregate external validity, and coherence and 

consistency. The quality and quantity of the studies and size and consistency of the results were 

used to grade the overall strength of the evidence. 

2. l. Peer Review 

We identified five individuals to serve as peer reviewers of our draft evidence report, 

including those with a range of personal, clinical, and professional interests in the use of health 

IT. Four federal organizations and four professional societies were also asked to review the draft 

report (Appendix G). 
21 

Chapter 3. Results 

In this section of the report we review existing evidence by key question. Full details 

abstracted from individual studies can be found in the Data Table in Appendix H* and Quality 

Assessment Table in Appendix I. Summary tables for each key question are referred to in that 

section (Summary Tables 1-5). Although in general we did not find direct comparative evidence 

within studies regarding the elderly, chronically ill, and underserved populations, a summary of 

studies that focus on one or more of these groups is provided (Summary Table 6). 

3. a. Findings for Key Question 1: Level of Use of 

Interactive Consumer Health IT 

To determine the capacity of interactive consumer health IT to provide benefit and improve 

patient outcomes, it is important to quantify individual participation in its use. Ideally, a measure 

of usage reflects the frequency of interactive consumer health IT use over time. 

We initially examined 31 studies that included some description of usage of the technology 

or system being evaluated.26-56 These studies are shown in Summary Table 1. 

While examining the evidence on the usage of interactive consumer health IT, two issues 

were identified. First, studies varied in the metrics employed to evaluate usage. Eight studies 

measured the time (minutes) for a subject to complete a task, such as entering data or responding 

to questions, or the time a subject was logged onto a Website.27, 38, 39, 52-54, 57, 58 This metric was 

considered a less appropriate method to quantify usage frequency, typically describing a singleuse or time-limited program; this measure was more related to usability. Six studies reported a 

count or proportion of subjects who used the technology, and did not provide insight into the 

frequency of use over time.26, 45, 47, 48, 50, 59 In six studies, the results did not offer sufficient 

clarity to extract usable usage data.29, 30, 40, 45, 60, 61 One study offered subject self-report of usage, 

presenting data as “transmissions per week,”55 the accuracy of this measure was of concern. 

 

*

Appendixes and Evidence Tables cited in this report are available electronically at http://www.ahrq.gov/clinic/tp/hitbartp.htm

Among elderly, chronically ill, and underserved populations, what is the level of usage of 

specific forms of interactive consumer health IT? 

a. What are the primary uses of interactive consumer health IT? 

b. How does interactive consumer health IT use vary? 

c. Does use vary in settings where consumers have access to interactive health IT tools? 

d. How does the level and type of interactive health IT use for the elderly, chronically ill, 

and underserved populations compare with that of the general population? 
 

22 

Second, there was the variation in the expected or “probable” use of each type of technology. 

This required that usage measurement be put in the context of intended technology use 

categories. Some technologies were expected to be used once or infrequently, such as 

educational programs or patient decision tools supporting a treatment choice. Many technologies, 

particularly Web-based applications with the purpose of patient self care and self-management, 

offered participants opportunities to use the application ad hoc or at repeated, specified times 

(e.g., once per day). Still other technologies, such as automatic reminders prompting a patient to 

adhere to a medication regimen, were not intended to be used at the discretion of the participant; 

these were not considered amenable to measuring usage. To achieve reasonable consistency 

while examining the level of technology usage, studies on usage were examined (in this Key 

Question) if the interactive consumer health IT application was intended for continuous use by 

consumers. 

Studies identified to offer data on frequency-related usage presented one of three types of 

measurement: (1) the usage of technology by subjects over a specified time interval, (2) the 

change in technology usage by subjects over time, and (3) the absolute or relative usage of 

specific functions compared to one another, within a particular technology application. Data 

sources measuring usage included automated computerized measurement, such as logins or use 

time, or self-reported information collected by surveys. 

Among studies presenting technology usage and examined here, all were quality rated for the 

primary study design. However, these studies did not have usage measurement as a primary 

objective of the study. As such, the methods used to rate the quality of the study did not readily 

apply to the usage findings. We included all usage results in this summary. 

3. a. i. Current Level of Use of Specific Forms of Interactive Consumer 

Health IT 

For frequency of technology usage, there was no standard terminology identified. The most 

common term used to quantify usage was reported as the number of “logins” by subjects during 

the study interval. Other terms presented included “hits,” “hit-days,” “logons,” and 

“transmissions” and were assumed to represent a similar metric. The term “hit-days” was a 

measure of the number of days a subject used the technology or program, perhaps 

underestimating the total number of logins if this occurred more than once daily. As previously 

discussed, frequency of usage by subjects was not appropriate for all types of interactive 

consumer health IT, such as automated voice messaging or clinic-based applications intended for 

single-time use. 

Seventeen studies either reported logins per subject per time interval, or allowed 

extrapolation of this data to generate an estimate of the mean number of logins per subject per 

month.

28, 31-37, 41-44, 46, 49, 51, 56, 62 These technology applications were accessible to subjects at their 

will. Among these studies, usage ranged from an average of <1 to over 23 logins per subject per 

month. Further description of these studies is discussed below and shown in Summary Table 1. 
 

23 

Seven studies described changes in interactive consumer health IT usage over time, all 

reporting some degree of overall reduction in usage over time.33, 34, 37, 44-46, 51 The metrics used to 

report trends varied widely, including descriptions of change in usage frequency over time, 

change in time (e.g., minutes) subjects used the technology, and change in the proportion of 

subjects who logged in within specified time intervals (e.g., first three months vs. last three 

months). While varied reporting did not allow direct comparisons among studies, some studies 

observed dramatic reductions in usage over weeks to months.34, 44, 45, 51 Among these studies--

which targeted a variety of conditions including heart failure, diabetes, tobacco, and chronic 

obstructive lung disease--none directly assessed the reasons for usage attrition. Some authors did 

discuss possible factors playing a role, such as technology inconvenience or reduced motivation 

over time. 

3. a. ii. Primary Uses of Interactive Consumer Health IT 

Nine studies reported on the comparative usage of specific components within an interactive 

consumer health IT application, relative to other components.34-37, 46, 51, 53, 54, 56 Studies that 

presented usage of components reported varied measures, including time (minutes) that subjects 

used a function, the frequency of functions used during logins, or the percentage of subjects 

using each function as part of a health IT application. 

Seven studies were randomized, controlled trials (RCTs) that examined the use of technology 

components among experimental group subjects. These studies employed Web-based programs 

that offered one or more of the following functions, beyond educational information: patient selfmonitoring tools (e.g., glucose, weight), tailored self-management tools (e.g., tailored feedback, 

decision aids), peer group discussion, ask-an-expert (posting questions to a clinical expert), posts 

to electronic bulletin boards, a patient’s view of their provider’s electronic medical record 

(EMR), secure messaging (secure electronic messaging with clinicians and staff), and/or text 

messaging.34, 35, 37, 46, 51, 53, 54 Overall, online peer group discussion and bulletin boards were the 

most commonly used functions, compared with other functions available within the applications. 

In two studies, self-management tools34 and ask-an-expert36 were also frequently used functions. 

In one study with patients having congestive heart failure who were provided access to their 

EMR, progress notes were most the commonly viewed component, followed by test results.51

Three cohort studies reported comparative usage of interactive consumer health IT 

components.32, 36, 56 One study showed that self-monitoring tools were used most frequently by 

patients with head and neck cancer.56 A second study, offering Web-based self-management and 

peer group support, found that subjects used the bulletin board function more frequently than 

self-management tool functions.36

One study, using a randomized design, tested the effect of adding diabetes self-management 

and peer support functionality to an information-based application.34 The study compared selfmanagement (tailored online coaching), self-management plus peer support (peer group 

discussion), and information only (non-tailored). Across 10 months of the study, higher usage 

was seen in both self-management conditions compared with information-only control, and 

highest in self-management plus peer support. While usage dropped over the study interval for 

all groups, highest usage was consistently observed in the condition that included the peer group 

discussion function. 
 

24 

3. a. iii. Variation of Use According To Type of Interactive Consumer 

Health IT Application 

Among the subset of 16 studies reporting the frequency of IT usage over time or allowing 

the estimation of this measure, 11 showed participant usage that was equal to or greater than 4.4 

logins per subject per month (range, 4.4 to 27). Five studies showed usage to fall under 2.2 mean 

logins per subject per month (range, 0.7 to 2.2). 

Studies observing a higher participant usage pattern (≥ 4.4 logins per subject/month) 

included the following interactive health IT functions: 

 

• Patient self-monitoring with secure messaging31, 33

• Patient self-monitoring with e-mail alerts49

• Peer group discussion with self-management tools34-37, 42, 44, 46

• Peer group discussion with secure messaging56

Studies observing a lower usage pattern (≤ 2.2 logins per subject/month) included the 

following interactive health IT functions: 

• Shared EMRs with secure messaging28, 32, 51

• Shared EMRs with quizzes43

• Automated paging and text messaging41

Studies reporting higher levels of technology usage appeared to have either peer group 

discussion functions or patient self-monitoring.31, 33-37, 42, 44, 46, 49, 54, 56 Studies reporting a lower 

frequency of interactive consumer health IT use included four with shared access to electronic 

records with or without secure messaging with clinicians,28, 32, 51 and one testing automated 

paging and text messaging,41 and one study using a self-monitoring application that also included 

quizzing subjects. 

This grouping was not based on any prior evidence about interactive consumer health IT 

usage; it was determined by empirical observation only. From a practical standpoint, the higher 

range of usage corresponds to logging in once per week or more frequently. 

The patient conditions for these studies varied widely, and included heart failure, diabetes, 

cancer, HIV, and lung transplant. One study included only subjects who were medically 

underserved women with breast cancer.54

3. a. iv. Variation of Use According To Settings and Availability of the 

Interactive Consumer Health IT Tools 

The studies included within the scope of this report did not specifically address the question 

of whether use varied according to setting or availability. Researchers typically provided study 

participants with the technology, if participants did not have it available. Some studies included 

participants who had little or no prior computer experience or access to the Internet. However, in 

one RCT testing Web-based diabetes self-management with or without peer support, 83 percent 

of subjects recruited into the study “had either no or extremely limited Internet experience prior 

to the study.”34 This study found high usage of peer group discussion and self-management tools.
 

25 

3. a. v. Level and Type of Use in Populations of Interest Compared 

With the General Population 

Few studies reported data describing individual-level factors related to usage. Three studies 

reported subject use of interactive consumer health IT by individual subject demographic factors. 

One study was an RCT of tobacco treatment medication with or without Web-based selfmanagement and peer group support.37 Analysis showed that technology usage was correlated 

with age, such that older participants had higher use frequency (p<0.05); this was not exclusively 

an elder population, however, in that this subgroup included participants aged 46 and older.

One RCT35 of individuals with AIDS/HIV reported that “women used the program 13 

percent more than [did] men”, and that “minorities used discussion somewhat less and 

information tools somewhat more” than did non-minority subjects; these were stated in the 

discussion, and data supporting these findings were not presented. A third study found that Web 

site use was associated with being Caucasian and Non-Hispanic, and with having more 

symptoms and a greater number of clinic visits; this study, however, showed only a trend toward 

signifcance.32

No studies were conducted comparing the use of interactive consumer health IT in a 

subpopulation of interest to a more general population. 

3. a. vi. Summary of Findings of the Use of Interactive Consumer 

Health IT 

The evidence on usage of consumer interactive technologies within the scope of this report is 

limited and presents challenges. Among the studies examined, measurements related to usage 

were varied and provided little standardization of terminology. Frequency of usage also need be 

placed in the context of expected or intended use of the technology. 

Some studies did report on the frequency of participant use of technology over time (i.e., 

mean logins per subjects per month), or allowed an estimation of this measure. These 

applications were tested across a variety of chronic conditions, including diabetes, tobacco use, 

chronic obstructive lung disease and congestive heart failure. Collectively, such studies observed 

reductions in usage over time. Among studies of Web-based interactive systems that offered 

usage frequency, findings ranged from <1 to 27 mean logins per subject month. Empiric 

observation revealed that higher usage applications offering functions that include patient selfmanagement tools and/or peer group support. Studies that compared the use of specific 

components within an application found the most frequently used functions to be online peer 

group support, bulletin boards, and self-management tools. 

3. b. Findings for Key Question 2: Usefulness and Usability 

of Interactive Consumer Health IT 

In the elderly, chronically ill and underserved populations, what type of interactive consumer 

health IT is most useful and easy for people to use? 

a. How useful are various types of interactive consumer health IT applications? 

b. What are the usability factors with various types of interactive consumer health IT? 
 

26 

We reviewed 54 studies that addressed issues of usefulness and usability for interactive 

health IT for consumers (Summary Table 2). The majority of studies (35) were qualitative, 

formative evaluations,33, 34, 56, 58, 63-93 and the rest were summative evaluations of interventions 

using study designs that included randomized control trials, cross-over designs, or cohort 

studies.29-31, 39, 41, 43, 44, 47, 52, 55, 94-102 Although a few of the studies were designed to exclusively 

look at issues of usability for a particular system, none compared the usability of different types 

of systems within a single study.65, 88, 95, 98, 103 In addition, most of the studies reported on 

usability issues as secondary findings, and not part of the primary study design. Given that our 

quality ratings for the summative evaluations usually did not apply to the qualitative findings on 

usability, we elected not to apply quality ratings on this qualitative information and considered 

all usability findings in our summary. 

In our review of these studies, we considered usability to be “the degree to which the system 

and services could be used effectively, efficiently and with satisfaction by the users.”94 This is 

consistent with the papers reviewed here. Researchers studying the usability of health IT most 

often referred to using the framework described in Jacob Nielsen’s 1993 book Usability 

Engineering.

104 In this framework, usability is considered to be a component of user acceptance, 

which refers to the willingness of users to use a system in a particular setting over a period of 

time. It also comprises social and practical acceptability. Nielsen’s Model of System 

Acceptability includes the concepts of attractiveness, controllability, efficiency, helpfulness, and 

“learnability.” 

For those studies that specifically addressed the usability issues associated with the 

interactive consumer health IT, most used protocol analysis with “talk aloud” techniques. 

However, many studies measured other outcomes and included questionnaire items to measure 

the usability issues. Those evaluating a Web site often used the 20-item WAMMI (Web site 

Analysis and Measurement Inventory) instrument105 to assess usability. The questionnaire items 

directly relate to the Nielsen framework and include a measure of global usability. The general 

acceptance of a common framework for discussing usability provided us with a useful structure 

for analyzing the results. 

3. b. i. Usefulness of Various Types of Interactive Consumer Health IT 

Applications 

With the wide mix of types of technology, even within our clusters of technology types, the 

usefulness of a system clearly depends very much upon how it was implemented. For those 

studies that measured the subjects’ perceived usefulness of the system, the results were generally 

very positive. For the Web-based interactive self-management systems,30, 31, 52, 55, 65, 71, 73, 79, 86, 87, 

94, 95, 97-102, 105 in each case the majority of users found the system to be helpful or useful, and 

often this was true for more than 90 percent of the users. An automated voice response system 

for calls to patients with diabetes was found to be very or somewhat helpful for 98 percent of the 

study participants.47 For single-use systems, such as the shared decisionmaking system for 

cancer patients,52 usefulness was also generally rated as high (in this case 4.62 out of 5 points). 

Additionally, efficiency is an aspect of usefulness. Although user efficiency was not often 

measured explicitly, occasionally perceived efficiency was reported in a study, again with the 

majority seeing improvements in efficiency by using the system.73 However, some studies 

detected efficiency problems if users were required to do a significant amount of data entry as 
 

27 

part of their self management.29, 30 For these systems, users expressed the opinion that the data 

entry should be automated. 

3. b. ii. Usability factors Associated With Various Types of Interactive 

Consumer Health IT 

Although, most users appreciated the usefulness of the systems we reviewed, there were 

clearly usability problems detected. The most frequent issue had to do with technical problems 

encountered during the study. These often thwarted the overall participation of subjects in a 

study. Technical issues consisted of Internet connection interruptions causing the loss of data,28, 

29 system errors,30, 99 and problems with the monitoring devices, including battery issues.30 In 

addition, sometimes the usability issues had to do with the physical format of the devices. For 

example, in a medication reminding study, the size and shape of the medication drawers used as 

part of the overall intervention made it difficult for study participants to small pills.106

Most studies that addressed usability issues measured the participants’ perception of the easeof-use of the technology. Naturally, this varied even within types of technology, depending on 

the particular implementation. Most often though, the vast majority of participants (> 90 

percent) felt that the technology they were using was understandable and easy to use.30, 31, 39, 43, 72, 

73, 95-98 Many of these studies also asked about the specific ease-of-use of system components. 

For example, with an asthma self-management system, 93 percent felt it was easy to record data 

and view data.30 Gomez et al. found that subjects using their Web-based HIV/AIDS 

management system reported that the graphics were easily understood.72 With a system for 

schizophrenia education, 93 percent of the subjects found the touch screen interface easy to use, 

but fewer (56 percent) found the overall system easy-to-use (all felt that it was at least “OK”). In 

this case, the usability issues arose from the organization of the content, as opposed to the 

usability issues associated with the more system-level interactions. At least one-third of the 

study participants encountered a display where they were confused on what to do next.39 Yet 

there were many studies with similar self-management systems with no apparent usability 

problems. For example, with one diabetes management system,43 most of the participants felt 

the system was easy to use, relevant, and useful and would recommend it to others. None of the 

participants reported needing technical help or had confusion about the system at any point. 

With a self-management system for congestive heart failure, all participants reported that the 

system was easy to use.31 Similarly, with another diabetes self-management system, all of the 

participants found the system easy and convenient to use, “with minimal intrusion on their 

lifestyle.”96 Another indicator used to measure usability was the participants’ willingness to 

continue using the system after the conclusion of the study. In this case, all participants reported 

wanting to continue with the program after the study terminated. 
 

28 

We found similar results for other types of interactive consumer health IT. For example, 

with a diabetes system that used a two-way automated messaging pager system to send tailored 

health-related messages to patients,41 the researchers found that most participants felt there were 

no problems with the pager (71 percent), enjoyed using the pager (79 percent), and would like to 

continue using it after the study (68 percent). Patients with bipolar depression were able to input 

symptoms into a PDA device,85 and although novice users reported being intimidated by the 

program at first, eventually all users felt that the system was easy to use. However, in a tablet 

PC for HIV+ patients study,88 many patients had difficulty making selections by tapping the 

screen, since that particular implementation did not respond to the users’ selections until they 

lifted the stylus. Many people naturally pressed instead of tapped at first. 

An important aspect of the findings from the studies that address the usability of technologies 

was discovering what system features needed to be modified. One change included revising a 

patient tutorial for a diabetes home management system and creating a field training program.75 

Another modification included improving the contrast, use of color, button size, and graphics 

clarity for an interactive health information system designed for older users.94 Often the largest 

usability changes needed to be made with regard to the data entry screens on Web-based 

systems,29, 30 and in some cases it was the graphic feedback for home monitoring data that 

needed improvements in clarity.30 Overall, we saw that the usability findings pointed to needing 

to keep systems simple and free from distracting elements, with an effort toward minimizing 

jumping from screen to screen. Researchers found that it was important to keep frequently used 

items on the main Web page with “one-click” access.63

3. b. iii. Usefulness and Usability Issues for Populations of Interest 

No studies directly compared differences in usefulness or usability issues for our populations 

of interest (elderly, chronically ill, and underserved) versus the general population. However, 

most systems were used by people with chronic illnesses and with a wide range of ages, 

including the elderly. In addition, some of the studies reporting usability issues were focused on 

the elderly,98 underserved populations70 or populations with physical disabilities.65, 79, 88 One of 

the studies that specifically looked at usability issues for an elderly population using an 

interactive system for obtaining tailored exercise recommendations98 showed that age was 

directly related to the need for more assistance with the technology. A study of low-income inner 

city asthma patients70 required subjects to perform routine spirometry tests and to interact with 

an electronic symptom diary. In this group, 2/3 of the subjects had never used a computer and 

nearly half had never used an ATM machine for banking. More than half of the subjects in this 

study were born outside the US and were non-native English speakers. Even in this group, more 

than 90 percent found the usability of the intervention “not complicated at all” or “not difficult at 

all.” However, in another study of an interactive educational system for cancer symptom 

control, where 26 percent of the subjects had never used a computer, only 71 percent of the 

subjects felt the system was easy to use. 
 

29 

There were three studies that dealt with usability issues for patients with physical 

disabilities.65, 79, 88 One study79 tested a Web-based intervention addressing physical activity for 

patients with spinal cord injury (33 percent), multiple sclerosis (21 percent) and cerebral palsy 

(19 percent). The ratings for ease-of-use of the system were still quite high (mean of 4.5 out 5 

points). Usability findings from two of the studies addressed issues of eyesight problems. 

Usability issues were identified in a tablet PC counseling tool for HIV+ patients’ medication 

management.88 Patients taking HIV medications frequently have eyesight impairment and the 

researchers found that some patients were having trouble seeing the content on the screen given 

the room lighting. They recommended increasing both the text size and the contrast level of the 

interface. Similarly, patients with diabetes often have vision problems. Interviews with patients 

with diabetes regarding various designs and interfaces for a cardiovascular risk prevention 

system showed that subjects had concerns about the clarity of the content on the screen, 

specifically mentioning that it would be common for people with diabetes to have trouble seeing 

detail on the screen.65

3. b. iv. Summary of Usefulness and Usability Issues 

In summary, we found 54 studies that addressed usefulness and usability issues of interactive 

consumer health IT. Most of these studies addressed usefulness and usability as a secondary 

component of the study; however, the information collected indicated that usability played a key 

role in the use of these systems. Most researchers measured usefulness as part of their usability 

assessment and found that the users’ perceived usefulness was generally rated high, especially 

for the self-management systems. With regard to the ease-of-use of systems, it is important to 

note that usability in these studies was always evaluated for a single type of system with a 

particular implementation, making it difficult to make general claims about usability findings. 

For each type of interactive consumer health information system there were examples of some 

that the users felt were easy to use and others with usability issues. There were a small number 

of studies that focused on usability testing early on in system design. In these cases, the goal was 

to discover and fix usability issues early on in the process. Most issues had to do with graphics 

clarity and simplicity for older or impaired users. 

3. c. Findings for Key Question 3: Barriers to Use of 

Interactive Consumer Health IT 

In the elderly, chronically ill and underserved populations, what barriers hinder the use of 

interactive consumer health IT? 

a. How do these barriers vary for these populations of interest? 

b. How do these barriers vary by type of interactive consumer health IT application? 
 

30 

We reviewed 52 studies that addressed the barriers to consumers’ use of interactive health IT 

for consumers, with a focus on patients with chronic illness, elderly users, and underserved 

populations (Summary Table 3). The majority of studies (33) were qualitative, formative 

evaluations,33, 34, 56, 58, 59, 63-71, 74-85, 87-89, 91-93, 101 and the rest were summative evaluations of 

interventions using study designs that included randomized control trials, cross-over designs, or 

cohort studies.28-32, 39, 41, 43, 44, 73, 86, 90, 95, 97-99, 106-108 As with the previous section on usability, most 

of the studies with information on barriers to system use reported this evidence as secondary 

findings, and not part of the primary study design. Given that our quality ratings for the 

summative evaluations usually did not apply to the qualitative findings on barriers, we elected 

not to apply quality ratings on this qualitative information and considered all findings relating to 

barriers in our summary 

Again, we used both Popay and Denyer’s approach to summarizing the evidence,22, 23 where 

we first summarized a subset of the evidence to cull out major technology design issues and then 

organized the rest of the evidence within that framework. The following subsections highlight 

the principle types of barriers to system use that we found in our review. 

3. c. i. Lack of Perceived Benefit 

The primary barrier to use that was described in studies that addressed this issue was the 

notion that if the study participants did not perceive that there was a benefit to be derived from 

using the system they were unlikely to engage with the intervention. This was especially true 

when there was a significant degree of inconvenience in data entry for the home-based selfmanagement systems. For example, one study found that participants’ use of their asthma selfmanagement system suffered when there was no perceptible immediate benefit to outweigh the 

inconvenience of doing self-management via Internet. Often improvements in asthma control 

are not perceptible by the patient.63 In their case, the benefits of using an asthma diary were 

often not recognized immediately. In a diabetes system, there were three types of patients who 

reported not seeing a benefit to the intervention: (1) study participants who had not experienced 

the problems yet, (2) those who did not understand the information, and (3) experienced users 

who felt they already knew the information.43 This last group also included people with well 

controlled diabetes who felt they did not need assistance. The researchers’ conclusion from these 

findings related to the need to tailor the intervention for these subpopulations.43 Many other 

interventions we reviewed did tailor their materials based on stages of change and related user 

model variables. 

For other types of interventions, such as a tailored medication reminding system, those study 

participants who were already doing well with their medications were more likely to report that 

the intervention was a nuisance and intrusive.106 In the case of support groups, we found that 

needing a critical mass in number of participants was often cited a barrier to successful use of the 

technology. For example, in a support group for diabetes and physical activity, there was a lack 

of perceived benefit by many participants (66 percent) because of an insufficient number of 

people actively participating in discussions.44
 

31 

In some cases, the technology was being evaluated in the context of replacing components of 

face-to-face interventions. For example, a schizophrenia technology intervention was tested with 

and without sessions with a community psychiatric nurse, as well as a combined intervention.39 

Of the people in the combination group, 13 out of 20 subjects preferred the sessions with the 

nurse, and 7 out of 20 preferred the sessions with the computer. Interestingly, all participants 

exposed to the community nurse intervention thought that the information provided by the nurse 

was relevant, but in the group receiving both the nurse and computer information, the good nurse 

information made the computer information seem less relevant (i.e., more participants in the 

computer only group found the computer information to be relevant).39

3. c. ii. Technology Intervention Did Not Fit Their Lifestyle 

A second type of reported barrier to the use of interactive consumer health IT was the issue 

of patients finding it difficult to fit the intervention into their everyday lives. In some studies, 

although patients reported liking the system, they indicated that they would be unlikely to use it 

long term because it was not easy to integrate into their lifestyle.63, 83, 91 One study reported 

factors that had to do with technical and psychological aspects.63 The issues in this study mainly 

had to do with the fact that their participants had to collect their monitoring data, write it down 

on paper prior to computer entry. This meant that participants often waited until the weekend to 

do the data entry and then would only receive feedback days later. They were not using the 

system continuously and this prevented the intervention from being more effective. The main 

point in these types of studies where data entry was a barrier was that monitoring needed to be 

unobtrusive and automatic. In addition, use of the Internet needed to be integrated into patients’ 

everyday lives. One study found that with reminder systems, it was necessary to consider the 

out-of home experience and to have a holistic approach to reminding in order to accommodate 

lifestyle issues.106 For example, some participants were frustrated by perceiving that they “had 

to build their life around the monitor/reminder” since it would not turn off when they were gone 

and they were not able to take the device with them when leaving the home. 

3. c. iii. Lack of Trust of the System 

Another major barrier to the use of the interactive consumer health IT was the lack of trust of 

the information provided. Findings showed that unexpected messages or advice that 

contradicted a patient’s experience caused them to disbelieve it and not follow the advice.63 

Systems that did not provide an explanation for the recommended action are less likely to have 

the intended behavior change effect on the users of the system.63 The authors’ recommendation 

based on this finding is to shift from providing automated treatment changes via the system to 

providing graded alerts on when to contact the clinician. 

3. c. iv. Technological Malfunctions 

Many studies encountered a wide variety of technical problems.28-30, 56, 73, 99 These technical 

problems represented barriers to successful system use, but were also problematic for the quality 

of the study. Many times, these technological problems were issues that could have been 

prevented with better pilot testing of the system prior to deployment, but many times the 

technological problems were representative of issues that system users might encounter 

independently of the quality of the health technology. 
 

32 

The most common technological problem encountered in the studies we reviewed was the 

lack of reliability of patients’ Internet connection.28-30, 73 As an example, more than half of users 

had Internet connection issues,30 and in two studies,28, 73 system users had connection problems 

leading to the loss of some of their data. Systems that required batteries, such as the home 

monitoring systems,30, 73 were also prone to having reduced patient use because of problems in 

keeping the batteries charges. The researchers saw a need to introduce battery checking software 

into their systems. 

Other technological issues encountered in the studies we reviewed included system errors,30, 

99 incorrect functioning of videos,28 and monitoring device problems.30 In general, many of the 

technological issues reflected the fact that the papers were reviewed dealt mostly with research 

systems, as opposed to commercial systems that would presumably be much more robust and 

reliable. However, the basic issues of Internet connectivity and short battery life are likely to 

plague future systems as well. 

3. c. v. Technology Too Cumbersome 

The most consistent issue with many of the self-monitoring systems that required the patients 

to enter their data into the computer by hand was that this process was cumbersome and took too 

long.29, 30, 101 In many cases, the technology exists to automate this process, but perhaps at a 

greater cost. As was noted before, some authors also reported that some of their participants had 

slow and/or intermittent Internet connections, making it cumbersome to use the intervention.63

3. c. vi. Confusion With the Technology and Content 

There were two ways in which users’ lack of understanding emerged as a barrier to the use of 

the health IT. The first was based on health literacy and numeracy.32, 82, 92, 93, 103 Many patients 

had difficulty in comprehending the medical jargon in their view of their provider’s EMR.32 The 

participants in this study felt that they could cope with this issue by using dictionaries or other 

methods, such as asking friends. They preferred this approach to having the material “watered 

down” or “sugar-coated.” 

Other sources of confusion had more to do with the use of the technology. Several studies 

mentioned lack of experience with computers and with the Internet as a significant barrier.56, 69, 

77-79, 81-83, 89, 101 For example, one study75 found it was necessary to improve their patient tutorial 

and create a field training program to ensure successful use of the home monitoring diabetes 

system. 

3. c. vii. Specific Barriers With Interactive Reminding Technologies 

Some of the barriers to system use were specific to the interactive reminding technologies. 

Users’ reactions to using their 2-way automated message paging system for diabetes care is one 

example revealed several challenges to providing this service.41 About 44 percent of the users 

found the messages to be annoying. Some found the system helpful for a few weeks, until they 

could anticipate the messages, and others found the messages too overwhelming. One 

suggestion arising from this work was that it would have been helpful for their messages to have 

been programmable. They also reported that patients would have liked to have changed the text. 

A separate barrier to this particular intervention was that the pager didn’t work in all locations 

(i.e., at work or while traveling).41
 

33 

3. c. viii. Clinician Factors as Barriers 

Our evidence review was limited to considerations of the patient or consumer’s use of health 

IT. However, several of the technological interventions we reviewed required active 

participation on the part of clinicians. Often these studies mentioned that patients perceived a 

lack of response and use of the system by physicians. For example, one study showed that if 

physicians did not examine the system’s report then patients (especially those with chronic 

illnesses) were less satisfied.109 In this particular study, fewer than half of the physicians looked 

at the report in front of the patient (49.2 percent) or discussed it with the patient (44.3 percent). 

Similarly, in a study of patient-provider messaging, monitoring, and email alerts, only 25 percent 

of the physicians involved used the system.56 In this case, the system was not integrated with 

the physician’s information system. Additionally, two investigations reported that physicians 

were reluctant to introduce the systems into their practice because of time constraints.28, 63

3. c. ix. Cost as a Barrier 

Although the interactive consumer health IT applications that we reviewed were designed 

with the intention to provide quality care in a timely manner that would be less expensive than 

having a clinician available in person, there were two ways in which cost was mentioned as a 

barrier to the use of these technologies. The most common was the fact that many patients did 

not have access to a computer in their homes.43, 70, 81-84, 87, 92, 101 This was particularly 

problematic for low-income populations, in an asthma study with low-income inner city 

subjects.70 Since 2/3 of the subjects had never used a computer, the cost associated with 

deploying this type of intervention would have to include both the computer hardware as well as 

computer training. The second factor was more of an artifact of the specific implementation. 

The protocol for the use of some systems, such as a tailored educational program for patients 

with schizophrenia,39 required that the patients come to the clinic to use a touch screen computer. 

The cost associated with bringing patients to the clinic was deemed to be too high for the 

intervention to be widely accepted. However, the alternative, being a person-to-person visit with 

the community nurse counselor, was even more expensive. A diabetes intervention also required 

technical personnel at hospital for technical support.73 Presumably this would be true for many 

of the systems if they were to be used in routine clinical care. 

The reimbursement for the clinical use of the types of technologies we reviewed was also 

brought up as a barrier to routine use outside of a study situation. For example, one study found 

that it would be difficult to have physicians routinely use electronic messaging with their patients 

if they did not receive payment or reimbursement for those interactions.28

3. c. x. Age and Disabilities as Barriers 

We only found only two studies of usability and usefulness that compared older and younger 

users of an interactive consumer health IT systems, both were diabetes interventions.47, 69 One 

study found that younger users were more interested in the technology.69 Looking at a wide 

range of ages using an automated voice response system, one study found that younger patients 

(less than 55 years of age) were less likely to listen to as many messages, but more likely to want 

to use the system in the future. Also the younger users more often reported that the diabetes 

voice system would make them more satisfied with their health care.47 
 

34 

In addition, given that most of the health IT interventions reviewed were focused on chronic 

conditions, many studies we reviewed included a substantial number of elderly participants. For 

example, one study focused on a low-income elderly disabled population living in a publicly 

subsidized housing project.82 Computer literacy and computer anxiety were determined to be 

major barriers to subjects’ use of the personal health record the researchers were testing. In this 

group 63 percent had computer literacy problems (needing help in learning to turn the computer 

on, use a mouse device, etc.) and 58 percent had computer anxiety problems (unwilling to 

attempt to use the computer). Other barriers identified in this study included cognitive 

impairment (34 percent) that limited subjects’ ability to remember their user name, password and 

Web URL address; health literacy (29 percent) that caused confusion about the content; and 

physical impairments (26 percent). The impairments included vision problems that made 

viewing the screen difficult, and decreased function of arms and hands due to cerebral vascular 

accidents, arthritis, multiple sclerosis, and Parkinson’s disease.82 All of these issues made using 

a computer much more time consuming and difficult. An additional study specifically looked at 

usability issues with a Web-based system designed to provide tailored exercise recommendations 

for older users.98 Within this study of users over the age of 60, they found a direct relationship 

between age and increasing need for technical assistance. 

3. c. xi. Summary of Barriers to the Use of Interactive Consumer 

Health It 

In summary, we found 53 studies that addressed the barriers to consumer’s use of interactive 

health IT. The most frequently mentioned barrier to consumers’ use of the interactive health IT 

across these studies was a lack of perceived benefit. The lack of convenience was an important 

barrier in many studies. For example, patients were less likely to use systems that required them 

to access equipment or technology that did not fit into their normal daily routine, or similarly if 

the data entry was overly cumbersome. Several studies also found that a lack of clinician 

involvement was associated with less system use. In addition, due to the fact that many studies 

were conducted on early prototype systems, technical issues often prevented consistent use of 

these systems. Finally, although in these research studies patients are generally provided with 

the technology, several articles mentioned that for use beyond a funded study, the costs and 

access to the technology would be a barrier. 

3. d. Findings for Key Question 4: Drivers and Facilitators 

of the Use of Interactive Consumer Health IT 

In the elderly, chronically ill and medically-underserved populations, what drivers or 

facilitators may stimulate or enable the use of interactive consumer health IT? 

a. How do these drivers vary for these populations of interest? 

b. How do these drivers or facilitators vary by type of interactive consumer health IT 

application? 
 

35 

We identified 60 studies that addressed factors that were drivers for interactive health IT for 

consumers (Summary Table 4). This included 36 qualitative, formative evaluations,33, 34, 39, 58, 59, 

63-72, 74-85, 87-93, 101, 110 and 24 summative evaluations of interventions using study designs that 

included randomized control trials, cross-over designs, or cohort studies.28, 29, 31, 32, 41, 43, 44, 46, 47, 56, 

73, 86, 94-96, 98, 99, 102, 106, 108, 111-114 As with the previous findings on usability and barriers, most of 

the studies with information on drivers or facilitators to system use reported this evidence as 

secondary findings, and not part of the primary study design. Given that our quality ratings for 

the summative evaluations usually did not apply to the qualitative findings on drivers, we elected 

not to apply quality ratings on this qualitative information and considered all findings on drivers 

in our summary. The following sections describe the clusters of key concepts that were reported. 

3. d. i. Perception of Health Benefit 

The most general and important driver of the use of interactive consumer health IT was that 

the user perceived a benefit to using the system. In the following section we will review the 

evidence for health and quality-of-life outcomes, but here we are making a distinction as to 

whether the patient actually perceives some benefit. Often with self-management systems there 

may be long term positive outcomes that are not perceptible to the patient at the time of system 

use. Some examples of systems where patients did perceive a benefit during system use are 

listed below. Most of these studies also measured increased use related to the perceived benefits. 

• Diabetes: In general, a driver of the use of interactive health IT interventions for patients 

with diabetes had to do with patients feeling empowered77, 83 with an increased sense of 

security about their health. In a study of a diabetes self-management system, patients felt 

more confident in adjusting their insulin doses in response to monitoring data feedback.96 

Similarly, one study showed that the majority of users perceived improved glycemic 

control, easier treatment changes, better communication with doctor, and better diabetes 

education.73 Also, participants in a study of a 2-way pager system for diabetes felt they 

received better health care when using the paging system. In addition, they perceived the 

messages to be comforting, helpful, convenient and useful. Some reported an additional 

benefit of reporting their blood glucose values in this way as improving their personal 

accountability.41

• Asthma: Patients using an asthma system perceived that keeping an electronic diary 

helped them manage their disease.63 However, with a pediatric asthma system,111 only 

the caregivers perceived quality-of-life improvements to a significant degree, while in 

children a similar trend was not statistically significant. Still, we considered this to be a 

driver of system use because satisfaction on the part of caregivers caring for asthmatic 

children improves the likelihood that the children will use the system. In this study, the 

children reported that using the system was fun and like that it helped them remember to 

take medication, compared with those with a paper diary who sometimes lost it or forgot 

to enter the data, in addition to reporting that they often forgot to take their 

medications.111 A study of low-income inner city adult patients with asthma revealed 

that a driver to use of the system was that the technology provided a sense of security 

(88.2 percent of the patients felt safer while being monitored) and all stated that it was 

either extremely or very important for them to know that the results were reviewed by 

clinicians immediately.70
 

36 

• Heart Failure: One study showed that reminders helped heart failure patients to stay on 

a schedule with noticeable improvements in medication management, diet and exercise, 

leading them to perceive that they felt better, that their blood pressure was more stable, 

and they had less worry.106 These patients also realized that they required less 

nitroglycerine in the management of their heart failure. In another heart failure system 

with access to the patient’s EMR, the patients perceived improvements in knowledge for 

self management saw an immediate benefit of having access to the electronic health 

record for reminding them about their most recent regimen changes, medication doses, 

and lab results.32 This group also saw an improvement in reassurance (they no longer had 

to assume “no news is good news” and could check for accuracy of their medical 

record).32

• Chronic Obstructive Pulmonary Disease (COPD): Of patients using a selfmanagement system for COPD, 73 percent were either very or completely satisfied with 

the system.113 Their primary perceived benefit to using the system was increased access 

to their health information. 

• Osteoarthritis: One intervention102 using the Web-based tailored feedback and 

recommendations based on guidelines for osteoarthristis care, showed that most 

participants felt that using the Website would help them receive better care from their 

physician (77 percent). 

• Hypertension: A study of an interactive voice response phone system for patients with 

hypertension.71 A driver to the use of this system was that nearly all of the subjects felt it 

made them more aware of their condition (95 percent) and most felt that it relieved their 

worries about the disease (79 percent). 

• HIV/AIDS: In a system for HIV/AIDS, the patients perceived that their health was 

better when using the system, but in addition they reported having a sense of greater 

control over their situation.72

• Physical Disabilities: One study79 of a Web-based system to promote physical activity 

for patients with physical disabilities (spinal cord injury (33 percent), multiple sclerosis 

(21 percent), and cerebral palsy (19 percent)) notes that a driver for the use of this system 

was the patients’ perception of increased health and performance, associated with 

motivational messages. 

3. d. ii. Perception of Convenience and Accessibility Benefits 

The perceptions of convenience and ease of access were also important drivers to the use of 

the health IT.32, 39, 46, 47, 69, 77, 84, 91, 92, 95, 110 Many patients with chronic conditions who were in 

support groups found it much more convenient to use the online format. In a study of breast 

cancer patients, many participants expressed difficulty finding or being able to attend support 

services in their home communities, and average travel time to receive breast cancer treatment or 

follow-up was over 1 hour.46 Similarly, with an automated voice response phone system for 

diabetes, patients liked being able to receive the calls at a time and place that was convenient for 

them.47 Also, heart failure patients reported that it was helpful to be able to check lab results and 

medication dosages while traveling.32 Diabetic patients received tailored recommendations via 

both Internet and mobile phone, and a driver to use was the convenience of immediate download 

of information and accessibility on a mobile device.115
 

37 

An additional perception of convenience was the time required to complete educational 

sessions. A study discovered that many participants in the schizophrenia tailored education 

intervention preferred the shorter length of the computer sessions to the longer sessions with the 

community nurse counselor.39

3. d. iii. Rapid Response From Clinicians 

A corollary to convenience as a facilitator of system use has to do with the participation of 

clinicians in health IT interventions. Active and timely response on the part of health care 

professionals was seen to be important when it was an expected component of the intervention. 

For example, one study reported that rapid electronic messaging turnaround and replies by their 

physicians increased patient satisfaction (the average turnaround time was 48 hrs in this study).28 

Similarly, patients receiving tailored printed feedback to share with their provider on smoking 

cessation and physical activity were more satisfied if the physician examined the report during 

the office visit.109

3. d. iv. Benefit of Anonymity for Sensitive Health Topics 

We found evidence of a perceived benefit to the use of health IT in the area of anonymity 

regarding sensitive health problems.72, 85, 87-89 For example, the HIV/AIDS technology study 

patients’ appreciated the anonymity of the communications and this appeared to lead to greater 

perceived control over their condition and increased use of the system.72 HIV+ participants also 

preferred to “discuss” sensitive topics with the computer because it reduced feelings of 

embarrassment and promoted privacy.88 In this study 17 percent of the patients reported being 

more honest with the computer. Similarly, with a PDA system for bipolar disorder, patients 

reported preferring to use the tool because it was discrete and not stigmatizing.85 Finally, for 

breast cancer patients using an online support group with the CHESS system, anonymity within 

the support groups appeared to foster a more equal participation among subjects.87

3. d. v. Benefit of Understanding Physicians and the Health Care 

System 

It appears from the studies we reviewed that having a better understanding of the health care 

system and a better connection with their doctors is important to patients and a driver of system 

use. A system for heart failure patients provided patients with secure web access to their medical 

records, a guide to heart failure and a messaging system for communicating with their doctor and 

sharing information with other doctors.32 The perceived benefits for the patients included: (1) 

they better understood the doctors’ thought processes and decisionmaking, (2) they had a better 

understanding of the medical care process, leading to the perception of more control, (3) they 

perceived they could communicate better with the doctor and felt that they had an increased 

participation in care—a more active role, and (4) they felt they had more of an appreciation for 

the doctors’ skill & expertise.32 Similarly, the HIV/AIDS system study participants perceived 

that it was beneficial to have access to experts and second opinions.72 Finally, in a patientprovider messaging/monitoring and email alert system, 83 percent of the patients felt the system 

provided a sense of security in being able to contact a health care provider in the hospital and 75 

percent felt the system helped them communicate better with their health care providers.56
 

38 

3. d. vi. Deliver Information on Devices Patients Use Every Day 

Another clear message about improving the use of interactive consumer health IT is to 

deliver the messages on devices the patients or consumers use every day. One study found that if 

the Internet is not part of one’s daily life, then SMS (Short Message Service for sending short 

messages to mobile devices) might work better for immediate feedback systems.63 These 

researchers suggested using rewards (free SMS units) to provide perceived benefit when the 

physiological benefit is not obvious to the patient.63 Text messaging was found to be especially 

appropriate for a younger mobile population. Rodgers et al. used mobile text messaging for 

smoking cessation intervention for young adults. Their arguments were that the use of cell 

phones with a young population is “popular, has caché among peers, is age appropriate ... and 

that the ownership of cell phones is much higher among lower SES groups than computers.” 

They anticipated that the use of the new multimedia phones will allow them to offer even more 

sophisticated distracter activities for smoking cessation that will still be low cost.114

3. d. vii. Tailor to Patients’ Interest 

Several literature reviews have demonstrated the benefits of tailoring health information for 

patients, both in terms of behavior change and satisfaction.116 In our review, we found similar 

evidence to support this. With a diabetes system, patients preferred tailored information on 

several counts: (1) it answered their questions better, (2) it related to their interests and current 

concerns, (3) it met their comprehension level, (4) was relevant to their family history, and (5) 

the information was ordered according to importance to them as individuals.43 Additionally, the 

system had most of the information geared toward newly diagnosed patients, and these were the 

participants who found the material most helpful. In a tailored versus generic information for 

diabetes and physical exercise, the participants using the system with tailored information had 

both greater system use and greater satisfaction with the system.44 A diabetes simulation program 

found that the participants were motivated to download the software because they are interested 

in how insulin therapy varies with respect to their own circumstances.110 With a similar diabetes 

system, the participants’ ability to view glucose value trends provided confidence for dramatic 

treatment change. Looking at their own trends allow patients to be able to test insulin dose / 

snack effects and to develop an understanding of the causal relationships.29

3. d. viii. Patients in Poor Health Benefit More 

Interactive consumer health IT systems seem to do better and have greater use when they are 

offered to patients who are sicker or more compromised, thus feeling the need for additional 

assistance. For example, in an online support group for women with breast cancer, those with 

lower perceived health status showed the greatest improvements over time. The intervention was 

most effective for those who started with greater expression of sadness, anxiety, and negative 

emotions. The women most recently diagnosed, and experiencing distress, were the most likely 

to benefit.46 In addition, patients with another diabetes system said it would be most useful when 

they might not be in good control, when there were weather changes, or when they were 

planning a long journey.29
 

39 

3. d. ix. Drivers for Older Users – Gradual Introduction of Technology 

In the studies that concentrated on older users, the main finding that appeared to drive 

increased use of the systems was to gradually introduce the new technology. One researcher 

recommended starting with familiar tools and having a gradual introduction of technology to 

ensure successful use by older participants.94 In addition, that study identified benefits to 

involving older users in the design process. They also found that using the technology to 

integrate older users with family caregivers and with society in general drove system use by 

elders. Similarly, another study reported that gradual introduction of technology worked best, 

stating that people who already had Internet access were more likely to accept and even want to 

try the new Web-based technology.28

3. d. x. Summary of the Drivers to the Use of Interactive Consumer 

Health IT 

In summary, we found 60 studies that addressed the drivers and facilitators of consumers’ 

use of health IT. Many of the findings are derived from the set of articles that also discuss 

barriers. Thus, many of the factors describing the drivers are the converse of the barriers listed 

in the previous section. The most frequent factor associated with increased use of the interactive 

consumer health IT was the patient’s perception of a health benefit. Most of the systems in this 

section were designed for use by patients with chronic conditions, and it was important that the 

patients feel that the system was helping them manage their condition. Convenience in terms of 

access to the technology and the amount of time required for the intervention was shown to be a 

factor driving technology use. It was shown to be beneficial to deliver the interventions on 

devices consumers use every day for other reasons. Again, clinician involvement was a driver to 

the use of the technology. A rapid response from the clinician was shown to improve patient 

satisfaction. Also, providing content tailored to an individual consumer was shown to increase 

both system use and satisfaction. Finally, an important driver of technology use relating to older 

users was the gradual introduction of technology. 

3. e. Findings for Key Question 5: Effectiveness of 

Interactive Consumer Health IT 

In the elderly, chronically ill, and underserved populations, is interactive consumer health IT 

effective in improving outcomes? 

a) How does the technology’s effectiveness vary for our populations of interest? 

b) How does the technology’s effectiveness in these populations of interest differ from the 

 effectiveness in the general population? 

We identified 74 studies reporting on the outcomes of interactive consumer health IT, 

including 50 randomized controlled trials,30, 35, 38, 41, 42, 44, 46, 48-51, 54, 62, 66, 78, 79, 93, 100, 102, 106-108, 114-

141 three studies using a cohort design,39, 97, 142 17 trials with a case series (or before-after) 

design,27, 29, 31, 33, 34, 95, 113, 143-152, three studies with cross sectional designs, 53, 99, 153 and one with a 

qualitative design.32
 

40 

Interactive consumer health IT systems are implemented to achieve a broad variety of 

outcomes, from enhancing self-efficacy, to reducing levels of glycated hemoglobin, to improving 

functional status as measured by the Medical Outcomes Study Short-Form 36 (MOS SF36). 

Because this body of literature is so variable in terms of the types of outcomes used to assess the 

impact of interactive consumer health IT, we were broadly inclusive in terms of the outcomes 

reported, including: 

• Attitudes, such as self-efficacy or the perception of social support. 

• Knowledge, of one’s condition or its treatment. 

• Health-related behaviors, such as diet, exercise, or medication adherence. 

• Disease status measures, such as glycated hemoglobin values, Beck Depression Inventory 

score, or peak expiratory flow rates. 

• Functional status measures, such as the six-minute walk test. 

• Quality of life measures, whether general or disease-specific. 

While broadly inclusive in terms of the study designs employed and the types of outcomes 

measured, we were more restrictive in terms of the quality and likely validity of included reports. 

Using established criteria as described in the Methods section of this report, each publication was 

independently assessed for study quality and assigned a rating of Good, Fair, or Poor. Findings 

from studies assigned a rating of Good or Fair are included in this research synthesis. Findings 

from studies assigned a rating of Poor are discussed only to the degree that they lend additional 

insight, such as illustrating the importance of ensuring that systems are technically sound prior to 

conducting clinical trials to assess their impact.154 

Taken together, this body of literature can be summarized in terms of a series of tentative 

conclusions or propositions23 about the effectiveness of interactive consumer health IT, as 

follows. 

3. e. i. Complete Feedback Loops 

A recurring pattern that is apparent in the evidence examined for this report is the 

effectiveness of systems that provide a complete feedback loop with regular communication 

between patient and clinician or system. Across a range of medical conditions, IT, geographic 

areas, and socioeconomic status, we found a consistent pattern of effectiveness of multicomponent systems that provide a timely response to information about the current or evolving 

status of the patient as part of a complete loop of interaction between patient and health 

professionals. These systems are highly varied and include differing combinations of component 

functions, but have in common components that enable: 

• Monitoring and transmission of data about the current status of the patient, such as daily 

blood pressure, weight, and symptoms in heart failure patients, or blood glucose, diet, and 

activity in diabetics. 

• Interpretation of this data about current patient status by comparison to previously 

established individual treatment goals or to published guidelines. 

• Adjustment of medications, diet, or provision of information as needed, according to the 

data received about patient status. 

• Timely feedback to the patient of adjustments in medications, diet, self-management 

education, or support, whether by email, voice messaging, telephone, or other means. 

• Repetition of this cycle at the appropriate intervals. 
 

41 

Systems that provide such a complete feedback loop are grouped here according to their 

intended duration of use: 

1. Ongoing chronic disease management systems, intended for indefinite, continuous use in 

the care of chronic conditions such as diabetes, heart failure, or asthma. 

2. Multiple session training or treatment systems, consisting of a finite series of modules 

that must be completed by the patient, usually over a period of weeks, either to provide 

training in self-care or as a means of delivering the treatment itself, in the case of some 

mental health disorders. 

3. Single session systems intended for one-time use, often as part of a visit to a health 

professional, to assist in assessment, decisionmaking, or goal setting. 

 3. e. i. 1. Ongoing chronic disease management 

Health professional feedback. We found ten randomized controlled trials78, 93, 115, 123, 

127, 131-133, 135, 136, 139 and two case series31, 147 of interactive consumer health IT for chronic 

conditions in which a health professional reviewed and responded to health status data sent 

by the patient. All of these showed benefits in at least some outcome measures. Positive 

effects were reported with systems that included a variety of health professionals reviewing 

and responding to the data, using a variety of technologies to communicate 

recommendations back to the patient, including: physician feedback using secure email;123

nurse practitioner,31 pharmacist,127 or case manager139 feedback through a the system 

Website accessible to the patient; nurse feedback and advice via text (SMS) messaging;78, 

115 and physician,136 nurse,131 or members of a diabetic care team including physicians, 

nurses, and dieticians depending on the message,132 using conventional telephone lines. 

We found three reports of systems for management of hypertension that provided a 

complete, interactive feedback loop. In a randomized trial of patients with hypertension, 

blood pressure control was improved in 261 patients who used a Website to report daily 

blood pressure values and who received biweekly medication adjustment from a 

pharmacist, compared with: (a) 259 controls who received training and advice to perform 

home BP monitoring, plus training in Web-based access to their EMR;155 and (b) 258 

control patients who received routine hypertension education and were given Web-based 

access to their EMR. Another randomized trial of adults with hypertension found 

reductions in blood pressure, more frequent treatment adjustments, and a higher odds ratio 

of hypertension being controlled when patients used a home system that combined blood 

pressure monitoring with reporting, review, and adjustment by a physician.136 Finally, a 

case series of 49 hypertensive adults reported that greater reductions in blood pressure 

correlated with greater adherence to using a system that included home blood pressure 

monitoring, medication reminding, and physician interaction, although this study had no 

control group and no accounting for confounders such as medication adherence.147
 

42 

We found four reports of randomized trials of persons with diabetes using systems with 

a complete, interactive feedback loop, each reporting improved outcomes. An initial 12-

week randomized trial132 and a subsequent 30-week randomized trial123 of a program in 

Taiwan combined (a) home monitoring of blood glucose, weight, and medications with (b) 

daily review of these data by a physician and (c) regular communication back to the patient 

of recommendations from a member of diabetic care team that included physicians, nurses, 

and dieticians. Intervention patients also had Web-based access to their EMR. Compared 

to patients receiving usual care, the intervention group had lower values of glycated 

hemoglobin and reduced fluctuation in their blood glucose values. Outcomes, including 

glycated hemoglobin, fasting blood glucose, and reported physical activity, were also 

improved in a 12-week trial to increase physical activity in adult diabetics who, following 

an initial assessment, performed and reported physical activity and received weekly nurse 

telephone contact to monitor progress and provide stage-appropriate recommendations.131 

In a 12-week randomized trial, adults with diabetes (N = 51) had improved glycated 

hemoglobin and post prandial glucose using a system that combined Web-based monitoring 

of blood glucose, medications, and events with weekly nurse review and text message 

(SMS) feedback including medication adjustments, education, and reinforcement.78, 115

Three studies reported improved outcomes in patients with heart failure using systems 

that included home monitoring by the patient and feedback from a health professional. 

Southard reported on a randomized trial that included weekly monitoring of weight, 

activity, diet, and other data by heart failure patients, with feedback and recommendations 

from a case manager and dietician.139 Patients using the intervention had greater weight 

loss during the 6-month trial, while both intervention and control patients had 

improvements in diet, activity, and serum lipids. Delgado reported on a case series of 16 

patients with heart failure using a Web-based system to report daily monitoring of weight, 

blood pressure, heart rate, and symptoms, and receive daily responses from a nurse 

practitioner.31 After 3 months, patients had improvements in three subscales of the 

Minnesota Living with Heart Failure Questionnaire. LaFramboise randomized 103 patients 

with heart failure to receive a) a health IT intervention that included monitoring, clinician 

assessment, and feedback with advice or encouragement; b) the health IT intervention plus 

home care; c) home care alone; or d) telephone care. Patients using the health IT 

intervention had increased self-efficacy, while all groups had equal improvement in health 

related quality of life, the, 6-minute walk test, and depression scores.133

One randomized trial included frail elderly veterans with at least one chronic 

condition: heart failure, chronic obstructive pulmonary disease, and/or diabetes. Patients 

randomized to a telecare system with regular monitoring, clinician review, and nurse case 

manager feedback for six months had fewer hospital days, fewer urgent care visits, and 

among those with diabetes, lower glycated hemoglobin, although cognition and quality of 

life were not affected.135

One study did not fit this pattern: adults recovering from bypass surgery improved 

equally after discharge with routine nurse telephone follow-up every 2 weeks compared 

with daily Web-based monitoring of symptoms with scripted management replies from a 

case manager.93
 

43 

Voice message feedback. We found one randomized trial reporting results of a voice 

message system for patients with heart failure that included daily monitoring of symptoms 

by the patient and, on the basis of current symptoms, selected daily voice messages about 

self care, reviewed for appropriateness by research nurses.119 Compared with patients 

receiving usual pre-discharge education, patients using the system had improved selfefficacy, health related behaviors, and improvements in some scales of the Medical 

Outcomes Study (MOS) Short Form-36 (SF36). 

Computer-assisted feedback. Three studies, two in persons with diabetes and one in 

persons with asthma, reported improved outcomes using systems with complete feedback 

loops where feedback was provided by computer alone or by a physician with computer 

assistance. A randomized trial of 105 adults with diabetes—-using online monitoring of 

diet and activity with expert system diet analysis and advice—reported improved diabetic 

knowledge and dietary behaviors.108 In a case series, persons with diabetes receiving 

computer automated dietary feedback based on periodic online dietary assessments (plus 

access to a diabetic self-management Website and diabetic information) had improved 

dietary fat compliance, diabetic support, and cholesterol, although exercise and glycated 

hemoglobin were unchanged.34 Computer assisted physician recommendations were 

examined in a randomized trial of 100 adults with asthma.

49 Using a touch tone phone or 

the Internet for daily monitoring of peak flow, subjects received secure e-mail feedback 

from a physician assisted by computerized decision support. Intervention patients had 

fewer symptoms, better quality of life, improved adherence to medications, and improved 

use of an action plan, compared with self management training with action plan alone or 

with usual care. 

No feedback. No benefit was found in a randomized trial of a system for patients with 

obstructive sleep apnea that provided daily monitoring of continuous positive airway 

pressure (CPAP) use and symptoms but no feedback such as personalization, adjustment, 

or support unless the patient was “high risk”.107 Functional status and CPAP use were no 

different after 30 days in 56 patients using the system compared with 56 controls. 

3. e. i. 2. Multiple session training or treatment 

Like interactive consumer health information systems intended for ongoing chronic 

disease management, systems intended for use in a finite series of sessions or modules 

tended to be effective when they incorporated a complete feedback loop that combined 

patient assessment or data entry with feedback based on information about the current state 

of the patient, but this finding was less clear and consistent for these systems. We found 

six studies showing improvement in outcomes with training or treatment of this type, two 

in patients with asthma,130, 144 three in depression,124, 143, 149 and one in panic or phobic 

disorder.97 We found one study showing a negative effect, where improvement in alcoholic 

behaviors occurred with face-to-face but not with secure e-mail counseling.118 One study of 

a system that provided no feedback showed no improvement,46 while two studies did not 

provide evidence on the question because of weaknesses in study design or reporting.44, 48
 

44 

Urban African-American teenagers with asthma had fewer days of symptoms, fewer 

days of missed school or missed activities, fewer hospitalizations, and improved access to 

controller medications 6 months after completing a four-session program on school 

computers that provided tailored education, including both normative (‘compared with 

other students’) and ipsative (related to self) feedback (‘compared with your last 

session’).130 Patients had fairly low rates of medication availability or use at baseline. Ten 

children with asthma had significantly improved inhaler technique and improved peak 

flows using video monitoring and case management. Of note, Web-based symptom diaries 

were used less frequently than paper-based diaries.144

Among 57 patients with depression randomized to Internet-based self-help modules 

that included therapist feedback by e-mail, measures of depression symptoms improved 

compared with 60 controls participating in a moderated discussion group.143 Improvement 

was also reported in a case series of 72 patients with depression who completed 8 weeks of 

Web-based monitoring, feedback, online cognitive behavioral therapy, e-consultation, and 

medication reminding. Of note, half of those enrolled did not complete the full 8 weeks, 

and adherence was higher with case manager reminding than with automated reminding.149 

Depressed patients solicited on the Internet had improvement in depression symptoms and 

depression literacy using either Web-based education or Web-based cognitive behavioral 

therapy compared with controls.124 The degree to which education or therapy was tailored 

or personalized could not be determined from the report. Panic and phobic disorder

symptoms improved equally well in patients using computerized cognitive behavioral 

therapy with brief therapist support, whether delivered over the Internet or delivered in a 

clinic.97

No Benefit. One study in this group had negative findings: alcoholic factory workers 

randomized to two face-to-face counseling sessions had improved knowledge and attitudes 

and reduced alcohol consumption, but workers randomized to e-mail counseling had no 

significant changes compared with controls.118

No Feedback. Owen found no difference in health-related quality of life, psychological 

well being, or physical function in a randomized trial of patients with breast cancer who 

completed self guided coping skills training in a system that also included peer support, a 

bulletin board, resource directory, and other features, but no mechanism for feedback, 

adjustment, or tailoring of information according to patient status.46 A possible ceiling 

effect due to the patients’ high baseline quality of life, and the small sample size suggest 

the potential for Type II error in this study. 

Indeterminate. Pike randomized smokers seeking to quit to a static Website with 

downloadable booklets or any of five smoking cessation Websites, some of which included 

personalized feedback features.48 Intervention patients and controls had similar abstinence 

rates (11 percent), but higher abstinence rates were found in sites that had higher rates of 

use. Data was combined for all five sites, so characteristics associated with higher use 

were not discernible from the study, and the influence of personalized feedback could not 

be determined from the report. McKay reported on a randomized trial of sedentary adults 

with diabetes offered an 8-week Web-based personalized training program with goal 

setting, monitoring, feedback, a personal coach, and peer forum.44 Controls simply given 

pointers to generic Internet sites had equivalent significant increase in physical activity, but 

little can be inferred from this because of limited actual use by the intervention group, 
 

45 

using the peer forum once on average, and the personal coach only twice in 8 weeks, on 

average. 

3. e. i. 3. Single session systems 

Four of the interactive consumer health IT studies we identified that involved a 

complete feedback loop were intended for use in a single session, providing tailored 

information in the form of computer-assisted goal setting100, 148 or tailored education and 

advice.99, 122 

In a case series 320 adults with diabetes using office-based education and computerassisted goal setting showed improvement in self-reported diet, but no change in glycated 

hemoglobin or body mass index.148 There was no difference if patients received follow-up 

calls, telephone calls, a binder of community resources, or both. Children with asthma

using an interactive CD-ROM to create a personalized treatment plan had greater 

knowledge of asthma at 1 month and fewer courses of steroids and fewer hospitalizations at 

6 months, though the latter finding was not significant with intention-to-treat analysis.100

Low-income women significantly increased their intake of fruit and vegetables after 

viewing a dietary education CD-ROM followed by two reminder phone calls; there was a 

nonsignificant dietary change after CD-ROM alone and after a control CD-ROM concerned 

with stress management.122 Of low-income women smokers using an interactive video 

with tailored advice, 79 percent reported at least one smoking cessation behavior, and 

favorable attitudes toward smoking declined.99

3. e. ii. Reminders, Alone and With Monitoring 

We identified five randomized trials of interactive consumer health IT systems that provided 

individualized reminders to patients, and these showed inconsistent results. No difference was 

found in glycated hemoglobin among 25 persons with diabetes receiving automated text pages or 

e-mail reminders about diet, medications, exercise, etc. compared with 25 controls,41 although 

patients had a positive attitude toward the technology. There was improvement in self-efficacy, 

self-reported adherence, and perceived support, but no difference in glycated hemoglobin among 

pediatric patients with diabetes receiving daily individualized reminders via mobile phone text 

(SMS) messages based on previous clinic visit goal-setting; knowledge of diabetes improved 

equally in intervention and control patients.125 Self reported and cotinine-verified quit rates 

were higher among adult smokers receiving SMS text reminder messages over 6 months 

compared with controls receiving periodic thank you messages.114 Adherence to medications 

and monitoring of peak flow, missed school days, urgent care calls, and days with severe 

obstruction were all improved among 66 urban children with asthma using a telephone-based 

monitoring and reminding device, compared with 68 controls using a paper-based diary.128

HIV/AIDS patients had nonsignificant increase in medication adherence (though a significant 

increase in the subgroup with impaired memory), lower HIV RNA load, and nonsignificant 

increase in CD4 counts 24 weeks after randomization to use of an automated reminding 

device.117
 

46 

3. e. iii. Monitoring Alone 

Because this report is focused on interactive health IT for use by patients or consumers, we 

excluded reports of surveillance systems that automatically transmit physiologic data such as 

heart rhythm to providers without direct participation by the patient. Monitoring by the patient 

was but one component of the multi-functional interactive consumer health IT described above 

under Complete Feedback Loops. Three reports that we identified in our search provide some 

evidence on the effect of consumer health IT for monitoring alone, and the results are 

inconsistent. As mentioned above, a randomized trial of patients with obstructive sleep apnea 

using daily monitoring of CPAP use and symptoms, without feedback or other interaction, found 

no change in functional status or CPAP use,107 suggesting no benefit of monitoring alone. On 

the other hand weight loss was correlated with adherence to monitoring, which overall was poor 

in a case control study of overweight adults using a handheld device to monitor calorie intake 

and weight.152 Finally, control group heart failure patients provided with a scale and weight log 

had improvement in self care and quality of life equal to that of intervention patients with a 

specialized device for transmitting daily weights, suggesting that monitoring alone may have an 

effect.106

3. e. iv. Peer Interaction 

We identified eight reports of studies of peer interaction in online forums or discussion 

groups, four concerned with peer interaction alone34, 121, 141, 151 and four in which peer interaction 

is one element in a complex, multi-component system.42, 46, 53, 129 

Peer interaction alone. We found three randomized trials that suggest that social and 

personal outcomes such as peer support and self efficacy are improved by interactive consumer 

health IT systems providing peer support. Peer interaction in an online forum among adults with 

diabetes increased perceived social support more than interaction with a Web-based health 

professional, or Web-based information access alone.121 Peer support services among persons 

with diabetes increased perceived peer support at 10 months, compared with subjects receiving 

automated Internet services alone or regular communication with a professional health coach.34 

Measures of depression, stress, and post-traumatic stress disorder symptoms were improved 

among 36 women with breast cancer involved in a Web-based, structured breast cancer social 

support group, actively moderated by a mental health professional though no difference was 

found in other behavior, attitude, or anxiety measures.141 

Peer interaction in multi-component systems. We found moderate, inconsistent evidence 

in three randomized trials and one correlation study that social and personal outcomes such as 

peer support and self efficacy are improved by interactive consumer health IT systems that 

provide peer support components as part of a complex, multi-component system. A randomized 

trial of HIV/AIDS patients using a complex multi-component system including information, 

decision aids, expert consultation, and peer support reported higher quality of life and lower 

utilization of health services during and after a 3 to 6 month trial.129 Though some services were 

used more heavily than others, it could not be determined from the available data which service 

or combination of services were responsible for the effects, but peer communication services 

were among the most heavily used. One correlation study of 231 breast cancer patients using a 

multi-component system including information, decision aids, expert consultation, and peer 

support found that information competence correlated with use of information and interactive 

services but not with communication or expert services.53 A randomized trial of patients with 
 

47 

chronic diseases receiving 6 weeks of peer-moderated Web-based training regarding diet, 

exercise, symptom management, medication use, and communication, along with online forum 

had no change in global health, illness intrusiveness, disability, aerobic exercise, or utilization, 

but did have improvements in pain, dyspnea, health distress, fatigue, and stretching exercises.42 

Women with breast cancer using self-guided coping training combined with peer support bulletin 

board, resource directory, library, and art and poetry repository had no change in health-related 

quality of life.46 

3. e. v. Education or Information Alone 

As described in the Methods, we excluded general-purpose Internet sites that provide 

information for the general public without tailoring, individualization, or direct patient 

interaction. We found one report that education alone impacts knowledge, and five reports that 

provide weak but consistent evidence that outcomes are not affected by systems that provide 

education alone. Children with asthma had improvement in some measures of knowledge about 

self-management, and possibly in self-efficacy, using an interactive CD-ROM game with roleplaying and education.138 Peer support was not affected in adults with diabetes in the control 

group provided with information alone, compared with interactions with health professionals or 

peers through an online forum.121 Hypertension control did not improve in the control group 

receiving education and a blood pressure monitor compared with the monitoring and feedback 

intervention.127 Teenagers in the control group receiving asthma education using generic asthma 

Websites were less likely to complete all four sessions and less likely to improve health-related 

behaviors than were those in the group receiving tailored education.130 Users of a telephonebased cancer information service (controls) had less new knowledge and knowledge of their 

condition of interest than did users of an instant messaging based cancer information service, 

although baseline differences in groups were significant and this could simply be different 

populations of users.153 Adult diabetics in the control group receiving educational materials 

alone did not have reduction in glycated hemoglobin and fasting blood glucose or increase in 

physical activity found in the intervention group using physical activity monitoring, feedback, 

education, and stage-appropriate prescription, including weekly nurse contacts to monitor 

progress and adjust recommendations.131

3. e. vi. Access to EMR Data and Secure Email System 

We found two studies that provide weak evidence that online access to the clinician’s EMR 

and optional secure email with clinicians does not by itself improve outcomes, except when 

combined with tailoring or more bidirectional information. Hypertension patients in a control 

group receiving online access to their EMR and the option of e-mail with clinicians did not 

improve their degree of hypertension control and showed limited actual use of the system, 

compared with the intervention group patients.127 Heart failure patients in the control group with 

online access to the EMR and the option of e-mail with clinicians had improved ‘general 

adherence,’ but more ED visits, more clinic contacts, and no difference in self efficacy, health 

status, medication adherence, hospitalizations, clinic visits, or deaths.51 
 

48 

3. e. vii. Web-based Assessment With Recommendations 

Our search retrieved one report of the outcomes of Web-based assessment and 

recommendations. In a randomized trial of in adults with knee osteoarthritis, satisfaction with 

prior care was not reduced when patients used a Website to self assess their condition and obtain 

with recommendations for care, compared with controls.102

3. e. viii. Systems May Not Be for Everyone 

Of the studies concerned with the effect of interactive consumer health IT on outcomes, 

patients in six demonstrated or stated a preference not to use the technology. These included (a) 

asthma patients who stated a preference for and had higher adherence with paper logs;30 (b) 

obese patients of whom 44 percent did not prefer a PDA to a paper-based diary for 

monitoring;152 (c) patients with schizophrenia, of whom those given nurse-based education 

showed more improvement in mental state at the end of the sessions, and who on the whole were 

more satisfied with the nurse-based education;39 and (d) children with asthma who used paperbased diaries twice as much as Web-based diaries in a study using video monitoring and 

feedback.144 Among persons with depression adherence was higher with case manager reminding 

than with automated reminding.149 Finally, problem drinkers and smokers in a primary care 

setting evaluated a brief tailored video doctor intervention favorably, but "Despite this overall 

positive response to the program, 72 percent of participants said they would be more comfortable 

answering questions about personal health from a ‘real doctor’ compared with a video doctor and 

71 percent said they believe a ‘real doctor’ would do a better job of offering patients 

recommendations about wellness and healthy living than would a video doctor."95 

3. e. ix. Summary of the Effectiveness of Interactive Consumer 

Health IT 

In summary, we reviewed 74 studies that reported the effect of interactive consumer health 

IT on health outcomes or health care process measures relevant to Key Question 5. The most 

important and consistent finding of this review was that systems were effective when they 

provided a complete feedback loop that included (a) monitoring and transmission of patient 

status; (b) interpretation of this data by comparison with previously established individual 

treatment goals or published guidelines; (c) adjustment of medications, diet, or information 

according to patient status; (d) timely communication back to the patient with tailored 

recommendations or advice; and (e) repetition of this cycle at appropriate intervals. These 

'complete loop' interventions were effective across a wide range of medical conditions, 

monitoring and communication technologies, geographical areas, and patients' socioeconomic 

status. 
 

49 

 Interactive consumer health IT that provided only one or a subset of these functions were 

less consistently effective. Systems that provided reminders, alone or based on monitoring, were 

less consistently effective. Systems that provided peer interaction, alone or as part of multicomponent systems, were generally effective for improving intermediate outcomes but not for 

health outcomes. Weak but consistent evidence showed that education or information alone had 

minimal effect on health outcomes. Online access to data in the clinicians' EMR and optional email with clinicians did not by itself improve outcomes, except when combined with tailoring or 

more bidirectional information flow. 

 When asked, in some cases patients sometimes expressed a strong preference for human 

interaction, even with systems that were effective. Many studies showed these systems to be 

effective in specific populations of interest: elders, those with chronic illness, members of 

underserved populations, but our search identified no direct comparisons of outcomes among 

subpopulations or between a target population and the general population. 

3. f. Specific Findings for Populations of Interest 

Each of our key questions had sub-questions about the relative use and effectiveness of the 

technology by our populations of interest (elderly, chronically ill, and underserved). However, 

we did not find evidence for relative comparisons between groups of interest on the same 

technology. There were certainly papers that primarily focused on special populations, such as a 

specific minority group, a low income area, an elderly population, or a rural setting, but they did 

not answer questions of relative use and outcomes. In most of the studies we identified, the 

interactive consumer health IT interventions were directed at a specific subpopulation, such as 

rural women with breast cancer54, 156 or inner city teens with asthma.128, 130 Although computers 

were sometimes provided for subjects,129, 130 access to e-mail or the Internet was often an 

inclusion criterion,30, 31, 34, 48, 51, 53, 54, 62, 102, 107, 116, 123, 127, 132, 139, 143, 149 making it difficult to study 

the underserved. Also, few of the studies we identified included samples of sufficient size to 

permit reliable inferences about differences in subgroups. As a consequence, limited and only 

tentative conclusions can be made about differences in effectiveness among subpopulations, 

based on extrapolation from this data or on indirect comparisons. We have summarized the 

evidence relating to the use of interactive consumer health IT by specific populations in 

Summary Table 6 and the sections below. 

3. f. i. African-American Population 

Fifteen studies provided ethnicity data for study participants ranging from 4-88 percent 

African American.47, 53, 86, 95, 102, 106, 107, 113, 128, 130, 141, 153, 157-159 Although other potentially 

underserved ethnic groups were listed in many of the studies, only African American populations 

made up the majority in any of the studies we reviewed (Summary Table 6). Three studies106, 128, 

160 focused primarily on African American populations in urban settings, with proportions 

ranging from 65 percent106 to more than 98 percent160. Subjects reported technical usability 

problems in these studies, specifically difficulties reading the screen or monitor. Moreover, 

problems with perception of letters due to their color and difficulties related to sound were also 

documented.106 The remainder of the studies providing ethnicity data had samples of less than 

50 percent African American, and no subgroup analysis was provided. As noted earlier, no 

studies provided direct comparisons for this subgroup with other populations. 
 

50 

3. f. ii. Rural Population 

There were a total of 9 studies that concentrated on rural underserved areas.36, 53, 54, 60, 75, 146, 

151, 156, 161 Of these, five36, 54, 60, 151, 156 were based wholly on rural areas whereas the remaining 

four53, 75, 146, 161 examined a combination of small city, rural, and urban settings. 

The Ferrer-Roca 2004 study reported that 71 percent of the intervention subjects and 92 

percent of the control subjects thought that the system was easy to use whereas 50 percent of the 

intervention subjects and 88 percent of the control subjects had difficulties in accessing the 

system (p<0.01). Besides these relevant findings, the same study also showed that 86 percent of 

the intervention subjects and 40 percent of the control subjects would recommend the site 

(p<0.01) whereas 33 percent of the intervention subjects and 12 percent of the control subjects 

did not find the system useful and efficient (p<0.05).60

Interestingly, the Kaufman 2003 study based on rural upstate New York and Hispanic New 

York City populations, classified usability concerns into system barriers and cognitive/skill 

barriers. Some of the system barriers documented in this study were problematic widgets, small 

fonts, bad spacing, complex tasks, screen transitions, and system stability. The cognitive/skill 

barriers included mouse-keyboard skill, mental model, literacy, and numeracy. Anxiety, selfefficacy, and motivation were also found to be important issues surrounding health IT use.75

3. f. iii. Underserved Population 

One study was based in public health clinics with one provider for 10 clinic sites and 63.3 

percent of the subjects having some chronic illness. A majority of the study subjects (81.1 

percent) felt that the computer was easy to use whereas 12 percent asked for help and 24.8 

percent had privacy concerns regarding use of the computer. Satisfaction with provider visit was 

significantly related to having a chronic condition, being a nonsmoker, and having a provider 

who reviewed the report with the patient.109 In addition, investigators specifically looked at the 

barriers to the use of personal health records by low-income elderly people living in a publicly 

subsidized housing project.82 They found that computer literacy and computer anxiety were the 

major barriers to use in this population, with 63 percent of the group having problems with 

computer literacy and 58 percent having problems with computer anxiety. Barriers due to 

cognitive impairment (34 percent), health literacy (29 percent), and physical impairments (26 

percent) were also significant factors affecting the use of the personal health records. In a study 

of low-income women who smoked,99 the factors associated with increased satisfaction and 

system use were readiness-to-change, minority, and a greater than high-school level of 

education. Finally, one study tested a computer tool for monitoring asthma symptoms and 

spirometry values in a low-income inner city area where 2/3 of the subjects had never used a 

computer and nearly half had never used an ATM machine for banking.70 In addition, the 

majority of the subjects were non-native English speakers. Their primary barriers to the use of 

the asthma monitoring system were a lack of computer experience, limited availability of 

instructional assistance for both the computer and the intervention, and the cost associated with 

providing the system beyond the study period. 
 

51 

3. f. iv. Elderly Population 

We found 13 studies with study subjects over the age of 65 years.36, 38, 45, 47, 62, 69, 75, 82, 98, 113, 

119, 120, 162 In two of the studies, the elderly population was examined as a subgroup.38, 62 Six 

studies were conducted on subjects aged 65 years and above36, 82, 119, 120, 162 and the remaining 

four had subjects with mean age ranging from 69.1 to 73.7 years.45, 75, 98, 113 One study found that 

younger users were more interested in their diabetes system.69 Another found that their younger 

patients with diabetes (less than 55 years of age) were less likely to listen to as many messages, 

but more likely to want to use the system in the future.47 With a physical exercise system 

specifically designed for elders, researchers found that even within an older group, age was 

directly related to increased need for assistance with the technology. Then as mentioned above, a 

study with low-income elderly participants using a personal health record found computer 

literacy and computer anxiety to be the major barriers to use of the system.82
 

53

Chapter 4. Discussion 

The results of our analysis of the literature pertaining to the barriers and drivers to the use of 

interactive consumer health IT by the chronically ill, elderly, and underserved is based on the 

relationships modeled in our analytic framework. As described in the methods section, we 

modeled the factors that influenced the degree of use of health IT as arising from patient 

characteristics, technology characteristics, and environmental factors. Our key questions were 

then generated to test these relationships, as well as the assumption that increased or improved 

use of the systems would improve intermediate outcomes, such as knowledge, self-efficacy, or 

health behaviors, as well as end outcomes, such as quality-of-life, satisfaction with care, or costs. 

The summarization of evidence described in this chapter is based on Denyer23 and Popay’s22

frameworks. For each of our five key questions, we first analyzed a subset of the papers to 

develop a model of how best to describe the technologies and interventions, as well as to 

describe the use characteristics and associated issues. This preliminary synthesis served as a 

framework for the rest of our summarization. Our focus in this review was on consumer health 

IT that was interactive, meaning that the consumer or patient was the primary user of the system 

and received computer-mediated patient-specific information in return. Through our review 

process, we found distinct technology types that needed to be assessed separately: 

• In-home monitoring, disease management, and self-management systems. 

• Online forums on health topics. 

• Patient access to their EMR and patient/physician e-mail. 

• Single or sporadic use of an interactive educational system. 

• Interactive training systems that monitor patient signals and provide immediate feedback. 

• Interactive and tailored reminding systems. 

There are several challenges to reviewing interventions based on interactive consumer health 

IT, primarily due to the fact that the interventions being studied in the articles we reviewed were 

highly varied. Firstly, the underlying technology varied significantly and could include Webbased applications, hand-held PDAs or cell phones, and touch-screen computers or kiosks. 

Secondly, the systems were designed for a wide variety of purposes, not a single intervention. 

Even though we focused the review on systems for the elderly, chronically ill, and underserved 

populations, we covered many application areas, such as systems for diabetes, asthma, cancer, 

mental disorders, smoking cessation, and medication reminding. Children with chronic 

conditions were included in our review, and we found that they have very different types of 

usage issues and systems designed for them. Thirdly, the frequency of intended use of the 

various systems could be quite different. Some were single use interactive educational systems 

or decision aids. Others were intended for daily use in the home. Fourth, these systems varied 

widely in complexity, from simple systems providing a single function to complex systems with 

an robust suite of diverse features. Finally, the embedded care protocols and the degree to which 

clinicians participated on the other end differed dramatically from study to study. All of these 

issues are critical when interpreting the results of these studies, and the variability in approaches 

has made us cautious in generalizing our results too broadly. 
 

54

Another issue we confronted in reviewing studies of interactive consumer health IT involved 

the quality ratings for our evidence. We incorporated two quality ratings, one based on the 

appropriateness of the methodology to the study question and the second based on the quality of 

the study execution. We based our ratings on a number of systems appropriate for rating 

evidence pertaining to our key questions on system usage and outcomes.18, 19, 163 However, for 

the key questions on usability, barriers, and drivers to system use, we needed to consider the 

quality of the evidence for those components of the study separately. Although some articles 

directly addressed these issues, in most cases our evidence for usability, barriers, and drivers 

came from studies where these issues were not a key part of the study design, but rather 

qualitative evidence that accompanied an outcomes study. For these studies we did not formally 

rate the quality of the evidence on usability, barriers, and drivers separately. 

The technologies and interventions we reviewed were typically “leading edge” and not in 

routine clinical use. Complex interventions such as these are difficult to deploy on a limited 

budget, especially in an academic setting. One of the primary barriers to system use in the 

studies we reviewed was that many users had technology malfunctions of some sort. Under 

these circumstances, it is difficult to determine whether the results of the study are representative 

of a more polished and tested intervention. Our evidence review of health IT has a smaller 

percentage of studies rated as high quality, compared with reviews of more straight-forward 

interventions, such as a pharmaceutical therapy. Yet this is appropriate for studies of evolving 

technologies. Devoting resources for a large randomized controlled trial of a complex 

technology early in its development stage is not warranted. Stead et al. proposed guidelines for 

matching the level of evaluation to the stage of development.154, 164 For example, as systems 

move from specification to component development to combining components and integrating 

them into the environment, the appropriate evaluations move from laboratory bench tests to tests 

of validity and then efficacy in the field. But it is only when systems have gone through 

thorough quality assurance and piloting in the field that large studies designed to provide high 

quality outcomes information are warranted. Many of the studies we reviewed for the barriers 

and drivers to the use of health IT were in the relatively early stages of development and not in 

routine clinical use. Thus, these studies often involved a small number of subjects and measured 

intermediate outcomes. Additionally, the information on barriers and drivers often came from 

qualitative additions to studies and not the main study design. 

4. a. Use of Interactive Consumer Health IT 

Findings regarding usage of interactive consumer health IT need to be interpreted with regard 

to the intended frequency of use of the system. For example, self-management and monitoring 

systems may be intended to be used on a regular and continuing basis, whereas many of the 

educational systems we reviewed were intended to be used only once. Availability was also an 

issue. Some systems were only available to patients in their clinician’s office. However, in 

studies of interventions with multiple components we were able to observe the relative frequency 

of patient access. For example, from these studies we found that online peer discussion and 

bulletin boards were the functionalities used most frequently compared with patient interactions 

with self-management, self-monitoring, health educational, or ask-an-expert functions within the 

same system. However, in general, self-management tools and systems with ask-an-expert 

modules were used frequently by subjects in the studies. 
 

55

In general, we have found that the measurement and understanding of the usage of interactive 

consumer health IT has begun to be addressed, however, there remains a lack of clarity or 

standardized metrics. These varied measures, along with differing expectations for use for each 

system, presents challenges in comparing usage between systems in a meaningful way. The 

issue gains relevance as the field strives to determine if the measurement of health technology 

usage can serve as a means of determining individual engagement, activation, or preference, and 

whether it can serve as a proxy for intervention exposure or “dose.” 

The findings describing the frequency of health IT usage (mean logins per subject per month) 

suggest that certain interactive consumer health IT functionality —peer group support, bulletin 

board, and secure messaging—may be associated with greater usage by subjects. Patient and 

consumer surveys have identified peer support (e-groups, chat rooms) as desirable services, 

providing anytime social support and person-to-person interaction. 

It also seems reasonable that some types of interactive consumer health IT would be expected 

to be used only on an intermittent or ad-hoc basis—shared access to electronic records, or 

messaging alone added to automatic paging—and not be used as frequently as social support and 

self-management applications. 

4. b. Usefulness and Usability of Interactive Consumer 

Health IT 

A great number of home care patients who require disease management are elderly and/or 

have functional limitations, such as reduced sensory, cognitive or motor capabilities. Although 

the Internet has the potential to revolutionize the process of health care delivery and empower 

patients to become more active in the care process, the fastest growing segment of the U.S. 

population— i.e., people over the age of 50 years—are at a disadvantage because designers of 

both software and hardware technology fail to consider them as a potential user group.14 

Usability and accessibility issues are important quality criteria for Web-based interventions, but 

are frequently ignored by designers and evaluators.15 The design of a usable Web-based 

information system for healthy users who are familiar with computer technology is a challenge. 

When a system needs to address age-related constraints and the functional limitations of 

inexperienced users, it becomes even more difficult. Designers of systems for home care patients 

should aim to increase functional accessibility165 and employ rigorous usability testing methods. 

Only two of the studies we reviewed were primarily targeted at discovering usability issues, 

and no studies compared the usability of more than one system at a time, or across populations of 

users. However, in all studies reporting usability results, a majority of the users found the 

systems under study to be helpful or useful. Often this was true for more than 90 percent of 

users. 

People with diabetes tended to have vision problems and were concerned about clarity and 

size of graphics. Several systems required improvements in the display contrast, use of color, 

and button size. In general, there was a need for clearer and simpler graphics for t older users or 

those with chronic disabilities. Often the largest usability changes needed to be made with 

regard to the data entry screens on Web-based systems, although in some cases it was the graphic 

feedback for home monitoring data that needed improvements in clarity. An additional general 

finding was that it was important to minimize the need for a user to jump from screen to screen. 

Researchers found that it was important to keep frequently used items on the main Web page 

with “one-click” access to important information or data entry screens. Overall, we saw that the 
 

56

usability findings pointed to needing to keep systems simple and free from distracting elements. 

These issues were particularly relevant for older users and those with functional limitations, 

however, the findings were still important across all populations. 

4. c. Barriers to the Use of Interactive Consumer Health IT 

Our summary of the evidence on the barriers to the use of interactive consumer health IT 

pointed mainly to issues of motivation, access, ease of use, and convenience. Most importantly, 

if users did not perceive a potential health benefit (regardless of an actual benefit or not) they 

were less likely to use the system. Additionally, if consumers or patients did not already know 

how to use computers, and especially if they did not have easy access to a computer in their 

home, there tended to be difficulties in using the intervention. 

Many of the technology interventions being studied suffered from usability and technology 

problems that prevented routine use as intended. Many systems were deployed for evaluation 

before sufficient quality testing to ensure that even motivated participants could use them. 

Common technology problems included unreliable Internet access, system crashes, and battery 

issues. Many of the systems also required updates to fix displays that generated confusion on the 

part of the users. Oftentimes these issues were not caught in advance of full deployment in a 

study. 

Feedback on the lack of use of some systems often pointed to the difficulty of finding a 

convenient time or situation to interact with the system. Similarly, data entry on the part of 

patients was often viewed as cumbersome and too time consuming. Interfaces for reminding 

systems for medications or for chronic disease management were particularly difficult to 

implement in a way that would encourage sustained use. 

One interesting barrier to use of some disease management or interactive self-management 

systems that provided either recommendations or changes in treatment based on computer 

protocols was a lack of trust on the part of some patients. If the system’s recommendation did 

not fit with the patient’s mental model or perception of the situation, the patient was unlikely to 

comply with the advice. In general, if patients did not trust the technology they were less likely 

to use the system or manage their health in accordance with the recommendations. 

Some of the interactive consumer health IT reviewed involved the participation of a clinician 

as part of the intervention. This could range from secure physician e-mails to a patient, 

supplemental phone interactions in addition to tailored computer feedback, or clinician review of 

monitoring data. For interventions where patients expected clinician interaction and did not 

receive timely responses, usage of the system and satisfaction on the part of the patient tended to 

drop. These studies reported that a barrier to the successful implementation of these technology 

interventions was the lack of appropriate resources and workflow modifications for the health 

care professionals. They identified issues of the lack of reimbursement for e-health encounters 

and the perception of additional time required. In general, cost was often mentioned as a barrier 

to system use on the part of both the patient and the health care provider. 

In the studies we reviewed, we did not find direct evidence for differential use of the 

interactive consumer health IT by populations in rural or frontier areas, minorities, low-income 

groups, or low education groups. There was weak evidence for age being a barrier to anticipated 

future use of some interventions. 

As mentioned in the previous section, usability issues plagued many studies and would also 

be considered to be a barrier to use. Older age was associated with lower usage in some studies 
 

57

of systems addressing chronic diseases, such as diabetes where vision and other functional 

disabilities required improved screen contrast and font size. In most studies, these issues were 

identified as barriers after the system had been deployed. 

4. d. Drivers to the Use of Interactive Consumer Health IT 

Patient perception that the system was helping them appeared to be the most important driver 

of system use. Generally, these were perceived health benefits. For applications that dealt with 

sensitive health topics, such as HIV/AIDS, patients identified the anonymity and nonjudgmental 

nature of interacting with a computer system as a benefit. Other perceived benefits, such as 

better understanding their doctors and the medical system, were also identified as drivers of 

technology use. 

A further driver corresponding to one of the identified barriers included information from 

studies where the care protocols involved clinician participation. They found that a rapid 

response from the participating clinician improved patient satisfaction and sustained use of the 

system. 

One important driver of system use identified in many studies was delivery of the 

intervention on familiar devices used by consumers or patients every day for other purposes. For 

example, cell phones with SMS messaging worked especially well for younger ages, and people 

who routinely used a computer throughout the day anyway would have an easier time adhering 

to protocols for interactions with self-management systems on the Web. In general, we found 

that it was important that patients feel the systems were convenient and easy to use, especially 

for interventions that required frequent sustained use. A particular driver for older individual’s 

use of new technologies was to have an implementation that provided a gradual stepwise 

introduction to the technology. Finally, several studies identified the tailoring of delivered 

content as a driver of patient satisfaction, perceived benefit, and system use. 

4. e. Effectiveness of Interactive Consumer Health IT 

Ongoing self management support for chronic conditions was effective if it included a 

‘closed feedback loop.’ Among the studies we reviewed, including 13 randomized trials and 

three studies with other designs, we found that consumer health IT systems that provided a 

complete feedback loop were generally effective at improving outcomes in chronic conditions. 

These systems included a combination of (a) monitoring and transmission of data about the 

current status of the patient; (b) interpretation of this data in light of previously established 

treatment goals or published guidelines; (c) adjustment of medications, diet, or patient education 

as required; (d) timely communication back to the patient of recommendations, advice, or 

information appropriate to their current state, and (e) regular repetition of this cycle. This 

process is analogous to the familiar Deming-Shewhart cycle for process improvement that serves 

as the basis for continuous quality improvement in healthcare.166 These systems provide the 

means for continuous improvement through repeated refinement or adjustment of the 

management of the patient according to their current condition. We observed this pattern across 

a spectrum of chronic conditions including diabetes, heart failure, hypertension, and asthma. 

Effective complete feedback loop systems most often incorporated a health professional in the 

loop—including pharmacists, dieticians, nurses, and physicians—alone or with computer 
 

58

decision support (Table 3), and used a variety of IT, including home computers, voice 

messaging, text messaging, e-mail, and the telephone.31, 34, 49, 78, 108, 115, 123, 127, 131-133, 135, 136, 139 

Table 3: Examples of complete feedback loop systems 

Author, 

Year, Design 

Condition 

Monitor 

Frequenc

y 

Feedback 

Frequency Feedback Source 

Nature of 

Information 

Cho, 2006, 

RCT123 DM Daily Semiweekly

Clinician e-mail State-specific

Delgado, 2003, 

Case series31 CHF Daily Daily Nurse Practitioner Website

State-specific

Glasgow, 

2003, 

Before/After34

DM/Diet

NR Quarterly "Automatic feedback" State-specific

Green, 2008, 

RCT127 HTN Daily Biweekly Pharmacist State-specific

Kashem, 2006 

RCT40 CHF > Weekly > Weekly RN, MD; phone, system

State-specific

Kim, 2006, 

RCT131 DM/ACT > Weekly Weekly RN phone State-specific

Kim, 2007, 

RCT115 DM > Weekly Weekly RN via SMS and web State-specific

Kwon, 2004, 

RCT132 DM

3x/Week > Weekly MD, RN, RD State-specific

LaFramboise, 

2003, RCT133 CHF Daily Daily RN, phone system State-specific

Noel 2004, 

RCT135

CHF, 

COPD, 

DM

Not clear Not clear Nurse case manager 

advice

State-specific

Rasmussen, 

2005, RCT49 Asthma Daily Regular Physician w/CDSS State-specific

Rogers, 2001, 

RCT136 HTN

3x/week Weekly Physician, by phone State-specific

Southard, 

2003, RCT139 CHF > Weekly Weekly Web use State-specific

Turnin, 1992, 

RCT 

crossover108

DM

6x/month 6x/month Expert system for diet, 

calorie analysis

State-specific

In essence, then, this group of closed feedback loop systems are health IT based forms of 

self-management support of the sort proposed in the Chronic Care Model. Of note, the systems 

described in the studies we examined depended on active engagement of patients and 

involvement of health professionals, supported by the specific technology interventions. 

Questions remain as to (a) the optimal frequency of monitoring, adjustment, and feedback, which 

are likely to be condition specific; (b) the optimal level of participation by members of the health 

care team; (c) the degree to which the process depends on familiarity and trust between the 
 

59

patient and the other elements of the system; and (d) the role of support and encouragement, as 

opposed to refinement of the management plan. Perhaps most challenging, these systems 

increase the frequency of patient-provider interaction while shifting the location of interaction 

away from episode-driven office-based care, and no mechanism exists to support these activities 

under current health care reimbursement mechanisms. 

Training or treatment through the use of multiple session or one-time use systems appear to 

be effective when it includes a similar complete loop of patient-specific information as the basis 

for tailored advice or education, although the evidence is less strong. Studies of these interactive 

consumer health IT systems were fewer in number and used weaker study designs, but overall 

the findings were comparable to the findings for chronic disease self management. Tailoring of 

information or adjustment of treatment based on patient state appear to be consistent elements in 

the effective systems, but robust studies of direct comparisons were not identified in this review. 

Systems providing such training or treatment were effective for patients with asthma,130, 144

depression,143, 149 and panic/phobic disorder,97 while a study with no feedback for patients with 

breast cancer showed no improvement,46 and a study of problem drinkers found face-to-face 

counseling to be effective while electronic counseling was not.118 It is unclear from the evidence 

available whether these systems will have a sustained impact, and they may be best suited for 

conditions requiring short-term treatment. Interactive consumer health IT systems that provided 

peer interaction, alone or as part of multi-component systems, were generally effective for 

improving intermediate outcomes but not for health outcomes. 

Consumer health IT systems that provide monitoring alone or reminding alone were not 

clearly effective. We found fewer studies of such systems, those identified had weaker study 

designs, and the evidence from them was not consistent. The heterogeneity of the systems 

described in these studies and the variety of chronic conditions to which they were applied make 

it difficult to generalize about their effects or lack of effects. The same can be said for systems 

that provide access to the EMR when they are not part of a “complete feedback loop”: fewer 

studies were identified; the studies reviewed employed weaker study designs; and the results did 

not consistently show benefit, in terms of peer support, self efficacy, or other outcomes 

(Appendix H∗

). 

4. f. Use of Interactive Consumer Health IT by Populations 

of Interest 

Each of our key questions had sub-questions about the relative use and effectiveness of the 

technology by key populations (elderly, chronically ill, and underserved). However, we did not 

find evidence for relative comparisons between groups of interest on the same technology. 

There were certainly studies that primarily focused on special populations, such as a specific 

minority group, a low income area, an elderly population, or a rural setting, but they did not 

answer questions of relative use and outcomes. 

4. g. Technical Difficulties Interfering With Evaluations 

Of the studies of interactive consumer health IT reporting health outcomes, nine studies were 

compromised by technical problems that limited use by patients and limited evaluation by 

 

∗ Appendixes and Evidence Tables cited in this report are available electronically at http://www.ahrq.gov/clinic/tp/hitbartp.htm
 

60

investigators, several of which had to be excluded on this basis. Depressed patients withdrew 

from a randomized trial of a self-help system, with 21 of 57 reporting the system was too 

demanding and only 65 percent completing all modules.143 Elderly heart failure patients had 

difficulty getting medications out of a reminder device and reported difficulty reading the screen, 

limiting potential effect on medication adherence.106 After initial enthusiasm, adult patients with 

diabetes failed to enter data, and only one of six entered data for three consecutive weeks as 

required for the study, citing loss of Internet connection and difficult-to-use data entry screens.29

Adults with asthma had higher adherence to peak flow monitoring using a paper diary than an 

Internet-based log, citing problems with Internet connection, system errors, and other technical 

problems, though subjects retained a positive attitude toward Internet monitoring.30 Geriatric 

diabetic patients had limited benefit from a voice message system, citing difficulty using buttons 

on the mobile phone and lost connections.145 Of 30 COPD patients, 14 dropped out because of 

technical and other difficulties using the system.113 An interactive voice response system failed 

to reach 30 percent of smokers after cardiac hospitalization and only 18 of 50 patients actually 

received all three calls,50 while another IVR system frequently failed to reach veterans with 

diabetes.47 Of 56 obstructive sleep apnea patients one in seven was unable to use the device to 

monitor sleep and CPAP use.107 In contrast, systems without such technical problems enjoyed 

more complete and effective use and a more robust evaluation.
 

61

Chapter 5. Future Research 

In our review of the evidence on the barriers and drivers of the use of interactive consumer 

health IT by the elderly, chronically ill, and underserved populations we identified several areas for 

future work. The most pressing need is for a principled taxonomy of interactive consumer health IT 

and related interventions, so that the resulting outcomes of studies involving these systems can be 

better interpreted by understanding effects of the various components. Currently, it is difficult to 

generalize across the wide variety of systems. Similarly, future research is needed to understand 

best practices for the design and implementation of these interactive health technologies for patients. 

A clear taxonomy will facilitate this effort. 

In addition to standardizing our descriptions of the variety of interactive consumer health IT 

applications, it will be important to develop standardized and clear definitions of the intermediate 

outcomes relating to the use of these technologies. For example, in the studies we reviewed, system 

usage has been measured by logins, Web clicks, or time within a session. These varied measures, 

along with differing expectations for use for each system, make it difficult to compare usage 

between systems in a meaningful way. The issue gains relevance as the field strives to determine if 

the measurement of the health technology usage can serve as a means of determining individual 

engagement, activation, or preference, and whether it can serve as a proxy for intervention exposure 

or “dose.” 

Finally, there is a paucity of research with direct comparison of the use and outcomes of these 

technologies by the general population versus disadvantaged populations, such as minorities, lowincome groups, elderly, disabled, and geographically remote populations. It would be very useful to 

test the same technology, protocol, and implementation interventions with comparison populations 

within the same study, to truly understand the barriers and drivers associated with these 

interventions. 
 

63

Chapter 6. Conclusions 

The findings from our evidence review on the barriers and drivers to the use of interactive 

consumer health IT by the elderly, chronically ill, and underserved were derived from our 

summarization of papers that met our inclusion criteria and quality standards (Appendix H*). 

These papers were reviewed according to each of our key questions on the level of use of the 

technology, the usability and usefulness of the technology, the barriers and drivers to the use of 

the technology, and the outcomes from the use of the technology. We found that it was 

important to characterize and specify the technology components, intended use, care protocols, 

content and system environment in order to interpret evidence from disparate technologies, 

populations, and implementations. The barriers to consumers’ use of interactive consumer health 

IT were heavily influenced by unreliable and difficult to use technology. This was partially due 

to many of the studies using early stage system prototypes, as is typical for a relatively new field. 

Few of the studies we reviewed were specifically designed to compare the elderly, chronically 

ill, or underserved to the general population. But in general, we did find that several types of 

interactive consumer health IT were usable and effective in many settings and with all of our 

populations of interest. We saw that convenience and ease-of-use were important drivers of 

system use, especially if the interventions could be delivered on technologies users already had 

and interacted with on a daily basis. It was critical that data entry not be cumbersome and that 

the intervention fit into the user’s daily routine. Perceived benefit, system trust, anonymity for 

sensitive health conditions, and rapid clinician feedback were also important factors influencing 

the successful use of interactive consumer health IT. 

The most important and consistent finding regarding the effectiveness of these technology 

interventions was that it was important that the systems provide a complete feedback loop that 

included some assessment of current patient status, interpretation of this status information in 

light of established treatment goals or plans, and communication back the patient with tailored 

recommendations or advice. Interactive consumer health IT that provided only one or a subset of 

these functions were less consistently effective. 

The systems described in the studies we examined depended on active engagement of 

patients and involvement of health professionals, supported by the specific technology 

interventions. Questions remain as to (a) the optimal frequency of use of the system by the 

patient, which is likely to be condition specific; (b) the optimal frequency of use or degree of 

involvement by the health professionals; (c) whether their success depends on repeated 

modification of the patient’s treatment regimen or simply ongoing assistance with applying a 

static treatment plan. Perhaps most challenging, these systems shift the locus of care away from 

traditional physician office visits and many of them involve the participation of a 

multidisciplinary health care team; it is difficult to support these activities financially under 

current episode-based, fee-for-service health care reimbursement mechanisms. 

 

 

*

Appendixes and Evidence Tables cited in this report are available electronically at http://www.ahrq.gov/clinic/tp/hitbartp.htm
 

65

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Summary Table 1. Level of Use of Interactive Consumer Health IT (KQ 1)

SUMMARY TABLES

1 of 6
Summary Table 1. Level of Use of Interactive Consumer Health IT (KQ 1)

Author, Year

Design, N

Condition and/or 

Population Interactive Health IT Findings: Technology Usage 

An, 200626

Cohort, N=47Experimental arm of RCT, N=257

SmokingCollege students

Web-based self-monitoring and goal setting; discussion board; quiz with tailored feedback; weekly email reminders; Question of the Week; peer coach emails weekly if not completing tasks

Website use by study: Pilot: % subjects using swas 53%, 21% and 26% for weeks 1, 2, and 5, respectivelyRCT: mean 95% over 20 weeks (range, 89% to 98%)

Bachofen, 199927

Cohort studyN=21

Obsessivecompulsive disorder

IVR self-management;IVR calls after reading a190-page manual on ERP(clinician-guided exposureand ritual prevention)

Subjects used system mean 67.2 +/- 38.3 days22 days for self-assessment;45 days for ERP

Cathala, 200328

Cohort, N=140

Prostate cancer; post-op radical prostatectomy

Web-based access to hospital medical records, secure messaging with surgical team, monitoring PSA level, and answer questions

Website use over 6 months: 95% “regularly connected”, mean logons 8/subject (range: 1-2Mean 2 PSA entries/subject; Mean 2 messages/subject

Cavan, 200329

Cohort, N=6

Type I Diabetes Web-based monitoring blood glucose 

and insulin use; food diary; feedback with glucose level simulation; patient decision support for insulin dosing 

Website use over 6 months:1 of 6 subjects input 4 days’ data on 3 occasionfirst month (study request). No input for several wks; Subjects input mean of 5 sets of data (rang2-8)

Cruz-Correia, 200730

Randomized, crossover study, N=21

Asthma Web-based self-monitoring of peak 

flow and symptoms; tailored action plan; decision support tool for patients and physician; Compared to paper-based monitoring

Website use during 4 week periods: variable monitoring, about half of scheduled monitoring conducted; paper tools more likely complete 

Delgado, 200331

Cohort, N=6

Congestive heart failure

Web-based self-monitoring of weight, BP, medication; secure messaging with providers; educational content

Website use over 3 months: Mean 73 logons/subject

Earnest, 200432

Randomized trial, N=107

Congestive heart failure

Web-based access to electronic records, secure messaging with clinical team; educational content

Website use over 1 year (N= 54): 80% used onor more, mean 8 hit-days/subject; Results sugguse associated with White, Non-Hispanic, more symptoms, more clinic visits (NS)

2 of 6
Summary Table 1. Level of Use of Interactive Consumer Health IT (KQ 1)

Author, Year

Design, N

Condition and/or 

Population Interactive Health IT Findings: Technology Usage 

Finkelstein, 199633Cohort, N=41

Lung transplant Web-based self-monitoring of 

symptoms, vital signs and automated spirometry readings; symptom diary; weekly transmission to clinicians

Monitoring over 1 year: 82% submitted weekly data; 8941 transmissions by 41 subjects, for meof 4.5 records/week sent; Initial 6.2 records/wk remained >3.9 records/wk for 43 weeks

Glasgow, 200334

Randomized trial, N=320

Type II Diabetes Web-based educational content, 

dietary goal setting (Basic); with glucose monitoring and feedback, online coach 2x/wk, Q & A forum (Self-Mgmt Condition); with online peer support monitored, e-news (Peer Support Condition )

Website use over 10 months: Highest in months 3, continued drop months 4-6 and 7-10; Mean logons/subject/month with Peer Support 18.7, wSelf-Mgmt 16.7, with Basic 9.4 and 11.4; Use higher in Peer Support for months 4-6 (13.2) an10 (6.7)

Gustafson, 199435

Randomized trial, N=204

HIV/AIDS CHESS: computerized information 

and problem-solving, Q & A, ask an expert, health profile, peer discussion group, personal stories, decision aids

Use over 3 to 6 months (N= 116): 116 subjects used 15,966 times; Mean 138 uses/subject; Metime use 39 hrs/subject; 34% of use occurred 9 – 7 am; 73% of use was peer discussion, 17% winformation and 4% was problem-solving; Use 13% more in women; Minorities used discussioslightly less and information slightly more

Gustafson, 199836

Cohort, N=38

Breast cancer CHESS: computerized information 

and problem-solving, Q & A, ask an expert, health profile, peer discussion group, personal stories, decision aids

Use over 10 weeks: Mean 6.8 uses/week/subje100% used discussion group, ask an expert, Q A, health profile; 71% of uses for social support16% of uses for information

Japuntich, 200637

Randomized trial, N=284

Smoking CHESS: web-based information and 

problem-solving, discussion group, chat room, Q & A, ask an expert, mood self-management

Use over 12 wks (N=140): Use declined over period; Mean logins 33.6/subject ; Mean total timon 486.4 min; Support tools (discussion, chat) umost and information least; Gender, race, education not associated with use; older age correlated with greater use

Jones, 199938

Randomized trial, N=525

Breast, cervical, prostate or laryngeal cancer

Computer touch-screen access to general (N=167) or personalized (n=93) information including electronic record, explanation of content, print-outs

Program use over 3 months: Mean time use wa12 minutes; 29% returned to use program; 11.8with personalized information used after wk 3, v2.6% with general information

3 of 6
Summary Table 1. Level of Use of Interactive Consumer Health IT (KQ 1)

Author, Year

Design, N

Condition and/or 

Population Interactive Health IT Findings: Technology Usage 

Jones, 200139Randomized trial, N=112

Schizophrenia Computer touch-screen access to 

general or personalized information including electronic record, explanation of content, feedback displays, print-outs

Program use: Median time for 5 sessions 69 mi(range 34-143)

Kashem, 200640

RCTN=36

Congestive heart failure

Web-based self-monitoring weight, vitals, medications, graphic data feedback, patient-provider text messaging

Patient data entry: 1,253Patient text messaging: 362Provider non-tailored messages: 836Provider tailored text messages: 416Total Internet interactions: 2,867

Leu, 200541

Randomized trial, N=50

Type I & II Diabetes

Wireless paging and messaging: automated reminders of subject’s choice (e.g. check glucose, eat, take Rx, exercise), 2-way text messaging 

Use over 3-6 months (N=25): 12,025 messages sent to subjects, with 1,676 responses; 114 additional messages sent from subjects, 16 needing action

Lorig, 200642

Randomized trial, N=958

Heart disease, Type II diabetes, or chronic lung disease

Web-based self-management, with bulletin board, discussion group, educational information, weekly postings and moderated activities for 6 weeks, buddy support by email, email reminders to participate

Website use over 1 year (N=354): Mean logon 2times/subject (range 0-177); 8 never logged on;

Ma, 200643

Cohort, N=12

Type II Diabetes Diabetes Information Profile (DIP): 

info tailoring and priority algorithms; Quiz Service: quiz tailoring and priority algorithms; Agenda Service: agenda algorithms.

Website use over 3 months: Median login 6 times/subject (range 2-15); total logins 82; 82 o257 information items visited for 10 sec or moreof 12 subjects made an agenda; of 5 subjects using quizzes, mean 61.8 questions conducted

Mckay, 200144

Randomized trial, N=78

Type II Diabetes who were sedentary

Web-based self-management, goal physical activity setting, tailored and graphic feedback, online coaching, peer discussion group; Control condition basic website

Use over 8 weeks: Total logons 341 for full program vs. 105 for informational website; Mealogon/wk 1.1 vs. 0.3; Mean group posting 1.1/subject; Mean messg to coach 2.1/subject; declined from mean 2.7 logons/subject/wk first 2 weeks to 0.5 in last 2 weeks

Nguyen, 200345

Cohort, N=16

Chronic obstructive pulmonary disease

Web-based self-management with exercise monitoring, goal setting and feedback, pulmonary function and symptom monitoring, weekly education, peer and professional chat, bulletin boards, email

First month registered most logins (330) compato 104 in the final month

4 of 6
Summary Table 1. Level of Use of Interactive Consumer Health IT (KQ 1)

Author, Year

Design, N

Condition and/or 

Population Interactive Health IT Findings: Technology Usage 

Owen, 200546Cohort, N=62

Breast cancer Web-based self-guided training for 

coping skills, posted exercises every 2 weeks, bulletin board, discussion group, symptom management, educational information, email reminders

Use over 12 weeks: Mean 35.5 total logins/subjMean 9.5 bulletin posts/subject; Coping skills component highest use for 8 weeks then steep drop; Bulletin board had highest continuous use

Piette, 199747

Cohort, N=65

Diabetes Automated voice messaging to 

communicate messages, collect information, and identify concerning issues (promting a clinician telephone call)

AVM contact over 4 weeks: 3.3 out of 4 calls completed; 57% contacted all 4 wks, 23% contacted 3 of 4 wks, 15% contacted twice, 5% contacted once

Pike, 200748

Cohort, N=NR

Smoking Recruitment to an interactive, tailored 

website

Wide variation in website use; 3 sites had higheuse (20% or more subjects making 5 or more visits) and 3 had lower use (<10% make 5 or mvisits); non-ITT quitting was 26% vs. 22.1% in hvs. low site use.

Rasmussen, 200549

Randomized trial, N=300

Asthma Web-based self-monitoring, input 

peak flow and symptoms, tailored action plans feedback, decision support for clinicians, email if treatment changes needed

Website use over 6 months: Mean use of selfmanagement tool was 4 times in 2-week period(range, 1-6)

Reid, 200750

Randomized trial, N=99

Smokers with heart disease

Interactive voice response after discharge from hospital on day 3, 14 and 30. If subject wanted to quit or had confidence <7 (out of 10), nurse counselor called 3 times over 8 week period

IVR contact (N=50):Day 3, 35/50 (70%)Day 14, 36/50 (72%)Day 30, 34/50 (68%)

Ross, 200451

Randomized trial, N=107

Congestive heart failure

Web-based access to electronic records, secure messaging with clinical team; educational content

Website use over 1 year (N=54): 43 subjects logged in 581 times; Use highest during month 3, thereafter 24% used it monthly; Subjects mosfrequently viewed clinical notes and lab results, education guide was viewed least; 41 subjects s63 e-messages; Message categories: appointm20%, refills 15%, Rx question 14%, tests 12%, feeling ill 8%, info 3%

5 of 6
Summary Table 1. Level of Use of Interactive Consumer Health IT (KQ 1)

Author, Year

Design, N

Condition and/or 

Population Interactive Health IT Findings: Technology Usage 

Ruland, 200352Randomized trial, N=52

Cancer Computerized touch-screen shared 

decision support for patients and clinicians, on patient symptoms, functional problems, and preferences, printed rank-order summary used for shared clinical decision-making

Use of decision tool: Median time 9 min to complete (range 0.5-49 minutes); 25% of subjecused <5 minutes

Shaw, 200753

Randomized trial, N=231

Breast cancer Underserved

CHESS: Web-based information, discussion group, ask an expert, and interactive services (tailored action plan, decision aids, health tracking

Use over 4 months, by category: Mean use discussion 506.2 min; Mean use information 35min; Mean use ask an expert 28 min; Mean interactive service 27.9 min

Smith, 200054

Randomized trial, N=30

DiabetesRural women

Computer with web-based health and diabetes information. ‘health chat’ selfmanagement training, monitored 

discussion group, ‘Mailbox’ private email, structured classroom, Q & A, bulletin board 

Website use over 5 months: Mean use time 63.min/month; First month use 129 min, then meatime declined to 68, 78, 44, an 37 min for month through 5; Highest use for discussion group, me111 times/subject total; Mailbox (email) 40 times/subject; Self-mgmt 38 times/subject

Tsang, 200155

Randomized crossover study, N=20

Diabetes Computer diabetes monitoring with 

food diary and nutritional feedback, blood glucose monitoring, messaging to and from clinicians

Use over 6 months (N=19): Transmissions per week reported as >7/week by 15.8%, 5-6 by 103-4 by 21%, 1-2 by 36.8%, and <1 by 15.8%

van den Brink, 200556

Cohort, N=36

Head and neck cancer

Web-based secure messaging, health and cancer information, support group forum, monitoring by questionnaire, e-mail alerts for concerning responses

Use over 6 weeks: Total sessions 982, with mesessions/subject 273 (range 4-69); Sessions lasmean 12 mins; 16% occurred after office hours; Use of functions: monitoring 100%, ask questio64%, read messages 75%, contact subjects 8%review information 61%

< X = less than X PSA = prostate-specific antigen> X = greater than X Q&A = question and answer> X or < X = greater than or equal to X or less than or equal to X RCT = randomized controlled trial

BP = blood pressure Rx = precriptionCHESS = Comprehensive Health Enhancement Support System sec(s) = second(s)DIP = diabetes information profile TBD = to be determinedmgmt = management wk(s) = week(s)min(s) = minute(s)

Abbreviations

6 of 6
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Andersson, 200294

RCT, N=44

Headaches Therapist initiated telephone 

contact via the internet

Gradual introduction AttractiveUser involvement in developmentFamiliar toolsControllability

Contrast, color, button size, graphics

HelLea

Anhoj, 200463

Qualitative, N=231

Asthma Website with tailored feedback 

and support

One-click access to frequently used items

Distracting news, flashy graphics, opinion polls

NR 

Bond, 200664 

Qualitative Study, N=35 users; 52 surveyed

Diabetes Tailored self-management 

instruction, chat/discussion, bulletin boards, advice, counseling, log of selfmanagement activities, and information

Tailored program greatly increased usability

Size, graphics NR 

Carroll, 200265

Qualitative Study, N=14

Type II Diabetes Specifically usability testing; 

CV risk prevention; used in clinic; qualitative interviews comparing designs and interfaces.

Plain, simple backgrounds for displayUsing pie charts

Concern about clarity of information

NR 

Cavan, 200329

Cohort, N=16

Type I Diabetes Web-based, feedback, patient 

decision support 

NR Data entry screens hard to 

follow

NR 

Chen, 200666 

Qualitative Study, N=77

Smoking (Cessation program)

Smoking cessation program (classroom), and Internetassisted instruction program

Very easy to use NR Diff

out low 

Cleland, 200767 

Qualitative Study, N=12

Asthma Electronic peak flow meter 

linked to a mobile phone with an interactive screen to record current asthma symptoms transmitted to and stored in a server

Fast, easy to use, time saver

Problematic cable attachments and data head, sensitivity of Piko meter, absence of a good cell signal

Veruse

Crosbie, 200668 

Qualitative Study, N=20

Stroke Stroke rehabilitation, user 

moves arms and hands in a virtual environment and interacts with familiar objects

VE allowed pts with limited movement to experience sense of 'presence'

Virtual reality made some of the healthy users motion sick

Eas

Usability Outcomes

1 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Cruz-Correia, 200730

RCT, N=21

Asthma Web-based self-monitoring, 

tailored action plan; decision support

East to record data, view data, 

User interface issues, difficult to interpret content

Use

Delgado, 200331

Cohort, N=6

Congestive heart failure

Web-based, secure messaging, educational content

Reliable responses from clinicGood quality of care

NR Eas

Feil, 200069

Qualitative Study, N=160

Type 2 Diabetes D-Net intervention: peer social 

support and personalized dietary intervention

NR Computer was initial barrier 

to program participants

NR 

Finkelstein, 199633 

Qualitative Study, N=41

Post Lung Transplant patients

Electronic spirometer/diary (patient daily selfmeasurements of standard spirometry, vital signs, and symptoms recorded at home)

NR Very few phone calls, only 

tech glitches were in the original developmental stages of device

All p

to reentr

Finkelstein, 199870 

Qualitative Study, N=17 

Asthma/underserved

Symptom diary and spirometry test

NR NR Not 

at ato u

Friedman, 199771 

Qualitative Study, N=516

Elderly/Chronically Ill

TLC: carries out automated, telephone-based health care encounters with patients in their homes

NR NR Maj

very 

Gerbert, 200395 

Randomized Trial, N=52

Smoking & Alcoholism (48% AA; 12%Latino; low income)

Brief motivational video risk assessment with tailored advice

Convenience (right amount of time;5.7 / 7); privacy(5.95/ 7 pts); confidential (5.9/7pts); honest (6.94/7 pts)

71% would feel more comfortable with a "real" doctor.

6.8 sca

Glasgow, 200334 

Qualitative Study, N=320

Diabetes Mellitus

Computer assessed, analyzed, and presented feedback on patients' dietary patterns, barriers to and support for dietary self-management, and tailored dietary fat reduction goal

NR NR NR 

Usability Outcomes

2 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Gomez, 2002a72 

Qualitative, N=NR

HIV/AIDS Web based self monitoring 

system for HIV/AIDS patient care

Graphics NR NR 

Gomez, 2002b73 

Qualitative, N=NR

HIV/AIDS Web based self monitoring 

system for HIV/AIDS patient care

Reliable NR Eas

Eas

Gustafson, 200174 

Qualitative Study, NR

HIV/AIDS, breast cancer

Elderly, have/had cancer, not specific to one study!!

NR NR NR 

Holman, 199696

Randomized, cross-over, N=6

Diabetes Hand held computer for self 

management and to make treatment recommendations

Convenient to use NR Eas

Jones, 200139

RCT, N=112

Schizophrenia Computer touch-screen, 

electronic record, feedback displays

Touch screen easy to use

Some displays confusing Eas

Kaufman, 200375

Qualitative, N=25

Diabetes Videoconferencing, vitals 

upload and review, education, email

NR Patient tutorial needs 

revision; creation of a field training program

NR 

Kenwright, 200497 

Cohort, N=27

Phobia or Panic Disorder

FearFighter - Internet self-help system

"gecomusinsys

Kerkenbush, 200376

Qualitative Study, NR

DM Track blood glucose levels, 

dietary intake, and activity level

NR NR Eas

Kim, 200577

Qualitative Study, N=42

Diabetes Mellitus

Access a website via their mobile phone or Internet and input their blood glucose levels every day; patients were sent the optimal recommendations by both the mobile phone and the internet

NR NR Eas

Usability Outcomes

3 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Kim, 200778

Qualitative Study, N=33

Diabetes Mellitus

Nurse short message service by mobile phone and internet on glycosylated hemoglobin (HbA1c) levels and adherence to diabetes control recommendations; patients input information and were sent weekly recommendations

NR NR NR 

Kosma, 200579

Qualitative Study, N=25

Physical Disability

Web-based physical activity motivational with and without discussion, weekly motivational messages

NR NR 4.5 

Kressig 200298 

Cohort, N=34

Physical activity for elderly

computer system for exercise promotion (questionnaire with tailored recommendations)

Most problems had to do with survey issues, not computer. 22% questions were mouse related.

4.6/

LaPlanteLevesque, 200680 Qualitative Study, N=3

Impaired hearing/hearing loss

Email communication between a new hearing aid user and the audiologist or from the audiologist to the client

All seemed 'comfortable' 

with the program-study is broken down into experience of each individual

NR NR 

Leu, 200541

RCT, N=50

Type I & II Diabetes

Wireless messaging, automated reminders

Pager useful NR NR 

Liu, 200581 

Qualitative Study, N=274

Type II Diabetes POEM: Education 

management system: patient specific educational materials, medication data, and laboratory test results, as well as reminders with emails and short messages via cell phone

NR NR NR 

Usability Outcomes

4 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Lober, 200682 

Qualitative Study, N=38

Elderly Personal health records with 

areas for communication with health professional

Ability to print out PHR Visibility, icon size, graphics NR 

Ma, 200643

Cohort, N=12

Type II Diabetes Information tailoring, question 

tailoring 

System was usefulSystem is relevant

None Eas

McDaniel, 200299 

Cohort, N=110

Smoking; Low income women

Interactive smoking cessation software

technology malfunctions; software errors

McKay, 200144RCT, N=78

Type II Diabetes, sedentary

Web-based, tailored and graphic feedback, peer discussion

No difference between groups in helpfulness of the PA section or relevance of the program 

NR No 

betwin e

McPherson, 2006100

Randomized Trial, N=101

Children with Asthma

Multimedia educational program

Piette, 199747

Cohort, N=65

Diabetes Voice messaging No difficulty entering 

answers into touchtone system

NR No 

undthe 

Ralston, 200483 Qualitative Study, N=9

Diabetes Mellitus

Living with diabetes program: web-based disease management module to support care

NR NR NR 

Reed, 200584 

Qualitative Study, N=NR

Diabetes Mellitus

Not specified, website review; www.diabetes-insight.info

Easy to navigate and comprehensiveFew advertisements

NR NR 

Usability Outcomes

5 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Ruland, 200352

Randomized trial, N=52

Cancer Computerized touch-screen 

shared decision support and decision-making

Use of touch pad system without assistanceSystem a useful tool

Not rated positively: Learning to use the computerTo get it to do what I wantClear and understandable on how to operateCan become skillful using the computer

Queto aDo oveuse

Schärer, 200285

Qualitative Study, N=600

Bipolar Disorder PDA; Patients encouraged to 

report most manic and depressive state not only in the presence of mood swings, information analyzed and sent to patient via email or paper

Experience with PDAs PDA intimidating to novices 

at beginning

All esatieasIT

Sciamanna, 1999101 

Qualitative Study, N(Pilot)=29, N(Study B)=503

Smoking Patients answer questions, 

these are analyzed and patient provided with tailored quitsmoking material, also physicians provided with information to help counsel patients

NR NR Eas

Sciamanna, 200486 

Cohort, N=150

Smoking & physical activity

Computer assessment with tailored printed feedback

NR NR Eas

Sciamanna, 2005102

Quasiexperimental, N=121

Osteoarthritis Computer assessment with 

tailored feedback about OA and OA care

NR NR Eas

Shaw, 200087 

Qualitative Study, N=20

Breast Cancer CHESS NR NR Fou

use

Skeels, 200688 

Cohort, N=30

HIV+ CARE+ Tablet PC counseling NR Tablet PC screen difficult to 

seeRadio buttonsConfusion on how to use tablet PC

NR 

Usability Outcomes

6 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

Author, Year, 

Design, N Condition

Intervention/Technology 

Type

Useful (technology 

elements)

Problematic technology 

elements Ea

Smaglik, 200889 

Qualitative Study, N=36

HIV/AIDS HIV CHESS support groups NS NS NS 

Tsang, 200155 Randomized crossover study, N=19

Diabetes Hand-held electronic diary with 

touch screen

NR Technical issues 95% 

was 

van den Brink, 200390

Qualitative Study, N=NR

Head and Neck Cancer

Not specified-summary of electronic information system

NR NR NR 

van den Brink, 200556

CohortN=36

Head & neck cancer

Electronic health information support system

NR Technical issues NR 

van't Riet, 200191 

Qualitative Study, N=14 families 

Amblyopia Interactive computer system 

that helps with communication between patients, their parents and caregiver

NR NR NR 

Wilkie, 200158 

Descriptive, N=41

Cancer The Symptom Report program 

was designed as an interactive, touch-screen method for assessment of pain and fatigue. 

NR NR Sys

eas

Yeh, 200692 

Qualitative Study, N=274

Diabetes Mellitus

POEM: Education management system: patient specific educational materials, medication data, and laboratory test results, as well as reminders with emails and short messages via cell phone

NR NR NR 

Zimmerman, 200493

Qualitative Study, N=52

CABG/Elderly (>65 yrs)

CABG patients access web for daily information on recovery, provider can observe patients' progress at home

NR NR NR 

Usability Outcomes

7 of 8
Summary Table 2. Usability of Interactive Consumer Health IT (KQ 2)

< X = less than X< X or > X = less than or equal to X or greater than or equal to 

X

> X = greater than XBP = blood pressureCABG = coronary artery bypass graft CHESS = Comprehensive Health Enhancement Support SystemCV = cardiovascularDM = diabetes mellitusHgBA1c = glycosylated hemoglobinNR = not reportedNS = not significantOA = osteoarthritisPA = peer assessmentPDA = personal digital assistantPHR = personal health recordsRCT = randomized controlled trialTBD = to be determinedVE = virtual entertainment

Abbreviations

8 of 8
Summary Table 3. Barriers to Use of Interactive Consumer Health IT (KQ 3)

Author, Year, Design, N

Condition and/or Population Interactive Computer Technology (ICT) Barriers

Anhoj, 200463

Qualitative, N=231

Asthma Website with tailored feedback and support - Doctors reluctant due to tim

- Did not fit into patients' ever- Used inefficiently- Contradictory information, cnot follow the advice- Access to the Internet- The benefits are not immed- Lack of trust with virtual exp

Artinian, 2003106

RCT, N=18

Congestive heart failure

Video cassette monitoring connected to telephone line

- 44% hated it- Perception that they had to monitor/reminder- Nuisance and intrusive- 44% had technical problems- Difficult to get small pills- Text on screen too small; coneeded longer message for c

Bond, 200664

Qualitative Study, N=35 users; 52 surveyed

Diabetes Tailored self-management instruction, 

chat/discussion, bulletin boards, advice, counseling, log of self-management activities, and information

- Lack of computer experienc- Non-tailored device/program and physical limitations such to be implemented)- Lack of access to a computegiven/installed for free in patie

Carroll, 200265

Qualitative Study, N=14

Type II Diabetes Specifically usability testing; CV risk prevention; 

used in clinic; qualitative interviews comparing designs and interfaces.

- Visibility- Lack of computer skills

Cathala, 200328

Cohort, N=140

Prostate cancer; post-op radical prostatectomy

Web-based monitoring with messaging - 11% had connection problem

- 14% had technical problems - Physician email not reimbur

Cavan, 200329

Cohort, N=16

Type I Diabetes Web-based, feedback, patient decision support - Took too much time

- Internet connection interrupt

Chen, 200666

Qualitative Study, N=77

Smoking (Cessation program)

Smoking cessation program (classroom), and Internet-assisted instruction program

- Lack of computer skills- Cost

1 of 6
Summary Table 3. Barriers to Use of Interactive Consumer Health IT (KQ 3)

Author, Year, Design, N

Condition and/or Population Interactive Computer Technology (ICT) Barriers

Cleland, 200767Qualitative Study, N=12

Asthma Electronic peak flow meter linked to a mobile 

phone with an interactive screen to record current asthma symptoms transmitted to and stored in a server

 - Lack of familiarization with - Failure of technology to wor -Problems with cable attachmtransfer of information)- Absence of a good cell signtransmission/sent reminders thad recorded it previously-fruredundant reminders)- Sensitivity of Piko meter (suhigh readings indicate that pts Piko meter')

Crosbie, 200668

Qualitative Study, N=20

Stroke Stroke rehabilitation, user moves arms and hands 

in a virtual environment and interacts with familiar objects

- Cost- Side effects associated with healthy patients experience hdisorientation, and balance d- Delays

Cruz-Correia, 200730

Randomized cross-over study, N=21

Asthma Web-based self-monitoring, tailored action plan; 

decision support

- Technical problems – 9/16 rconnection issues (9) , system batteries (5), meter technical - Difficult data input for monito- Poor graphic feedback

Delgado, 200331

Cohort, N=6

Congestive heart failure

Web-based, secure messaging, educational content

- Technical problems – system months

Earnest, 200432

Randomized trial, N=107

Congestive heart failure

Web-based records, secure messaging, educational content

- Not comprehending medica- Watered down or “sugar-coa

Feil, 200069

Qualitative Study, N=160

Diabetes Mellitus D-Net intervention: peer social support and 

personalized dietary intervention

- Cost of computer/internet- Lack of computer/internet sk- Computer anxiety

Finkelstein, 199633 

Qualitative Study, N=41

Post Lung Transplant patients

Electronic spirometer/diary (patient daily selfmeasurements of standard spirometry, vital signs, and symptoms recorded at home)

- Lack of computer/internet sk- Cost of computer/internet- Failure to replace mouthpiec

Finkelstein, 199870 

Qualitative Study, N=17 

Asthma/underserved

Symptom diary and spirometry test - Lack of computer/internet sk

- Cost- Limited personal instruction

Friedman, 199771 

Qualitative Study, N=516

Elderly/Chronically Ill

TLC: carries out automated, telephone-based health care encounters with patients in their homes

- Lack of human interaction

2 of 6
Summary Table 3. Barriers to Use of Interactive Consumer Health IT (KQ 3)

Author, Year, Design, N

Condition and/or Population Interactive Computer Technology (ICT) Barriers

Gerbert 200395 Randomized Trial, N=52

Smoking & Alcoholism

Brief motivational video risk assessment with tailored advice

- 71% would feel more comfo

Glasgow, 200334 

Qualitative Study, N=320

Diabetes Mellitus Computer assessed, analyzed, and presented 

feedback on patients' dietary patterns, barriers to and support for dietary self-management, and tailored dietary fat reduction goal

- Cost- Level of commitment- Lack of long-term weight los

Gomez, 2002b73

Cross Over, N=NR

HIV/AIDS Web based self monitoring system for HIV/AIDS 

patient care

- No battery checking softwar- Low reliability of research sy- Required technical personnetrial- Required training users on w

Gustafson, 200174

Qualitative Study, NR

HIV/AIDS, breast cancer

Elderly, have/had cancer, not specific to one study!!

- Cost- Lack of internet/computer- Lack of computer skills

Jones, 200139

Randomized trial, N=112

Schizophrenia Computer touch-screen, electronic record, 

feedback displays

- Some preferred the nurse bempathy, and talking rather th- Bringing pts into the clinic fotoo costly for schizophrenia tr

Kaufman, 2003b75 

Qualitative Study, N=25

Diabetes IDEATel project; web-based diabetes diary 

(Diabetes Manager) that allows patients to track their own progress for five variables: BP, glucose, medications, exercise, and viewing Hgba1c; Videoconferencing, electronic transmission of glucose and blood pressure readings, secure email, web-based review of clinical data, access to web-based educational material

- Literacy and numeracy- Visibility- Psychomotor skills associat- Lack of computer skills- Numerous technical difficult

Kenwright, 200497

Cohort, N=27

Phobia or Panic Disorder

FearFighter - Internet self-help system - 3/10 would prefer face-to-fa

Kerkenbush, 200376 

Qualitative Study, NR

DM Track blood glucose levels, dietary intake, and 

activity level

- APNs have to evaluate patiefor PDAs (excluding people w-Cost-Technological demands- The fact that PDAs operate when they run down complete

3 of 6
Summary Table 3. Barriers to Use of Interactive Consumer Health IT (KQ 3)

Author, Year, Design, N

Condition and/or Population Interactive Computer Technology (ICT) Barriers

Kim, 200577Qualitative Study, N=42

Diabetes Mellitus Access a website via their mobile phone or 

Internet and input their blood glucose levels every day; patients were sent the optimal recommendations by both the mobile phone and the internet

- Lack of mobile phone techn- Cost

Kim, 200778

Qualitative Study, N=33

Diabetes Mellitus Nurse short message service by mobile phone 

and internet on glycosylated hemoglobin (HbA1c) levels and adherence to diabetes control recommendations; patients input information and were sent weekly recommendations

- Lack of mobile phone/intern- Cost

Kosma, 200579

Qualitative Study, N=25

Physical Disability Web-based physical activity motivational with and 

without discussion, weekly motivational messages

- Patient adherence/motivatio- Lack of computer/internet sk

Kressig 200298

Cohort, N=34

Physican activity for elderly

computer system for exercise promotion (questionnaire with tailored recommendations)

- Age directly related to increa

LaPlante-Levesque, 200680

Qualitative Stud

y, N=

3

Impaired hearing/hearing loss

Email communication between a new hearing aid user and the audiologist or from the audiologist to the client

- Cost- Time

Leu, 200541

Randomized trial, N=50

Type I & II Diabetes

Wireless messaging, automated reminders - 44% found messages annoy

- Anticipating messages- Messages too overwhelming- Pager didn’t work in all locat- Messages not programmab- Security issues, was this HI

Liu, 200581

Qualitative Study, N=274

Type II Diabetes POEM: Education management system: patient 

specific educational materials, medication data, and laboratory test results, as well as reminders with emails and short messages via cell phone

- Cost- Lack of computer/internet sk- Lack of cell phone

Lober, 200682

Qualitative Study, N=38

Elderly Personal health records with areas for 

communication with health professional

- Lack of computer/internet- Lack of computer/internet sk- Literacy and numeracy- Legal restrictions to PHR

Ma, 200643

Cohort, N=12

Type II Diabetes Information tailoring, question tailoring - Redundancy of information 

- Well controlled DM- Lack of computer at home

4 of 6
Summary Table 3. Barriers to Use of Interactive Consumer Health IT (KQ 3)

Author, Year, Design, N

Condition and/or Population Interactive Computer Technology (ICT) Barriers

McDaniel, 200299Cohort, N=110

Smoking; Low income women

Interactive smoking cessation software - Participants with less than a 

satisfied as were those not se- Technology malfunctions an

McKay, 200144

Randomized trial, N=78

Type II Diabetes who were sedentary

Web-based, tailored and graphic feedback, peer discussion

- Only 33% found the peer-to- Low number of people in sucritical mass

Ralston, 200483

Qualitative Study, N=9

Diabetes Mellitus Living with diabetes program: web-based disease 

management module to support care

- Lack of computer/internet sk- Lack of computer/internet- Unmet expectations of the p- program was Incompatible w

Reed, 200584

Qualitative Stud

y, N=N

R

Diabetes Mellitus Not specified, website review; 

www.diabetes-insight.info

- Lack of computer/internet

Schärer, 200285 

Qualitative Study, N=600

Bipolar Disorder PDA; Patients encouraged to report most manic 

and depressive state not only in the presence of mood swings, information analyzed and sent to patient via email or paper

- Cost- Visibility (small font)

Sciamanna, 1999101 

Qualitative Study, N(Pilot)=29, N(Study B)=503

Smoking Patients answer questions, these are analyzed 

and patient provided with tailored quit-smoking material, also physicians provided with information to help counsel patients

- Cost of internet/computer- Lack of computer/internet sk- Program requires personal r

Sciamanna, 200486

Cohort, N=150

Smoking Tailored printed feedback (primary care setting) 

on smoking & physical activity for patients and physicians

- Lack of review/discussion of 

Shaw, 200087

Qualitative Stud

y, N=1

2

Breast Cancer CHESS support groups - Lack of computer/internet sk

- Lack of interest

Skeels, 200688

Qualitative Study, N=30

HIV/AIDS CARE+; Tablet PC Counseling Tool for HIV+; 

medication adherence and secondary HIV prevention; observation & semi-structured interview after use Specifically designed to test barriers & drivers with HIV+ pts

- Visibility- Lack of human interaction/c- Unable to ask clarifying que

Smaglik, 200889

Qualitative Stud

y, N=3

6

HIV/AIDS HIV CHESS support groups - Lack of computer/internet sk

Taylor, 2006107

RCT, N=114

Sleep apnea HealthBuddy; CPAP adherence - Telephonic delay between p

Turnin, 1992108RCT, N=105

Diabetes Internet expert system for diabetes - Diabeto - Cost

van den Brink, 200390Qualitative Stud

y, N=N

R

Head and Neck Cancer

Not specified-summary of electronic information system

- Lack of computer/internet sk- Lack of interest in site conte

5 of 6
Summary Table 3. Barriers to Use of Interactive Consumer Health IT (KQ 3)

Author, Year, Design, N

Condition and/or Population Interactive Computer Technology (ICT) Barriers

van den Brink, 200556Prospective Study, N=36

Head and Neck Cancer

Patient-provider messaging; forum; monitoring; email alerts

- Technical difficulties- Lack of computer/internet sk- Program technology not intesystem

van't Riet, 200191

Qualitative Study, N=14 families 

Amblyopia Interactive computer system that helps with 

communication between patients, their parents and caregiver

- Lack of computer/internet- Cost- Lack of time

Wilkie, 200158

Qualitative Stud

y, N=41

Cancer Cancer symptom control; Symptom report 

questionnaire; interactive tailored education 

- Lack of computer skills- Not a comprehensive quest

Yeh, 200692

Qualitative Study, N=274

Diabetes Mellitus POEM: Education management system: patient 

specific educational materials, medication data, and laboratory test results, as well as reminders with emails and short messages via cell phone

- Lack of computer/internet sk- Cost- Literacy

Zabinski, 200159

Pilot study, N=4

Eating Disorders Women at risk of eating disorders; synchronous 

Internet relay chat; based on cognitive behavioral treatment; facilitated by a moderator

- Lack of body language/facia- Unable to control distraction- Unable to control forwarding 

Zimmerman, 200493

Qualitative Study, N=52

CABG/Elderly (>65 yrs)

CABG patients access web for daily information on recovery, provider can observe patients' progress at home

- Lack of computer/internet sk- Literacy/numeracy

< X = less than X HgBA1c = glycosylated hemo< X or > X = less than or equal to X or greater than or equal to 

X HIPAA = Health Insurance Po

> X = greater than X NR = not reportedAPN = advanced practice nurse PDA = personal digital assistaBP = blood pressure PHR = personal health recordCABG = coronary artery bypass graft RCT = randomized controlled CHESS = Comprehensive Health Enhancement Support System TBD = to be determinedCPAP = continuous positive airway pressure VE = virtual entertainmentDM = diabetes mellitusGP = general practitioner

Abbreviations

6 of 6
Summary Table 4. Drivers and Facilitators of the Use of Interactive Consumer Health IT (KQ 4

Author, Year, Design, N

Condition and/ or Population Interactive Computer Technology (ICT) Drivers

Andersson, 200294

RCT, N=44

Headaches Therapist initiated telephone contact via the 

internet

Gradual introduction AttractiveUser involvement in developmentFamiliar toolsControllability

Anhoj, 200463

Qualitative, N=231

Asthma Website with tailored feedback and support - Trustworthy and reliable technology

- Perception that an electronic asthma disease

Artinian, 2003106

RCT, N=18

Congestive heart failure

Video cassette monitoring connected to telephone line

- Reminders

Bond, 200664 

Qualitative Study, N=35 users; 52 surve

yed

Diabetes Tailored self-management instruction, 

chat/discussion, bulletin boards, advice, counseling, log of self-management activities, and information

- One-on-one instruction- Tailored program

Carroll, 200265

Qualitative Study, N=14

Type II Diabetes Specifically usability testing; CV risk prevention; 

used in clinic; qualitative interviews comparing designs and interfaces.

- Patient enthusiasm in gaining knowl- Empowering patient adherence to be

Cathala, 200328

Cohort, N=140

Prostate cancer; post-op radical prostatectomy

Web-based monitoring with messaging - People who already have Internet ac

accept and even want to try the Web 

Cavan, 200329

Cohort, N=16

Type I Diabetes Web-based, feedback, patient decision support - Increased confidence

- Not in good control- Planning a long journey- Weather changes

Chen, 200666 

Qualitative Study, N=77

Smoking (Cessation program)

Smoking cessation program (classroom), and Internet-assisted instruction program

- Quit smoking- Ease of use

Cleland, 200767

Qualitative Study, N=12

Asthma Electronic peak flow meter linked to a mobile 

phone with an interactive screen to record current asthma symptoms transmitted to and stored in a server

 - Intervention that uses a technologicmost pts were familiar with cell phone problems)- Digital Piko meter also was though tyielding a more satisfactory result for 

Crosbie, 200668 

Qualitative Study, N=20

Stroke Stroke rehabilitation, user moves arms and 

hands in a virtual environment and interacts with familiar objects

- Site of stroke that limits arm movemcan be remedied by VE- Low levels of interaction between pt - Ineffective traditional paralysis thera- Very small percentage of time actuatraditional therapy tasks

1 of 6
Summary Table 4. Drivers and Facilitators of the Use of Interactive Consumer Health IT (KQ 4

Author, Year, Design, N

Condition and/ or Population Interactive Computer Technology (ICT) Drivers

Delgado, 200331Cohort, N=6

Congestive heart failure

Web-based, secure messaging, educational content

- Technology available at any time ne

Earnest, 200532

Randomized trial, N=107

Congestive heart failure

Web-based records, secure messaging, educational content

- Perceived more control- Perceived better communication with - Perceived increased participation in - Perceived increase in efficiency of g

Feil, 200069 

Qualitative Study, N=160

Diabetes Mellitus D-Net intervention: peer social support and 

personalized dietary intervention

- Availability of access to intervention the internet- Internet/computer competence

Finkelstein, 199633 

Qualitative Study, N=41

Post Lung Transplant patients

Electronic spirometer/diary (patient daily selfmeasurements of standard spirometry, vital signs, and symptoms recorded at home)

- Daily self-reporting by transplant pts of disease/illness

Finkelstein, 199870

Qualitative Study, N=17 

Asthma/ underserved

Symptom diary and spirometry test - Perceived sense of security

- Importance of knowing that results w

Friedman, 199771 

Qualitative Study, N=516

Elderly/Chronically Ill

TLC: carries out automated, telephone-based health care encounters with patients in their homes

- Increased awareness of disease- Relieves worries about the disease

Gerbert 200395 

Randomized Trial, N=52

Smoking & Alcoholism

Brief motivational video risk assessment with tailored advice

- Convenience (right amount of time;5- Privacy(5.95/ 7 pts)- Confidential (5.9/7pts)- Honest (6.94/7 pts)- Felt they learned something new(4.9

Glasgow, 200334 

Qualitative Study, N=320

Diabetes Mellitus Computer assessed, analyzed, and presented 

feedback on patients' dietary patterns, barriers to and support for dietary self-management, and tailored dietary fat reduction goal

- Empowerment of individual (goal set- Focus on behavioral changes vs. we

Gomez 2002a72 

NR, N=NR

HIV/AIDS Web based self monitoring system for HIV/AIDS 

patient care

- Anonymity- Access to experts / 2nd opinions - Perceived greater control over their - Perceived improved health

Gomez 2002b73

NR, N=NR

HIV/AIDS Web based self monitoring system for HIV/AIDS 

patient care

- Perceived improved glycemic contro- Better communication with doctor, - Good DM education

2 of 6
Summary Table 4. Drivers and Facilitators of the Use of Interactive Consumer Health IT (KQ 4

Author, Year, Design, N

Condition and/ or Population Interactive Computer Technology (ICT) Drivers

Gustafson, 200174Qualitative Study, NR

HIV/AIDS, breast cancer

Elderly, have/had cancer, not specific to one study!!

- Accessibility of internet by many- Ability of internet to more information originally have access (underserved p

Holman, 199696

Randomized, cross-over, N=6

Diabetes Hand held computer for self management and to 

make treatment recommendations

- No increased worry about DM- Monitoring data feedback

Jan, 2007111

RCT, N=176

Asthma Education, monitoring, self-management advice - Fun

- Reminders

Jones, 200139

Randomized trial, N=112

Schizophrenia Computer touch-screen, electronic record, 

feedback displays

- Short

Kaufman, 200375

*Qualitative, N=25

Diabetes Videoconferencing, vitals upload and review, 

education, email

- Field training program- Tutorial

Kenwright, 2005112

Randomized trial, N=44

Obsessive compulsive disorder

Live help-line support during office hours - Proactive, brief support by phone

Kerkenbush76 

2003, Qualitative Stud

y, N

R

DM Track blood glucose levels, dietary intake, and 

activity level

- Tracking data on their own empowehealthcare providers to guide appropr

Kim, 200577 

Qualitative Study, N=42

Diabetes Mellitus Access a website via their mobile phone or 

Internet and input their blood glucose levels every day; patients were sent the optimal recommendations by both the mobile phone and the internet

- Cost and time effectiveness of mobi

Kosma, 200579 

Qualitative Study, N=25

Physical Disability Web-based physical activity motivational with 

and without discussion, weekly motivational messages

- Increase health- Increase physical activity- Motivational

Kressig 200298

Cohort, N=34

Physican activity for elderly

computer system for exercise promotion (questionnaire with tailored recommendations)

- More computer experience related to and less need for assistance

LaPlanteLevesque, 200680 

Qualitative Study, N=3

Impaired hearing/hearing loss

Email communication between a new hearing aid user and the audiologist or from the audiologist to the client

- Enables people to give feedback on allows manufacturers/audiologist makadjustments/recommendations

3 of 6
Summary Table 4. Drivers and Facilitators of the Use of Interactive Consumer Health IT (KQ 4

Author, Year, Design, N

Condition and/ or Population Interactive Computer Technology (ICT) Drivers

Leu, 200541Randomized trial, N=50

Type I & II Diabetes

Wireless messaging, automated reminders - Perceived pager helped their health 

- Reminders useful when unusual life - Monitoring treatment changes- Personal accountability

Liu, 200581 

Qualitative Study, N=274

Type II Diabetes POEM: Education management system: patient 

specific educational materials, medication data, and laboratory test results, as well as reminders with emails and short messages via cell phone

- Ability to review medical information- Appointment reminders- Access to education materials

Lober, 200682 

Qualitative Study, N=38

Elderly Personal health records with areas for 

communication with health professional

- Empowering patients to keep track o- Reassurance that PHR will remain in

Ma, 200643

Cohort, N=12

Type II Diabetes Information tailoring, question tailoring - Tailored information

- Newly diagnosed patients found info

McDaniel, 200299 

Cohort, N=110

Smoking; Low income women

Interactive smoking cessation software - Minority participants were significant

computer program as well as those weducation and those who were thinkinmonth

McKay, 200144

Randomized trial, N=78

Type II Diabetes who were sedentary

Web-based, tailored and graphic feedback, peer discussion

- Tailored action plan with personal fe- Personal coach- Lesson: focus on keeping people en

Nguyen, 2005113

NR, N=23

COPD Web site with activities information - Perceived increase access to inform

Owen, 200546Cohort, N=62

Breast cancer Web-based self-guided trainings, bulletin board, 

discussion group, symptom management, educational information, email reminders

- Low perceived health status- More severe depressive and anxiou- Newly or recently diagnosed

Piette, 199747

Cohort, N=65

Diabetes Voice messaging - Convenience

- Fewer messages for younger patien

Ralston, 200883

Qualitative Study, N=9

Diabetes Mellitus Living with diabetes program: web-based 

disease management module to support care

- Empowerment of patient- Non-acute concerns uniquely valued - Enhanced sense of security about h- Providing valuable feedback

Reed, 2005a110 

Qualitative Study, N=NR

Diabetes Mellitus Not specified, website review; 

www.diabetes-insight.info

- Accessibility of information

Reed, 2005b84

NR, NR

Diabetes Website with static information, email support 

groups, video Web cast interviews, transcripts and articles

- Patient specific information about ins

4 of 6
Summary Table 4. Drivers and Facilitators of the Use of Interactive Consumer Health IT (KQ 4

Author, Year, Design, N

Condition and/ or Population Interactive Computer Technology (ICT) Drivers

Rodgers, 200584RCT, N=1705

Smoking Tailored text messages - Use of cell phones with younger indi

- Low cost- Distracting activities

Schärer, 2002114 

Qualitative Study, N=600

Bipolar Disorder PDA; Patients encouraged to report most manic 

and depressive state not only in the presence of mood swings, information analyzed and sent to patient via email or paper

- Discrete- Not a 'stigmatizing' tool

Sciamanna, 1999101 Qualitative 

Study, N(Pilot)=29, N(Study B)=503

Smoking Patients answer questions, these are analyzed 

and patient provided with tailored quit-smoking material, also physicians provided with information to help counsel patients

- Tailored quit-smoking materials- Easy/fun to use

Sciamanna, 200486 

Cohort, N=150

Smoking Tailored printed feedback (primary care setting) 

on smoking & physical activity for patients and physicians

- Tailored printed feedback

Sciamanna, 2005102 

Quasiexperimental, N=121

Osteoarthritis Web-tailored feedback and recommendations 

based on guidelines for osteoarthritis care

- Better care from doctors- Try/recommend website to others

Shaw, 200087 

Qualitative Study, N=12

Breast Cancer CHESS support groups - Anonymity within the support group 

participation

Skeels, 200688 

Qualitative Study, N=30

HIV/AIDS CARE+; Tablet PC Counseling Tool for HIV+; 

medication adherence and secondary HIV prevention; observation & semi-structured interview after use Specifically designed to test barriers & drivers with HIV+ pts

- Computer as "objective" and "non-ju- Privacy- Accessibility and convenience

Smaglik, 200889 

Qualitative Study, N=36

HIV/AIDS HIV CHESS support groups - Availability of support group via the i

- Anonymity- Availability of information- Access to professionals who answe

Turnin, 1992108

RCT, N=105

Diabetes Internet expert system for diabetes - Diabeto -24 hour home access

- Free of charge

van den Brink, 200390

Qualitative Study, N=NR

Head and Neck Cancer

Not specified-summary of electronic information system

- Accessibility and availability of inform

5 of 6
Summary Table 4. Drivers and Facilitators of the Use of Interactive Consumer Health IT (KQ 4

Author, Year, Design, N

Condition and/ or Population Interactive Computer Technology (ICT) Drivers

van der Brink, 200556Prospective Study, N=36

Head and Neck Cancer

Patient-provider messaging; forum; monitoring; email alerts

- Sense of security- Enable communication between heapatients

van't Riet, 200191 

Qualitative Study, N=14 families 

Amblyopia Interactive computer system that helps with 

communication between patients, their parents and caregiver

- Ability to access information and sup

Wilkie, 200158 

Qualitative Study, N=41

Cancer Cancer symptom control; Symptom report 

questionnaire; interactive tailored education 

- Enabling communication- Beneficial for the novice patient

Yeh, 200692 

Qualitative Study, N=274

Diabetes Mellitus POEM: Education management system: patient 

specific educational materials, medication data, and laboratory test results, as well as reminders with emails and short messages via cell phone

- Ability to review medical information- Appointment reminders- Access to education materials

Zabinski, 200159 

Pilot study, N=4

Eating Disorders Women at risk of eating disorders; synchronous 

Internet relay chat; based on cognitive behavioraltreatment; facilitated by a moderator

- Chat room forum- Low cost

Zimmerman, 200493

Qualitative Study, N=52

CABG/Elderly (>65 

yrs)

CABG patients access web for daily information on recovery, provider can observe patients' progress at home

- Empowerment of patients to manage 

CABG = coronary artery bypass graftCHESS = Comprehensive Health Enhancement Support SystemCOPD = chronic obstructive pulmonary disease CV = cardiovascularDM = diabetes mellitusNR = not reportedPDA = personal digital assistantPHR = personal health recordsRCT = randomized controlled trialTBD = to be determinedVE = virtual entertainment

Abbreviations

6 of 6
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Andrade, 2005117 

RCT, N=64

HIV/AIDS, initial Rx and failed Rx, good and poor memory; 42% female, 88% African American

Rx reminder device; reports to clinician; monthly counseling; autoadherence monitor

monthly counseling; auto- adherence monitor

24 week; Baltimore; (1999-2002)

Rx adherence in memory imv 57% (p=.00(NS); HV RNA v 41% (p=0.02RNA 34% v 3

Araki, 2006118 

RCT, N=36

Alcoholic factory workers (GGTP high on screen); age 44 yrs

tailored email counseling tailored face to face 

counseling; no counselling

2 mos, 2 sessions; Tokyo

alcohol consudecreased, knscores increasface to face gno change in controls

Artinian, 2003106

RCT, N=18

Heart Failure,1 of 18 female, age ~ 68 yrs;LVEF<40, 61% African American

daily monitoring; reminding, daily clinician review and adjustment; (videocassette size device with meds and phone connection)

digital scale; BP monitor, logs, incentive pmts, educ booklet, HF clinic visits, pharmacist review meds

3 mos, Virginia both groups im

medication adlife; neither chNYHA class; t'most pts did ninstructions"; 4it

Barnason, 2003119

RCT, N=18

post CABG HF24 Males71 yrs (65-85 yrs)LVEF<40NYHA I, II

daily monitoring Sx, standardized advice acc to Sx,reviewed by RN, education, encouragement; no human co-intervention (Health Buddy); telephone attachment with 4 buttons (Health Buddy)

17 predischarge education re CABG, CAD RFs, exercise

3,Midwestern hospitalslarger study of 180 CABG patients

SELF EFFICA43.2 50.1 50.8 43 45.7 47.4CV RF MODsalt, exercise, greater in TX; MOS SF36overall improvscales, but bacloud the pictu

Barrera, 2002120

RCT, N=160

DM 2,75 males; 59 yrsno internet access, has phone, speak English (of 650 sent letters)

www personal self mgmt coach (with data monitoring and review) vs www social support vs www coach plus support vs education only

none 3,

primary care Eugene Oregon

DM Support S(1.14) CCH 0.(1.44) SS+CCISEL items CT−0.08 (0.52) C0.45 (0.82) SS 

1 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Block, 2004122

RCT, N=481

Midlife, low income women; 48.4% African American; 75% overwt

CDROM + reminder calls; CDROM alone

stress management CDROM

2 mos; California fruit & vegetab

1.32/day (p=0(p=0.52) v 0.7greater effect stress reductioincreased frui

Chan, 2003144

Case series, N=10

Asthma children; 50% F, ~ 7 Y, 

web based daily PEF, Sx; education, video monitor 2x/wk by case mgr; case mgr email or phone re PEF, Sx, technique, mgmt plan

paper based daily monitor PEF, Sx, office based education at 2, 6, 12, 24 weeks

6 mos, Hawaii inhaler techniq

increased in 

Cho, 2006123

RCT, N=120

DM253 yrs (62%) MHbA1c 7.6BMI 23webaccess, > 30 years old, no HF, ESLD, Ckd, insulin pump, prior webprogram

>2x per week monitoring BG, weight; EHR (lab, Hx) access; feedback; daily review by clinic, MD message biweekly; weekly reminder to logon, w/ quarterly clinic visit

education diet, self management; glucometer, paper record quarterly visit, recommendation to monitor at least 3x a week

30 moTaiwan

HBA1c 6.7 v 7HFI .47 v .78 ~20% 2-3 logins/mo NS trend to loHbA1c in 80%+ adherence t1586 recommpatients (45.3 modificaiton le

Christensen, 2004124

RCT, N=525

Depressed adults, 71% female, age ~36 yrs

weekly contact, web based education vs. web based cognitive behavioral therapy with assessments

weekly contact about lifestyle, health habits

6 weeks, Canberra depression sy

depression liteboth interventuntreated con

2 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Cruz-Correia, 200730

RCT, crossover, N=19

asthma29 yrs (16-65)no bad comorbidity, have internetnone doing selfmonitoring

monitoring, graphic feedback, tailored reminding, education vs paper diary, paper action plan

none NR

Asthma clinicPorto, Portugal

Technical/Usaconnection prerrors (3), wormeter (5); proTime to compHIT Satisfactiohigh overall sappr and www; www viewing Health Behavhigher monitopaper (cleares

Delgado, 200331

Case series, N=16

CHF,10 males53.3 yrs (+12.5)

NYHA 7II, 9IIICHF and internet access and >6 months life expectancy

www daily monitoring, display, education, daily communication

none 3, 

University HF clinicToronto

HIT Satisfactiohigh mean scoetcHIT Useone 24 hr breaMorb/Util2 hospitalizedQOL59.75 to 49.87 relationships, home, side ef

Ewald, 2006147 

Case Series, N=49

Hypertensive adults, 43% female, age ~58

BP monitor twice daily, physician-patient communication, medication reminding

none 9 weeks, Germany greater BP red

more frequent controls

Finkelstein, 199633

Case series, N=NR

lung transplant monitoring none NR HIT Use 

3 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Franklin, 2006125

RCT, N=92

DM 149 males12.7, 14.1, 12.6 yrs (Conv Ins, Conv Ins + INT, Int Ins +INT, respectively) (range=8-18 yrs)Diagnosis >1 yrNo social problems, learning difficulties, or needle phobias

daily scheduled individualized text message reminders; text message education; cellphone

none Tayside, UK HbA1c: no ch

Efficacy DM: 5adherence: 70Knowledge ScINT; DM Social Sumore from teafamily/friendsDKAno changeHypoglyc req no changeBMI: no changVisits: not comHIT Satisfactio80% ‘it helpedof msg 90% w

Gerbert, 200395 

Case series, N=52

smokers, problem drinkers, 51% women, 77% nonwhite

1.5 - 3 min tailored advice OR 3-5 min motivational intervention

pamphlets immediate favorable view

72% would printention to qudrinking after 

Glasgow, 2000148

Case Series, (RCT for components), N=320

DM 259 yrs 43.5 % male

Single use dietary goal setting intervention with touch screen in office computer asst goal setting (all groups); followup phone calls; 3 ring binder of community resources

none 3 and 6,

Primary careDenver, COEugene, OR

HbA1c, weighno changeself efficacy ndiet behaviors 

4 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Glasgow, 200334

Case series (RCT for components), N=320

DM 259 yearsDM for 8 years83% minimal www expindependent, English, phone, 

TSM: professional coach online 2x/wk, online resources, Q&A, optional diet data entry and display; PS: online peer support forum, newsletters, 

PC in home, periodic online assessment with automated dietary change goals, DM self mgmt website, DM information

12,Primary careDenver, COEugene, OR

Behaviorsdiet better, DM notBiological chol better, HPsychosocialDM support bCES MDD beHIT usedrops off in 3 support group

Green, 2008127

RCT, N=778

HTN48% maleage 59.1 yrs25% some post bacc; 90% retired or employed, 87% white, 7% smokeInclusion: BP > 140, on Rx, no DM or KD, use PC, email, web access

biweekly pharmacist adjustment, BP monitoring, webaccess EHR

training in BP monitoring and web EHR (I1), or HTN education and web EHR (CTRL)

12 mo HMO in Seattle, WA

SBP C, I1, I2:139.1; DBP 86, 84.9BP CNTRL 0.BMI 32.5, 32.5phone calls Cvisits specialiscare, urgent cprofessional inC/I1/I2: 4.6, 7patient initiate2.7, 4.2; ASA use C/I1/HTN Rxs 1.69Gen 66.7, 66.Phys 88.1, 77Emot 71.5, 72

5 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Guendelman, 2002128

RCT, N=134

Asthma 73% mod or severe12 yrs57 male10 white, 90% public asst

home phone device daily monitoring (sx, activity, med use, peak flow), feedback, information, no change in regimen between visits

asthma diary inner city hospital clinic Missed schoo

CTRL .44 - .2INT .52 - .15 Peakflow red CTRL 20/32 2INT 22/36 – 1Activity limit CTRL .72 - .4INT .67 - .32cough/wheezedifferent UTILno other diff Hadherent to mmonitoring (.8

Gustafson, 1999129

RCT, N=107

HIV/AIDS90% male

information (FAQ, library, resource guide, dictionary); expert consultation, peer support forum, stories, assessment, decision aids, action planPC

97 3 to 6 months 

implementation with 2-3 month follow-up (total 5 to 9 months)Madison, Milwaukee

QoL improved and participatother scales; hospitalzationmore phone cintervention p

Jones, 200139

Cohort, N=112

Schizophrenia > 65 yrs, recent education uncertain Dx, acutely ill, persistent defaulter

5 session personalised computer education1-3-1 hybrid education

5 session psychiatric nurse education

community psychiatric clinic in Glasgow

NS higher satsessions in aloutcomes impcompleting segroups, NS di

Joseph, 2007130

RCT, N=314

Symptomatic Asthma46% male 15.2 yearsurban, 98% African American, high school, 49% medicaid

4 sessions tailored webbased asthma education; referral coordinator interaction based on input

4 sessions timed access to generic asthma website; referral coordinator avoids interaction

6 mo INT 12 mo follow-upschool computers in Detroit

SX/FXN feweSx fewer days activity UTIL fewer hoED visits and obtained contgreater controgreater rescue QOL no chang

6 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Kenwright, 200497

Cohort, N=27

Panic disorder and phobias, 40% female, ~37 years

Self help CBT online Self help CBT in office 16 weeks, London both groups im

and work and scales

Kim, 2006a131RCT, N=167

DM255 yrs53% maleover 21, FBS under 240, A1c under 10, no complications, no insulin

general information, assessment tool, screening instrument, exercise test individualized physical activity prescription, five stage-matched interventions, a questionand-answer board, weekly nurse phone feedback, update, revision; TTM info available on web

general information, assessment tool, screening instrument, exercise test individualized physical activity prescription, five stage-matched interventions, a question-and-answer board, weekly nurse phone feedback, update, revision; TTM info available in print, (C1) standard DM information (C2)

12 weeks University clinic in South Korea

HbA1c 7.00 – (PRINT), 7.87 FBS 162 – 14161;Activity 17 – 2mets/week, 14 

Kim, 2007115

RCT, N=51

DM2, 47 yrs, 57% female

monitor and view CBG, Rx doses, events; weekly RN review, adjustment, education, reinforcment.

1 or 2 office based specialty visits

12 weeks University clinic in South Korea

improved HbAto 7.7 in contrpostprandial g

Kwon, 2004132

RCT, N=110

DM254 years67% malesA1c 7.3, BMI 24, webaccess, over 30, no HF or ESLD, Ckd, insulin pump, prior webprogram

3x per week monitoring BG, weight, etc; EHR (lab, Hx) access; feedback; daily review by MD; MD, RN, dietician recommendations; weekly reminder to logon, with quarterly clinic visit

monthly clinic visit, same recommendation to monitor at least 3x a week

12 weeks in Taiwan HBA1c 7.59 - 

CTRL HDL increaseBG monitor 72 weeks;23/50 got remw/o);Login 42±32/ 

La Porta, 2007a153

Cross sectional, N=2525

Cancer24% malemore often learned of service onlineall ages, IM users 

instant messaging with cancer information specialist

telephone cancer information service

NR IM users a littl

increase, new discussed or pdoctor

7 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

LaFramboise, 2003133

RCT, N=103

Heart Failure monitoring, review by 

RN, tailored advice, education, encouragement; telephone advice if needed; with OR without home visit

telephone care OR home visit

2 mo self efficacy in

visits and HITmin walk test, depression im

Leu, 200541

RCT, N=50

DM1/251 yrsuncontrolled DM1, DM2 (A1c 8-9)

automated text pager/email reminders about BG, Rx, eat, exercise, appointments, labs; clinician replies to requests

could page investigator ~ 5 mo 

UW clinics in Seattle

HbA1cCTRL 8.5 – 7INT 8.5 – 8.2BP > 130 CTRL 1INT 16 – 8 of ATT minimal dINT liked page

Levetan, 2002134

RCT, N=128

Diabetic adults, 67.% female, age ~58 yrs, 86% African American

computer generated goals poster, wallet card, monthly postcards, a phone call, and report to physician

usual diabetes care 6 mo, Washington DC HbA1c 8.85 -

8.39 -> 7.78 Cdifference in w

Lorig, 200642

RCT, N=958

Chronic disease (heart, lung, DM2)29% male57.5 yr15+ yrs of education, 88% whiteINCL: internet, e-mailEXCL: cancer

peer moderated webbased education: (diet, exercise, meds, communication, cognitive behavioral training, selfmanagement), forums, reference book Chronic Disease SelfManagement Program (CDSMP) 3/wk x 6 wk

usual care 12 mo Health; no diff 

intrusiveness, health distress(fatigue); no dHRB; no diff aMD comm; beUTIL; no diff M

McDaniel, 200299

Cross-sectional, N=100

Smokers, low income women, age ~42 yrs, 23% African American 

interactive video with tailored advice

none 1 week followup 79% reported 

or more behavas cutting dowfamily, etc.; ovfavorable attit

8 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

McKay, 200144

RCT, N=68

DM2, sedentary 47% male52.3% yrs50% bacc deg, 62% employedEXCL <40

personalized goal setting, feedback, strategies, online monitoring, forum online coach biweekly prompts 8 week

generic internet information

8 weeksUS, Canada

USE higher in sharplyEXINT had no increased walexercise

McPherson, 2006100

RCT, N=101

Asthmatic children, age 7-14 yrs 

CDROM interactive asthma and self management education, role playing, tailored goal setting

asthma booklet 6 mo, United Kingdom At 1 mo: grea

knowledge, no rates; At 6 mosteroids and snot only a trenanalysis

Noel, 2004135 

RCT, N=104

CHF, COPD, DM frail, elderly veterans 3% female, ~71 yrs

monitoring signs and symptoms, messaging, clinician advice and case management

home health care and case menagment

6 mo, Connecticut fewer hospital 

care, lower Hbchange in cog

Owen, 200546

RCT, crossover, N=62

breast cancer100% female52 yrs

self guided coping skills trainig, email reminders, library, peer forum, resources, art/poetry

academic center in Southeast US

no difference psychological fxn; subgroup baseline show

Pike, 200748

RCT, N=6451

30% male41 yrs21 ciggarettes/day6 prior attempts to quit, smokers visiting American Cancer Society website, frequent internet users

5 independent smoking cessation sites

internet accessible booklets

4 mo US 54% follow-up 

7 day prev 23sites, v 22.3% slips 11% bot

Rasmussen, 200549

RCT, N=300

Asthma25% mod, 25% severe, 5% mild101 male30 yrs

daily monitoring (web or touch tone) peak flow, action plan; CDSS for MD; MD email or phone adjustment; 1 mo steroids for severe

CTRL-1 patient given peak flow meter, action plan, instruction Rx adjustment CTRL-2 told to contact GP with spirometer results;

6 mo in Denmark Sx improved: 

Asthma QoL iFEV1 .32, .13HAVE PLAN .ADHERE .32-VISITS .037, ED/Hosp 2, 1

9 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Reid, 200750

RCT, N=100

Smoking CAD54 yrs61% males91% 12+ years education, 80% ACS, 80% 1st smoke in 30 min from awakeadults, recent ACS, PCI, card cath

pre discharge smoking cessation then IVR at 3, 14, 30 days, then nurse phone counseling 3 x 20 min on quit request

pre discharge smoking cessation only

12 mos hospital in Ottawa

7day abstinen(NS)

Robertson, 2006149

Case series, N=144

MDD54% malewebaccess, willingness, cognitive intact

weekly then biweekly then monthly monitoring w/ feedback, education, online CBT, econsultation, med list, information, strategies, clinician review, automated or case manager reminding

none 12 mo 

Perth, AU

adherence higmanager than reminding' dereduced in 72 

Rodgers, 2005114

RCT, N=1705

Smokers42% male25 yrsover 15, have mobilephone

set quit date, regular, personalised (person but not state specific) text messages providing smoking cessation advice, support, and distraction; quit buddy, quit quiz, quit messages on request

txt message thanks every 2 weeks, incentive to continue to follow-up measurements leading to higher complete participation rate

6 mo New Zealand self report qui

weeks;cotinue 

Rogers, 2001136 

RCT, N=121

Hypertension, 50% female, age ~61 yrs

BP monitoring 3x/wk; fax weekly reports to patient, physician, physician adjustment by phone and/or visit

usual care 8 weeks, Syracuse, NY SBP, DBP, m

mo; adjusted of BP 2.6 - 2.8

African Ameri

more treatmenintervention g

10 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Ross, 200451

RCT, N=107

Heart failure56 yrs77% malesNYHA II, over 18, webuser, not health proffessional

webaccess to EHR labs, notes; email to providers

HF clinic in Colorado No difference 

status, Improvmore ED visitsv 21) visits (32v 6);INT 287 calls, CTRL 267 ms

Sciamanna, 2005102

RCT, N=121

osteoarthritis in the knee 48 yrs23% male89% white, 25% 4+years, college65% regular webuser 88% surgery and md visit for knee surgery limited by OA

webbased assessment w/ recommendations for care

same, but measured satisfaction before assmt

specialty website no difference 

prior care aftereceiving ~8 s

Shaw, 200753

Cross-sectional, N=231

Breast cancer52 yrs100% female< 250% poverty, not homeless, literate

information (FAQ, library, resource guide, dictionary); expert consultation, peer support forum, stories, assessment, decision aids, action plan, PC, CHESS training

none 4 mo Detroit, 

MI/Madison, WI 

perceived infocorrelated witinteractive sercommunicaito

Shegog, 2001138

RCT, N=71

Asthmatic children, 8-13 yrs, 35% female, 59% nonwhite

CDROM interactive asthma and self management education, role playing

not described 3 sessions, 3 weeks, 

Houston

Some knowlein intervention possibly highe

Southard, 2003139

RCT, N=104

CAD or CHF62 yrs96% white50% retired25% college degree

weekly monitoring surgery and signs, feedback, case manager and dietician led education, risk factor managementt, online forum; phone and mail incentive gifts for participation

usual care 6 mo wt loss (3#) IN

improved activLDL, depress

11 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

Author, Year 

Design, N Patients Intervention(s)

Comparison 

Group(s) Duration, Setting O

Strecher, 2006116

RCT, N=3971

smokers44% male37 yrs23 cigarettes/daysmoke 1st in 30 min of awakening with set date, email, purchased patch 

tailored webbased smoking education, newsletters, supportive messages by email, and support person, tailored account to initial questionnaire

generic web based smoking intervention, not tailored to initial questionnaire, no newsletters, no support person

12 weeks UK 10 week absti

moderators: tochildren in houconsumption;aand less seve

Taylor, 2006107

RCT, N=114

45 yrs88% maleOSA, new Dx, no prior CPAP

monitoring symptoms, CPAP use; education; contact from sleep medicine clinic by phone if high risk

stratified age, severity, 30 days specialty clinic Walter Reed

no difference hours CPAP usatisfaction

Turnin, 1992108 

RCT crossover, N=105

Diabetic adults, 42% female, age ~45 yrs, ~ 70% on IDDM

monitoring, dietary analysis, expert system advice, email, education

crossover condition not described

12 mo, Toulouse, FR DM knowledg

proportional to diet behaviors weight was unimproved 11.0 subgroup.

Winzelberg, 2003141

RCT, N=72

breast cancer36% some grad school80% whitenot suicidal, in CA, English speaking

Web-based MH professional actively moderated structured breast cancer social support group, personal journal, others’ stories

wait list control 12 weeks Palo Alto INT improved 

(PTSD), PSS 

Yon, 2007152

Case series, N=176

obesity13% maleBMI 25-39, over 18 yrs, web access not pregnant, moving, major illness, unable to meet weekly

PDA dietary monitoring, part of 24 week weight loss program, weekly email encouragement monitoring

historical controls using paper based monitoring, weekly encouragement

24 weeks Vermont no difference 

diet complianccomplaint with actual data nocorrelated witattendance, aexercise

Zimmerman, 200493

RCT, N=45

CABG daily monitoring 

symptoms, daily review and scripted response

predischarge teaching, followup phone calls at 2, 4, and 6 weeks

6 weeks, no significant 

functioning or 

12 of 13
Summary Table 5. Studies of Effectiveness of Interactive Consumer Health IT (KQ 5)

ASA = aspirin HTN = hypertension

BG = blood glucose INCL = inclusionBP = blood pressure INT = interventionCBT = computer based test KD = kidney diseaseCDSS = clinical decision support system M = maleCHESS = Comprehensive Health Enhancement Support System MD = doctorCHF = chronic heart failure MDD = Major Depressive Disorderchol = cholesterol mgmt = managementCOPD = chronic obstructive pulmonary disease mo(s) = month(s)CTRL = control msg = messageDBP = diastolic blood pressure OA = osteoarthritisdiff = difference OSA = sleep apneaDM = Diabetes Mellitus PDA = personal digital assistantDx = diagnosis QoL = quality of lifeED = emergency department Rx = prescriptionEHR = electronic health records SPB = systolic blood pressureESLD = end stage liver disease sx = symptomsEXCL = exclusion txt = textf/u = follow-up UTIL = utilizedfxn = function wk(s) = week(s)HbA1c = glycosylated hemoglobin wt = weightHF = heart failure yr=year(s)hosps = hospitals

Abbreviations

13 of 13
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Artinian, 2003106

RCT, N= 18

65 % Black (11 pts)35% White (6 pts)

Congestive heart failure

video cassette (Med - Monitor) that sat in a cradle connected to a telephone line

Majority (56%) loved itLifestyle change seen in 67%BP more stable by remembering nitroglycerineImproved self care behaviors anSome technical difficulties repo

Brennen, 2001157

RCT, N= 140

12% African American86% Caucasian

CABG Computerized, internet-based 

information and support system that provides extension and enhancement of traditional nursin

g services

For one week snapshot: Group a total of 451 times, an average 

Gerbert, 200395

RCT, N=52

48% African American; 12%Latino; low income

Smoking & Alcoholism

Brief motivational video risk assessment with tailored advice

Convenience (right amount of tipts); confidential (5.9/7pts); hon(6.8 /7 pts); satisfaction (5.7 /7p71% would feel more comfortab

Guendelman, 2002128

RCT, N= 134

Gp 1: 79% African American8% CaucasianGp 2: 74% African American12% Caucasian

Asthma Interactive communication 

device with question and response tasks. Questions provided by the nurse to the patient for their respones

Reduction in the number of urge(p= 0.21), hospitalizations, (0.96Reduction in limitation of activity Decrease in the number of peop(p= 0.83)Reduction in number of subjects 

Joseph, 2007130

RCT, N= 314

>98% students were African American 

Asthma Web based program focusing 

on controller medication adherence, rescue inhaler availability,smoking cessation/reduction.

Decrease in symptom days, symmissed/30 days,Decreased days of restricted acDecrease in days having to chaDecreased Hospitalizations (p= months, p= (0.08)Positive behavior in medication rescue inhaler activity (p=0.01)Trend towards better 

qualit

y of 

African Americans

1 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

La Porta, 2007a153Survey, N= 2485

11% African American75% Caucasian6% Hispanic

Cancer and Smoking

Cancer Information Service and LiveHelp provide cancer patients and family members with real time information and advice

73% reported their knowledge hservice95% said they were very satisfie88% said service met or exceed67% said they felt more confidecancer information following the  60% of those who used the serfamily member) and were dx w/their ability to actively participaOf those actively smoking at timcompletion: 14% had quit, 35% or cut back in future

La Porta, 2007b158

Survey, N= 2485

11% African American75% Caucasian6% Hispanic

Cancer and Smoking

Cancer Information Service (CIS)and LiveHelp provide cancer patients and family members with real time information and advice

Live help group was more likely knowledge compared to CIS tel 61% in Live help grp and 59% 39% in Live help and 35% in CI38% in Live Help and 29% in Cexpections, 56% in Live Help and 59% in Cexpectations

Meigs, 2003159

RCT, N= 598

Grp 1 vs Grp 2Black: 19.2% vs 18.9%White: 71% vs 71.1%Other: 9.8% vs 10.0%

Type 2 Diabetes

web based decision support tool, displaying interactive patient specific clinical data,treatment advice, and links to other web based care resources

Positive change seen in all glycMean HbA1c (% Hb)Positive change seen in all cholPositive change seen in all BP cMean systolic BP (mmHg)Increase in the number of subjeexamination by an eye care proin the last 12 months

Nguyen, 2005*113

Between group, N= 23

6% African-American (1)13% White (13)6% Native American (1)6% Other (1)

Chronic ObstructivePulmonary disease

web site to help individuals with COPD by logging onto website at least 3 times/week, 2 weekly chat sessions, online bulletin board and email support, individualized exercise plans

No significant differences in phy

African Americans

2 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Piette, 1997*47Prospective study, N= 65

9.2% Black61.5% White18.5% Hispanic10.8% Asian/Pacific Islander

Diabetes AVM system using specialised 

computer technology to telephone patients, communicate messages, and collect information

AVM system with multiple phoncost 10% of the human calling Program cost for second year is (assuming progam paid for itselAlmost all of the subjects reportand would like to receive AVM.Almost all had no difficulty respo37.5% - 85.6% listened to 2 + 72.2-90.9 % thought AVM would with VA care

Sciamanna, 2004*86

Cohort, N= 150

4.1% Black (6)83.1% White (123)11.4% Hispanic (17)1.4% Other (2)

chronic illness computer assessment on 

smoking and physical activity with tailored printed feedback

12% of patients asked for help u81.1% patients felt computer eaSatisfaction with provider visit schronic conditon, being a nonsmwho reviewed the report with th

Sciamanna, 2005102Quasi experimental, N= 121

6.6% Black86.8% White2.5% American Indian3.3% Hispanic5% Other

osteoarthritis of the knee

computer assessment on OA, satisfaction with care;computer personalized feedback about OA quality of care and treatment recommendations

website ratings-23.1% excellent97.5 % said website was easy tMajority thought that Website cohelp understand and manage odoctor

Shaw, 2007*53

RCT, N= 231

35.9% African American (83)62.3% Caucasian (144) 1.7% Other minorities 

Metastatic Breast Cancer

CHESS: Web-based information, discussion group, ask an expert, and interactive services (tailored action plan, decision aids, health tracking

Positive change in information cUse of all four components of ICUse over 4 months by categoryMean use discussion 506.2 minMean use information 35.5 min Mean use ask an expert 28 min Mean interactive service 27.9 m

African Americans

3 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Taylor, 2006107RCT, N= 114

UC vs Exp40% vs 42% African American60% vs 49% Caucasian0% vs 5% Hispanic0% vs 5% Asian

Obstructive sleep apnea

OSAS library with customized information; Daily questions on sleep and CPAP use, responses monitored and categorized into low/medium/high risk, sleep specialist call patient after 3 days of high-risk responses

83% found HealthBuddy very ea2 out of 3 reported it a positive e 83% had no difficulty using it; 75% wer likely or very likely to c69% satisfied with Health Buddy71% satisfied with communicatiMean hours of CPAP use (hrs): p=.87CPAP mean proportion (hrs):UC Functional status, Usual care 2.

Winzelberg, 2002141

RCT, N= 72

4% African American80% Caucasian4% Asian6% Hispanic/Latina6% Other

Breast cancer 12 week, structured, web 

based support group moderated by a mental health professional

Group experience evaluation (% deal)getting support and encouragemhelping others: 56learning that problems are not udevelop new friendships: 63get advice: 55express true feelings: 65

Estabrooks, 2005146

RCT, N= 422

Urban as well as rural Type 2 

Diabetes

Computer CD-ROM with Goal setting, Tailored action plan, assessments of dietary intake and physical activity, hard cop

y

of pts personal action plan

Decreased fat consumtionincreased veggie and fruit consIncreased moderate/vigorous ac

Ferrer-Roca, 200460

Case series, N= 172(Gp 1 (DM): 12Gp 2 (website review only): 160)

Rural Islanders Diabetes Website with patient and doctor 

interfaces for inputting data and sending advice

 Out of those who entered the tr86% would recommend the siteMajority (71%) thought the syste33% did not find the system use50% had difficulties in accessinAmbiguous results on HIT satisOut of those who reveiwed the w40% would recommend the siteMajority (92%) thought the syste12%did not find the system use 88% had difficulties in accessin

Rural

African Americans

4 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Gustafson, 1998*36Cohort, N=38

28[76%] of 27 potentially eligible urban vs. 10 [71%] of 14 rural

Breast Cancer CHESS: computerized 

information and problemsolving, Q & A, ask an expert, health profile, peer discussion group, personal stories, decision aids

Use over 10 weeks: Mean 6.8 uses/week/subject100% used discussion group, aprofile 71% of uses for social support, 16% of uses for information

Gustafson, 200174

RCT, N = 295

Urban, small city and rural areas

Breat cancer Computer based patient 

support system providing information, decision-making and emotional support

Increased participation and leveIncreased confidence in doctorsIncreased information competenDecreased unmet information nIncreased partici[ation and behaBetter qualit

y of lif

e

Hill, 2006156RCT, N= 120

Ranch, Farm or Small town(Rural- lived 25 miles outside an urbanized area (a city of 12,500 or more),having to travel almost 57 miles one way)

Diabetes, Rheumatoid conditions, heart disease, cancer, multiple sclerosis

Online, asynchronous, peer-led support group and health teaching units using WebCT

Trend toward increasing self esIncrease in feeling of empowermIncreased seld efficacy (p=0.10Decreased feelings of depressio0.342, 0.206, 0.343 respt)High levels of social support, (p

Kaufman, 2003*75

usability cognitive walkthrough and field observation,N= 14 NYC, 11 upstate (but really 2 case studies)

rural upstate NY and hispanic NYC

Diabetes Mellitus

videoconferencing, BP & BG uploading and review, education, email

Usability issuessystem barriers- problematic wispacing, complext tasks, screen  Cognitive/Skill barriers-mouse-literacy, numeracy. Also anxiety, self-efficacy, motiv

Shaw, 2007*53

RCT, N= 231

Detroit cohort: unknown. Wisconsin cohort: rural.

Metastatic Breast Cancer

CHESS: Web-based information, discussion group, ask an expert, and interactive services (tailored action plan, decision aids, health tracking

Positive change in information cUse of all four components of ICUse over 4 months by categoryMean use discussion 506.2 minMean use information 35.5 min Mean use ask an expert 28 min Mean interactive service 27.9 m

Rural

5 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Smith, 200054RCT, N= 30

Rural-at least 25 miles away from 6 major cities of Montana

Diabetes web based computer 

intervention on general health and info on diabetes with Conversation area, mailbox, health chat area and resource rack

Time spent using the HIT decrePsychosocial adjustment to illnecomputer group as compared to Raw mean scores of quality of lcomputer group

Woodruff, 2001151

pre post cohort study, N= 26

Rural alternative schools Smoking chat room, facilitated by 

tobacco counselor, using Active World 

 38.9% self reported not smokin

Finkelstein, 199633

Qualitative Study, N=41

Reside in low-income inner city area, 2/3 had never used a computer, almost 50% had never used a computer or an ATM, >50% born outside US and English was their 2nd language, age range was on older side (most >40 yrs, and 1/3 >50 yrs), 

Asthma/underserved

Symptom diary and spirometry test

Not complicated at all, very eas

Lober, 2006*82

N=38

Low-income elderly and disabled, mean age 69 years (range 49-92 years), 82% Female, 18% Male, many had chronic diseases

Many chronic illnesses

Personal health records with areas for communication with health professional

Empowering patients to keep traif a natural disaster hits, their PHthey are not paper-form- 93% of providers found the PeSystem useful when treating pa

McDaniel, 200299

Cohort, N=110

35% did NOT have HS diploma

Smoking Interactive smoking cessation 

software

Technology malfunctions; softw60.2 / 70pts; minority participan

Sciamanna 2004*86

CohortN= 150

Public health clinics1 provider at 10 clinic sites;

63.3% had some chronic illness

computer assessment on smoking and physical activity with tailored printed feedback

12% of patients asked for help u81.1% patients felt computer eaSatisfaction with provider visit schronic conditon, being a nonsmwho reviewed the report with th

Rural

Underserved

6 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Barnason, 2003119RCT w/ repeated measures, N= 35

Age range 65 -85 years Coronary 

Artery disease

Health buddy:small device attached to patient's telephone. Provides asssessment, strategies, education, and positive reinforcement

Significantly higher self efficacy functioning mean scores, adjustmean scores, mental health funfunctioning in intervention groupSignificant time effect [F(1,29)=emotional functioning and pain <.002]Fewer subjects reported the use eating at 4 weeks, 6 weeks, and Significantly higher exercise adstress control

Baranson, 2006120

randomized, experitmental twogroup repeated measures design,N= 50

Average age- 73.3 years Coronary 

Artery disease

Health Buddy-12 weeks of a desktop telehealth device with daily interventions with recovery assessment, strategies to manage problems, education and positive reinforcement

Improvement seen in physical a6 wks and 3 months after bypas(Physical- F=9.42, p<0.01; MenDecreased number of emergenintervention grp (p= 0.15) More number of non routine progrp( Mean =2.8) as compared to Nursing visit average higher in tcompared to control group

Feil, 200069

Qualitative Study, N=160

Ages 40-75 years Type 2 

Diabetes

D-Net intervention: peer social support and personalized dietary intervention

Computer was initial barrier to p

Gustafson, 1998*36

Cohort, N=38

Mean age- 71.8 , SD 6.06 Breast Cancer CHESS: computerized 

information and problemsolving, Q & A, ask an expert, health profile, peer discussion group, personal stories, decision aids

Use over 10 weeks: Mean 6.8 uses/week/subject100% used discussion group, aprofile 71% of uses for social support, 16% of uses for information

Holbrook, 2007162

Randomised trial (no control), N= 98

> 65 years Not specified Decision board, decision 

booklet with audiotape , or interactive computer program with either pie graphs or pictograms

Higher post intervention scores Information Questionnaire (p<096% of participants felt that the aids helped them make their treMean difference scores by formsimilar in correspondin

g

groups 

Elderly

7 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Jones, 199938RCT, N= 525

>60 examined as variable Cancer Personalized computer based 

information i.e. Cancer information tailored by data in EHR vs not tailored vs printed booklets

NS change depression and can 84% improved anxiety scores, especially but also BKLT (booklPhysician assessed 'above avg tailored), 25% PINF, 20% BKLTassessed pt knowledge below a12 min (1-44) 1st use; 71% no r4/155 GINF 80% would prefer 1PINF, 80% GINF, 90% BKLT); osatisfaction with information hig

Kaufman, 2003*75

usability cognitive walkthrough and field observation,N= 14 NYC, 11 upstate (but really 2 case studies)

Age-69.6±6 (NYC); 73.7±8 upstate

Diabetes Mellitus

videoconferencing, BP & BG uploading and review, education, email

Usability issuessystem barriers- problematic wispacing, complext tasks, screen  Cognitive/Skill barriers-mouse-literacy, numeracy. Also anxiety, self-efficacy, motiv

Kressig, 200298

Cohort, N=34

>60 years Physical 

activity for elderly

computer system for exercise promotion (questionnaire with tailored recommendations)

Most problems had to do with s22% questions were mouse rela21.45/24pts on acceptability of 

Lee, 200762

Quasiexperimental, N= 274

Elderly examined as subgroup

Type 2 DM Web-based system for 

managing patients' care of their diabetes

Significant reduction in fasting b0.001), total cholesterol levels p(p=0.001)Subjects in "College or Universioften than those in "Illiterate" ohigh school" (P=0.007 and P=0Subjects in the age category of in 50-59, 60-69, and 70-79 (P=respectively)Those in the age category of 40than those in the age category oand P=0.040, respectively)

Elderly

8 of 9
Summary Table 6. Specific Findings for Populations of Interest

Author, Year, 

Design, N Population description Condition

Interactive Computer 

Technology (ICT) Find

Lober, 2006*82N=38

Low-income elderly and disabled, mean age 69 years (range 49-92 years), 82% Female, 18% Male, many had chronic diseases

Many chronic illnesses

Personal health records with areas for communication with health professional

Empowering patients to keep traif a natural disaster hits, their PHthey are not paper-form- 93% of providers found the PeSystem useful when treating pa

Nguyen, 200345

Non randomized trial, N= 16

Mean age = 69.1 (grp 2)range- 55-82

Dyspnoea nurse facilitated and peer 

supported internet based dyspnoea self management program

FEV1/FVC: 41 +/- 7%; Improve(effect size=0.86)Endurance exercie 0.27 for the Improvement in self efficacy for First month registered most logthe final month

Nguyen, 2005*113

Between groups, N= 23

Age- 69.1 +/- 7.0 (55-82) Chronic 

ObstructivePulmonary disease

web site to help individuals with COPD by logging onto website at least 3 times/week, 2 weekly chat sessions, online bulletin board and email support, individualized exercise plans

No significant differences in phy

Piette, 1997*47

Prospective study, N= 65

9.2% Black61.5% White18.5% Hispanic10.8% Asian/Pacific Islander

Diabetes AVM system using specialised 

computer technology to telephone patients, communicate messages, and collect information

AVM system with multiple phoncost 10% of the human calling Program cost for second year is (assuming progam paid for itselAlmost all of the subjects reportand would like to receive AVM.Almost all had no difficulty respo37.5% - 85.6% listened to 2 + 72.2-90.9 % thought AVM would with VA care

*Study appears in more than one category on this table

Elderly

9 of 9
APPENDIX A. TECHNICAL EXPERT PANEL MEMBERS 

A-2 

S. Ann Becker, PhD 

Professor in College of Business and College of Engineering, Florida Institute of Technology, 

Melbourne, Florida 

Patty Flatley Brennan, RN, PhD 

Moehlman Bascom Professor of Nursing & Industrial Engineering, School of Nursing & 

College of Engineering, University of Wisconsin-Madison

M. Chris Gibbons, MD, MPH 

Assistant Professor, Associate Director, Johns Hopkins Urban Health Institute 

Jennie Chin Hansen, RN, BS, MS 

AARP Board Member and a Senior Fellow at the University of California, San Francisco, 

Center for the Health Professions 

Warren Jones, MD 

Executive Director, Mississippi Institute for the Improvement of Geographical Minority Health; 

Distinguished Professor of Health Policy and Senior Health Policy Advisor at the University of 

Mississippi Medical Center 

Tom Landauer, PhD 

Professor in the Department of Psychology of the University of Colorado, Founder of 

Knowledge Analysis Technologies (now Pearson Knowledge Technologies) 

Anna M. Nápoles-Springer, PhD 

Assistant Adjunct Professor, Department of Medicine, University of California at San 

Francisco 

John Santa, MD, MPH 

Adjunct Associate Professor in the Center for Evidence-based Policy at Oregon Health and 

Science University 
APPENDIX B. SEARCH STRATEGIES

A-2 

MEDLINE (1950 to November Week 2 2007) 

1 exp Chronic Disease/ (173377) 

2 exp Cardiovascular Diseases/ (1438368) 

3 exp Lung Diseases, Obstructive/ (130454) 

4 exp Diabetes Mellitus/ (220904) 

5 exp Arthritis/ (157266) 

6 exp Neurodegenerative Diseases/ (145561) 

7 exp Cognition Disorders/ (33934) 

8 exp Mental Retardation/ (67825) 

9 exp Memory Disorders/ (14524) 

10 exp Spinal Cord Diseases/ (75315) 

11 exp Sensation Disorders/ (115283) 

12 exp Dyskinesias/ (39533) 

13 exp gait disorders, neurologic/ (1434) 

14 exp Movement Disorders/ (68290) 

15 exp paralysis/ (58765) 

16 exp neuromuscular diseases/ (191261) 

17 exp communication disorders/ (41578) 

18 exp developmental disabilities/ (9477) 

19 exp Mental Disorders/ (684932) 

20 exp Neoplasms/ (1911694) 

21 exp Disabled Persons/ (34321) 

22 exp Nervous System Diseases/ (1539072) 

23 exp hiv infections/ (169604) 

24 exp Palliative Care/ (27918) 

25 exp terminal care/ (31467) 

26 exp home care/ (32404) 

27 exp self care/ (26229) 

28 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 

or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 (5437291) 

29 exp Aged/ (1674702) 

30 exp Health Services for the Aged/ (11511) 

31 Minority Groups/ (7142) 

32 exp Ethnic Groups/ (74714) 

33 exp Urban Population/ (33470) 

34 Urban Health Services/ (1769) 

35 Urban Health/ (11071) 

36 exp Rural Health/ (18287) 

37 exp Rural Health Services/ (5217) 

38 exp Rural Population/ (26232) 

39 exp Socioeconomic Factors/ (237725) 

40 exp "Emigration and Immigration"/ (19800) 

41 exp "Transients and Migrants"/ (6397) 

42 exp Homeless Persons/ (4401) 
APPENDIX B. SEARCH STRATEGIES (continued) 

A-3 

43 exp vulnerable populations/ (2562) 

44 exp Complementary Therapies/ (124194) 

45 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 or 37 or 38 or 39 or 40 or 41 or 42 or 43 or 44 

(2103285) 

46 exp Information Systems/ (98874) 

47 exp Telemedicine/ (8789) 

48 exp Computer Systems/ (103462) 

49 exp Telemetry/ (5208) 

50 exp Telecommunications/ (33833) 

51 exp computer assisted instruction/ (6068) 

52 exp User-Computer Interface/ (14124) 

53 exp video games/ (534) 

54 exp computer literacy/ (1012) 

55 46 or 47 or 48 or 49 or 50 or 51 or 52 or 53 or 54 (221934) 

56 attitude/ or attitude to computers/ or exp attitude to health/ (215232) 

57 exp Health Behavior/ (58229) 

58 exp Health Education/ (102644) 

59 exp Consumer Participation/ (22463) 

60 exp Access to Information/ (1956) 

61 56 or 57 or 58 or 59 or 60 (315151) 

62 28 and 55 and 61 (4166) 

63 45 and 55 and 61 (3288) 

64 62 or 63 (6051) 

65 limit 64 to english language (5756) 

66 limit 65 to yr="2005 - 2008" (1751) 

67 limit 65 to yr="2001 - 2004" (1819) 

68 from 67 keep 1-1819 (1819) 

America Association of Retired Persons Ageline (1978 to December 2007) 

1 ((Health$ or patient$ or medicin$ or medical$) adj5 (Behav$ or educat$ or promot$ or 

enhanc$ or assess$ or evaluat$ or participat$ or informat$ or access$ or instruct$ or monitor$ or 

"use" or uses or using or user$ or utiliz$)).mp. [mp=abstract, descriptors, identifiers, title] 

(18699) 

2 ((Computer$ or Information$ or internet or telephon$ or telecommun$ or communicat$) 

adj5 (technol$ or system$ or access$ or literac$ or interact$ or "use" or uses or using or user$ or 

utiliz$)).mp. [mp=abstract, descriptors, identifiers, title] (3131) 

3 telemedic$.mp. [mp=abstract, descriptors, identifiers, title] (141) 

4 1 and 2 (899) 

5 3 or 4 (984) 

6 limit 5 to yr="1990 - 2008" (743) 

7 from 6 keep 1-743 (743) 

PsycINFO (1806 to February Week 4 2008) 

1 exp Chronic Illness/ (12434) 
APPENDIX B. SEARCH STRATEGIES (continued) 

A-4 

2 exp Cardiovascular Disorders/ (21984) 

3 exp Lung Disorders/ (1501) 

4 exp Diabetes Mellitus/ (2029) 

5 exp Arthritis/ (2244) 

6 exp Neurodegenerative Diseases/ (24809) 

7 exp Cognitive Ability/ (49657) 

8 exp Mental Retardation/ (34481) 

9 exp Memory Disorders/ (6470) 

10 exp Sense organ disorders/ (12090) 

11 exp Movement Disorders/ (12584) 

12 exp paralysis/ (3316) 

13 exp neuromuscular diseases/ (0) 

14 exp communication disorders/ (34610) 

15 exp developmental disabilities/ (7651) 

16 exp Mental Disorders/ (307576) 

17 exp Neoplasms/ (17431) 

18 exp Disabilities/ (37156) 

19 exp Nervous System Disorders/ (117832) 

20 exp hiv/ (20142) 

21 exp Palliative Care/ (3092) 

22 exp terminal care/ (0) 

23 exp home care/ (2706) 

24 exp self care/ (2581) 

25 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 

or 19 or 20 or 21 or 22 or 23 or 24 (559924) 

26 exp Aged/ (1662) 

27 exp elder care/ (1877) 

28 Minority Groups/ (6897) 

29 exp Ethnic Groups/ (64791) 

30 exp Urban environments/ (11755) 

31 exp Rural Environments/ (7802) 

32 exp socioeconomic status/ (26347) 

33 exp Sociocultural factors/ (61600) 

34 exp psychosocial factors/ (20491) 

35 exp Immigration/ (7012) 

36 exp human migration/ (4334) 

37 exp Homeless/ (3740) 

38 exp disadvantaged/ (4831) 

39 exp poverty/ (3162) 

40 exp at risk populations/ (21985) 

41 exp alternative medicine/ (3589) 

42 exp holistic health/ (1015) 

43 exp "medicinal herbs and plants"/ (742) 

44 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 or 37 or 38 or 39 or 40 or 41 

or 42 or 43 (203244) 

45 exp Information Systems/ (11332) 
APPENDIX B. SEARCH STRATEGIES (continued) 

A-5 

46 exp Telemedicine/ (819) 

47 exp Computers/ (8211) 

48 exp computer applications/ (36570) 

49 exp Telemetry/ (85) 

50 exp computer assisted instruction/ (8563) 

51 exp Human Computer Interaction/ (3852) 

52 exp video games/ (1146) 

53 45 or 46 or 47 or 48 or 49 or 50 or 51 or 52 (52851) 

54 exp attitudes/ (199020) 

55 exp Health Behavior/ (10516) 

56 exp Health Education/ (9901) 

57 exp Client Participation/ (826) 

58 exp information literacy/ (53) 

59 exp digital divide/ (102) 

60 54 or 55 or 56 or 57 or 58 or 59 (214695) 

61 25 and 53 and 60 (321) 

62 44 and 53 and 60 (270) 

63 61 or 62 (562) 

64 limit 63 to yr="1990 - 2008" (521) 

65 from 64 keep 1-521 (521) 

CINAHL - Cumulative Index to Nursing & Allied Health Literature (1982 to February 

Week 4 2008) 

1 exp Chronic Disease/ (13105) 

2 exp Cardiovascular Diseases/ (109186) 

3 exp Lung Diseases, Obstructive/ (15894) 

4 exp Diabetes Mellitus/ (30301) 

5 exp Arthritis/ (11830) 

6 exp Neurodegenerative Diseases/ (14138) 

7 exp Cognition Disorders/ (5017) 

8 exp Mental Retardation/ (6988) 

9 exp Memory Disorders/ (1631) 

10 exp Spinal Cord Diseases/ (2715) 

11 exp Sensation Disorders/ (12372) 

12 exp Dyskinesias/ (1982) 

13 exp Movement Disorders/ (5096) 

14 exp paralysis/ (5061) 

15 exp neuromuscular diseases/ (16050) 

16 exp communication disorders/ (9672) 

17 exp developmental disabilities/ (1961) 

18 exp Mental Disorders/ (113649) 

19 exp Neoplasms/ (77516) 

20 exp Disabled/ (14893) 

21 exp Nervous System Diseases/ (150650) 

22 exp hiv infections/ (27143) 
APPENDIX B. SEARCH STRATEGIES (continued) 

A-6 

23 exp Palliative Care/ (7652) 

24 exp terminal care/ (18364) 

25 exp home health care/ (20414) 

26 exp self care/ (11961) 

27 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 

or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 (491273) 

28 exp Aged/ (160564) 

29 exp Health Services for the Aged/ (2866) 

30 Minority Groups/ (3560) 

31 exp Ethnic Groups/ (35621) 

32 exp Urban Areas/ (5715) 

33 Urban Health Services/ (300) 

34 Urban Health/ (1069) 

35 exp Rural Health/ (1943) 

36 exp Rural Health Services/ (2130) 

37 exp Rural Areas/ (6210) 

38 exp Socioeconomic Factors/ (83125) 

39 exp "Emigration and Immigration"/ (1717) 

40 exp "Transients and Migrants"/ (676) 

41 exp Homeless Persons/ (1341) 

42 exp vulnerable populations/ (975) 

43 exp Complementary Therapies/ (52130) 

44 28 or 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 or 37 or 38 or 39 or 40 or 41 or 42 or 43 

(316072) 

45 exp Information Systems/ (32862) 

46 computer systems/ or computer hardware/ or computer peripherals/ or computer processor/ 

or computer types/ or interactive voice response systems/ or software/ or communications 

software/ or computer graphics/ or multimedia/ (9023) 

47 electronic bulletin boards/ or electronic mail/ or internet/ or wireless communications/ 

(11359) 

48 exp Telemedicine/ (1864) 

49 exp Telemetry/ (434) 

50 exp computer assisted instruction/ (2868) 

51 exp User-Computer Interface/ (1682) 

52 exp video games/ (224) 

53 exp computer literacy/ (527) 

54 45 or 46 or 47 or 48 or 49 or 50 or 51 or 52 or 53 (54149) 

55 attitude/ or attitude to abortion/ or attitude to adoption/ or attitude to aging/ or attitude to 

change/ or attitude to computers/ or exp attitude to death/ or attitude to disability/ or exp attitude 

to health/ or exp attitude to illness/ or attitude to life/ or attitude to obesity/ or attitude to 

pregnancy/ or attitude to risk/ or exp attitude to sexuality/ or consumer attitudes/ or cultural bias/ 

or cultural sensitivity/ or exp family attitudes/ or gender bias/ or patient attitudes/ or exp personal 

satisfaction/ or exp social attitudes/ or uncertainty/ (81195) 

56 exp Health Behavior/ (22660) 

57 exp Health Education/ (46582) 

58 exp Consumer Participation/ (4961) 
APPENDIX B. SEARCH STRATEGIES (continued) 

A-7 

59 exp Access to Information/ (5087) 

60 55 or 56 or 57 or 58 or 59 (136133) 

61 27 and 54 and 60 (1444) 

62 44 and 54 and 60 (1117) 

63 61 or 62 (2094) 

64 limit 63 to english language (2065) 

65 limit 64 to yr="1990 - 2008" (2052) 

66 limit 65 to yr="1990 - 2003" (832) 

67 limit 65 to yr="2004 - 2008" (1220) 

68 from 67 keep 1-1220 (1220) 

EBM Reviews - Cochrane Database of Systematic Reviews (1st Quarter 2008) 

1 ((health$ or patient$ or home or homes or ((self or selves) adj2 (help$ or care or caring or 

cares))) adj5 (internet$ or telemedic$ or telehealth$ or web-base$ or (information$ adj2 

(technol$ or system$ or computer$)))).mp. (73) 

2 from 1 keep 1-73 (73) 

EBM Reviews - Cochrane Central Register of Controlled Trials (1st Quarter 2008) 

1 ((health$ or patient$ or home or homes or ((self or selves) adj2 (help$ or care or caring or 

cares))) adj5 (internet$ or telemedic$ or telehealth$ or web-base$ or (information$ adj2 

(technol$ or system$ or computer$)))).mp. (498) 

2 from 1 keep 1-498 (498) 
APPENDIX C. INCLUSION/EXCLUSION CRITERIA 

A-8 

Abstract Level 

In or Out Reason 

Outcome data likely; ALL DESIGNS 

Elderly (E) or chronically ill (CI) or medically underserved (MU)* 

or possible analysis as a subpopulation AND 

Appropriate Health information technology* 

Review or meta-analysis with relevant information as specified above 

Foreign language, meets include criteria and may have data, for use later

Include 

Uncertain (pull paper): May be relevant, can’t tell from abstract 

Background Retrieve for use in introduction or discussion 

Wrong population (e.g., not elderly, underserved, or chronically ill, no subpopulation analysis) 

Wrong intervention/technology: see definition below 

No data by design (e.g., opinion, letter, editorial) 

No data by other parameter (e.g., description of technology only) 

Foreign language, no data

No relevance to evidence report key questions 

Exclude 

Related to topic but does not meet inclusion criteria, unspecified 

Paper Level 

Population 

Task Order 

Interest 

Potential Categories Description 

elderly over 65 years of age 

elderly functionally elderly by life experience or health concerns; need clarity/inter-observer 

agreement 

chronic condition over 1 year + benefits from ongoing monitoring or treatment 

impairment impairment in body function or structure 

activity limitation difficulty in executing activities 

chronically 

ill or 

disabled participation restriction problems experienced in participating in life situations 

medically underserved 

population (MUP) or 

medically underserved 

area (MUA) 

study uses Bureau of Health Professions/Health Resources and 

Service Administration designated MUP or MUA 

medically underserved 

area 

region where individuals are inhibited in their ability to receive 

services 

medically 

underserved 

medically underserved 

group 

population which is inhibited in its ability to receive services 

Technology 

The most challenging part of classifying the literature relating to the barriers and drivers of health 

information IT by the elderly, chronically ill and underserved has been in classifying the types of 

technology – both for the purposes of deciding which types to include in our review and also in our 

summarization and discussion of the results. Our inclusion / exclusion criteria are: 
APPENDIX C. INCLUSION/EXCLUSION CRITERIA (continued) 

A-9 

Include: Health information technology program where: 

• Patient interacts with the technology, 

• Computer / technology processes the information in some way, and

• Patient receives information in return and has access to patient-specific information. 

Examples: tailored health advice; personal medical record/access; risk assessment tools, health 

calculators, predictive tools; interactive online support or chat groups; health games that adapt to the 

patient (more than simple “next page”); programs or sites with storage, indexing, retrieval, display, or 

advanced features (summarization and decision support). 

Exclude: Health information technology program where:

• Limited or no patient or consumer driven involvement or 

• Web browsing of general health information databases) or 

• Patient / clinician communication without computer information support (simple videoconferencing or 

phone) or 

• Systems where the patient interacts but doesn’t receive patient-specific information back (e.g., survey 

– no feedback) 

• Systems primarily or entirely for health professionals or caregivers 

Examples: Passive monitoring; direct video or phone conferencing; general web-based content; WebMD, 

Medline Plus, etc.; .Education, information based only programs; surveys without feedback; frequently 

asked question sites; where a tele-visit replaces the face-to-face interaction with a clinician; video 

interaction 
APPENDIX D. LITERATURE SEARCH TREE 

A-10 

 

 

Review titles/abstracts 

with eligibility criteria 

Study meets all inclusion 

criteria; abstract data and 

quality rate; included in results 

Database Searches* 

Download Citations 

8,522 

563 Total full text papers retrieved and 

reviewed with refined eligibility criteria† 

Exclude (n= 434)††

Wrong intervention/technology n=243 

Wrong population n=54 

No data n=42 

Other n=18 

Not on Excluded Studies List (Appendix E) 

Background/check grey literature n=77

129

* includes Medline, Cinahl, Psychinfo, ERIC, Ageline, Cochrane (See Appendix B for search strategies) 

† see Appendix C for details on inclusion/exclusion criteria 

††Studies may be excluded for more than one reason 

**Many studies are included in more than one Key Question 

7,959 

excluded

31 54 52

KQ1 KQ2 

60 74

KQ3 KQ4 KQ5 

Studies included in the evidence 

report by Key Question** 
Appendix E. Excluded Studies List

A-11 

EXCLUDE CODE

E: Other 

E1: Wrong Population 

E2: No Data 

E3: Wrong Intervention/Technology 

1. Use the Internet to reach more patients 

with your demand management 

programs. Healthcare Demand & 

Disease Management, 1997. 3(5): p. 75-

7. Exclude Code: E1 

2. Elderly patients embrace touch-screen 

technology. Telemedicine & Virtual 

Reality, 1998. 3(12): p. 144. Exclude 

Code: E3 

3. Online advice on problem drinking could 

fulfill unmet need: success of services 

depends on proper targeting. Alcoholism 

& Drug Abuse Weekly, 2006. 18(13): p. 

5-7. Exclude Code: E2 

4. Telehealth helps hospital cut 

readmissions by 75%. Healthcare 

Benchmarks & Quality Improvement, 

2007. 14(8): p. 92-4. Exclude Code: E2 

5. Abroms, L.C., et al., The STRENGTH 

Ezine: an application of e-mail for health 

promotion in adolescent girls. American 

Journal of Health Promotion, 2004. 

19(1): p. 28-32. Exclude Code: E1 

6. Adams, W.G., et al., TLC-Asthma: an 

integrated information system for 

patient-centered monitoring, case 

management, and point-of-care decision 

support. AMIA, 2003. Annual 

Symposium Proceedings/AMIA 

Symposium.: p. 1-5. Exclude Code: E 

7. Adamsen, L. and J.M. Rasmussen, 

Exploring and encouraging through 

social interaction: a qualitative study of 

nurses' participation in self-help groups 

for cancer patients. Cancer Nursing, 

2003. 26(1): p. 28-36. Exclude Code: E3 

8. Adkins, J.W., et al., Home-based 

behavioral health intervention: Use of a 

telehealth model to address poor 

adherence to type-1 diabetes medical 

regimens. Telemedicine Journal & EHealth, 2006. 12(3): p. 370-2. Exclude 

Code: E3 

9. Adler, K.G., Web portals in primary care: 

an evaluation of patient readiness and 

willingness to pay for online services.

Journal of Medical Internet Research, 

2006. 8(4): p. e26. Exclude Code: E3 

10. Agnew, T., User friendly at any age.

Nursing Older People, 2006. 18(8): p. 8-

9. Exclude Code: E2 

11. Agre, P., Interactive computer 

technology. Cancer Practice, 1994. 2(1): 

p. 74-6. Exclude Code: E 

12. Agrell, H., S. Dahlberg, and A.F. Jerant, 

Patients' perceptions regarding home 

telecare. Telemedicine Journal & EHealth, 2000. 6(4): p. 409-15. Exclude 

Code: E3 

13. Ahmed, M., F. Bayog, and C.M. 

Boisvert, Computer-facilitated therapy 

for inpatients with schizophrenia.

Psychiatric Services, 1997. 48(10): p. 

1334-5. Exclude Code: E1 

14. Ahring, K.K., et al., Telephone modem 

access improves diabetes control in 

those with insulin-requiring diabetes.

Diabetes Care, 1992. 15(8): p. 971-5. 

Exclude Code: E3 

15. Alemi, F., et al., Electronic self-help and 

support groups. Medical Care, 1996. 

34(10 Suppl): p. OS32-44. Exclude 

Code: E 

16. Alemi, F., et al., Automated monitoring 

of outcomes: application to treatment of 

drug abuse. Medical Decision Making, 

1994. 14(2): p. 180-7. Exclude Code: E3 

17. Alemi, F., et al., Educating patients at 

home. Community Health Rap. Medical 

Care, 1996. 34(10 Suppl): p. OS21-31. 

Exclude Code: E3 
Appendix E. Excluded Studies List (continued) 

A-12

18. Allen, A. and J. Hayes, Patient 

satisfaction with telemedicine in a rural 

clinic. American Journal of Public 

Health, 1994. 84(10): p. 1693. Exclude 

Code: E3 

19. Allen, D.N., et al., Teaching memory 

strategies to persons with multiple 

sclerosis. Journal of Rehabilitation 

Research & Development, 1998. 35(4): 

p. 405-10. Exclude Code: E3 

20. Alonso, A., A new model for home care 

for COPD. Studies in Health Technology 

& Informatics, 2004. 103: p. 368-73. 

Exclude Code: E3 

21. Als, A.B., The desk-top computer as a 

magic box: patterns of behaviour 

connected with the desk-top computer; 

GPs' and patients' perceptions. Family 

Practice, 1997. 14(1): p. 17-23. Exclude 

Code: E3 

22. Alterman, A.I. and T.G. Baughman, 

Videotape versus computer interactive 

education in alcoholic and nonalcoholic 

controls. Alcoholism: Clinical & 

Experimental Research, 1991. 15(1): p. 

39-44. Exclude Code: E3 

23. Alwan, M., et al., Impact of monitoring 

technology in assisted living: outcome 

pilot. IEEE Transactions on Information 

Technology in Biomedicine, 2006. 10(1): 

p. 192-8. Exclude Code: E3 

24. Anderson, E.S., et al., A computerized 

social cognitive intervention for nutrition 

behavior: direct and mediated effects on 

fat, fiber, fruits, and vegetables, selfefficacy, and outcome expectations 

among food shoppers. Annals of 

Behavioral Medicine, 2001. 23(2): p. 88-

100. Exclude Code: E1 

25. Andreassen, H.K., et al., Patients who 

use e-mediated communication with 

their doctor: new constructions of trust in 

the patient-doctor relationship.

Qualitative health research, 2006. 16(2): 

p. 238-48. Exclude Code: E1 

26. Andrews, G., ClimateGP - web based 

patient education. Australian Family 

Physician, 2007. 36(5): p. 371-2. 

Exclude Code: E3 

27. Anhoj, J. and C. Moldrup, Feasibility of 

collecting diary data from asthma 

patients through mobile phones and 

SMS (short message service): response 

rate analysis and focus group evaluation 

from a pilot study. Journal of Medical 

Internet Research, 2004. 6(4): p. e42. 

Exclude Code: E 

28. Anogianakis, G., S. Maglavera, and A. 

Pomportsis, ATTRACT--applications in 

telemedicine taking rapid advantage of 

cable television network evolution.

Studies in Health Technology & 

Informatics, 1998. 50: p. 60-6. Exclude 

Code: E3 

29. Ansari, M., et al., Improving guideline 

adherence: a randomized trial 

evaluating strategies to increase betablocker use in heart failure. Circulation, 

2003. 107(22): p. 2799-804. Exclude 

Code: E3 

30. Aoki, N., et al., Edutainment tools for 

initial education of type-1 diabetes 

mellitus: initial diabetes education with 

fun. Medinfo, 2004. 11(Pt 2): p. 855-9. 

Exclude Code: E3 

31. Aoki, N., et al., INSULOT: a cellular 

phone-based edutainment learning tool 

for children with type 1 diabetes.

Diabetes Care, 2005. 28(3): p. 760. 

Exclude Code: E3 

32. Aoki, N., et al., u-SHARE: web-based 

decision support/risk communication 

tool for healthcare consumers with 

unruptured intracranial aneurysms.

Medinfo, 2007. 12(Pt 2): p. 1012-6. 

Exclude Code: E3 

33. Artinian, N.T., et al., Effects of nursemanaged telemonitoring on blood 

pressure at 12-month follow-up among 

urban African Americans. Nursing 

research, 2007. 56(5): p. 312-22. 

Exclude Code: E3 

34. Artinian, N.T., et al., Effects of nurse-
Appendix E. Excluded Studies List (continued) 

A-13

managed telemonitoring on blood 

pressure at 12-month follow-up among 

urban African Americans. Nursing 

Research, 2007. 56(5): p. 312-22. 

Exclude Code: E3 

35. Artinian, N.T., et al., Effects of nursemanaged telemonitoring on blood 

pressure at 12-month follow-up among 

urban African Americans. Nursing 

Research, 2007. 56(5): p. 312-22. 

Exclude Code: E3 

36. Artinian, N.T., O.G. Washington, and 

T.N. Templin, Effects of home 

telemonitoring and community-based 

monitoring on blood pressure control in 

urban African Americans: a pilot study.

Heart & Lung, 2001. 30(3): p. 191-9. 

Exclude Code: E3 

37. Atkinson, N.L., et al., Assessment of the 

nutrition and physical activity education 

needs of low-income, rural mothers: can 

technology play a role? Journal of 

Community Health, 2007. 32(4): p. 245-

67. Exclude Code: E3 

38. Baba, M., D. Seckin, and S. Kapdali, A 

comparison of teledermatology using 

store-and-forward methodology alone, 

and in combination with Web camera 

videoconferencing. Journal of 

Telemedicine & Telecare, 2005. 11(7): 

p. 354-60. Exclude Code: E3 

39. Baehring, T.U., et al., Using the World 

Wide Web--a new approach to risk 

identification of diabetes mellitus.

International Journal of Medical 

Informatics, 1997. 46(1): p. 31-9. 

Exclude Code: E3 

40. Baker, L., et al., Use of the Internet and 

e-mail for health care information: 

results from a national survey. JAMA, 

2003. 289(18): p. 2400-6. Exclude 

Code: E1 

41. Bakken, S., et al., Development, 

validation, and use of English and 

Spanish versions of the telemedicine 

satisfaction and usefulness 

questionnaire. Journal of the American 

Medical Informatics Association, 2006. 

13(6): p. 660-7. Exclude Code: E3 

42. Balas, E.A., et al., Electronic 

communication with patients. Evaluation 

of distance medicine technology. JAMA, 

1997. 278(2): p. 152-9. Exclude Code: 

E3 

43. Baranowski, T., et al., The Fun, Food, 

and Fitness Project (FFFP): the Baylor 

GEMS pilot study. Ethnicity & disease, 

2003. 13(1 Suppl 1): p. S30-9. Exclude 

Code: E3 

44. Barber, J.G., Computer-assisted drug 

prevention. Journal of Substance Abuse 

Treatment, 1990. 7(2): p. 125-31. 

Exclude Code: E2 

45. Barnes, N., et al., Liverpool Telecare 

Pilot: case studies. Informatics in 

Primary Care, 2006. 14(3): p. 197-202. 

Exclude Code: E3 

46. Barnett, T.E., et al., The effectiveness of 

a care coordination home telehealth 

program for veterans with diabetes 

mellitus: a 2-year follow-up. American 

Journal of Managed Care, 2006. 12(8): 

p. 467-474. Exclude Code: E3 

47. Baron, J., et al., Effect of assessment 

method on the discrepancy between 

judgments of health disorders people 

have and do not have: a web study.

Medical Decision Making, 2003. 23(5): 

p. 422-34. Exclude Code: E1 

48. Bartholomew, L., et al., Watch, 

Discover, Think, and Act: Evaluation of 

computer-assisted instruction to improve 

asthma self-management in inner-city 

children. Patient Education and 

Counseling, 2000. 39(2-3): p. 269-280. 

Exclude Code: E2 

49. Bartholomew, L.K., et al., Watch, 

Discover, Think, and Act: evaluation of 

computer-assisted instruction to improve 

asthma self-management in inner-city 

children. Patient Education & 

Counseling, 2000. 39(2-3): p. 269-80. 

Exclude Code: E2 

50. Bartholomew, L.K., et al., Watch, 
Appendix E. Excluded Studies List (continued) 

A-14

Discover, Think, and Act: a model for 

patient education program development.

Patient Education & Counseling, 2000. 

39(2-3): p. 253-68. Exclude Code: E2 

51. Basch, E., et al., Patient online selfreporting of toxicity symptoms during 

chemotherapy. Journal of Clinical 

Oncology, 2005. 23(15): p. 3552-61. 

Exclude Code: E3 

52. Basch, E., et al., Evaluation of an online 

platform for cancer patient self-reporting 

of chemotherapy toxicities. Journal of 

the American Medical Informatics 

Association, 2007. 14(3): p. 264-8. 

Exclude Code: E3 

53. Bastian, L.A., et al., Evaluating 

participants' use of a hormone 

replacement therapy decision-making 

intervention. Patient Education & 

Counseling, 2002. 48(3): p. 283-91. 

Exclude Code: E3 

54. Baum, L.S., Internet parent support 

groups for primary caregivers of a child 

with special health care needs. Pediatric 

Nursing. 30(5): p. 381-8. Exclude Code: 

E3 

55. Beale, I.L., et al., Improvement in 

cancer-related knowledge following use 

of a psychoeducational video game for 

adolescents and young adults with 

cancer. Journal of Adolescent Health, 

2007. 41(3): p. 263-70. Exclude Code: 

E3 

56. Beaudin, J.S., S.S. Intille, and M.E. 

Morris, To track or not to track: user 

reactions to concepts in longitudinal 

health monitoring. Journal of Medical 

Internet Research, 2006. 8(4): p. e29. 

Exclude Code: E1 

57. Bechtel-Blackwell, D.A., Computerassisted self-interview and nutrition 

education in pregnant teens. Clinical 

Nursing Research, 2002. 11(4): p. 450-

62. Exclude Code: E1 

58. Beck, H., et al., Computer skills in 

patients with movement disorders.

Parkinsonism & Related Disorders, 

2005. 11(7): p. 421-6. Exclude Code: E3 

59. Benatar, D., et al., Outcomes of chronic 

heart failure. Archives of internal 

medicine, 2003. 163(3): p. 347-52. 

Exclude Code: E3 

60. Bendixen, R.M., K. Horn, and C. Levy, 

Using telerehabilitation to support elders 

with chronic illness in their homes.

Topics in Geriatric Rehabilitation, 2007. 

23(1): p. 47-51. Exclude Code: E3 

61. Bensink, M., et al., Investigating the 

cost-effectiveness of videotelephone 

based support for newly diagnosed 

paediatric oncology patients and their 

families: design of a randomised 

controlled trial. BMC Health Services 

Research, 2007. 7: p. 38. Exclude 

Code: E3 

62. Berry, D.L., et al., Actual use and 

perceived usefulness of a web-based, 

decision support program for men with 

prostate cancer. Studies in Health 

Technology & Informatics, 2006. 122: p. 

781-2. Exclude Code: E3 

63. Bielli, E., et al., A Wireless Health 

Outcomes Monitoring System 

(WHOMS): development and field 

testing with cancer patients using mobile 

phones. BMC Medical Informatics & 

Decision Making, 2004. 4: p. 7. Exclude 

Code: E3 

64. Biermann, E., et al., Are there time and 

cost savings by using telemanagement 

for patients on intensified insulin 

therapy? A randomised, controlled trial.

Computer Methods & Programs in 

Biomedicine, 2002. 69(2): p. 137-46. 

Exclude Code: E3 

65. Bik-Multanowski, M., et al., Use of 

handheld computers for assessment of 

prefrontal cortex function in patients with 

phenylketonuria. Molecular Genetics & 

Metabolism, 2005. 86 Suppl 1: p. S142-

4. Exclude Code: E3 

66. Birkmann, C., R.C. Dumitru, and H.U. 

Prokosch, Evaluation of health-related 

Internet use in Germany. Methods of 

Information in Medicine, 2006. 45(4): p. 
Appendix E. Excluded Studies List (continued) 

A-15

367-76. Exclude Code: E1 

67. Block, G., et al., An interactive CD-ROM 

for nutrition screening and counseling.

American Journal of Public Health, 

2000. 90(5): p. 781-5. Exclude Code: E 

68. Bock, B., et al., Acceptability of 

computer assessments among 

ethnically diverse, low-income smokers.

American Journal of Health Promotion, 

1999. 13(5): p. 299-304. Exclude Code: 

E3 

69. Boisen, E., et al., Copability, coping, and 

learning as focal concepts in the 

evaluation of computerised diabetes 

disease management. International 

Journal of Medical Informatics, 2003. 

70(2-3): p. 353-63. Exclude Code: E2 

70. Boissy, P., K. Jacobs, and S.H. Roy, 

Usability of a barcode scanning system 

as a means of data entry on a PDA for 

self-report health outcome 

questionnaires: a pilot study in 

individuals over 60 years of age. BMC 

Medical Informatics & Decision Making, 

2006. 6: p. 42. Exclude Code: E3 

71. Bosworth, H.B., et al., Hypertension 

Intervention Nurse Telemedicine Study 

(HINTS): testing a multifactorial tailored 

behavioral/educational and a medication 

management intervention for blood 

pressure control. American Heart 

Journal, 2007. 153(6): p. 918-24. 

Exclude Code: E3 

72. Bowen, A.M., K. Horvath, and M.L. 

Williams, A randomized control trial of 

Internet-delivered HIV prevention 

targeting rural MSM. Health Education 

Research, 2007. 22(1): p. 120-7. 

Exclude Code: E3 

73. Bratan, T., et al., Evaluation of the 

practical feasibility and acceptability of 

home monitoring in residential homes.

Journal of Telemedicine & Telecare, 

2005. 11 Suppl 1: p. 29-31. Exclude 

Code: E3 

74. Brenner, B., Is the provision of 

laboratory results via the Internet 

acceptable to patients? A survey of 

private patients in a large, specialist 

gynaecology practice. New Zealand 

Medical Journal, 2003. 116(1187): p. 

U711. Exclude Code: E1 

75. Brown, C.L., N. Tangsinmankong, and 

P.J. Emmanuel, Improving care of HIVinfected patients in the outpatient setting 

with patient data flow sheets. Journal of 

the Association of Nurses in AIDS Care, 

2002. 13(4): p. 58-63. Exclude Code: E3 

76. Brown, S.J., et al., Educational video 

game for juvenile diabetes: results of a 

controlled trial. Medical Informatics, 

1997. 22(1): p. 77-89. Exclude Code: E3 

77. Bruning Brown, J., et al., An evaluation 

of an Internet-delivered eating disorder 

prevention program for adolescents and 

their parents. Journal of Adolescent 

Health, 2004. 35(4): p. 290-6. Exclude 

Code: E1 

78. Bulmer, P.J., et al., A randomized trial 

comparing the effectiveness and 

preference of a touch-screen computer 

system with a leaflet for providing 

women with information on urinary 

symptoms suggestive of detrusor 

instability. BJU International, 2001. 

88(6): p. 532-5. Exclude Code: E3 

79. Burkhart, P.V., et al., Testing an 

intervention to promote children's 

adherence to asthma self-management.

Journal of Nursing Scholarship, 2007. 

39(2): p. 133-40. Exclude Code: E 

80. Bush, N., et al., Development, feasibility 

and compliance of a web-based system 

for very frequent QOL and symptom 

home self-assessment after 

hematopoietic stem cell transplantation.

Quality of Life Research, 2005. 14(1): p. 

77-93. Exclude Code: E3 

81. Butler, S.F., et al., Working it out: 

development and testing of a 

multimedia, vocational education 

program. Substance Use & Misuse, 

2004. 39(13-14): p. 2525-58. Exclude 

Code: E3 
Appendix E. Excluded Studies List (continued) 

A-16

82. Campbell, E., et al., Encouraging 

underscreened women to have cervical 

cancer screening: the effectiveness of a 

computer strategy. Preventive Medicine, 

1997. 26(6): p. 801-7. Exclude Code: E1 

83. Campbell, M.K., et al., Randomized trial 

of a tailored nutrition education CDROM program for women receiving food 

assistance. Journal of Nutrition 

Education and Behavior, 2004. 36(2): p. 

58-66. Exclude Code: E3 

84. Carlbring, P., et al., Remote treatment of 

panic disorder: a randomized trial of 

internet-based cognitive behavior 

therapy supplemented with telephone 

calls. The American journal of 

psychiatry, 2006. 163(12): p. 2119-25. 

Exclude Code: E3 

85. Carlbring, P., et al., Treatment of social 

phobia: randomised trial of internetdelivered cognitive-behavioural therapy 

with telephone support. British Journal 

of Psychiatry, 2007. 190: p. 123-8. 

Exclude Code: E3 

86. Carlbring, P., et al., Treatment of panic 

disorder via the Internet: A randomized 

trial of a self-help program. Behavior 

Therapy, 2001. 32(4): p. 751-764. 

Exclude Code: E3 

87. Carpenter, K.M., et al., Teaching brief 

interventions for smoking cessation via 

an interactive computer-based tutorial.

Journal of Health Psychology, 2003. 

8(1): p. 149-60. Exclude Code: E3 

88. Chen, H.H. and M.L. Yeh, Developing 

and evaluating a smoking cessation 

program combined with an Internetassisted instruction program for 

adolescents with smoking. Patient 

education and counseling, 2006. 61(3): 

p. 411-8. Exclude Code: E 

89. Cherry, J.C., et al., Diabetes disease 

management program for an indigent 

population empowered by telemedicine 

technology. Diabetes Technology & 

Therapeutics, 2002. 4(6): p. 783-91. 

Exclude Code: E3 

90. Chou, M., et al., Exploring the selflearning experiences of patients with 

depression participating in a multimedia 

education program. Journal of Nursing 

Research, 2004. 12(4): p. 297-305. 

Exclude Code: E3 

91. Christensen, D.B., et al., Assessing 

compliance to antihypertensive 

medications using computer-based 

pharmacy records. Medical Care, 1997. 

35(11): p. 1164-70. Exclude Code: E3 

92. Christenson, M.A., Assessing an elder's 

need for assistance: one technological 

tool. Generations, 1995. 19(1): p. 54-55. 

Exclude Code: E2 

93. Chung, M. and M. Akahoshi, Reducing 

home nursing visit costs using a remote 

access infusion pump system. Journal 

of Intravenous Nursing, 1999. 22(6): p. 

309-14. Exclude Code: E3 

94. Clemensen, J., S.B. Larsen, and N. 

Ejskjaer, Telemedical treatment at home 

of diabetic foot ulcers. Journal of 

Telemedicine & Telecare, 2005. 11 

Suppl 2: p. S14-6. Exclude Code: E3 

95. Crosbie, J.H., et al., Virtual reality in the 

rehabilitation of the upper limb after 

stroke: the user's perspective.

Cyberpsychology & Behavior, 2006. 

9(2): p. 137-41. Exclude Code: E3 

96. Cunningham, J.A., K. Humphreys, and 

A. Koski-Jannes, Providing personalized 

assessment feedback for problem 

drinking on the Internet: a pilot project.

Journal of Studies on Alcohol, 2000. 

61(6): p. 794-8. Exclude Code: E1 

97. Cunningham, J.A., et al., Internet and 

paper self-help materials for problem 

drinking: is there an additive effect?

Addictive Behaviors, 2005. 30(8): p. 

1517-23. Exclude Code: E1 

98. Cunningham, J.A., P. Selby, and T. van 

Mierlo, Integrated online services for 

smokers and drinkers? Use of the check 

your drinking assessment screener by 

participants of the Stop Smoking Center.

Nicotine & Tobacco Research, 2006. 8 
Appendix E. Excluded Studies List (continued) 

A-17

Suppl 1: p. S21-5. Exclude Code: E2 

99. Cytryn, K.N. and V.L. Patel, Reasoning 

about diabetes and its relationship to the 

use of telecommunication technology by 

patients and physicians. International 

Journal of Medical Informatics, 1998. 

51(2-3): p. 137-51. Exclude Code: E 

100. Dale, J., S. Connor, and K. Tolley, An 

evaluation of the west Surrey 

telemedicine monitoring project. Journal 

of Telemedicine & Telecare, 2003. 9 

Suppl 1: p. S39-41. Exclude Code: E3 

101. Datto, C.J., et al., The pilot study of a 

telephone disease management 

program for depression. General 

Hospital Psychiatry, 2003. 25(3): p. 169-

77. Exclude Code: E3 

102. De Bourdeaudhuij, I., et al., Evaluation 

of an interactive computer-tailored 

nutrition intervention in a real-life setting.

Annals of Behavioral Medicine, 2007. 

33(1): p. 39-48. Exclude Code: E 

103. de Lusignan, S., et al., A pilot study of 

radiotelemetry for continuous 

cardiopulmonary monitoring of patients 

at home. Journal of Telemedicine & 

Telecare, 2000. 6 Suppl 1: p. S119-22. 

Exclude Code: E3 

104. DeLenardo, C., Web-based tools steer 

patient-focused care: portal technology 

empowers patients to track and improve 

outcomes. Holistic Nursing Practice, 

2005. 19(2): p. 90-2. Exclude Code: E2 

105. Delgado, M. and J. Santiago, HIV/AIDS 

in a Puerto Rican/Dominican 

community: a collaborative project with 

a botanical shop. Social Work, 1998. 

43(2): p. 183-6. Exclude Code: E3 

106. Delic, D., O. Polasek, and J. Kern, 

Internet health-information seekers in 

Croatia--who, what, and why? Medical 

Informatics & the Internet in Medicine, 

2006. 31(4): p. 267-73. Exclude Code: 

E3 

107. DePalma, A., Prostate Cancer Shared 

Decision: a CD-ROM educational and 

decision-assisting tool for men with 

prostate cancer. Seminars in Urologic 

Oncology, 2000. 18(3): p. 178-81. 

Exclude Code: E2 

108. Diamond, B.J., et al., Telerehabilitation, 

cognition and user-accessibility.

Neurorehabilitation, 2003. 18(2): p. 171-

7. Exclude Code: E3 

109. Diefenbach, M.A. and B.P. Butz, A 

multimedia interactive education system 

for prostate cancer patients: 

development and preliminary evaluation.

Journal of Medical Internet Research, 

2004. 6(1): p. e3. Exclude Code: E3 

110. Dobscha, S.K., et al., Measuring 

depression and pain with home health 

monitors. Telemedicine Journal & EHealth, 2006. 12(6): p. 702-6. Exclude 

Code: E3 

111. Donald, S., et al., Evaluation of use of 

cellphones to aid compliance with drug 

therapy for HIV patients. AIDS Care, 

2007. 19(5): p. 605-7. Exclude Code: E 

112. Dragone, M.A., et al., Development and 

evaluation of an interactive CD-ROM for 

children with leukemia and their families.

Patient Education & Counseling, 2002. 

46(4): p. 297-307. Exclude Code: E3 

113. Duff, A., et al., Betterland: an interactive 

cd-rom guide for children with cystic 

fibrosis. Paediatric Nursing, 2006. 18(7): 

p. 30-3. Exclude Code: E2 

114. Dunbar, P.J., et al., A two-way 

messaging system to enhance 

antiretroviral adherence. Journal of the 

American Medical Informatics 

Association, 2003. 10(1): p. 11-5. 

Exclude Code: E3 

115. Edworthy, S.M. and G.M. Devins, 

Improving medication adherence 

through patient education distinguishing 

between appropriate and inappropriate 

utilization. Patient Education Study 

Group. Journal of Rheumatology, 1999. 

26(8): p. 1793-801. Exclude Code: E3 

116. Eisermann, U., I. Haase, and B. Kladny, 
Appendix E. Excluded Studies List (continued) 

A-18

Computer-aided multimedia training in 

orthopedic rehabilitation. American 

Journal of Physical Medicine & 

Rehabilitation, 2004. 83(9): p. 670-80. 

Exclude Code: E3 

117. Emery, J., et al., The GRAIDS Trial: a 

cluster randomised controlled trial of 

computer decision support for the 

management of familial cancer risk in 

primary care. British Journal of Cancer, 

2007. 97(4): p. 486-93. Exclude Code: 

E1 

118. Engelbrecht, R., et al., DIABCARD--an 

application of a portable medical record 

for persons with diabetes. Medical 

Informatics, 1996. 21(4): p. 273-82. 

Exclude Code: E3 

119. Eriksson, J., A. Ek, and G. Johansson, 

Design and evaluation of a software 

prototype for participatory planning of 

environmental adaptations. IEEE 

Transactions on Rehabilitation 

Engineering, 2000. 8(1): p. 94-106. 

Exclude Code: E3 

120. Finkel, S., et al., E-care: a 

telecommunications technology 

intervention for family caregivers of 

dementia patients. American Journal of 

Geriatric Psychiatry, 2007. 15(5): p. 

443-448. Exclude Code: E1 

121. Finkelstein, J., M.R. Cabrera, and G. 

Hripcsak, Internet-based home asthma 

telemonitoring: can patients handle the 

technology? Chest, 2000. 117(1): p. 

148-55. Exclude Code: E 

122. Fortney, J.C., et al., Design and 

implementation of the telemedicineenhanced antidepressant management 

study. General hospital psychiatry, 

2006. 28(1): p. 18-26. Exclude Code: E3 

123. Franck, L.S. and M. Jones, Computertaught coping techniques for 

venepuncture: preliminary findings from 

usability testing with children, parents 

and staff. Journal of Child Health Care, 

2003. 7(1): p. 41-54. Exclude Code: E3 

124. Friedman, R.H., et al., A 

telecommunications system to manage 

patients with chronic disease. Medinfo, 

1998. 9 Pt 2: p. 1330-4. Exclude Code: 

E2 

125. Frosch, D.L., R.M. Kaplan, and V.J. 

Felitti, A randomized controlled trial 

comparing internet and video to facilitate 

patient education for men considering 

the prostate specific antigen test.

Journal of General Internal Medicine, 

2003. 18(10): p. 781-7. Exclude Code: 

E3 

126. Gerber, B.S., et al., Implementation of 

computer multimedia for diabetes 

prevention in African-American women.

AMIA, 2005. Annual Symposium 

Proceedings/AMIA Symposium.: p. 

962. Exclude Code: E3 

127. Giallauria, F., et al., Efficacy of 

telecardiology in improving the results of 

cardiac rehabilitation after acute 

myocardial infarction. Monaldi Archives 

for Chest Disease, 2006. 66(1): p. 8-12. 

Exclude Code: E3 

128. Glasgow, R.E., et al., Long-term effects 

and costs of brief behavioural dietary 

intervention for patients with diabetes 

delivered from the medical office.

Patient Education & Counseling, 1997. 

32(3): p. 175-84. Exclude Code: E3 

129. Glazebrook, C., et al., Impact of a 

multimedia intervention "Skinsafe" on 

patients' knowledge and protective 

behaviors. Preventive Medicine, 2006. 

42(6): p. 449-54. Exclude Code: E1 

130. Goldfield, G.S. and A. Boachie, Delivery 

of family therapy in the treatment of 

anorexia nervosa using telehealth.

Telemedicine Journal & E-Health, 2003. 

9(1): p. 111-4. Exclude Code: E3 

131. Gomez, E.J., et al., Telemedicine as a 

tool for intensive management of 

diabetes: the DIABTel experience.

Computer Methods & Programs in 

Biomedicine, 2002. 69(2): p. 163-77. 

Exclude Code: E3 

132. Graham, A.L., et al., Characteristics of 

smokers reached and recruited to an 
Appendix E. Excluded Studies List (continued) 

A-19

internet smoking cessation trial: a case 

of denominators. Nicotine & Tobacco 

Research, 2006. 8 Suppl 1: p. S43-8. 

Exclude Code: E3 

133. Grant, R.W., et al., Design and 

implementation of a web-based patient 

portal linked to an ambulatory care 

electronic health record: patient gateway 

for diabetes collaborative care. Diabetes 

Technology & Therapeutics, 2006. 8(5): 

p. 576-86. Exclude Code: E2 

134. Gray, J., et al., Baby CareLink: 

development and implementation of a 

WWW-based system for neonatal home 

telemedicine. Proceedings / AMIA, 

1998. Annual Symposium.: p. 351-5. 

Exclude Code: E1 

135. Gray, J.E., et al., Baby CareLink: using 

the internet and telemedicine to improve 

care for high-risk infants. Pediatrics, 

2000. 106(6): p. 1318-24. Exclude 

Code: E1 

136. Gustafson, D.H., et al., CHESS: 10 

years of research and development in 

consumer health informatics for broad 

populations, including the underserved.

International Journal of Medical 

Informatics, 2002. 65(3): p. 169-77. 

Exclude Code: E2 

137. Hahn, E.A., et al., The talking 

touchscreen: a new approach to 

outcomes assessment in low literacy.

Psycho-Oncology, 2004. 13(2): p. 86-

95. Exclude Code: E3 

138. Han, H.R. and A.E. Belcher, Computermediated support group use among 

parents of children with cancer--an 

exploratory study. Computers in 

Nursing, 2001. 19(1): p. 27-33. Exclude 

Code: E1 

139. Hebert, M.A. and B. Korabek, 

Stakeholder readiness for 

telehomecare: implications for 

implementation. Telemedicine Journal & 

E-Health, 2004. 10(1): p. 85-92. Exclude 

Code: E3 

140. Hebert, M.A., M.J. Paquin, and S. 

Iversen, Predicting success: stakeholder 

readiness for home telecare diabetic 

support. Journal of Telemedicine & 

Telecare, 2002. 8 Suppl 3: p. S3:33-6. 

Exclude Code: E3 

141. Heiberg, T., et al., Daily health status 

registration (patient diary) in patients 

with rheumatoid arthritis: a comparison 

between personal digital assistant and 

paper-pencil format. Arthritis & 

Rheumatism, 2007. 57(3): p. 454-60. 

Exclude Code: E3 

142. Heidgerken, A.D., et al., Telehealth 

intervention for adolescents with type 1 

diabetes. Journal of Pediatrics, 2006. 

148(5): p. 707-8. Exclude Code: E3 

143. Heisler, M., et al., "I am not alone": the 

feasibility and acceptability of interactive 

voice response-facilitated telephone 

peer support among older adults with 

heart failure. Congestive Heart Failure, 

2007. 13(3): p. 149-57. Exclude Code: 

E3 

144. Hill, W., C. Weinert, and S. Cudney, 

Influence of a computer intervention on 

the psychological status of chronically ill 

rural women: preliminary results.

Nursing Research, 2006. 55(1): p. 34-

42. Exclude Code: E3 

145. Hill, W.G. and C. Weinert, An evaluation 

of an online intervention to provide 

social support and health education.

CIN: Computers, Informatics, Nursing, 

2004. 22(5): p. 282-8. Exclude Code: E1 

146. Hoffmann, T., et al., Randomised trial of 

a computer-generated tailored written 

education package for patients following 

stroke. Age & Ageing, 2007. 36(3): p. 

280-6. Exclude Code: E3 

147. Hoffmann, T., T. Russell, and K. 

McKenna, Producing computergenerated tailored written information for 

stroke patients and their carers: system 

development and preliminary evaluation.

International Journal of Medical 

Informatics, 2004. 73(11-12): p. 751-8. 

Exclude Code: E3 
Appendix E. Excluded Studies List (continued) 

A-20

148. Hofmann, M., et al., Interactive 

computer-based cognitive training in 

patients with Alzheimer's disease.

Journal of Psychiatric Research, 1996. 

30(6): p. 493-501. Exclude Code: E3 

149. Holbrook, A., et al., Influence of decision 

aids on patient preferences for 

anticoagulant therapy: a randomized 

trial. CMAJ Canadian Medical 

Association Journal, 2007. 176(11): p. 

1583-7. Exclude Code: E3 

150. Hong, O., et al., Efficacy of a computerbased hearing test and tailored hearing 

protection intervention. International 

Journal of Behavioral Medicine, 2006. 

13(4): p. 304-14. Exclude Code: E3 

151. Hudson, L.R., et al., Remote 

physiological monitoring: clinical, 

financial, and behavioral outcomes in a 

heart failure population. Disease 

Management, 2005. 8(6): p. 372-81. 

Exclude Code: E3 

152. Hunkeler, E.M., et al., Efficacy of nurse 

telehealth care and peer support in 

augmenting treatment of depression in 

primary care. Archives of Family 

Medicine, 2000. 9(8): p. 700-8. Exclude 

Code: E3 

153. Huss, K., et al., Computer game for 

inner-city children does not improve 

asthma outcomes. Journal of Pediatric 

Health Care, 2003. 17(2): p. 72-8. 

Exclude Code: E3 

154. Illyes, M., T. Mengden, and A. Tisler, 

The virtual hypertension clinic. Blood 

Pressure Monitoring, 2002. 7(1): p. 67-

8. Exclude Code: E2 

155. Im, E.-O. and W. Chee, Evaluation of 

the decision support computer program 

for cancer pain management. Oncology 

Nursing Forum Online, 2006. 33(5): p. 

977-82. Exclude Code: E3 

156. James, S.D. and R.E. Thomas, 

Problems in community healthcare 

information systems: a case study.

Health Informatics Journal, 1999. 5(3): 

p. 146-53. Exclude Code: E2 

157. Jantz, C., J. Anderson, and S.M. Gould, 

Using computer-based assessments to 

evaluate interactive multimedia nutrition 

education among low-income 

predominantly Hispanic participants.

Journal of Nutrition Education and 

Behavior, 2002. 34(5): p. 252-60. 

Exclude Code: E3 

158. Jenkins, R.L. and M. McSweeney, 

Assessing elderly patients with 

congestive heart failure via in-home 

interactive telecommunication. Journal 

of Gerontological Nursing, 2001. 27(1): 

p. 21-7. Exclude Code: E3 

159. Jerant, A.F., R. Azari, and T.S. Nesbitt, 

Reducing the cost of frequent hospital 

admissions for congestive heart failure: 

a randomized trial of a home telecare 

intervention. Medical Care, 2001. 

39(11): p. 1234-1245. Exclude Code: E3 

160. Jibaja-Weiss, M.L., et al., Entertainment 

education for informed breast cancer 

treatment decisions in low-literate 

women: development and initial 

evaluation of a patient decision aid.

Journal of Cancer Education, 2006. 

21(3): p. 133-9. Exclude Code: E3 

161. Johnson, M.J., et al., The development 

and evaluation of alternative 

communication strategies to facilitate 

interactions with Somali refugees in 

primary care: a preliminary study.

Informatics in Primary Care, 2006. 

14(3): p. 183-9. Exclude Code: E3 

162. Johnston, B., et al., Outcomes of the 

Kaiser Permanente Tele-Home Health 

Research Project. Archives of Family 

Medicine, 2000. 9(1): p. 40-5. Exclude 

Code: E3 

163. Kaminer, Y., et al., An interactive voice 

response (IVR) system for adolescents 

with alcohol use disorders: a pilot study.

American Journal on Addictions, 2006. 

15 Suppl 1: p. 122-5. Exclude Code: E3 

164. Kaplan, B., R. Farzanfar, and R.H. 

Friedman, Personal relationships with 

an intelligent interactive telephone 
Appendix E. Excluded Studies List (continued) 

A-21

health behavior advisor system: a 

multimethod study using surveys and 

ethnographic interviews. International 

Journal of Medical Informatics, 2003. 

71(1): p. 33-41. Exclude Code: E1 

165. Karagiannis, G.E., et al., Health and 

lifestyle management via interactive TV 

in patients with severe chronic 

cardiovascular diseases. Journal of 

Telemedicine & Telecare, 2006. 12 

Suppl 1: p. 17-9. Exclude Code: E3 

166. Karp, W.B., et al., Use of telemedicine 

for children with special health care 

needs. Pediatrics, 2000. 105(4 part 1): 

p. 843-7. Exclude Code: E3 

167. Katz, N., et al., Interactive virtual 

environment training for safe street 

crossing of right hemisphere stroke 

patients with unilateral spatial neglect.

Disability & Rehabilitation, 2005. 27(20): 

p. 1235-43. Exclude Code: E3 

168. Kaufman, D.R., et al., A cognitive 

framework for understanding barriers to 

the productive use of a diabetes home 

telemedicine system. AMIA, 2003. 

Annual Symposium 

Proceedings/AMIA Symposium.: p. 

356-60. Exclude Code: E2 

169. Kearney, N., et al., Utilising handheld 

computers to monitor and support 

patients receiving chemotherapy: results 

of a UK-based feasibility study.

Supportive Care in Cancer, 2006. 14(7): 

p. 742-52. Exclude Code: E3 

170. Keaton, L., et al., An E-rehabilitation 

team helps caregivers deal with stroke.

Internet Journal of Allied Health 

Sciences & Practice, 2004. 2(4): p. 17p. 

Exclude Code: E1 

171. Keener, R.E., Tracking HIV treatment: 

technology and drugs make AIDS a 

manageable disease. Health 

Management Technology, 2000. 21(10): 

p. 48, 50-1. Exclude Code: E3 

172. Kerr, C., et al., Internet interventions for 

long-term conditions: patient and 

caregiver quality criteria. Journal of 

Medical Internet Research, 2006. 8(3): 

p. e13. Exclude Code: E 

173. Keulers, B.J., et al., Can face-to-face 

patient education be replaced by 

computer-based patient education? A 

randomised trial. Patient Education and 

Counseling, 2007. 67(1-2): p. 176-82. 

Exclude Code: E3 

174. Kim, H.-S., Y.-S. Yoo, and H.-S. Shim, 

Effects of an Internet-based intervention 

on plasma glucose levels in patients 

with type 2 diabetes. Journal of Nursing 

Care Quality, 2005. 20(4): p. 335-40. 

Exclude Code: E3 

175. Kinsella, A., Managing technology. 

Telehealthcare under PPS: tools to 

make it work. Home Healthcare Nurse, 

2001. 19(9): p. 579-81. Exclude Code: 

E2 

176. Kinzie, M.B., J.B. Schorling, and M. 

Siegel, Prenatal alcohol education for 

low-income women with interactive 

multimedia. Patient Education and 

Counseling, 1993. 21(1/2): p. 51-60. 

Exclude Code: E3 

177. Kleinpell, R.M. and B. Avitall, Integrating 

telehealth as a strategy for patient 

management after discharge for cardiac 

surgery: results of a pilot study. Journal 

of Cardiovascular Nursing, 2007. 22(1): 

p. 38-42. Exclude Code: E3 

178. Kreisel, K., Evaluation of a computerbased nutrition education tool. Public 

Health Nutrition, 2004. 7(2): p. 271-7. 

Exclude Code: E1 

179. Krishna, S., et al., Internet-enabled 

interactive multimedia asthma education 

program: a randomized trial. Pediatrics, 

2003. 111(3): p. 503-10. Exclude Code: 

E3 

180. Krishna, S., et al., Internet-enabled 

interactive multimedia asthma education 

program: a randomized trial. Pediatrics, 

2003. 111(3): p. 503-10. Exclude Code: 

E3 

181. Kukafka, R., et al., Web-based tailoring 
Appendix E. Excluded Studies List (continued) 

A-22

and its effect on self-efficacy: results 

from the MI-HEART randomized 

controlled trial. Proceedings / AMIA, 

2002. Annual Symposium.: p. 410-4. 

Exclude Code: E1 

182. Kully, D., Telehealth in speech 

pathology: applications to the treatment 

of stuttering. Journal of Telemedicine & 

Telecare, 2000. 6 Suppl 2: p. S39-41. 

Exclude Code: E3 

183. Laron, Z., et al., Four years experience 

with the microcomputer system "Diacon" 

in the treatment and education of 

diabetes. Hormone & Metabolic 

Research - Supplement, 1990. 24: p. 

129-40. Exclude Code: E3 

184. Latessa, R.A., et al., Changing practices 

in the use of pneumococcal vaccine.

Family Medicine, 2000. 32(3): p. 196-

200. Exclude Code: E3 

185. Lawton, D.F., Older Adults Eager To 

Explore Cyberspace. 2001. Exclude 

Code: E3 

186. Leahy, A., et al., Computerised 

biofeedback games: a new method for 

teaching stress management and its use 

in irritable bowel syndrome. Journal of 

the Royal College of Physicians of 

London, 1998. 32(6): p. 552-6. Exclude 

Code: E3 

187. Lee, J.H., et al., A telemedicine system 

as a care modality for dementia patients 

in Korea. Alzheimer Disease & 

Associated Disorders, 2000. 14(2): p. 

94-101. Exclude Code: E3 

188. Lehmann, E.D., Spontaneous 

comments from users of the AIDA 

interactive educational diabetes 

simulator. Diabetes Educator. 26(4): p. 

633-8. Exclude Code: E3 

189. Lehmann, E.D., Preliminary experience 

with the Internet release of AIDA--an 

interactive educational diabetes 

simulator. Computer Methods & 

Programs in Biomedicine, 1998. 56(2): 

p. 109-32. Exclude Code: E3 

190. Lehmann, E.D., Experience with the 

Internet release of AIDA v4.0--

http://www.diabetic.org.uk.aida.htm--an 

interactive educational diabetes 

simulator. Diabetes Technology & 

Therapeutics, 1999. 1(1): p. 41-54. 

Exclude Code: E3 

191. Lehmann, E.D., The freeware AIDA 

interactive educational diabetes 

simulator--http://www.2aida.org--(1). A 

download survey for AIDA v4.0. Medical 

Science Monitor, 2001. 7(3): p. 504-15. 

Exclude Code: E3 

192. Lehmann, E.D., Why are people 

downloading the freeware AIDA 

diabetes computing software program: a 

pilot study. Diabetes Technology & 

Therapeutics, 2002. 4(6): p. 793-808. 

Exclude Code: E3 

193. Lehmann, E.D., Who is downloading the 

freeware AIDA v43 interactive 

educational diabetes simulator? An 

audit of 2437 downloads. Diabetes 

Technology & Therapeutics, 2002. 4(4): 

p. 467-77. Exclude Code: E3 

194. Lehmann, E.D., Who is downloading the 

free AIDA v4.3a interactive educational 

diabetes computer software? A 1-year 

survey of 3864 downloads. Diabetes 

Technology & Therapeutics, 2003. 5(5): 

p. 879-90. Exclude Code: E3 

195. Lehmann, E.D., Why people download 

the freeware AIDA v4.3a diabetes 

software program: a proof-of-concept 

semi-automated analysis. Diabetes 

Technology & Therapeutics, 2003. 5(3): 

p. 477-90. Exclude Code: E3 

196. Lehmann, E.D., Research use of the 

AIDA www.2aida.org diabetes software 

simulation program: a review-part 1. 

decision support testing and neural 

network training. Diabetes Technology & 

Therapeutics, 2003. 5(3): p. 425-38. 

Exclude Code: E2 

197. Lehmann, E.D., Usage of the 

www.2aida.org AIDA diabetes software 

Website: a pilot study. Diabetes 

Technology & Therapeutics, 2003. 5(1): 
Appendix E. Excluded Studies List (continued) 

A-23

p. 75-89. Exclude Code: E3 

198. Lehmann, E.D., British Diabetic 

Association review of the AIDA v4 

diabetes software simulator program.

Diabetes Technology & Therapeutics, 

2004. 6(1): p. 87-96. Exclude Code: E3 

199. Lehmann, E.D. and T. Deutsch, An 

interactive, educational model for insulin 

dosage and dietary adjustment in type I 

diabetes mellitus. Proceedings - the 

Annual Symposium on Computer 

Applications in Medical Care, 1992: p. 

205-9. Exclude Code: E3 

200. Levetan, C.S., et al., Impact of 

computer-generated personalized goals 

on HbA(1c). Diabetes Care, 2002. 25(1): 

p. 2-8. Exclude Code: E3 

201. Lewis, D., J. Cooper, and S. 

Gunawardena, Caring Connection: 

Internet resources for family caregivers 

of children with cancer. AMIA, 2006. 

Annual Symposium 

Proceedings/AMIA Symposium.: p. 

1007. Exclude Code: E1 

202. Lillis, K., Electronically enhanced 

therapy: behavioral health plan rolls out 

online, interactive "coaching" series for 

members. Health Management 

Technology, 2004. 25(8): p. 42. Exclude 

Code: E2 

203. Lin, Z., J. Effken, and Y. Li, Designing 

and evaluating a Web-based, tailored 

breast cancer education program. CIN: 

Computers, Informatics, Nursing, 2006. 

24(5): p. 300. Exclude Code: E 

204. Linke, S., 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(2): p. e10. 

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205. Linne, A.B. and H. Liedholm, Effects of 

an interactive CD-program on 6 months 

readmission rate in patients with heart 

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[NCT00311194]. BMC Cardiovascular 

Disorders, 2006. 6: p. 30. Exclude 

Code: E3 

206. Lo, R., et al., The development and 

evaluation of a computer-aided diabetes 

education program. Australian Journal 

of Advanced Nursing, 1996. 13(4): p. 

19-27. Exclude Code: E3 

207. Long, J.D., et al., Pilot using world wide 

Web to prevent diabetes in adolescents.

Clinical Nursing Research, 2006. 15(1): 

p. 67-79. Exclude Code: E 

208. Lopez, A.M., et al., Increasing access to 

care via tele-health: the Arizona 

experience. Journal of Ambulatory Care 

Management, 2005. 28(1): p. 16-23. 

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209. Lovell, K., et al., Telephone 

administered cognitive behaviour 

therapy for treatment of obsessive 

compulsive disorder: randomised 

controlled non-inferiority trial. BMJ, 

2006. 333(7574): p. 883-6. Exclude 

Code: E3 

210. Lowe, P., H. Hearnshaw, and F. 

Griffiths, Attitudes of young people with 

diabetes to an Internet-based virtual 

clinic. Journal of Telemedicine & 

Telecare, 2005. 11 Suppl 1: p. 59-60. 

Exclude Code: E3 

211. Maaske, L., A study of interactivity in 

educational patient hypermedia. Journal 

of Biocommunication, 1999. 26(3): p. 2-

11. Exclude Code: E3 

212. Mackenzie, S.L.C., et al., Patient and 

staff perspectives on the use of a 

computer counseling tool for HIV and 

sexually transmitted infection risk 

reduction. Journal of Adolescent Health, 

2007. 40(6): p. 572.e9-16. Exclude 

Code: E3 

213. Mahoney, D.F., et al., Effects of a 

multimedia project on users' knowledge 

about normal forgetting and serious 

memory loss. Journal of the American 

Medical Informatics Association, 2002. 

9(4): p. 383-94. Exclude Code: E3 

214. Malasanos, T.H., et al., School nurse, 
Appendix E. Excluded Studies List (continued) 

A-24

family and provider connectivity in the 

FITE diabetes project. Journal of 

Telemedicine & Telecare, 2005. 11 

Suppl 1: p. 76-8. Exclude Code: E3 

215. Marco, C.A. and G.L. Larkin, Public 

education regarding resuscitation: 

effects of a multimedia intervention.

Annals of Emergency Medicine, 2003. 

42(2): p. 256-60. Exclude Code: E3 

216. Markman, M., et al., Profile of ovarian 

cancer patients seeking information 

from a web-based decision support 

program. Journal of Women's Health, 

2006. 15(3): p. 312-8. Exclude Code: E3 

217. Matheson, D. and C. Achterberg, 

Ecologic study of children's use of a 

computer nutrition education program.

Journal of Nutrition Education, 2001. 

33(1): p. 2-9. Exclude Code: E1 

218. Matthew, A.G., et al., Serial personal 

digital assistant data capture of healthrelated quality of life: a randomized 

controlled trial in a prostate cancer 

clinic. Health & Quality of Life 

Outcomes, 2007. 5: p. 38. Exclude 

Code: E3 

219. McGrew, K.B. and C.A. Quinn, 

Examining the effectiveness of 

telephone assessment and care 

planning for homecare services.

Generations, 1997. 21(1): p. 66-67. 

Exclude Code: E3 

220. Meneghini, L.F., et al., An electronic 

case manager for diabetes control.

Diabetes Care, 1998. 21(4): p. 591-6. 

Exclude Code: E3 

221. Mengden, T., et al., Telemonitoring of 

blood pressure self measurement in the 

OLMETEL study... Proceedings from the 

"European Society of Hypertension 

Working Group on Blood Pressure 

Monitoring Scientific Workshop: What's 

New in Blood Pressure Measurement?"; 

held June 2004 in Paris, France. Blood 

Pressure Monitoring, 2004. 9(6): p. 321-

5. Exclude Code: E3 

222. Mermelstein, R. and L. Turner, Webbased support as an adjunct to groupbased smoking cessation for 

adolescents. Nicotine & Tobacco 

Research, 2006. 8 Suppl 1: p. S69-76. 

Exclude Code: E3 

223. Meyerhoff, C., F. Bischof, and E.F. 

Pfeiffer, Long-term experiences with a 

computerized diabetes management 

and glucose monitoring system in 

insulin-dependent diabetic patients.

Diabetes Research & Clinical Practice, 

1994. 24(1): p. 1-7. Exclude Code: E3 

224. Miloud, G.L., Response to: Heartcare: 

an Internet-based information and 

support system for patient home 

recovery after coronary artery bypass 

graft (CABG) surgery by P. Flatley 

Brennan, S.M. Moore, G. Bjornsdottir, J. 

Jones, C. Visovsky and M. Rogers 

(2001) Journal Of Advanced Nursing 35, 

699-708. Journal of Advanced Nursing, 

2002. 38(4): p. 425; author reply 425-6. 

Exclude Code: E 

225. Molenaar, S., et al., Exploring the black 

box of a decision aid: what information 

do patients select from an interactive 

Cd-Rom on treatment options in breast 

cancer? Patient Education & 

Counseling, 2007. 65(1): p. 122-30. 

Exclude Code: E3 

226. Molenaar, S., et al., Decision support for 

patients with early-stage breast cancer: 

effects of an interactive breast cancer 

CDROM on treatment decision, 

satisfaction, and quality of life. Journal 

of Clinical Oncology, 2001. 19(6): p. 

1676-87. Exclude Code: E3 

227. Morlion, B., et al., Internet-based home 

monitoring of pulmonary function after 

lung transplantation. American Journal 

of Respiratory & Critical Care Medicine, 

2002. 165(5): p. 694-7. Exclude Code: 

E3 

228. Mullan, B., et al., Home monitoring for 

lung transplant candidates. Progress in 

Transplantation, 2003. 13(3): p. 176-82. 

Exclude Code: E3 

229. Murtagh, M.J., et al., Qualitative 

methods in a randomised controlled 
Appendix E. Excluded Studies List (continued) 

A-25

trial: the role of an integrated qualitative 

process evaluation in providing 

evidence to discontinue the intervention 

in one arm of a trial of a decision 

support tool. Quality & Safety in Health 

Care, 2007. 16(3): p. 224-9. Exclude 

Code: E3 

230. Nakajima, K., et al., Low-cost, emailbased system for self blood pressure 

monitoring at home. Journal of 

Telemedicine & Telecare, 2006. 12(4): 

p. 203-7. Exclude Code: E3 

231. Nakamura, K., T. Takano, and C. Akao, 

Effectiveness of videophones in home 

healthcare for the elderly. Medical Care, 

1999. 37(2): p. 117-125. Exclude Code: 

E3 

232. Neafsey, P.J., et al., An interactive 

technology approach to educate older 

adults about drug interactions arising 

from over-the-counter self-medication 

practices. Public Health Nursing, 2002. 

19(4): p. 255-62. Exclude Code: E3 

233. Nebel, I.-T., et al., Evaluation of a 

computer based interactive diabetes 

education program designed to train the 

estimation of the energy or carbohydrate 

contents of foods. Patient Education & 

Counseling, 2002. 46(1): p. 55-9. 

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234. Nebel, I.-T., et al., Comparative analysis 

of conventional and an adaptive 

computer-based hypoglycaemia 

education programs. Patient Education 

& Counseling, 2004. 53(3): p. 315-8. 

Exclude Code: E3 

235. Neville, R., et al., Mobile phone text 

messaging can help young people 

manage asthma. BMJ, 2002. 325(7364): 

p. 600. Exclude Code: E3 

236. Neville, R., et al., E-mail consultations in 

general practice. British Journal of 

General Practice, 2004. 54(504): p. 546. 

Exclude Code: E 

237. Neville, R.G., et al., Email consultations 

in general practice. Informatics in 

Primary Care, 2004. 12(4): p. 207-14. 

Exclude Code: E1 

238. Nijeweme-d'Hollosy, W.O., et al., Teletreatment of patients with amyotrophic 

lateral sclerosis (ALS). Journal of 

Telemedicine & Telecare, 2006. 12 

Suppl 1: p. 31-4. Exclude Code: E3 

239. Oberleitner, R. and S. Laxminarayan, 

Information technology and behavioral 

medicine: impact on autism treatment & 

research. Studies in Health Technology 

& Informatics, 2004. 103: p. 215-22. 

Exclude Code: E2 

240. Ogawa, H., et al., A web-based carerequiring client and Home Helper mutual 

support system. Biomedical Sciences 

Instrumentation, 2005. 41: p. 159-62. 

Exclude Code: E 

241. O'Grady, L.A., Consumer e-health 

education in HIV/AIDS: a pilot study of a 

web-based video workshop. BMC 

Medical Informatics & Decision Making, 

2006. 6: p. 10. Exclude Code: E1 

242. Osman, L.M., et al., Reducing hospital 

admission through computer supported 

education for asthma patients. BMJ, 

1994. 308(6928): p. 568-71. Exclude 

Code: E3 

243. Owens, B.H. and K.C. Robbins, 

CHESS: comprehensive health 

enhancement support system for 

women with breast cancer. Plastic 

Surgical Nursing. 16(3): p. 172-5. 

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244. Page, S.J. and P. Levine, Modified 

constraint-induced therapy extension: 

using remote technologies to improve 

function. Archives of Physical Medicine 

& Rehabilitation, 2007. 88(7): p. 922-7. 

Exclude Code: E3 

245. Palermo, T.M., D. Valenzuela, and P.P. 

Stork, A randomized trial of electronic 

versus paper pain diaries in children: 

impact on compliance, accuracy, and 

acceptability. Pain, 2004. 107(3): p. 213-

9. Exclude Code: E3 

246. Papadaki, A. and J.A. Scott, The 

Mediterranean Eating in Scotland 
Appendix E. Excluded Studies List (continued) 

A-26

Experience project: evaluation of an 

Internet-based intervention promoting 

the Mediterranean diet. British Journal 

of Nutrition, 2005. 94(2): p. 290-8. 

Exclude Code: E 

247. Papakonstantinou, E., et al., 

Assessment of perceptions of nutrition 

knowledge and disease using a group 

interactive system: the PERCEPTION 

ANALYZER. Journal of the American 

Dietetic Association, 2002. 102(11): p. 

1663-8. Exclude Code: E1 

248. Peters, A.L. and M.B. Davidson, 

Application of a diabetes managed care 

program. The feasibility of using nurses 

and a computer system to provide 

effective care. Diabetes Care, 1998. 

21(7): p. 1037-43. Exclude Code: E3 

249. Petersen, M., What are blood counts? A 

computer-assisted program for pediatric 

patients. Pediatric Nursing, 1996. 22(1): 

p. 21-5; quiz 26-7. Exclude Code: E3 

250. Phelps-Fredette, S., Cardiology patient 

page. Learn, take action, and live: a 

guide to using online tools to prevent 

and manage heart disease and stroke.

Circulation, 2005. 112(20): p. e318-9. 

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251. Pierce, L.L., V. Steiner, and A.L. 

Govoni, In-home online support for 

caregivers of survivors of stroke: a 

feasibility study. CIN: Computers, 

Informatics, Nursing, 2002. 20(4): p. 

157-64. Exclude Code: E1 

252. Pierce, L.L., et al., Internet-based 

support for rural caregivers of persons 

with stroke shows promise.

Rehabilitation Nursing. 29(3): p. 95-9. 

Exclude Code: E1 

253. Plougmann, S., O.K. Hejlesen, and D.A. 

Cavan, DiasNet--a diabetes advisory 

system for communication and 

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Journal of Medical Informatics, 2001. 

64(2-3): p. 319-30. Exclude Code: E2 

254. Port, K., K. Palm, and M. Viigimaa, Daily 

usage and efficiency of remote home 

monitoring in hypertensive patients over 

a one-year period. Journal of 

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Suppl 1: p. 34-6. Exclude Code: E3 

255. Porter, S.C., et al., The asthma kiosk: a 

patient-centered technology for 

collaborative decision support in the 

emergency department. Journal of the 

American Medical Informatics 

Association, 2004. 11(6): p. 458-67. 

Exclude Code: E 

256. Porter, S.C., et al., Impact of patientcentered decision support on quality of 

asthma care in the emergency 

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e33-42. Exclude Code: E3 

257. Porter, S.C., et al., Parents as direct 

contributors to the medical record: 

validation of their electronic input.

Annals of Emergency Medicine, 2000. 

35(4): p. 346-52. Exclude Code: E1 

258. Pyper, C., et al., Access to electronic 

health records in primary care-a survey 

of patients' views. Medical Science 

Monitor, 2004. 10(11): p. SR17-22. 

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259. Redsell, S.A., et al., Multimedia versus 

written information for nocturnal 

enuresis education: a cluster 

randomized controlled trial. Child: Care, 

Health & Development, 2003. 29(2): p. 

121-9. Exclude Code: E3 

260. Reis, J., B. McGinty, and S. Jones, An 

e-learning caregiving program for 

prostate cancer patients and family 

members. Journal of Medical Systems, 

2003. 27(1): p. 1-12. Exclude Code: E3 

261. Rice, V.H., et al., Exemplary program 

development: hypermedia interactive 

smoking cessation intervention program 

for pregnant women... including 

commentary by Budin WC. Journal of 

Perinatal Education, 1997. 6(3): p. 47-

61. Exclude Code: E3 

262. Riley, E.D., et al., Use of audio 

computer-assisted self-interviews to 

assess tuberculosis-related risk 
Appendix E. Excluded Studies List (continued) 

A-27

behaviors. American Journal of 

Respiratory & Critical Care Medicine, 

2001. 164(1): p. 82-5. Exclude Code: E3 

263. Riva, A., R. Bellazzi, and M. Stefanelli, 

A Web-based system for the intelligent 

management of diabetic patients. MD 

Computing, 1997. 14(5): p. 360-4. 

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264. Robbins, K.C., CHESS: the 

Comprehensive Health Enhancement 

Support System. A computer based 

program for patients and families.

Connecticut Nursing News, 1999. 72(1): 

p. 14. Exclude Code: E2 

265. Rogers, A. and N. Mead, More than 

technology and access: primary care 

patients' views on the use and non-use 

of health information in the Internet age.

Health and Social Care in the 

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266. Rosen, J., et al., Educating the families 

of nursing home residents: a pilot study 

using a computer-based system.

Journal of the American Medical 

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128-34. Exclude Code: E1 

267. Roumie, C.L., et al., A three-part 

intervention to change the use of 

hormone replacement therapy in 

response to new evidence. Annals of 

Internal Medicine, 2004. 141(2): p. 118-

25. Exclude Code: E1 

268. Rutscher, A., E. Salzsieder, and U. 

Fischer, KADIS: model-aided education 

in type I diabetes. Karlsburg Diabetes 

Management System. Computer 

Methods & Programs in Biomedicine, 

1994. 41(3-4): p. 205-15. Exclude Code: 

E 

269. Sabati, N., et al., Facilitators and 

barriers to adherence with home 

monitoring using electronic spirometry.

AACN Clinical Issues, 2001. 12(2): p. 

178-85. Exclude Code: E3 

270. Sanchez-Franco, M.J., Exploring the 

influence of gender on the web usage 

via partial least squares. Behaviour & 

Information Technology, 2006. 25(1): p. 

19-36. Exclude Code: E3 

271. Scherr, D., et al., Mobile phone-based 

surveillance of cardiac patients at home.

Journal of Telemedicine & Telecare, 

2006. 12(5): p. 255-61. Exclude Code: 

E3 

272. Schinke, S.P., et al., Feasibility of 

interactive videodisc technology to teach 

minority youth about preventing HIV 

infection. Public Health Reports, 1992. 

107(3): p. 323-30. Exclude Code: E3 

273. Schrot, R.J., et al., A computerized 

model for home glucose monitoring 

proficiency testing: efficacy of an 

innovative testing program. Diabetes 

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274. Sciamanna, C.N., et al., Effects of a 

Website designed to improve the 

management of migraines. Headache, 

2006. 46(1): p. 92-100. Exclude Code: 

E3 

275. Sciamanna, C.N., et al., Visit 

satisfaction and tailored health behavior 

communications in primary care.

American Journal of Preventive 

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276. Sequist, T.D., et al., A randomized trial 

of electronic clinical reminders to 

improve quality of care for diabetes and 

coronary artery disease. Journal of the 

American Medical Informatics 

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277. Sevick, M.A., et al., A preliminary study 

of PDA-based dietary self-monitoring in 

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278. Shah, N.B., et al., Prevention of 

hospitalizations for heart failure with an 

interactive home monitoring program.

American Heart Journal, 1998. 135(3): 

p. 373-8. Exclude Code: E3 
Appendix E. Excluded Studies List (continued) 

A-28

279. Shames, R.S., et al., Effectiveness of a 

multicomponent self-management 

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& Immunology, 2004. 92(6): p. 611-8. 

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280. Shaw, B., et al., How Underserved 

Breast Cancer Patients Use and Benefit 

From eHealth Programs: Implications for 

Closing the Digital Divide. American 

Behavioral Scientist, 2006. 49(6): p. 

823-834. Exclude Code: E2 

281. Sims, T.L., et al., Multimedia quality of 

life assessment: advances with FLAIR.

AMIA, 2005. Annual Symposium 

Proceedings/AMIA Symposium.: p. 

694-8. Exclude Code: E3 

282. Sixsmith, A.J., An evaluation of an 

intelligent home monitoring system.

Journal of Telemedicine & Telecare, 

2000. 6(2): p. 63-72. Exclude Code: E3 

283. Slootmaker, S.M., et al., Promoting 

physical activity using an activity monitor 

and a tailored web-based advice: design 

of a randomized controlled trial 

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2005. 5: p. 134. Exclude Code: E2 

284. Soghikian, K., et al., Home blood 

pressure monitoring. Effect on use of 

medical services and medical care 

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855-65. Exclude Code: E3 

285. Starren, J., et al., Columbia University's 

Informatics for Diabetes Education and 

Telemedicine (IDEATel) project: 

technical implementation. Journal of the 

American Medical Informatics 

Association, 2002. 9(1): p. 25-36. 

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286. Steel, S., et al., A feasibility study of 

remote monitoring of asthmatic patients.

Journal of Telemedicine & Telecare, 

2002. 8(5): p. 290-6. Exclude Code: E3 

287. Steier, J., et al., Optical feedback 

training of inhalation with Autohaler and 

Turbuhaler in COPD patients. Lung, 

2003. 181(4): p. 183-92. Exclude Code: 

E3 

288. Stillman, J.K., et al., Telepaediatrics and 

diabetic retinopathy screening of young 

people with diabetes in Queensland.

Journal of Telemedicine & Telecare, 

2004. 10 Suppl 1: p. 92-4. Exclude 

Code: E3 

289. Stricklin, M.L.V., S. Jones, and S.A. 

Niles, HOME TALK/healthy talk: 

improving patients' health status with 

telephone technology. Home Healthcare 

Nurse, 2000. 18(1): p. 53-62. Exclude 

Code: E3 

290. Stromberg, A., et al., Interactive 

education on CD-ROM-a new tool in the 

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Patient Education & Counseling, 2002. 

46(1): p. 75-81. Exclude Code: E3 

291. Sun, P., et al., Internet accessibility and 

usage among urban adolescents in 

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web-based health research.

Cyberpsychology & Behavior, 2005. 

8(5): p. 441-53. Exclude Code: E 

292. Suzuki, L.K. and I.L. Beale, Personal 

Web home pages of adolescents with 

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dissemination, and interpersonal 

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61. Exclude Code: E3 

293. Sweeney, N.M., D. Glaser, and C. 

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Journal of Pediatric Health Care, 2007. 

21(1): p. 13-21. Exclude Code: E3 

294. Tam, S., et al., Evaluating the efficacy of 

tele-cognitive rehabilitation for functional 

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Occupational Therapy International, 

2003. 10(1): p. 20-38. Exclude Code: E3 

295. Tang, P.C., et al., PAMFOnline: 

integrating EHealth with an electronic 

medical record system. AMIA, 2003. 

Annual Symposium 

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Appendix E. Excluded Studies List (continued) 

A-29

296. Tessaro, I., et al., Effectiveness of a 

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Health Behavior, 2007. 31(1): p. 35-43. 

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297. Thomson, R.G., et al., A patient decision 

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298. Tomuro, K., Oral home telecare tutorials 

for the community-dwelling elderly.

Journal of Medical & Dental Sciences, 

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E3 

299. Tudiver, F., et al., Primary care 

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telemedicine care in the IDEATel 

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23(1): p. 55-61. Exclude Code: E1 

300. Van Biervliet, A. and T.R. Gest, A 

multimedia guide to spinal cord injury: 

empowerment through self instruction.

Medinfo, 1995. 8 Pt 2: p. 1701. Exclude 

Code: E3 

301. Van Diest, S.L., et al., Sex therapy 

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115-33. Exclude Code: E3 

302. van Eijken, M., et al., Interventions to 

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Drugs & Aging, 2003. 20(3): p. 229-40. 

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303. van Lankveld, W.G.J.M., A.M. Derks, 

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Annals of the Rheumatic Diseases, 

2006. 65(1): p. 121-3. Exclude Code: E2 

304. Verheijden, M., et al., Web-based 

targeted nutrition counselling and social 

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p. e44. Exclude Code: E 

305. Verheijden, M.W., et al., Rates and 

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Code: E1 

306. Walker, H., Computer-based education 

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Standard, 2006. 20(30): p. 49-56. 

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307. Waller, A., et al., Participatory design of 

a text message scheduling system to 

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Health Informatics Journal, 2006. 12(4): 

p. 304-18. Exclude Code: E3 

308. Wangberg, S.C., E. Arsand, and N. 

Andersson, Diabetes education via 

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309. Weber, B., et al., Computerized selfassessment in psychiatric in-patients: 

acceptability, feasibility and influence of 

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310. Weinert, C., S. Cudney, and C. Winters, 

Social support in cyberspace: the next 

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15. Exclude Code: E 

311. Welch, J., S. Dowell, and C.S. Johnson, 

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312. Wells, M., et al., Online mental health 

treatment: concerns and considerations.

Cyberpsychology & Behavior, 2007. 
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A-30

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313. Westlake, C., et al., Evaluation of a 

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Code: E 

314. White, C., V. Sheedy, and N. Lawrence, 

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Code: E1 

315. White, M.J., et al., Implementing a 

computer-assisted telephone interview 

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61(3): p. 419-28. Exclude Code: E1 

316. Whitlock, W.L., et al., Telemedicine 

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317. Whittaker, S.L., et al., Success factors in 

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318. Whitten, P., B. Collins, and F. Mair, 

Nurse and patient reactions to a 

developmental home telecare system.

Journal of Telemedicine & Telecare, 

1998. 4(3): p. 152-60. Exclude Code: E3 

319. Whitten, P., G. Doolittle, and M. 

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2004. 21(3): p. 191-5. Exclude Code: E3 

320. Whitten, P., et al., Telehospice carries 

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321. Whitten, P. and E. Kuwahara, A multiphase telepsychiatry programme in 

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322. Whitten, P. and B. Love, Patient and 

provider satisfaction with the use of 

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294-300. Exclude Code: E2 

323. Whitten, P., F. Mair, and B. Collins, 

Home telenursing in Kansas: patients' 

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Journal of Telemedicine & Telecare, 

1997. 3 Suppl 1: p. 67-9. Exclude Code: 

E3 

324. Whitten, P. and M. Mickus, Home 

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p. 69-73. Exclude Code: E3 

325. Whitten, P.S. and F. Mair, Telemedicine 

and patient satisfaction: current status 

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23. Exclude Code: E2 

326. Wiecha, J.M. and W.G. Adams, 

BostonBreathes: improving pediatric 

asthma care with a home-based 

interactive website for patient education, 

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AMIA, 2006. Annual Symposium 

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1144. Exclude Code: E2 

327. Wilkie, D.J., et al., Usability of a 

computerized PAINReportIt in the 

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213-24. Exclude Code: E3 

328. Williams, C., et al., Efficacy of a drug 

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2005. 40(6): p. 869-78. Exclude Code: 

E1 

329. Williams, P., D. Nicholas, and P. 

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information kiosks examined in an 
Appendix E. Excluded Studies List (continued) 

A-31

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20(2): p. 95-103. Exclude Code: E1 

330. Williams, T., et al., Patient satisfaction 

with store-and-forward teledermatology.

Journal of Telemedicine & Telecare, 

2001. 7 Suppl 1: p. 45-6. Exclude Code: 

E3 

331. Williams, T.L., et al., Patient satisfaction 

with teledermatology is related to 

perceived quality of life. British Journal 

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332. Wilson, A., et al., Investigating patients' 

and general practitioners' views of 

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333. Wilson, A.E. and P.D. Home, A dataset 

to allow exchange of information for 

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The Diabetes Audit Working Group.

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90. Exclude Code: E2 

334. Wilson, P.N., N. Foreman, and D. 

Stanton, Virtual reality, disability and 

rehabilitation. Disability & Rehabilitation, 

1997. 19(6): p. 213-20. Exclude Code: 

E2 

335. Winkelman, W.J., K.J. Leonard, and 

P.G. Rossos, Patient-perceived 

usefulness of online electronic medical 

records: employing grounded theory in 

the development of information and 

communication technologies for use by 

patients living with chronic illness.

Journal of the American Medical 

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306-14. Exclude Code: E3 

336. Wise, M., et al., Internet telehealth for 

pediatric asthma case management: 

integrating computerized and case 

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Health Promotion Practice, 2007. 8(3): 

p. 282-91. Exclude Code: E 

337. Wolf, J.M., et al., Access and use of the 

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Hand Surgery, 2004. 9(1): p. 29-33. 

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338. Wolf, S.L., et al., Reducing frailty and 

falls in older persons: an investigation of 

Tai Chi and computerized balance 

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44(5): p. 489-97. Exclude Code: E3 

339. Woodall, W.G., et al., Effect of emailed 

messages on return use of a nutrition 

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Medical Internet Research, 2007. 9(3): 

p. e27. Exclude Code: E3 

340. Woods, K.F., et al., Sickle cell disease 

telemedicine network for rural outreach.

Journal of Telemedicine & Telecare, 

2000. 6(5): p. 285-90. Exclude Code: E3 

341. Woods, K.F., et al., Sickle cell 

telemedicine and standard clinical 

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1999. 5(4): p. 349-56. Exclude Code: E3 

342. Wright, P., et al., Text entry on handheld 

computers by older users. Ergonomics, 

2000. 43(6): p. 702-16. Exclude Code: 

E3 

343. Wright, P., S. Belt, and C. John, Helping 

people assess the health risks from 

lifestyle choices: comparing a computer 

decision aid with customized printed 

alternative. Communication & Medicine, 

2004. 1(2): p. 183-92. Exclude Code: E1 

344. Wright, P., et al., Enhancing an 

appointment diary on a pocket computer 

for use by people after brain injury.

International Journal of Rehabilitation 

Research, 2001. 24(4): p. 299-308. 

Exclude Code: E 

345. Wu, A.W., et al., A randomized trial of 

the impact of a programmable 

medication reminder device on quality of 

life in patients with AIDS. AIDS Patient 
Appendix E. Excluded Studies List (continued) 

A-32

Care & Stds, 2006. 20(11): p. 773-81. 

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346. Wu, S.-J., et al., Integrating hospital 

medical care data with pharmaceutical 

education materials for diabetes self 

management. Studies in Health 

Technology & Informatics, 2006. 122: p. 

660-3. Exclude Code: E 

347. Wydra, E.W., The effectiveness of a 

self-care management interactive 

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Forum, 2001. 28(9): p. 1399-407. 

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348. Wylie-Rosett, J., et al., Computerized 

weight loss intervention optimizes staff 

time: the clinical and cost results of a 

controlled clinical trial conducted in a 

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American Dietetic Association, 2001. 

101(10): p. 1155-62; quiz 1163-4. 

Exclude Code: E 

349. Xavier, A., et al., Cognition, interaction 

and ageing: an Internet workshops 

exploratory study. Studies in Health 

Technology & Informatics, 2004. 103: p. 

289-95. Exclude Code: E3 

350. Yager, J., Clinical computing: monitoring 

patients with eating disorders by using 

e-mail as an adjunct to clinical activities.

Psychiatric Services, 2003. 54(12): p. 

1586-8. Exclude Code: E2 

351. Yon, B.A., et al., The use of a personal 

digital assistant for dietary selfmonitoring does not improve the validity 

of self-reports of energy intake. Journal 

of the American Dietetic Association, 

2006. 106(8): p. 1256-9. Exclude Code: 

E3 

352. Young, R.J., et al., Pro-active call center 

treatment support (PACCTS) to improve 

glucose control in type 2 diabetes: a 

randomized controlled trial. Diabetes 

Care, 2005. 28(2): p. 278-82. Exclude 

Code: E3 

353. Young, T.L. and C. Ireson, Effectiveness 

of school-based telehealth care in urban 

and rural elementary schools.

Pediatrics, 2003. 112(5): p. 1088-94. 

Exclude Code: E3 

354. Yousef, J. and A.N. Lars, Validation of a 

real-time wireless telemedicine system, 

using bluetooth protocol and a mobile 

phone, for remote monitoring patient in 

medical practice. European Journal of 

Medical Research, 2005. 10(6): p. 254-

62. Exclude Code: E3 

355. Yueh, B., et al., Randomized trial of 

amplification strategies. Archives of 

Otolaryngology-Head & Neck Surgery, 

2001. 127(10): p. 1197-204. Exclude 

Code: E3 

356. Zaylor, C., Clinical outcomes in 

telepsychiatry. Journal of Telemedicine 

& Telecare, 1999. 5 Suppl 1: p. S59-60. 

Exclude Code: E3 

357. Zaylor, C., P. Whitten, and C. Kingsley, 

Telemedicine services to a county jail.

Journal of Telemedicine & Telecare, 

2000. 6 Suppl 1: p. S93-5. Exclude 

Code: E3 
APPENDIX F. QUALITY ASSESSMENT CRITERIA 

A-33 

Study Design United States Preventive Services Task Force quality rating criteria1

Systematic reviews and 

meta-analyses 

• Comprehensiveness of sources considered/search strategy used 

• Standard appraisal of included studies 

• Validity of conclusions 

• Recency and relevance are especially important for systematic 

reviews 

Case-control studies • Accurate ascertainment of cases 

• Nonbiased selection of cases/controls with exclusion criteria 

applied equally to both 

• Response rate 

• Diagnostic testing procedures applied equally to each group 

• Measurement of exposure accurate and applied equally to each 

group 

• Appropriate attention to potential confounding variables 

Randomized controlled 

trials (RCTs) 

• Initial assembly of comparable groups employs adequate 

randomization, including first concealment and whether potential 

confounders were distributed equally among groups 

• Maintenance of comparable groups (includes attrition, crossovers, 

adherence, contamination) 

• Important differential loss to followup or overall high loss to followup 

• Measurements are equal, reliable, and valid (includes masking of 

outcome assessment) 

• Clear definition of the interventions 

• All important outcomes considered 

Cohort studies • Initial assembly of comparable groups employs consideration of 

potential confounders with either restriction or measurement for 

adjustment in the analysis; consideration of inception cohorts 

• Maintenance of comparable groups (includes attrition, crossovers, 

adherence, contamination) 

• Important differential loss to followup or overall high loss to followup 

• Measurements are equal, reliable, and valid (includes masking of 

outcome assessment) 

• Clear definition of the interventions 

• All important outcomes considered

Diagnostic accuracy 

studies 

• Screening test relevant, available for primary care, adequately 

described 

• Study uses a credible reference standard, performed regardless 

of test results 

• Reference standard interpreted independently of screening test 

• Handles indeterminate result in a reasonable manner 

• Spectrum of patients included in study 

• Sample size 

• Administration of reliable screening test 
APPENDIX F. QUALITY ASSESSMENT CRITERIA (continued) 

A-34 

CRD's Guidance for those Carrying Out or Commissioning Reviews 

Quality criteria for assessment of experimental studies2

1. Was the assignment to the treatment groups really random? 

• Adequate approaches to sequence generation 

- Computer-generated random numbers 

- Random numbers tables 

• Inadequate approaches to sequence generation 

- Use of alternation, case record numbers, birth dates or week days 

2. Was the treatment allocation concealed? 

• Adequate approaches to concealment of randomization 

- Centralized or pharmacy-controlled randomization 

- Serially-numbered identical containers 

- On-site computer based system with a randomization sequence that is not 

• readable until allocation 

- Other approaches with robust methods to prevent foreknowledge of the allocation 

• sequence to clinicians and patients 

• Inadequate approaches to concealment of randomization 

- Use of alternation, case record numbers, birth dates or week days 

- Open random numbers lists 

- Serially numbered envelopes (even sealed opaque envelopes can be subject to 

• manipulation) 

3. Were the groups similar at baseline in terms of prognostic factors? 

4. Were the eligibility criteria specified? 

5. Were outcome assessors blinded to the treatment allocation? 

6. Was the care provider blinded? 

7. Was the patient blinded? 

8. Were the point estimates and measure of variability presented for the primary outcome 

measure? 

9. Did the analyses include an intention to treat analysis 

Some quality criteria for assessment of observational studies 

Cohort studies 

• Is there sufficient description of the groups and the distribution of prognostic factors? 

• Are the groups assembled at a similar point in their disease progression? 

• Is the intervention/treatment reliably ascertained? 

• Were the groups comparable on all important confounding factors? 

• Was there adequate adjustment for the effects of these confounding variables? 

• Was a dose-response relationship between intervention and outcome demonstrated? 

• Was outcome assessment blind to exposure status? 

• Was follow-up long enough for the outcomes to occur? 

• What proportion of the cohort was followed-up? 

• Were drop-out rates and reasons for drop-out similar across intervention and unexposed groups? 

Case-control studies 

• Is the case definition explicit? 

• Has the disease state of the cases been reliably assessed and validated? 

• Were the controls randomly selected from the source of population of the cases? 

• How comparable are the cases and controls with respect to potential confounding factors? 

• Were interventions and other exposures assessed in the same way for cases and controls? 

• How was the response rate defined? 

• Were the non-response rates and reasons for non-response the same in both groups? 
APPENDIX F. QUALITY ASSESSMENT CRITERIA (continued) 

A-35 

• Is it possible that over-matching has occurred in that cases and controls were matched on factors 

related to exposure? 

• Was an appropriate statistical analysis used (matched or unmatched)? 

Case series 

• Is the study based on a representative sample selected from a relevant population? 

• Are the criteria for inclusion explicit? 

• Did all individuals enter the survey at a similar point in their disease progression? 

• Was follow-up long enough for important events to occur? 

• Were outcomes assessed using objective criteria or was blinding used? 

• If comparisons of sub-series are being made, was there sufficient description of the series and 

the distribution of prognostic factors? 

Critical Appraisal Skills Programme (CASP)3

Making sense of evidence, 10 questions to help you make sense of qualitative research 

Currently awaiting approval to re-publish criteria here. 

References 

1. Harris RP, Hefland M, Woolf SH, et al. Methods Work Group, Third US Preventive Services Task 

Force. Current methods of the US Preventive Services Task Force: a review of the process. 

American Journal of Preventive Medicine. 2001;20(3 Suppl):21-35. 

2. Khan K, ter Riet G, Popay J, Nixon J, Kleijnen J. Undertaking Systematic Reviews of Research 

on Effectiveness: Stage II: Conducting the Review. York: University of York; 2001. 

3. Critical Appraisal Skills Programme. 10 questions to help you make sense of qualitative research: 

Milton Keynes Primary Care Trust; 2002. 
APPENDIX G. PEER REVIEWERS 

A-36 

Majd Alwan 

President of CAST (Center for Aging Services Technologies), Washington DC 

Cheryl Austein-Casnoff 

Office of Health Information Technology, (HRSA), Rockville, Maryland 

Joyce Dubow 

Senior Advisor AARP Office of Policy and Strategy, Washington DC 

Daryle Gardner-Bonneau, MD 

Human Factors and Ergonomics Society, Portage, Mississippi 

Margaret Giannini, MD, FAAP 

Director, Office on Disability at HHS, Washington DC 

Linda Golodner, BA 

President and CEO of the National Consumer's League and chairs the Alliance Against Fraud 

in Telemarketing and electronic Commerce and serves on the Board of the National Council 

on Patient Information and Education, the National Patient Safety Foundation, the Patient 

Safety Institute and the American National Standards Institute, Washington DC 

Donald Grantt

Director, Evidence-Based Disease and Disability Prevention Program U.S. Administration on 

Aging Office of Planning and Policy Development, Washington DC 

Rita Kukafka, DrPH, MA 

Assistant Professor of Sociomedical Sciences, Mailman School of Public Health and 

Assistant Professor of Biomedical Informatics, College of Physicians and Surgeons, New 

York City, New York 
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Albisser 2001 Mixed model HMO with a 

membership of 275,000

Primary and/or subspecialty care physicians all generally in a mixed model HMO with a membership of 275,000

Homes Quasi-experimental, 

longitudinal observation study design

AH-1
Appendix H. Data Table

Author Year

Albisser 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 12 months *Enrollment in the HMO-sponsored 

disease management programs.

NR NR

AH-2
Appendix H. Data Table

Author Year

Albisser 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR NR Mean Age: 58

Range: 19-81

AH-3
Appendix H. Data Table

Author Year

Albisser 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

NR NR NR NR Male body we

baseline: 63-1Female body baseline: 56-1

Patient Characteristics*

AH-4
Appendix H. Data Table

Author Year

Albisser 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-5
Appendix H. Data Table

Author Year

Albisser 2001

Intervention Described Intervention Groups Type of Device* Functions*

Each patient received and continued to receive standardized diabetes education. The second initiative sought to augment the education component by adding ongoing, selfmanagement training. The third initiative added computer-assisted selfcare to the same education component.

Group 1: (E-alone) Education aloneGroup 2: (E+SMT) education supplemented with self-management trainingGroup 3: (E+caSC) education supplemented with computer-assisted selfcare

Group 4: (Control) (DCCT initiative) Glycolated Hemoglobin A1c values in Diabetes patients.

Touch-tone telephone (recorded to a computer)

To record data as repoby patients, pass this information through an algorithm, and pass intervention instructionthe patient.

Intervention*

AH-6
Appendix H. Data Table

Author Year

Albisser 2001

Staff Training Subject Training co-interventions Physiolo

One case worker was dedicated to the computerassisted self-care initiative, which was under the direction of an endocrinologist to whole the difficult-to-manage patients were referred.

Received standardized diabetes education

NR HbA1c Group 2 (E+S

Significant improvemebaseline (8.8 ± 1.5%) 3 months (p < 0.01). Simprovement from 3 m1.8% at 12 months (p 3 (E+caSM): had startHBA1c of 9.5 ± 1.7% significantly (p < 0.01) 8.6 ± 1.6%, then decli8.4 ± 1.6% at 12 montGroup 4, DCCT initiatiimprovement from bas1.43%) to 7.3 ± 1.4% < 0.01). Significant im3 months to 6.7 ± 0.8% (p < 0.01). Body weight Group 2 showed a significant (change from body wei(82 ± 15.9 kg) over the months to (94 ± 18.2 k

AH-7
Appendix H. Data Table

Author Year

Albisser 2001

Health Service Utilization Health Care Costs Knowledge

NR Cost of care Per member per month 

chargesE-Alone: no additional charges E+SMT: $18 E+caSM: $1.31 DCCT: $11.88

NR

AH-8
Appendix H. Data Table

Author Year

Albisser 2001

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-9
Appendix H. Data Table

Author Year

Albisser 2001

Health Satisfaction Self-efficacy Health Behavior

NR NR NR

Outcomes

AH-10
Appendix H. Data Table

Author Year

Albisser 2001

Quality of Life Support Withdrawals

NR NR NR

AH-11
Appendix H. Data Table

Author Year

Albisser 2001

Adverse Effects Other

Body weight gain occurred in both the DCCT and the education and selfmanagement training initiatives.

NR

AH-12
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

An 2006 E-mail college students NA WWW Pilot Study and Cohort 

Study (experimental arm RCT)

AH-13
Appendix H. Data Table

Author Year

An 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

2004-2005 5 mos *Students completing survey after an 

email recruitment*Smoke in the past 30 days*Age 18-24*In school for next 2 semesters

NR NR

AH-14
Appendix H. Data Table

Author Year

An 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR NR NR Pilot: 27.7%

RCT: 29.6%

MeanPilot: 20.1RCT: 20.1

AH-15
Appendix H. Data Table

Author Year

An 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR All in college NA NR

AH-16
Appendix H. Data Table

Author Year

An 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR RealU

AH-17
Appendix H. Data Table

Author Year

An 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Pilot: subjects send weekly email asking to track their smoking, take an interactive quiz giving tailored feedback, and Question of the Week (answers posted to discussion board), tobacco topic only; RealU modifications to Pilot: Content on broad topics related to college life rather than only tobacco, users guided weekly to complete Tracker, Quiz, Question of the Week emailed, subjects encouraged to set goals weekly, peer coaches sent emails proactively to those not completing weekly tasks.

Pilot study and RealU experimental arm (not reporting on control group, or outcomes)

Tailored interactive website with email reminders and questions

NR

AH-18
Appendix H. Data Table

Author Year

An 2006

Staff Training Subject Training co-interventions Physiolo

NR NR NR NA

AH-19
Appendix H. Data Table

Author Year

An 2006

Health Service Utilization Health Care Costs Knowledge

NA NA NR

AH-20
Appendix H. Data Table

Author Year

An 2006

Use of Technology Usability HIT Satisfaction

Website ParticipationPilot StudyWeek 1: 53%Week 2: 21%Week 5: 26%RealU experimental armMean: 95% over 20 weeksRange: 89%-98%)

NR NR

AH-21
Appendix H. Data Table

Author Year

An 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-22
Appendix H. Data Table

Author Year

An 2006

Quality of Life Support Withdrawals

NR NR NR

AH-23
Appendix H. Data Table

Author Year

An 2006

Adverse Effects Other

NR NR

AH-24
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Andersson 2005 Press release, articles, 

advertisements

NR Homes RCT 

AH-25
Appendix H. Data Table

Author Year

Andersson 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Probability of .55 or more for diagnosis 

of major depression on CIDI-SFt*Total score on the MADRS-S between 15-30, including a score of less than 4 on item 9 (zest for life)*No psychosis (based on meds status)*No bipolar disorder*No antidepressant meds begun or changed during the last month*No history of cognitive behavioral therapy for depression*Age 18 or older*Prepared to work on self-help program for several hours/week*No travel abroad or major surgery planned*Completion of pre-treatment assessment

NR NR

AH-26
Appendix H. Data Table

Author Year

Andersson 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DEPRESSION NR Grp 1: 22%

Grp 2: 28%Withdrawal: 28%

Mean (SD)Grp 1: 36.4 (+ 

Grp 2: 36.3 (+ Grp 3: 35.6 (+

AH-27
Appendix H. Data Table

Author Year

Andersson 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Education university 

levelGrp 1: 64%Grp 2: 61%Grp 3: 50%

45% City dwellers Fewer than 3 

reported episodepressionGrp 1: 33%Grp 2: 39%Withdrawal: 2No previous Treatment for depressionGrp 1: 44%Grp 2: 39%Grp 3: 44%Current antidepressanmedicationGrp 1: 22%Grp 2: 37%Grp 3: 28%Baseline BDI Grp 1: 20.5 (+

Grp 2: 20.9 (+Grp 3: 21.6 (+Baseline QoL(SD)Grp 1: -0.1 (+

Grp 2: -0.2 (+Grp 3: -0.2 (+

AH-28
Appendix H. Data Table

Author Year

Andersson 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR Swedish NR Internet Base

AH-29
Appendix H. Data Table

Author Year

Andersson 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Recruiting ads sent those interested to a website. To sign up a computer administered CIDI-SF, MADRS-S and gathered background information. Randomized participants were assigned groups and sent an email with log-in and user name. 89 pages of text as 5 modules were presented. Each module ended with a quiz. Therapist received response and gave email feedback w/i 24h. Participants could read website or download pdfs. 8 weeks was advised for completion. Discussion groups offered separately and monitored. 7 days had to have passed between log ins so depression levels could be monitored. 

Group 1: Treatment GroupGroup 2: Control GroupGroup 3: Withdrawal Group

Website Administer psychiatric 

tests, offer discussion groups, offer therapist feedback. 

AH-30
Appendix H. Data Table

Author Year

Andersson 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-31
Appendix H. Data Table

Author Year

Andersson 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-32
Appendix H. Data Table

Author Year

Andersson 2005

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-33
Appendix H. Data Table

Author Year

Andersson 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR BDI (0-63 range) 

Pre, Post, pre-post DifferenceGrp 1: 20.5 (6.7 s.d.), 12.2 (6.8), 8.3 (5.7 to 10.9), Grp 2: 20.9 (8.5), 19.5 (8.1), 1.4 (-1.1 to 3.9)Follow up, Pre to follow up difference Grp 1: n36 - 13.1 (9.1), 7.8 (4.6 to 11.3)Grp 2: n: 35 - 13.1 (7.6), 7.4 (4.0 to 10.7)MADRS-S (range 0-54)Pre, Post, pre-post DifferenceGrp 1: 20.1 (5.7), 12.7 (8.3), 5.5 (4.6 to 10.1)Grp 2: 21.6 (7.2), 19.0 (7.6), 2.6 (-0.4 to 4.8)Follow up, Pre to follow up difference Grp 1: 14.6 (9.2), 6.3 (3.2 to 9.3)Grp 2: 14.5 (9.3), 6.8 (3.9 to 9.7)

Follow up, Pre to follow up diff

AH-34
Appendix H. Data Table

Author Year

Andersson 2005

Quality of Life Support Withdrawals

Pre-Treatment, Post treatment, PrePost differenceGrp 1: -0.1 (1.1), 0.5 (1.6), 0.6 (0.2 to 1.1)Grp 2: -0.2 (1.6), 0.0 (1.5), 0.2 (-0.2 to 0.6)Follow Up and Pre-treatment to follow up differenceGrp 1: 0.7(1.7), 0.9 (0.4 to 1.4)Grp 2: 0.9 (1.8), 1.0 (0.5 to 1.4)

Group 1: Treatment Modules and discussion groupGroup 2: Discussion group only

N = 32

AH-35
Appendix H. Data Table

Author Year

Andersson 2005

Adverse Effects Other

Those who were not chosen for the treatment group received no help. After the study they were given access to the treatment modules. 

Limitations include no formal diagnosis of depression made by clinician, those on med regimens were not excluded, short study

AH-36
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Andersson 2003 NR NA Homes RCT 

AH-37
Appendix H. Data Table

Author Year

Andersson 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 weeks *Pts need to have access to a 

computer, modem and an internet connection.

Pts with specific cluster headache or sign indicating serious physical or psychological disorders and pts with headache less than 6 months were excluded.Pts with Horton headache, whiplash, head trauma, fibromyalgia, major depression, tinnitus, or other medically unclear neurologic symptoms were excluded. Pts who just submitted the interest form and did not return to the site (or who were not ableto call or reach via email) were also excluded.

NR

AH-38
Appendix H. Data Table

Author Year

Andersson 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HEADACHE NR N = 8 (18%)

Grp 1: N = 4 (17%)Grp 2: N = 4 (20%)

Mean (RangeGrp 1: 36.2 (1Grp 2: 44.9 (2Total: 40.3 (1

AH-39
Appendix H. Data Table

Author Year

Andersson 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Probable diag

Migrane TotaGrp 1: 12Grp 2: 8Tension type headache totaGrp 1: 9Grp 2: 6Tension type headache and migraine totalGrp 1: 2Grp 2: 3Nonspecific toGrp 1: 1Grp 2: 3Duration of he0.5-1 yr total; Grp 1: 0Grp 2: 21-5 yr total: 14Grp 1: 10Grp 2: 46-10 yr total: 5Grp 1: 3

AH-40
Appendix H. Data Table

Author Year

Andersson 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Internet based 

with telephon

AH-41
Appendix H. Data Table

Author Year

Andersson 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Therapist initiated telephone contact in the treatment of headache via the internet

The program delivered in 6 weekly treatment modules, information regarding headache and role of psychological factors provided, applied relaxation delivered in separate parts, Rationales, common questions and examples of potential problems added, Relaxation program included various aspects such as tense relax, relax only, cue controlled breathing, rapid relaxation and application training.Weekly report cards for the exercise, problem solving section with pts able to identify, cope with problems including headache related ones.Cognitive behavioral techniques for handling negative thoughts and core beliefs added with part of these being optional.The self help group had all of the above, 

Computer Information, questions 

answers, email, learn relaxation techniques downloadable sound fand online presentatioexercises.

AH-42
Appendix H. Data Table

Author Year

Andersson 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR Grp 1 vs. Grp 2

No of subjects- 17 vs. Headache indexPretreatment (pre)- 4.(2.6)Post treatment (post)- (2.4)Headache daysPre- 8.4 (4.4) vs. 7.2 (Post- 8.5 (4.1) vs. 6.4 Peak headachePre- 2.4 (0.6) vs. 2.4(0Post- 2.3 (1.1) vs. 2.7(Rated intensityPre- 2.1 (0.7) vs. 2.0(0Post- 2.0(1.1) vs. 2.3 (DurationPre- 9.8(6.3) vs. 7.1 (4Post- 7.0(6.7) vs. 9.5(

AH-43
Appendix H. Data Table

Author Year

Andersson 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-44
Appendix H. Data Table

Author Year

Andersson 2003

Use of Technology Usability HIT Satisfaction

NR NR Author mentions that most sub

were pleased with the treatmelack of time was the most comreason for not having practiced extent that they wished

AH-45
Appendix H. Data Table

Author Year

Andersson 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Grp 1 vs. grp 2

No of subjects- 15 vs. 9QuestionnairesHospital and Anxiety depression scale- depression subscalepre- 5.7(4.3) vs. 6.8 (2.5)post- 4.5 (3.9) vs. 4.9(3.3)Hospital and anxiety depression scale- anxiety subscalepre- 8.7(4.8) vs. 7.7 (4.5)posy- 7.9(5.1) vs. 7.0 (4.8)Headache disability inventorypre- 46.6(18.4) vs. 48.4 (13.3)post- 33.6 (16.4) vs. 34.4 (14.8)perceived stress scalepre- 31.3 (7.8) vs. 28.3(3.9)post- 25.7(2.0) vs. 24.7(7.3)

Coping strategies questionnairGrp 1 vs. Grp 2No of subjects- 15 vs. 9Diverting attentionPre- 7.4 (3.6) vs. 7.6 (4.6)Post- 8.6(6.7) vs. 9.6(3.5)Reinterpreting pain sensationsPre- 3.8(5.1) vs. 2.1 (2.6)post- 6.1 (6.1) vs. 4.6 (4.6)Coping self statementsPre- 17.7 (6.4) vs. 16.3 (5.2)Post- 14.7 (5.2) vs. 18.7 (5.5)Ignore pain sensationsPre- 16.9(5.1) vs. 17.1 (3.3)Post- 13.7(3.3) vs. 18.2 (7.0)Praying and hopingPre- 9.1 (5.2) vs. 13.9(9.2)Post- 8.8 (5.5) vs. 15.4 (10.9)CatastrophizingPre- 17.1 (7.5) vs. 17.9 (8.8)Post- 11.1 (8.5) vs. 16.0 (8.3)Increased behavioral activitiesPre- 11.3 (6.5) vs. 12.0(6.6)Post- 10.4( 5.1) vs. 13.2 (7.4)

AH-46
Appendix H. Data Table

Author Year

Andersson 2003

Quality of Life Support Withdrawals

NR NR Dropout rate

Grp 1: 29% Grp 2: 35%Difference was not statistically significantN= 44Grp 1: 24Completed treatment and registrations: 17Completed treatment and questionnaires: 15Failed to respond after exercismodules: 7Grp 2: 20Completed treatment and registrations: 13Completed treatment and questionnaires: 9Failed to respond after exercismodules: 7

AH-47
Appendix H. Data Table

Author Year

Andersson 2003

Adverse Effects Other

NR Significant interaction was found for 

duration F (1, 28) =7.16, p= 0.012Post hoc tests showed that the grp 1 had significant decreased durationNumber of subjects reaching a 50% decrease in the headache indexgrp 1 vs. grp2= 29 % vs. 23% the difference was not statistically significant (chi square test)Significant main effect was found on HADS depression subscaleF (1,22)=4.53, p=0.045Disability a/w headache , as measured by HDI decreased significantly for both groups with a main effect of timeF(1, 22)= 26.49, p=0.0001PSS showed main effect of timeF (1,22) =7.1, p=0.014Significant main effect of time was found for subscale reinterpreting pain sensationsF (1,22) = 11.2, p=0.029significant reductions found for catatrophizing with amin effect of time

AH-48
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Andersson 2006 Newspaper articles in 

national and regional papers, notices in health magazines, and link on homepage of Swedish National Anxiety Association

Sweden Homes RCT 

AH-49
Appendix H. Data Table

Author Year

Andersson 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 1 year *Fulfill the DSM-4 criteria for social 

phobia according to the SPSQ*Be afraid of giving a public speech*Have a total score of less than 31 on MADRS-S depression scale and less than 4 on the suicide item of this scale*Undergo no other psychological treatment for the duration of the study and have no history of earlier CBT*If prescribed drugs for anxiety/depression, dosage had to be constant for 3 months before the start of the treatment and the patient had to agree to keep the dosage constant throughout the study*Be at least 18 years or older*Attend an interview that used the SCID*Social phobia had to be the most severe disorder present in the patient*Not a substance abuser

Any criteria against the Inclusion criteria

NR

AH-50
Appendix H. Data Table

Author Year

Andersson 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SOCIAL PHOBIA NR IVR: 44%

CG: 53%Total: 48.4%

IVR: 36.4 (+9

Range: 21-53CG: 38.2 (+11

Range: 18-67Total: 37.3 (+

Range: 18-67

AH-51
Appendix H. Data Table

Author Year

Andersson 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Highest educational 

level (N)Nine-year compulsory school, %IVR: 0, 0CG: 2, 6.3Total: 2, 3.1Secondary school (not completed), %IVR: 1, 3.1CG: 1, 3.1Total: 2, 3.1Vocational school (completed), %IVR: 1, 3.1CG: 4, 12.5Total: 5, 7.8Secondary school (completed), %IVR: 8, 25.0CG: 7, 21.9Total: 15, 23.4College/university (not completed), %IVR: 7, 21.9CG: 5, 15.6Total: 12, 18.8

NR EMPLOYMEN

Full timeIVR: 21, 65.6CG: 21, 65.6Total: 42, 65.6Part timeIVR: 1, 3.1CG: 3, 9.4Total: 4, 6.3UnemployedIVR: 2, 6.3CG: 2, 6.3Total: 4, 6.3StudentIVR: 8, 25CG: 4, 12.5Total: 12, 188Registered sicIVR: 0, 0CG: 0, 0Total: 0, 0Disability penIVR: 0, 0CG: 1, 3.1Total: 1, 1.6Retired

AH-52
Appendix H. Data Table

Author Year

Andersson 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR Swedish NR NR

AH-53
Appendix H. Data Table

Author Year

Andersson 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Self-help manual that was adapted for use via the internet; divided into 9 modules

IVR: 32CG: 32 (wait-listed, and received treatment after the IVR group did)

Home computer with access to the internetaccess a self-help manual divided into modules

Education, feedback, motivation, encouragement, goalsetting, behavioral experiments, self-focusafety behaviors

AH-54
Appendix H. Data Table

Author Year

Andersson 2006

Staff Training Subject Training co-interventions Physiolo

NR NR Exposure sessions: two 

separate 3-hr group exposure sessions facilitated by therapists

Outcome Measures at Assessment PointPretreatment, Post tre1 year F/UEffect SizeB = btwn grp effect siztreatmentW=w/in grp effect size grpW/FU=1 yr F/u w/in grtotal grpLiebowitz Social AnxieIVR: 68.5 (+22.5), 45.6 39.0 (+21.2)CG: 66.7 (+20.9), 62.8 46.5 (+21.5)B: 0.73W: 0.91W/FU: 1.29Social Phobia ScaleIVR: 35.8 (+16.7), 20.7 17.8 (+11.6)CG: 32.5 (+13.1), 31.0 24.2 (+11.8)B: 0.67W: 0.96W/FU: 1.12Social Interaction Anx

AH-55
Appendix H. Data Table

Author Year

Andersson 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-56
Appendix H. Data Table

Author Year

Andersson 2006

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-57
Appendix H. Data Table

Author Year

Andersson 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Data for the Proportion of Part

Reaching the Criteria of ClinicaSignificant Improvement as Deby Jacobson and Truax (1991)%, NPost treatment1 yr F/uLiebowitz Social Anxiety ScaleIVR: 43.3, 1357.1, 28CG: 18.7, 6Social Phobia ScaleIVR: 56.3, 1853.0, 26CG: 25.0, 8Social Interaction Anxiety ScalIVR: 60.0, 1846.9, 23CG: 37.5, 12Social Phobia Screening QuestionnaireIVR: 73.0, 269.4, 34CG: 15.6, 5Personal Report of Confidence Speaker

AH-58
Appendix H. Data Table

Author Year

Andersson 2006

Quality of Life Support Withdrawals

QoL InventoryPretreatment, Post treatment1 yr F/uB = btwn grp effect size at post treatmentW=w/in grp effect size for treatment grpW/FU=1 yr F/u w/in grp effect size for total grpIVR: 1.5 (+1.1), 2.2 (+1.2)

2.2 (+2.1)

CG: 1.1 (+1.4), 1.1 (+1.7)

1.6 (+2.4)

B: 0.83W: 0.61W/FU: 0.43Data for the Proportion of Participants Reaching the Criteria of Clinical Significant Improvement as Defined by Jacobson and Truax (1991)%, NPost treatment1 yr F/uQoL InventoryIVR: 66.7, 2053.1, 26CG: 34.4, 11

NR 2 people dropped out during th

course of the study12 failed to finish all weekly mo2 pts did not return their questionnaires

AH-59
Appendix H. Data Table

Author Year

Andersson 2006

Adverse Effects Other

NR NR

AH-60
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Andrade 2005 NR The John Hopkins Moore 

HIV clinic in Baltimore

disease management assistance system device

RCT 

AH-61
Appendix H. Data Table

Author Year

Andrade 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

1999 to 2001 24 weeks *Age >18 yrs

*Able to self medicate*Currently receiving care at John Hopkins Moore clinic*Subjects were either previously treatment naïve and initiating HAART for the first time or antiretroviral experienced and switching HAART regimen*In the latter group, only those who received < 3 HAART regimens before 

study enrollment included

Inability to self medicate; presence of severe dementia; and institutionalization.

NR

AH-62
Appendix H. Data Table

Author Year

Andrade 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HIV NR IG: 16 (55)

CG: 18 (62)

IG: 38 (+7)

CG: 38 (+7)

AH-63
Appendix H. Data Table

Author Year

Andrade 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

BlackIG: 26 (90)CG: 25 (86)

NR NR NR CD4 cell coun

IG vs. CG:235 (+141) vs

(+245)

Plasma HIV Rload,IG vs. CG:4.4 (+0.2) vs. 

(+0.2)

Memory impaIG vs. CG:14 (48%) vs. Neuropsychotest scores, dAIDS, Drug upast 4 days, Methadone trHAART status study entry, Canalysis givenlisted here. (Ndifferences bethe two groupstatistically sig

AH-64
Appendix H. Data Table

Author Year

Andrade 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR The disease m

assistance sy

AH-65
Appendix H. Data Table

Author Year

Andrade 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

The DMAS is a portable battery powered electronic device that uses a digital signal processor to produce a timed, programmed voice message that prompts subjects to take their antiretrovirals. When a subject pushes a response button, the data can be uploaded and printed. The device can store up to 3 months of messages for up to 25 different medications.

All subjects attended an individualized, 30 min adherence counseling session each month and received adherence feedback from a standardized transcript that provided general education about barriers to adherence, hazards of non adherence and their prescribed HAART regimen. Mean adherence scores obtained from eDEM caps.IG group- provided with DMAS programmed with HAART regimen data to provide verbal reminders at dosing time. When a subject pushes a response button, the data can be uploaded and printed. CG- received only adherence counseling.

Battery powered electronic device

Voice reminders, storadosage times and date

AH-66
Appendix H. Data Table

Author Year

Andrade 2005

Staff Training Subject Training co-interventions Physiolo

NR Subjects trained on usage 

of eDEM caps

NR Plasma HIV RNA load

Week 12 IG vs. CG: Undetectable plasma 14%, p=0.014Week 24 IG vs. CG:Undetectable loads:34% vs. 38%, p=0.49Week 12 IG vs. CG: Decrease in load of at 

copies/mm3: 55% vs. Week 24 IG vs. CG: Decrease in load of at copies/mm3: 72% vs. Overall mean reductio-2.10 vs. -0.98, p=0.02Mean CD4 cell countsWeek 12 IG vs. CG:337 (+183) vs. 258 (+

Week 24 IG vs. CG: 301 (+172) vs. 250 (+Overall increase IG vs68 vs. 45 , p=0.62

AH-67
Appendix H. Data Table

Author Year

Andrade 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-68
Appendix H. Data Table

Author Year

Andrade 2005

Use of Technology Usability HIT Satisfaction

Week 20: 88% of pts in IG reported that they used the device some or all of the time to manage HAART use. 1 subject reported never having used the device.

NR NR

AH-69
Appendix H. Data Table

Author Year

Andrade 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Mean percentage adherence (

IG vs. CGWeek 4: 76 ( 5.7) vs. 69 (5.2)Week 8: 75 (6.3) vs. 68 (6.1)Week 12: 87 (4.9) vs. 64 (6.3)Week 16: 83 (5.9) vs. 62 (7.4)Week 20: 80 (5.8) vs. 61(7.3)Week 24: 80 (6.3) vs. 64 (8.2)Overall: 80 (2.3) vs. 65(2.7)Mean adherence percentage (impaired memory (IG : CG) vsmemory (IG : CG)Week 4: 70 (8.8) : 63(6.8) vs. (7.0) : 77 (7.7)Week 8: 75(8.8) : 62 (8.4) vs. (9.4) : 76(8.5)Week 12: 81 (7.3) : 56 (8.4) vs(7.7) : 77 (8.1)Week 16: 72 (9.3) : 55 (9.8) vs(6.2) : 76(9.6)Week 20: 73 (7.5) : 49(8.7) vs. (8.9) : 80 (9.3)Week 24: 79(10.8) : 56 (10.6) v(8.0) : 76 (12.5)Overall: 77 (3.4) : 57(3.5) vs. 877 (3.5)Among memory impaired subjescores were statistically signifiand greater in the IG (p=0.001

AH-70
Appendix H. Data Table

Author Year

Andrade 2005

Quality of Life Support Withdrawals

NR NR Intervention group:

1 died, 1 lost to follow up, 29 inin ITT analysisControl group:3 lost to follow up, 29 included analysis

AH-71
Appendix H. Data Table

Author Year

Andrade 2005

Adverse Effects Other

NR Virological and immune responses to 

medication between memory impaired and memory intact given, not listed here (NS).

AH-72
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Andrade 2001 Johns Hopkins NR Home/with patient RCT 

AH-73
Appendix H. Data Table

Author Year

Andrade 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

6 months NR *Patients who were starting highly active 

antiretroviral therapy for the first time or who had failed it once or twice in the past

NR NR

AH-74
Appendix H. Data Table

Author Year

Andrade 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HIV/AIDS NR NR NR

AH-75
Appendix H. Data Table

Author Year

Andrade 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

American NR NR NR NR

AH-76
Appendix H. Data Table

Author Year

Andrade 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR Disease Mana

Assistance Sy

AH-77
Appendix H. Data Table

Author Year

Andrade 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

The DMAS emits a beeping sound when patients are supposed to take their medication. They then press "play" button on the DMAS and the device plays a verbal medication reminder. Once the patients have taken their medication they press the "yes" button and the device records the date and time of their medication.

IVR Grp: 50CG: 50

Pager like Medication reminder, 

adherence recorder

AH-78
Appendix H. Data Table

Author Year

Andrade 2001

Staff Training Subject Training co-interventions Physiolo

NR NR Monthly antiretroviral 

education and counseling

Viral loads (Mean)IVR: 1.5 log decreaseCG: 0.5 log decreaseCD4 cell count (MeanIVR: 50 CD4 cell increCG: 10 CD4 cell incre

AH-79
Appendix H. Data Table

Author Year

Andrade 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-80
Appendix H. Data Table

Author Year

Andrade 2001

Use of Technology Usability HIT Satisfaction

Preliminary data analysis of the first 19 patients (of 86, 52 who were enrolled through June 2001) showed a mean adherence score of 92% for IVR and 83 for CG. Memoryimpaired subjects in IVR group had adherence of 90% vs. 80% in CG.

Reported that younger employees tended to use the e-mail as a communication tool for health counseling more than older employees, and there are different levels of skill and acceptability for email among employees in administrative and manufacturing divisions

NR

AH-81
Appendix H. Data Table

Author Year

Andrade 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-82
Appendix H. Data Table

Author Year

Andrade 2001

Quality of Life Support Withdrawals

NR NR NR

AH-83
Appendix H. Data Table

Author Year

Andrade 2001

Adverse Effects Other

NR All pts were also given EDEM caps 

which recorded the time and date pts took medications, to help verify the accuracy of the DMAS recording system

AH-84
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Andrewes 1996 NR General teaching hospital 

(Royl Melbourne hospital) and a private hospital (Melbourne clinic)

Homes Pre- post 

Anhoj 2004 WWW NR WWW Cohort (survey, interview

AH-85
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NA NR NR NR2002 NA *Survey responders NR NR

AH-86
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR Yes- DSM-III-R 

diagnosed eating disorders (anorexia nervosa and bulimia)

NR NR Age in years - 

(SD)DIET vs. Con22.4 (+5.6) vs

(+5.0)

NR ASTHMA NR 29% (N = 17/59) Mean

Male: 41Female: 36

AH-87
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NRNR NR NR NR NR

AH-88
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR DIETNR NR NR NR LinkMedica

AH-89
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Health education package containing information aimed at educating patients on issuesregarding the dangers and myths surrounding certain methods of dieting and the underlying mechanism which have been found to maintain eating disorders

Grp 1 (DIET): Aim of the program is to educate about anorexia and bulimia and to debunk some of the myths concerning weight control. Areas covered were laxative inefficiency, adverse effects of self induced vomiting, rebound effect of diuretics, effects on the brain due to starvation, natural metabolic rate drops, myths surrounding weight loss and exercise, weight reduction and self esteem, media influence, challenging aspects of puberty and family influences.Grp 2 (CARL): Subjects interacted with the CARL program by typing in responses toquestions. on directional counseling stimulation.

Computers Health education, 

questions and answer(typing in responses)

Website with tailored feedback and support

NA WWW Web-based diary, grap

report of peak flow, medication managemefeedback, knowledge center, support group, the expert

AH-90
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Staff Training Subject Training co-interventions Physiolo

NR NR NR NRNR NR NR NA

AH-91
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Health Service Utilization Health Care Costs Knowledge

NR NR Eating disorder knowledge 

questionnaireGrp 1 vs. Grp 2 mean (SD)Pre intervention:30.1 (+8.0) vs. 34.4 (+8.0) 

Post intervention:44.4 (+10.8) vs. 37.1 (+7.9)

F (1, 50)= 50.3, p < 0.001

NR NR NR

AH-92
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Use of Technology Usability HIT Satisfaction

NR Ease of use, Mean (SD)

Grp 1: 8.5 (+2.6) to 9.8 (+0.6) 

Difficult to operate :Grp 2: 4.6 (+2.6)

Mean (SD)Usefulness and relevance Grp1: 7.1 (+2.9) to 8.6 (+2.0)

Wanting to receive more info6.3 (+2.5) to 7.9 (+2.2)

Grp 2:Not useful: 2.0 (+1.5)

Not enjoyable: 4.1 (+3.0)

NR NR Users rated quality of website 

functions, but combined patienproviders and survey instrumepoor

AH-93
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Eating disorder attitude questionnaire, Mean (SD)Grp 1 vs. Grp 2 Pre-intervention: 149 (+42.7) vs. 148.6 (+40.4) 

Post-intervention:166.9 (+49.0) vs. 149.5 (+41.9) 

F (1,50) = 6.8, p=0.01

NR NR

NR NR NR

AH-94
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Quality of Life Support Withdrawals

NR NR NRNR NR NR

AH-95
Appendix H. Data Table

Author Year

Andrewes 1996Anhoj 2004

Adverse Effects Other

NR NRNR Qualitative description of users, 

categorized as 'controllers' or 'disease neglecters'; patient users felt "doctors ignorant"

AH-96
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Araki 2006 Manufacturing Plant NR Manufacturing plant RCT Armstrong 2005 Patients at Cancer Risk 

Evaluation Program at U of Pennsylvania were screened for inclusion

NR NR Double blind RCT 

AH-97
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

2 months NR *Employed at a manufacturing plant in 

Tokyo*Had health data (for the annual checkup) available as of May 20th*Abnormal levels of gamma-GTP

Requiring immediate medical attention, past history of hepatic dysfunction, those who drank less than once a month

NR

2000-2003 6 weeks *Have undergone oophorectomy or 

mastectomy

No significant residual breast or ovarian cancer risk, those with ovarian cancer or metastatic breast cancer

NR

AH-98
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ALCOHOLISM NR 100% Mean Age (SD

Grp 1: 43.4 (+

Grp 2: 44.3 (+Grp 3: 43.8 (+

NR CANCER Risk, BRCA 

1/2 Mutations

NR N = 0% Mean (Range

Total: 43 (26-Grp 1: 45 (30Grp 2: 42 (26

AH-99
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Japanese NR NR NR NRCaucasian: 100% NR Overall 74% College 

educatedGrp 1: 70%Grp 2: 85%

NR NR

AH-100
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR Japanese Based on the 

Knowledge-AttitudeBehavior (KAB) model, precise knowledge on appropriate drinking behaviors would likely lead to a favorable attitude to adopting an appropriate behavior, and in turn it would result in an actual behavior

NR

NR NR English NR Tailored Deci

System

AH-101
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

E-mail counseling or faceto-face counseling that included short lectures on potential health risks of excess alcohol consumption and appropriate drinking behaviors. Also included goal setting.

Grp 1 (Face-to-Face): 12Grp 2 (Email): 12Grp 3 (CG): 12

Computer/email, brochure, live counselor

Email was used to encourage cessation oheavy drinking and oftused attached brochuFace-to-face counselinemployed real counseand used brochures a

Provides individualized survival and cancer incidence curves specific to outcomes of alternative management strategies

Group 1: InterventionGroup 2: Control

Computer was used to create curves, unclear as to patient interaction

Offer decision strategidealing with BRCA 1/2

AH-102
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR Baseline

Reported alcohol consEthanol/day)Grp 1: 24.8 (+15.7)

Grp 2: 35.0 (+18.6)Grp 3: 19.8 (+17.9)p=0.1074Serum gamma-GTPGrp 1: 140.7 (+63.6)

Grp 2: 138.1 (+65.4)Grp 3: 117.4 (+40.2)p=0.5613After IVR

Grp 1: 116.3 p=0.05Grp 2&3: not significan

NR 1:1 meeting with research 

coordinator, review of educational booklet

NR NR

AH-103
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-104
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Use of Technology Usability HIT Satisfaction

NR Reported that younger employees 

tended to use the e-mail as a communication tool for health counseling more than older employees, and there are different levels of skill and acceptability for email among employees in administrative and manufacturing divisions

NR

NR NR NR

AH-105
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Baseline/After IVR

Knowledge:Grp 1: 3.2 (+1.7)/5.9 p<0.001

Grp 2: 4.2 (+1.1)/4.8Grp 3: 3.6 (+2.2)/NSBaseline p=0.3654Attitude:Grp 1: 5.6 (+2.2)/7.1 p<0.05Grp 2: 5.2 (+2.5)/NSGrp 3: 6.6 (+2.1)/NSBaseline p=0.3521Behavior:*see physiologic

NR NR NR

AH-106
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Quality of Life Support Withdrawals

NR NR NRNR NR 5-3 unable to travel or commit 

research coordinator, one undprophylactic mastectomy and oophorecotmy, 1 unreachable follow up

AH-107
Appendix H. Data Table

Author Year

Araki 2006Armstrong 2005

Adverse Effects Other

NR NRNR NR

AH-108
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Artinian 2003 Urban, Detroit VA medical 

center

Urban CHF clinic Homes RCT 

AH-109
Appendix H. Data Table

Author Year

Artinian 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 months *Age equal to or more than 18 yrs

*Symptomatic left ventricular dysfunction *Ejection fraction of 40% or less*Ownership or rental home with a telephone*Ability to read and understand English*Plans to remain in the city for the study period

H/o dementia, mental illness, symptomatic infectious diseaseor advanced liver disease, self reported substance abuse, hemodialysis, coronary angioplasty within last 60 days or coronary artery bypass graft surgery within last 90 days, inability to participate in 6 min walk test or terminal stages of cancer

NR

AH-110
Appendix H. Data Table

Author Year

Artinian 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CHF NR N = 17 (94%) Mean (SD): 6

yrs)

AH-111
Appendix H. Data Table

Author Year

Artinian 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Black: 65% (11 pts)Caucasian: 35% (6 pts)

< $ 10,000: 2 pts (13%)$10,000-19,000: 8 pts (53%)$ 20,000-29,999: 4 pts (27%)>$ 40,000: 1 pt (6%)*3 pts chose not to disclose their income

Mean educational level: 13 yrs Range: 8-19yrs

Urban Marital status

Unmarried: 14 (78%)Single: 2 pts (Divorced/sepa10 pts (56%)Widowed: 2 pMarried: 4 pts Live alone: 11 (61%)Live with spouchild: 7 pts (3

AH-112
Appendix H. Data Table

Author Year

Artinian 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Med-eMonitor

AH-113
Appendix H. Data Table

Author Year

Artinian 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

*Med-eMonitor device-5 compartments for supplying up to 5 medications using alarm to remind the pts daily when to take, which to take and how many to take.*25 virtual compartments with daily tailored reminders or questions regarding other medications, monitoring, daily weight and BP, reducing salt intake, eating heart healthy and engaging in physical activity*Date and time stamped record of pill taking*Recordings of pts's responses to specific questions*Updates to pts's regimen downloaded and pts info automatically transmitted to server via telephone

Grp 1 : usual care + web based intervention (compliance device)Grp 2: Usual careAll received self care behavior booklet, Grp 1 included visits to the cardiologist in the clinic at regular intervals (frequency determined by severity of HF). On each visit, pharmacist assesses medication taking and provided pts with written info about name, frequency and action of medication. Grp2 received all above + Med- eMonitor

Videocassette (Med - Monitor) that sat in a cradle connected to a telephone line

Compliance device wialarms and remindersmonitoring of weight aBP, and answers to spquestions

AH-114
Appendix H. Data Table

Author Year

Artinian 2003

Staff Training Subject Training co-interventions Physiolo

NR All pts received an 

educational booklet describing CHF self care behaviorspts were instructed on how to use their weight scale and BP monitor

Unclear Functional status

Grp 1 vs. Grp 2NYHA Functional clasBaseline Mean (SD)3 (+0.83) vs. 3 (+0.50)

3 month Mean (SD)3 (+1.01) vs. 2 (+1.01)ANOVATime---F(1,16)=0.67,pGroup by time interactF(1,16)=0.00,p=1.06 MWT distance in feeBaseline Mean (SD)846 (+347) vs. 946 (+2

3 month Mean (SD)983(+299) vs. 995 (+2ANOVATime-- F(1,16)=1.28,pgroup by time interactF(1,16)=0.00,p=0.968

AH-115
Appendix H. Data Table

Author Year

Artinian 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-116
Appendix H. Data Table

Author Year

Artinian 2003

Use of Technology Usability HIT Satisfaction

NR Technical difficulties

Difficulties with monitor: 4 pts (44%)Difficulty getting out small pills from compartments: 4 pts(44%)Difficulty reading the screen: 2 ptsColor of lettering hard to see: some ptsMessage needs to be long enough for clarity: manyCould not hear beep of the monitor: 1 pt

Loved it: 5pts(56%)Hated it: 4 pts (44%)Quotes of the pts included, not here

AH-117
Appendix H. Data Table

Author Year

Artinian 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Changing lifestyles (i.e. helped establish better routines): 6 (67%)

NR Self care behaviors

Grp 1 vs. Grp 2Baseline Mean(SD)95 (+22) vs. 92 (+8)

3 month Mean (SD)108 (+22) vs. 106 (+21)

ANOVATime: F(1,16)=6.81,p=0.019Grp by time interaction: F(1,16p=0.902Post hoc paired samples t test Grp 1: t (8)= 1.85, p=0.102Grp 2: t (8)=1.85, p= 0.102Compliance with daily monitoriWeight: Grp 1 vs. Grp 2: 79% v85% , t (15)= 0.57,p=0.577BP: Grp 1 vs. Grp 2 : 51% vs. (15)= 1.83, p= 0.095Grp 2 rates as measured by mAverage compliance rates for wweight and BP: 96%Medication compliance rate: 94

AH-118
Appendix H. Data Table

Author Year

Artinian 2003

Quality of Life Support Withdrawals

Total Quality of life (QOL)Grp 1 vs. Grp 2Baseline mean (SD)43 (+22) vs. 51 (+24)

3 month mean28 (+27) vs. 38 (+25)ANOVATime: F (1,16) = 10.00, p= 0.006Grp by time interaction-: F (1,16)= 0.05, p=0.832Post hoc paired samples t testDifference between baseline and follow up mean Grp 1: t (8)=1.78, p=0.113Grp 2: t (8)=4.63, p=0.002Physical QOLGrp 1 vs. Grp 2Baseline mean(SD)21 (+13) vs. 26 (+9)3 month mean14 (+11) vs. 19 (+11)ANOVATime: F (1,16)= 6.20, p=0.024Grp by time interaction: F (1,16)= 0.03, p=0.868

NR NR

AH-119
Appendix H. Data Table

Author Year

Artinian 2003

Adverse Effects Other

NR Clinical characteristics of sample at 

baseline viz NYHA FC, Perceived health, smoking, BMI -given but not listed hereCronbach alpha values for reliability across time given, not listed here

AH-120
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Bachofen 1999 NR NR Homes Cohort 

AH-121
Appendix H. Data Table

Author Year

Bachofen 1999

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 mos NR NR NR

AH-122
Appendix H. Data Table

Author Year

Bachofen 1999

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR OCD NR N = 56.6% Mean (SD): 3

Range: 21-54

AH-123
Appendix H. Data Table

Author Year

Bachofen 1999

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Mean age at o

OCD (SD): 19 

Range: 10-34Mean OCD du(SD): 12 yrs (

Range: 2-28 yOCD rituals (NWashing: 4Checking: 9Harming self others: 6Numbers: 4PerfectionismAll but 1 takinpsychotropic medication at of study

AH-124
Appendix H. Data Table

Author Year

Bachofen 1999

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Self therapy w

AH-125
Appendix H. Data Table

Author Year

Bachofen 1999

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Self-guiding manual and touch tone phone used to access computer driven interviews via an interactive voice response system

Pts given 190 pg BT STEPS manual and a PIN to access the IVR systemA toll free call could be made from any touch tone telephone in the UK to a computer in Madison, WICoordinator explained steps, pts left to personalize their self treatment program under guidance of the manual and the IVR systemAfter call completion, computer generated and faxed a feedback sheet that summarized the call.After pts started doing the ERP sessions, a personalized ERP homework diary sheet, based on the goals they had entered, was mailed to them each weekPts who did not call the IVR system for a week were contacted by phone or mail to inquire the reason.

Touch Tone telephone Question and answers

feedback sheet, personalized ERP diasheet,helped personalize setreatment program

AH-126
Appendix H. Data Table

Author Year

Bachofen 1999

Staff Training Subject Training co-interventions Physiolo

NR A coordinator spent 5 mins 

with each pt, explaining how to use the BT steps and make IVR calls and encouraging daily use of the system. For pts who lived far away, manual was mailed and coordinator explained the steps over the phone

NR Y-BOCS (Yale Brown 

Compulsive scale) TotBaseline vs. CompleteMean (SD)25 (+6.2) vs. 20 (+7.5)

Completers (N=19) (t, analysis (t, df, p)3.19, 18, 0.005 vs. 3.1RitualsBaseline vs. CompleteMean (SD)13 (+3) vs. 10 (+4.4)

Completers (N=19) (t, analysis (t, df, p)3.54, 18, 0.002 vs. 3.4ObsessionsBaseline vs. CompleteMean (SD)11 (+3.8) vs. 10 (+3.4)

Completers (N=19) (t, analysis (t, df, p)1.89, 18, 0.074 vs. 1.8PGI- Patient Global imCompleters (12 week) 2.8 (+1.0)

AH-127
Appendix H. Data Table

Author Year

Bachofen 1999

Health Service Utilization Health Care Costs Knowledge

NR The cost of the system compares 

favorably to that of medication and clinician guided care. They save 80 % of the per patient time neededby a clinician to guide ERP

NR

AH-128
Appendix H. Data Table

Author Year

Bachofen 1999

Use of Technology Usability HIT Satisfaction

Pts used the IVR system over a period of 67.2 (+38.3) days

Pts took a mean of 34.6 (+26.5) days 

from the time they made call 1 until they completed their first ERP sessionPts who did 2 or more ERP sessions took a mean of 64 days doing ERP after completing call 4

NR NR

AH-129
Appendix H. Data Table

Author Year

Bachofen 1999

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Hamilton rating scale for depre

Baseline vs. Completers (12 wMean (SD)22 (+8.1) vs. 17 (+8.3)

Completers (N=19) (t, df, p) vsanalysis (t, df, p)2.99, 18, 0.008 vs. 2.93, 20, 0.Work and social adjustment scTotalBaseline vs. Completers (12 wMean (SD)20 (+7.3) vs. 17 (+7.6)Completers (N=19) (t, df, p) vsanalysis (t, df, p)2.28, 18, 0.035 vs. 2.27, 19, 0.Work itemBaseline vs. Completers (12 wMean (SD)5.2 (+2.7) vs. 4.5 (+2.3)

Completers (N=19) (t, df, p) vsanalysis (t, df, p)1.49, 18, 0.154 vs. 1.49, 19, 0.Home managementBaseline vs. Completers (12 wMean (SD)6.1 (+1.6) vs. 4.9 (+2.2)Completers (N=19) (t, df, p) vsanalysis (t, df, p)2.15, 17, 0.046 vs. 2.14, 18, 0.

AH-130
Appendix H. Data Table

Author Year

Bachofen 1999

Quality of Life Support Withdrawals

NR NR 2 pts had to leave the study as 

turn on the waiting list arrived tbegin clinician guided therapy

AH-131
Appendix H. Data Table

Author Year

Bachofen 1999

Adverse Effects Other

NR Data on improvement in OCD 

patients who completed 2 or more versus 1 or no exposure and ritual prevention session given, not listed here

AH-132
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Barnason 2003 4 Midwestern hospitals Hospital Homes RCT w/ repeated 

measures

AH-133
Appendix H. Data Table

Author Year

Barnason 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 months *From previous parent study of N = 180

*Ischemic Heart Failure (HF) *CABG*Written consent*Age >65

Age < 65 yrs Ye

yrs

AH-134
Appendix H. Data Table

Author Year

Barnason 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CAD NR HCI: 78% 

RC: 59%

Mean (SD)HCI: 73.72 (+

RC: 72.82 (+ 

AH-135
Appendix H. Data Table

Author Year

Barnason 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR "There were n

statistically sigdifferences by for any of the demographic characteristicgender, or lenhospital stay)

AH-136
Appendix H. Data Table

Author Year

Barnason 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Health Buddy

AH-137
Appendix H. Data Table

Author Year

Barnason 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Small device attached to patient's telephone. Provides assessment, strategies, education, and positive reinforcement

Group 1: (Home Communication Intervention): Health Buddy: daily sessionGroup 2: (Routine Care): RC consisted of the usual patient education and counseling provided to all CABG subjects prior to hospital discharge.Ability to ask questions: NR.

Phone attachment /screen Provides daily: 

assessment, strategieeducation, and positivreinforcement

AH-138
Appendix H. Data Table

Author Year

Barnason 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-139
Appendix H. Data Table

Author Year

Barnason 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-140
Appendix H. Data Table

Author Year

Barnason 2003

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-141
Appendix H. Data Table

Author Year

Barnason 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Self-efficacy score:

[F (1,29) = 6.40, P < 0.02]HCI: 50.6 (standard error = 1.1)RC: 46.5 (standard error = 1.1)

Salt Fewer subjects HCI subjects rethe use of salt when cooking aeating at 4 weeks, 6 weeks, anmonths postoperatively.There was as significant differe4 weeks using salt with cookin6.92, P > .01):RC: 47.1%HCI: 6.3 %Exercise Adherence The HCI group reported signifihigher exercise adherence (t = P < .01) and stress control (t=3< .01) at 3 months postoperativ

AH-142
Appendix H. Data Table

Author Year

Barnason 2003

Quality of Life Support Withdrawals

Physical FunctioningHCI subject has significantly [F(1,29) = 4.56, P <.04] higher adjusted physical functioning mean scores (Mean: 84.4, SD: +3.9) compared 

with the RC group (Mean:71.5, SD: +3.9)

General Health FunctioningHCI subjects had significantly [F(1,29) = 6.99, P <.01] higher adjusted general health functioning mean scores (Mean: 79.8, SD: +2.9) 

compared with the RC group (Mean: 69.4, SD: +2.8)

PainThere was a significant time effect [F(1,29)=11.25, P <.002] on the pain subscale both HCI and RC:6 weeks: 77.4 (+3.3)3 months: 87.8 (+3.7)

Mental HealthHCI subjects had significantly [F (1,29)= 8.33, P <.007] higher mental health functioning (Mean: 88.7, SD: +3.09) compared with RC group 

(Mean: 78.0, SD: +3.0).

Validity HCI subjects had significantly [F 

NR NR

AH-143
Appendix H. Data Table

Author Year

Barnason 2003

Adverse Effects Other

NR NR

AH-144
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Barnason 2006 Convenience sample "Midwestern Medical 

Center"

Homes Randomized, experimen

two-group repeated measures design

AH-145
Appendix H. Data Table

Author Year

Barnason 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 months *Speak/read English

*Have telephone service*Undergoing coronary artery bypass graft*Pre-discharge*Ages 65 and older

NR NR

AH-146
Appendix H. Data Table

Author Year

Barnason 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CORONARY 

ARTERY DISEASE

NR 56% Mean: 75.3

AH-147
Appendix H. Data Table

Author Year

Barnason 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-148
Appendix H. Data Table

Author Year

Barnason 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR Health Buddy

AH-149
Appendix H. Data Table

Author Year

Barnason 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

12 weeks of a desktop telehealth device with daily interventions with recovery assessment, strategies to manage problems, education and positive reinforcement

Group 1: Experimental - received Healthcare Intervention (HCI)Group 2: Control - Routine care (HHC)

Desktop telehealth device Illuminated screen wit

large buttons - daily feedback

AH-150
Appendix H. Data Table

Author Year

Barnason 2006

Staff Training Subject Training co-interventions Physiolo

NR Screened for vision and 

key use no reported training

Medical Outcomes Short Form-36 at 6 weeks and 3 mos. after bypass. Physiological Functioning Scales: IVR mean 77.6, CG mean 69.59Time improvement Physical: (F [1,36]=9.42, P<.01)Role Physical:(F[1,36]=5.74, P<.05)Time Effect Mental Health: (F[1,36]=7.97, P<.01) both groups

Non-Routine provider mean 2.8,CG mean 2Emergency room visitIVR 1 subject, CG 6 sstat. significant (squarNursing visits: IVR avg. 9.68(+5.4), C

7.48(+6.8)

AH-151
Appendix H. Data Table

Author Year

Barnason 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-152
Appendix H. Data Table

Author Year

Barnason 2006

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-153
Appendix H. Data Table

Author Year

Barnason 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Self report instrument developed by 

research team

NR

AH-154
Appendix H. Data Table

Author Year

Barnason 2006

Quality of Life Support Withdrawals

WITHIN group time effects differences: Both groups improved:Mental Health: (F[1,36=7.97, P<.01)other within group mental health outcomes listed. 

NR NR

AH-155
Appendix H. Data Table

Author Year

Barnason 2006

Adverse Effects Other

Chest pain, fluid in leg, pneumonia, heart failure, wound infection, dysrhythemias, blood clot: leg, Very nervous, GI problems, constipation

NR

AH-156
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Barrera 2002 16 Primary care medical 

practices

Primary care medical practices

Homes RCT 

AH-157
Appendix H. Data Table

Author Year

Barrera 2002

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 mos *Age: 40-75 years old

*Live in local area*Have a telephone*Read and write English*Be diagnosed with Type 2 diabetes for least 1 year*Did not previously have Internet access at home or work

NR NR

AH-158
Appendix H. Data Table

Author Year

Barrera 2002

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 2 DIABETES NR N = 46.9%

Control: 47.5%Coach: 42.5%Social Support: 52.5%Social Support + Coach: 45%

Mean (SD)Total: 59.3 (+

Control: 60.8 

Coach: 57.6 (Social Suppo(+ 9.2)

Social SuppoCoach: 62.1 (

AH-159
Appendix H. Data Table

Author Year

Barrera 2002

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR Income (this was a 6-

point scale where 1 = less than $10,000, and where 6 > 

$50,000)N = 160, 2.9 (1.4)Control: (n=40), 2.9 (1.2)Coach: (n=40), 2.7 (1.4)Social Support: 3.1 (1.6)Social Support + Coach: 2.7 (1.3)

Education (This was a 7-point scale where 1 = 6th grade or less, 2 = 7th - 9th grade, 3 = 10th - 11th grade, 4 = high school graduate, 5 = some college, 6 = college graduate, 7 = graduate or professional school)N = 160, 4.8 (1.3)Control: (n=40), 4.8 (1.2)Coach: (n=40), 5.0 (1.2)Social Support: 4.7 (1.4)Social Support + Coach: 4.9 (1.3)

NR NR

AH-160
Appendix H. Data Table

Author Year

Barrera 2002

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate English NR NR

AH-161
Appendix H. Data Table

Author Year

Barrera 2002

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

This study used a new measure that was designed to provide a fairevaluation of a Internetbased support group that focused on diabetes.Information Onlycomputer access to an extensive number of informational articles that concerned medical, nutritional, andlifestyle aspects of diabetes. Personal Coach OnlyInformation Only intervention plus they had computer-mediated access to a professional who had expertise in providing dietary advice to diabetes patients. Participants worked with their coach, viewed resources via the web, had access to an online dietary conference, and accessed a personal database where they could enter information on their daily intake of fruits, 

Computers with internet access. Social support websites with forums, live chat, and informational articles.

Computer-based interventions to assess changes in participant perceptions of social support.

NR

AH-162
Appendix H. Data Table

Author Year

Barrera 2002

Staff Training Subject Training co-interventions Physiolo

NR Each participant received 

training in using the features of the computer that would allow them to access and navigate the website.

NR NR

AH-163
Appendix H. Data Table

Author Year

Barrera 2002

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-164
Appendix H. Data Table

Author Year

Barrera 2002

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-165
Appendix H. Data Table

Author Year

Barrera 2002

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-166
Appendix H. Data Table

Author Year

Barrera 2002

Quality of Life Support Withdrawals

NR Age was significantly related to 

change on the DSS, r(123) = -.22, p < .05, and did not show interactions with this condition.Age (as covariate) and DSS change scores (as criterion), ANCOVA showed significant effect for both age, F(1,118) = 6.46, p < .05, and condition, F(3, 118) = 4.88, p < .01, f = .33. Information controls contrasted with Social Support Only and Combined Conditions that contained Internetbased social support was significant t(119) = -3.82, p < .001.

Total = 37. Support group = 10Information, personal coach, acombined condition groups: 9 No significant effects for conditinteraction between groups.

AH-167
Appendix H. Data Table

Author Year

Barrera 2002

Adverse Effects Other

NR NR

AH-168
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Block 2004 NR Berkeley, California Homes RCT Brennan 2001 Hospital NR Homes RCT 

AH-169
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

9 months NR *Female

*African American or non-Hispanic white*Age 40-65 yrs*Low-income

Any criteria against the Inclusion criteria

NR

NR 6 mos NR NR NR

AH-170
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YESBelow poverty: 68.0%100-185% of poverty: 24.5%Above 185% of poverty: 7.5%

NR NR 0.00% 50.1 (39-65)

NR CABG NR N = 78% Mean (Range

(38-82) 

AH-171
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

African American: 48.4%White non-Hispanic: 51.6%

<$10,000: 23.3%10,000-15,000: 9.6%15,000-20,000: 8.5%20,000-25,000: 7.7%25,000-35,000: 17.1%35,000-50,000: 11.6%50,000-65,000: 6.7%>65,000: 15.6%

Elementary only: 0.6%Junior high only: 2.1%HS grad: 31.0%More than HS: 66.3%

NR Fruit and vege

occurrence/da0: 6.2%1: 15.4%2: 18.3%3: 15.6%4: 16.6%5 or more: 27

Caucasian: 86%African American: 12%

NR NR NR NR

AH-172
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR Research 

overwhelmingly implicates low intakes of fruits and vegetables as factors influencing the prevalence of several chronic disease and poor health

Little by Little

NR NR NR NR HeartCare

AH-173
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

CD-ROM that includes a brief assessment of fruit and vegetable intake as well as messages and tips to increase intake

CD-ROM Only: 160CD-ROM + 2 phone calls: 162CG: 159

CD-ROM To increase the level o

and vegetable intake bincome women

Computerized, internetbased information and support system that provides extension and enhancement of traditional nursing services

Group 1: HeartCareGroup 2: CHIPGroup 3: Usual care

Computer/internet Education, support, ta

health information

AH-174
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Staff Training Subject Training co-interventions Physiolo

Interviewers were provided with scripts for the two brief telephone calls made to the CD-ROM + telephone call groups

NR NR Change in Fruit/Veget

Occurrences:CD-ROM + telephone p=0.016CD-ROM only: 1.20, pCG: 0.71, p=NA

None NR NR NR

AH-175
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-176
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Use of Technology Usability HIT Satisfaction

NR NR NRFor one week snapshot: Group 1 used the HeartCare system a total of 451 times, an average of 64 accesses per day

NR NR

AH-177
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Change in Stage of Readiness 

ChangeCD-ROM Only: 0.41, p=0.01CD-ROM + phone calls: 0.31, CG: 0.17, p=NA

NR NR NR

AH-178
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Quality of Life Support Withdrawals

NR NR N=10NR NR NR

AH-179
Appendix H. Data Table

Author Year

Block 2004Brennan 2001

Adverse Effects Other

NR NRNR NR

AH-180
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Carlbring 2007 NR NR Homes RCT 

AH-181
Appendix H. Data Table

Author Year

Carlbring 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 1 year *Fulfill the DSM-4 criteria for social 

phobia according to the SPSQ*Have a total score of below 31 on MADRS-S depression scaleand a score of less than 4 on the suicide item of this scale*Agree to undergo no other psychological treatment for the durationof the study, and have no history of earlier cognitive- behavior therapy*If taking prescribed drugs for anxiety or depression, the dosage had to be constant for 3 months before the start of the treatment, and the participants had to agree to keep the dosage constant throughout the study*Have access to a computer with internet connection*Be at least 18 yrs old*Live in Sweden*Be able to speak to the therapists by phone on a weekly basis*Following telephone administration must have social phobia as the primary diagnosis if other co morbid conditions present*Not currently meet diagnostic criteria for psychosis or substance abuse

NR No

AH-182
Appendix H. Data Table

Author Year

Carlbring 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SOCIAL PHOBIA NR IVR vs. CG:

41 % vs. 29%

Mean (SD)IVR vs. CG:32.4 (+9.1) vs

(+9.2)

AH-183
Appendix H. Data Table

Author Year

Carlbring 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Education %

IVR vs. CGElementary: 7 vs. 7High schoolNot completed: 0 vs. 4Completed: 28 vs. 18Community college: 3 vs. 4College/universityNot completed: 34 vs. 34Completed: 28 vs. 32

NR Self-rated com

experienceIVR vs. CGFar above avevs. 32

Above averag

21Average: 34 vBelow averag14

AH-184
Appendix H. Data Table

Author Year

Carlbring 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Cognitive beh

therapy with tsupport

AH-185
Appendix H. Data Table

Author Year

Carlbring 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

9 week Internet based cognitive behavioral selfhelp designed to increase treatment adherence by minimum therapist contact by email supplementation and short weekly telephone calls.

CG: received no treatmentIVR: Intervention modules included information, exercises and interactive quiz that ended with 3-8 essay questions. Pts encouraged to explain the most important sections of the module, provide thought records, describe experience with and outcome of their exposure exercise. For each module, pts required to post at least one online message in an online discussion group about a predetermined topic.Feedback on homework assign given within 24 hrsOne weekly telephone call made weekly to each pt to provide positive feedback and to answer questions the pt had about the module.

Computer Information modules, 

email, online discussioboard

AH-186
Appendix H. Data Table

Author Year

Carlbring 2007

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-187
Appendix H. Data Table

Author Year

Carlbring 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-188
Appendix H. Data Table

Author Year

Carlbring 2007

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-189
Appendix H. Data Table

Author Year

Carlbring 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR IVR vs. CG

Liebowitz social anxiety scale Fear/AnxietyPre treatment score: Mean (SD)36.0 (+11.7) vs. 34.2 (+10.6)Post treatment score ; Mean (SD)24.2 (+12.0) vs. 36.1 (+12.3)Main effectTime: F=16.8, (p<0.001), group; F= 3.1Interaction effect: 32.2 (p<0.001)Effect sizeWithin group: 1.00 vs. - 0.17Between group: 0.98AvoidancePre treatment score: Mean (SD)35.2 (+12.9) vs. 33.8 (+11.0) Post treatment score ; Mean (SD)21.6 (+12.8) vs. 33.3 (+11.9)Main effectTime: F= 34.2 , (p<0.001), group, F= 3.0Interaction effect: 29.2 (p<0.001)Effect sizeWithin group: 1.06 vs. 0.04

NR

AH-190
Appendix H. Data Table

Author Year

Carlbring 2007

Quality of Life Support Withdrawals

Quality of life inventory, Summary scorePre treatment score: Mean (SD) 0.7 (+2.0) vs. 0.5 (+1.7)

Post treatment score ; Mean (SD)1.4 (+1.8) vs. 0.7 (+1.8)

Main effectTime: F= 9.9 , (p<0.0125), group, F= 1.16Interaction effect: 3.2 Effect sizeWithin group: 0.37 vs. 0.11Between group: 0.39

NR Total randomized= 60, 

Treatment grp=30Began other therapy=1eligible for analysis=29Did not return completed measures=1Post treatment assessment= 2Did not complete follow up=1Follow up data = 27Control grp= 30Began other therapy=1No computer access=1Eligible for analysis=28Post treatment survey= 28No FU for control grp

AH-191
Appendix H. Data Table

Author Year

Carlbring 2007

Adverse Effects Other

NR

AH-192
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Cathala 2003 Outpatient dept. at Institute 

Monstouris

Dept. of Urology, Institute Monstouri

Homes Cohort 

Cavan 2003 NR NR Homes Cohort 

AH-193
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

Preliminary study: JanuaryMarch 2000November 2000-April 2002 

NR *Patients with localized prostate cancer 

treated with radical prostatectomy*Computer in home

NR NR

NR 6 months NR NR NR

AH-194
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR PROSTATE CANCER NR N = 100% Mean: 63

Range: 46-70

Unclear DIABETES NR NR Mean: 36 

Range: 29-61

AH-195
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR Senior executives and 

professionals: 58%Tradesmen: 12%

NR NR NR

NR NR NR Unclear NR

AH-196
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English, French NR WebsiteNR NR NR NR Diasnet

AH-197
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Website to securely house data and answer questions for those with a radical prostatectomy

NR Website History of surgery - 

techniques and complications, video ooperation, glossary, acto medical files includidischarge, operative ahistology, QOL questionnaire, patient/dialogue

A structured patient education program which utilizes the DiasNet computer modelto display and manipulate patient data. The program used as a training exercise in carbohydrate assessment and insulin dose adjustment to help patients optimize their metabolic control

N=6Pts asked to enter from home or work PC, blood glucose values, insulin doses and a food diary.The computer model generated the a simulation of the blood glucose concentration forthe data collection period. It then suggested alternative insulin doses (regimes), or meal sizes, to reduce risk of hypo and hyperglycemia. Pts entered four days data on three occasions in the first month and then as often as they wished.

Computer Data entry, simulation 

blood glucose concentration, suggesof alternative medicatiregimes

AH-198
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR NRNR Pts attended a one hour 

training session, which covered accessing the system, data entry and interpretation.

NR There is a graph show

levels but difficult to givalues, as author has reported the same.

AH-199
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-200
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Use of Technology Usability HIT Satisfaction

11% had problems accessing site, 14% had tech problems attributed to incorrect PSA data entry or not having the right software to run the video

NR Satisfied with site sections: 98

Satisfied with medical file: 94%

Pts did not enter any data for several weeks, only one entered data on three consecutive as requested.

All pts found the graphical display and ability to manipulate their data very useful.Qualitative responses of pts given, not listed here

NR

AH-201
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-202
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Quality of Life Support Withdrawals

NR NR NRNR NR NR

AH-203
Appendix H. Data Table

Author Year

Cathala 2003Cavan 2003

Adverse Effects Other

NR Older patients used email follow up 

more than younger ones, email follow up was greater for patients living further from the hospital

NR NR

AH-204
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Cawsey 2000 NR Beatson Oncology center, 

Glasgow

Homes RCT (more details given 

Jones 1999)

AH-205
Appendix H. Data Table

Author Year

Cawsey 2000

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NR NR Pts receiving palliative 

treatment, those with no knowledge of their diagnosis, those with only a single treatment planned and those not giving consent to participate

NR

AH-206
Appendix H. Data Table

Author Year

Cawsey 2000

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CANCER NR NR NR

AH-207
Appendix H. Data Table

Author Year

Cawsey 2000

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-208
Appendix H. Data Table

Author Year

Cawsey 2000

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Personalized 

system

AH-209
Appendix H. Data Table

Author Year

Cawsey 2000

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Evaluation of a personalized information system for patients with cancer. The system dynamically generates hypertext pages that explain treatment, diseases, measurements etc related to the patient's condition, using information in the patients medial record as the basis for the tailoring

Grp 1: personalized information; access to the system that generated personalized explanation using the medical record, pts received a printout of the dialogue with the system after the sessionGrp 2: generalized information; access to system with similar look and feel but only contained generalized information, main differences were the opening screen and detailed content of individual screens.Grp 3: combined group; this grp had access to both the above systems, an initial screen provided links to both.Grp 4: leaflets group; grp given opportunity to browse and select from a folder containing a wide range of appropriate leaflets

Computer General and personal

information

AH-210
Appendix H. Data Table

Author Year

Cawsey 2000

Staff Training Subject Training co-interventions Physiolo

NR The first 3 grps were invited 

to carry out a computer consultation at the beginning of their course of radiotherapy in the researcher's office; pts also offered to use a computer again after their first use, but one sited in the waiting room

NR NR

AH-211
Appendix H. Data Table

Author Year

Cawsey 2000

Health Service Utilization Health Care Costs Knowledge

NR NR Small but not significant effect 

doctor's assessment of how knowledgeable pts were-- with result that more pts in the genegroup were viewed as above average, (35% vs. 27%, p=0.1

AH-212
Appendix H. Data Table

Author Year

Cawsey 2000

Use of Technology Usability HIT Satisfaction

NS difference in the average time spent using the computer and only a small difference (p=0.11) in which group used the computer at any time after the first session.Pts receiving personalized information was more likely to use the computer between 3 weeks and 3 months (12/80 vs. 4/155, chi sq=12.8, p<0.001)The majority of the pts used the printouts of their sessions with the computer later at home, 73% of personalized information grp and 59% of general information grp (chi square=3.4, 1 df, p=0.07)Pts in the personal grp were more likely to report using it with their family

NR Pt views on information given; 

general vs. personalWas info useful: 76 (60) vs. 51 p=0.16Did it tell you anything new: 63 vs. 46 (64), p=0.05Was information relevant: 85 (63 (85), p= 0.004Find information easily:109 (8563 (89), p= 0.41Feel overwhelmed with info: 37 vs. 17 (23), p=0.41Was it too technical:18 (14) vsp=0.23Was it too limited: 71 (56) vs. 3p= 0.04Satisfaction score: 44 (26) vs. (42), p= 0.04A t test between patient satisfascores between the two groupshows that the mean score of for the personal group is signifhigher than the mean score of 

AH-213
Appendix H. Data Table

Author Year

Cawsey 2000

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NS difference found in the 

psychological state between the groups as measured by the HADS and MAC questionnaires.NS difference on the doctor's assessment of their anxiety, or of how active/passive they were

NR

AH-214
Appendix H. Data Table

Author Year

Cawsey 2000

Quality of Life Support Withdrawals

NR NR 789 pts were eligible for the stu

74 excluded and 190 refused tpart in the study(more details in Jones et al 19

AH-215
Appendix H. Data Table

Author Year

Cawsey 2000

Adverse Effects Other

NR At 3 months, pts asked if they would 

prefer unlimited time with their computer for getting information, or 10 mins with a specialist nurse or radiographer. Most preferred latter, but pts using personalized design were slightly more likely to choose time with the computer(32% vs. 20%), (chi square=2.9, 1df, p=0.09)

AH-216
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Chan 2003 Pediatric clinic Honolulu, HI Homes Cohort 

AH-217
Appendix H. Data Table

Author Year

Chan 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

6 mos NR *Pediatric patients

*Ages 6-17*Persistent asthma*Written informed consent obtained from parents/guardians*Assent obtained from all children over the age of 7

NR NR

AH-218
Appendix H. Data Table

Author Year

Chan 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ASTHMA NR Total: 50%

Office grp: 80%Virtual: 20%

Mean (SD)7.6 (+2)

AH-219
Appendix H. Data Table

Author Year

Chan 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-220
Appendix H. Data Table

Author Year

Chan 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Home monito

AH-221
Appendix H. Data Table

Author Year

Chan 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Pts used the digital video camera and computer to record peak flow meter readings and their daily use of the MDI or DPI two times a week

Office group: had scheduled office visits to receive asthma educationVirtual group: received asthma education via the internet

Home computer system, video camera, microphone, cable to access the internet

Receive asthma educvia the internet, record peak flow meter use avalues, transmit this d

AH-222
Appendix H. Data Table

Author Year

Chan 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR Asthma Control

Days 0-90Peak flow values (% obest)Total: 84.3Virtual: 82.7Office: 86.0Total # unscheduled avisitsTotal: 2Virtual: 1Office: 1% symptom-free days diaryTotal: 75.1Virtual: 70.8Office: 80.4Mean (SD) total sympscoreTotal: 4.5 (+0.5)

Virtual: 4.5 (+0.6)

Office: 4.4 (+0.4)

Mean (SD) # beta-agodiaryTotal: 10.8 (+18.0

Virtual: 3.0 (+3.3)

Office: 20.5 (+24.9)

AH-223
Appendix H. Data Table

Author Year

Chan 2003

Health Service Utilization Health Care Costs Knowledge

Therapeutic AdherenceDays 0-90Virtual vs. Office vs. TotalTotal # corticosteroid inhalers refilled:8 vs. 16 vs. 24Mean (SD) # corticosteroid inhalers per patient per mo0.5 (+0.5) vs. 1.1 (+0.6) vs. 08. (+0.6)

Total # videos of inhaler use sent84 vs. 109 vs. 193Mean (SD) # videos of inhaler use submitted per person16.8 (+3.4) vs. 21.3 (+6.7) vs. 19.3 

(+5.6)

Adherence to submission of videos of 

inhaler use (%)70.0 vs. 90.8 vs. 80.4DPI or MDI plus space technique score (%)87.2 vs. 87.3 vs. 87.3Days 91-180Virtual vs. Office vs. TotalTotal # corticosteroid inhalers refilled:6 vs. 8 vs. 14

NR Asthma Knowledge Test and S

Results, Mean (SD)Virtual vs. Office vs. TotalKnowledge test score (%)Before Study82.0 (+8.8) vs. 77.0 (+5.2) vs. 

(+7.5)

After Study84.8 (+7.6) vs. 8.3 (+5.5) vs. 8

(+6.6)

AH-224
Appendix H. Data Table

Author Year

Chan 2003

Use of Technology Usability HIT Satisfaction

Diagnostic AdherenceVirtual vs. Office vs. TotalDays 0-90Total # asthma diary entries119 vs. 293 vs. 412Mean (SD) # asthma diary entries per patient23.8 (+23.8) vs. 58.6 (+39.4) vs. 41.2 

(+35.7)

Asthma diary adherence (%)18.7 vs. 65.1 vs. 41.9Total # videos of peak flow meter use submitted83 vs. 111 vs. 194Mean (SD) # videos of peak flow meter use submitted per patient16.6 (+3.2) vs. 22.2 (+7.5) vs. 19.4 

(+6.2)

Adherence to submission of videos of 

peak flow meter use (%)69.2 vs. 92.5 vs. 80.8Peak flow meter technique score (%)99.7 vs. 99.2 vs. 99.5Days 91-180

NR Virtual vs. Office vs. Total

Satisfaction survey score4.0 (+0.3) vs. 4.2 (+0.6) vs. 4.1 

Satisfaction with electronic che3.8 (+0.8) vs. 4.3 (+1.0) vs. 4.0 

AH-225
Appendix H. Data Table

Author Year

Chan 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-226
Appendix H. Data Table

Author Year

Chan 2003

Quality of Life Support Withdrawals

Virtual vs. Office vs. TotalQoL ScoreBefore StudyCaregiver5.6 (+1.2) vs. 6.4 (+0.7) vs. 6.0 (+1.0)

Patient6.6 (+0.4) vs. 6.5 (+0.7) vs. 6.5 (+0.5)After studyCaregiver6.2 (+1.0) vs. 6.4 (+0.6) vs. 6.3 (+0.8)Patient6.6 (+0.2) vs. 6.6 (+0.6) vs. 6.6 (+0.4)

NR NR

AH-227
Appendix H. Data Table

Author Year

Chan 2003

Adverse Effects Other

NR NR

AH-228
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Chen 2006 NR Senior High school 

students

Homes Non-Random Group 

AH-229
Appendix H. Data Table

Author Year

Chen 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NR NR NR NR

AH-230
Appendix H. Data Table

Author Year

Chen 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SMOKING NR Grp 1 vs. Grp 2:

100% vs. 87.2%

NR

AH-231
Appendix H. Data Table

Author Year

Chen 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR High school students NR Grp 1 vs. Grp 

Using antioxid0% vs. 10.3%Having part ti10.5% vs. 20.Experiencing cessation: 57.9% vs. 79.Having family who is smokin65.8% vs. 69.

AH-232
Appendix H. Data Table

Author Year

Chen 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Smoking cess

program

AH-233
Appendix H. Data Table

Author Year

Chen 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Comparing effects of auricular acupressure with or without an internet assisted program on smoking cessation and self efficacy of adolescents

Grp 1: received 4 weeks of auricular acupressure plus an internet smoking cessation programGrp 2: received only auricular acupressure

Auricular acupressure: five 

common points used, shenmen, lung, mouth, stomach and endocrine points, seed embedding method used, pts asked to press each point for at least a minute, 3-5 times/day for 4 weeks, adhesive patch with the ear seed renewed weekly.Internet assisted smoking cessation program: website composed of interactive pages with the following unitsa) Impact of smoking (smoking and health, second hand smoking, difficulty of quitting and legal regulationb) Auricular acupressure for smoking cessation 

Computers Forums, counseling, 

education, hyperlinked website surfing 

AH-234
Appendix H. Data Table

Author Year

Chen 2006

Staff Training Subject Training co-interventions Physiolo

NR NR NR Mean serum cotinine l

1 vs. Grp 2:Before intervention:346.77 ng/ml (142.51) ng/ml (169.71)After intervention:342.29 ng/ml (182.70) ng/ml (187.68)In terms of quitting rat100 ng/ml (Grp 1vs Gr6/38 (15.78%) vs. 1/39 For the degree of nicodependenceBefore intervention, GLow: 24 (63.2%) vs. 3Medium: 10 (26.3%) vHigh: 4 (10%) vs. 2 (5After intervention, grp Low: 31 (81.6%) vs. 3Medium: 6 (15.8%) vsHigh: 1 (2.6%) vs. 3 (7Group 1 showed a stasignificant difference odependence before anintervention (z= - 2.81Grp 2 did not show andifference (z= - 0.47, p 

AH-235
Appendix H. Data Table

Author Year

Chen 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-236
Appendix H. Data Table

Author Year

Chen 2006

Use of Technology Usability HIT Satisfaction

NR NR Usefulness of website, Mean (

Overall usefulness of website: (+1.49)

Discussion forum: 5.53 (+1.47

Auricular acupressure for smocessation: 5.15 (+1.46)

Impact of smoking: 4.98 (+1.37

Critical issues in smoking cess4.82 (+1.50)

Hot topics: 4.76 (+1.72) 

Hyperlinked websites: 4.66 (+1

Professional counseling: 4.64 

Online questionnaire: 4.51 (+1

AH-237
Appendix H. Data Table

Author Year

Chen 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Self efficacy, M (SD), pretest vs. 

posttestGrp 1: 45.80 (+15.14) vs. 58.57 

(+15.67), t= 5.77, p < 0.001

Grp 2: 43.47 (+14.88) vs. 48.97 

(+12.54), t= 2.32, p< 0.05

NR

AH-238
Appendix H. Data Table

Author Year

Chen 2006

Quality of Life Support Withdrawals

NR NR NR

AH-239
Appendix H. Data Table

Author Year

Chen 2006

Adverse Effects Other

NR NR

AH-240
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Cho 2006 NR Kang nam St Mary's 

hospital diabetes center

Homes RCT 

AH-241
Appendix H. Data Table

Author Year

Cho 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

February 2002-August 2004

30 months *Age > 30 yrs

*Followed up for more than 6 months in the clinic

Disabling conditions or disease like heart failure, hepatic dysfunction, a creatinine level > 0.133 mmol/l, severe complications of diabetes, or treatment with an intensified insulin regimen. Also people who did not have internet access in their homes or offices, did not knowhow to use the internet, or did not wish to participate in the study were excluded. Pts having history of participating in other programs that provided similar information or if they received diabetes management education from any other website other than the study website were excluded.

NR

AH-242
Appendix H. Data Table

Author Year

Cho 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 2 DIABETES NR CG: 57.5%

IG: 65%

Mean (SD) CG: 54.6 (+8.

IG: 51.3 (+9.1

AH-243
Appendix H. Data Table

Author Year

Cho 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Mean (SD) 

CG vs. IGDiabetes durayrs: 6.9 (+5.7) 

(+5.3), p=0.86

Hypertension(+32.5) vs. 11 

p=0.808BMI (kg/m2): (+2.8) vs. 22.8 

p=0.139SBP (mmHg)(+16.1) vs. 12

(+16.5), p= 0.DBP (mmHg)(+9.1) vs. 74.0 (+11.6), p= 0.Glucose contrmethodsCG vs. IGLifestyle mod3 vs. 4Oral medicati30 vs. 25Oral + InsulinInsulin only: 4 

AH-244
Appendix H. Data Table

Author Year

Cho 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Internet Base

Monitoring Sy(IBGMS)

AH-245
Appendix H. Data Table

Author Year

Cho 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Internet based system for blood glucose monitoring with additional information such asuse of current medication, blood pressure and weight with appropriate recommendationsprovided by the study team

IG: Pts logged on to biodang.com and uploaded glucose levels on a blood glucose board on the online chart. Additional information on the use of current medication, blood pressure and weight were uploaded. Pts also recorded changes in lifestyle and any questions or detailed information that the pt wished to discuss. The clinical instructors in the team monitored the system and sent appropriate recommendations to each pt every 2 weeks. Every 3 mos, pts had a face to face interview with their physician and provided a blood sample for follow up lab testing.CG: Pts used a conventional note keeping record system. They were given the clinic's usual recommendations about medications, dosage and lifestyle modification from 

Computer Uploading glucose lev

mail communications.

AH-246
Appendix H. Data Table

Author Year

Cho 2006

Staff Training Subject Training co-interventions Physiolo

NR Pts were taught how to use 

the system

NR Mean (SD): CG vs. IG

FBG (mmol/l) ; Baseline: 7.67 (+2.8) v(+3.36), p= 0.52615 months: 7.99 (+1.8(+2.61)30 months: 7.87 (+1.8(+2.68)A1C (%)Baseline: 7.5 (+1.3 ) vp= 0.45715 months: 7.4 (+1.3) 30 months: 7.4 (+1.3) Total Cholesterol (mmBaseline: 4.8 (+0.9) vsp=0.40315 months: 4.27 (+0.8(+0.72)30 months: 4.49 (+0.7(+0.67)Triglyceride (mmol/l)Baseline: 1.68 (+1.22) (+0.8), p= 0.06215 months:1.5 (+0.95) (+1.29)30 months: 1.28 (+0.7(+0.73)

AH-247
Appendix H. Data Table

Author Year

Cho 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-248
Appendix H. Data Table

Author Year

Cho 2006

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-249
Appendix H. Data Table

Author Year

Cho 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-250
Appendix H. Data Table

Author Year

Cho 2006

Quality of Life Support Withdrawals

NR NR NR

AH-251
Appendix H. Data Table

Author Year

Cho 2006

Adverse Effects Other

NR Graphs of the long term results given

There is a table of staff recommendations and patient reports given, not listed here

AH-252
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Christensen 2004 NR NR Homes RCT 

AH-253
Appendix H. Data Table

Author Year

Christensen 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

August 2002- May 2003

6 weeks *Access to internet

*Scores of 22 or above on the Kesler psychological distress scale*Not receiving clinical care from either a psychologist or a psychiatrist

NR NR

AH-254
Appendix H. Data Table

Author Year

Christensen 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DEPRESSION NR Bluepages: 31%

MoodGym: 25%Control: 30%

Bluepages: 37(+9.4)

MoodGym: 35(+9.5)Control: 36.29 

AH-255
Appendix H. Data Table

Author Year

Christensen 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Years (SD) spent in 

educationBluepages: 15.0 (+2.4)

MoodGym: 14.6 (+2.4)

Control: 14.4 (+2.3)

NR Marital status 

Bluepage vs. Moodgym vs. Married/cohab61 vs. 54 vs. Divorced/sepa15 vs. 14 vs. Never married30 vs. 31 vs. Mean score (SKessler psychdistress scaleBluepage vs. Moodgym vs. 17.5 (+4.9) vs

(+5.0) vs. 18.0 

Info on intervepreference, mscore on centepidemiologicstudies deprescale given, nhere 

AH-256
Appendix H. Data Table

Author Year

Christensen 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR Blue page and 

MoodGym

Blue page an

AH-257
Appendix H. Data Table

Author Year

Christensen 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Blue page: information about depression with evidence based information at 8th grade reading level from the site http://bluepages.anu.edu.auMoodGym: offered cognitive behavioral therapy for the prevention of depression from site http://moodgym.anu.edu.au 

Bluepage (Grp 1): website available freely in world wide web, pts given a login identification number and directed each week to one of the five sections of the website with an overview at 6 weeks, post intervention questionnaire at 6 weeks.MoodGym (Grp 2): pts given a login identification number and undertook online assessments, MoodGym consisted of five interactive modules which were made available sequentially weekly; pts revised all aspects of the program in the sixth week, post intervention questionnaire at 6 weeks.Control Grp (Grp 3): Pts phoned weekly by interviewers to discuss lifestyle and environmental factors influencing depression.(Topics included education and hobbies, social, financial and family roles, work 

Computer Education, cognitive 

behavioral therapy (onassessments)

AH-258
Appendix H. Data Table

Author Year

Christensen 2004

Staff Training Subject Training co-interventions Physiolo

Interviewers received instruction booklets providing timetables for tracking phone calls and verbatim instructions.

Pts sent detailed guidelines outlining navigation and weekly assignments for MoodGym or Bluepages

NR NR

AH-259
Appendix H. Data Table

Author Year

Christensen 2004

Health Service Utilization Health Care Costs Knowledge

NR NR Intention to treat (n=525)

Medical LiteracyBluepages vs. MoodGym vs. C-0.6 (+0.7) vs. -0.1 (+0.5) vs. -0(+0.5)Difference (95% CI)Bluepages vs. MoodGym:-0.5 (-0.7 to -0.4) (S)MoodGym vs. Control:0.0 (-0.1 to 0.2)Bluepages vs. Control:-0.5 (-0.6 to -0.3) (S)Psychological LiteracyBluepages vs. MoodGym vs. C-0.7 (+1.1) vs. -0.5 (+1.0) vs. - (+0.9)Difference (95% CI)Bluepages vs. MoodGym:-0.3 (-0.5 to 0.0) (S)MoodGym vs. Control:-0.4 (-0.7 to -0.2) (S)Bluepages vs. Control:-0.7 (-1.0 to -0.4) (S)Lifestyle LiteracyBluepages vs. MoodGym vs. C-0.6 (+0.9) vs. -0.0 (+0.5) vs. 0(+0.8)Difference (95% CI)Bluepages vs. MoodGym:

AH-260
Appendix H. Data Table

Author Year

Christensen 2004

Use of Technology Usability HIT Satisfaction

Bluepage: Site visited an avg of 4.49 (SD= 1.35, n=113) with an average of 67.2 (23.9) hitsMoodGYM: pts completed half of 29 exercises (14.8, 9.7)

NR The author states that both the 

were acceptable to pts, with drrates of 25% and 15% (these rare remarkably low in compariwith other interventions)

AH-261
Appendix H. Data Table

Author Year

Christensen 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Intention to treat (n=525)Center for epidemiological studies depression scale, Mean (SD)Bluepages vs. MoodGym vs. Control:3.9 (+9.1) vs. 4.2 (+9.1) vs. 1.0 (+8.4)

Difference (95% CI)Bluepages vs. MoodGym:-0.3 ( -2.6 to 2.0)MoodGym vs. Control:3.2 (0.9 to 5.4) (significant at 0.05 level-S)Bluepages vs. Control:2.9 (0.6 to 5.2) (S)Automatic thoughts questionnaireBluepages vs. MoodGym vs. Control= 6.4 (+18.1) vs. 9.3 (+16.9) 

vs. 3.1 (+15.8)

Difference (95% CI)Bluepages vs. MoodGym:2.8 ( -7.2 to 1.5) MoodGym vs. Control:6.1 (1.9 to 10.4 ) (S)Bluepages vs. Control:3.3 (- 1.1 to 7.7)Completers (n=414)Center for epidemiological studies depression scale, Mean (SD)Bluepages vs. MoodGym vs. Control:

NR NR

AH-262
Appendix H. Data Table

Author Year

Christensen 2004

Quality of Life Support Withdrawals

NR NR MoodGYM: 25%

Bluepage: 15%BluepagesAssigned= 165Total dropouts= 25(Too busy=5, not contactable=trouble with internet= 1, ill=1, dlike=1, incorrectly included=1, reason given=4)MoodGymAssigned= 182Total dropout= 46(No reason given=12, not contactable=10, too busy=7, fareasons=3, didn't like it=6, trouwith internet=5, other=3)ControlAssigned= 178Total dropout=19(no reason given=14, lost interfamily problem=1, too busy=1, contactable= 1, ill=1)

AH-263
Appendix H. Data Table

Author Year

Christensen 2004

Adverse Effects Other

NR NS

AH-264
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Cross 2007 University of Maryland IBD 

program and gastroenterology clinic at VA in Baltimore

Maryland Homes Cohort 

AH-265
Appendix H. Data Table

Author Year

Cross 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

2004-2005 6 months *Diagnosable IBD

*Comprehend simple instructions in English*Have telephone at home*Ages > 18 years

Posessing other types of colitis (infectious, ischemic, microscopic), no telephone at home, uncontrolled medical or psychiatric disease,had had surgical ostomy, had undergone IBD surgery in the past 6 months, had had a previous colectomy and ileoanal anastomosis, were pregnant, or were less than 18 years of age

NR

AH-266
Appendix H. Data Table

Author Year

Cross 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR IBD NR 52% 43.1 + 15.3

AH-267
Appendix H. Data Table

Author Year

Cross 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 76%Hispanic: 8%African American: 16%

NR Years (SD) spent in 

educationBluepages: 15.0 (+2.4)

MoodGym: 14.6 (+2.4)

Control: 14.4 (+2.3)

NR Type of IBD

Ulcerative CoCrohn's DiseaIndeterminate 8%Disease seveprevious monNone: 16%Mild: 24%Moderate: 44Severe: 16%EmploymentFull-time: 56%Part-time: 16%Student: 8%None: 20%Medical TheraAminosalicylaAntibiotis: 20%Steroids: 52%Immunomodu32%Infliximab: 20Cyclosporine: Internet UseOnce a day: 7Once a weekOnce a month 

AH-268
Appendix H. Data Table

Author Year

Cross 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR Home Telema

System (HAT

AH-269
Appendix H. Data Table

Author Year

Cross 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Laptop connected to an electronic scale: desgined for pt self-test and symptom diary recording along with medication side effect recording and automated feedback (tailored to the symptoms/side effects of each pt)

All pts received their normal IBD care along with HAT

Laptop and a Scale Education, data input, 

collection, tailored feedback

AH-270
Appendix H. Data Table

Author Year

Cross 2007

Staff Training Subject Training co-interventions Physiolo

NR 30-40 minute instruction 

session at home

Regular IBD care NR

AH-271
Appendix H. Data Table

Author Year

Cross 2007

Health Service Utilization Health Care Costs Knowledge

NR NR Pre-Intervention

Very limited: 32%Good: 64%Excellent: 4%CCKNOW Scores Baseline vsmonths12 + 4.4 vs. 9.2 + 5.3

AH-272
Appendix H. Data Table

Author Year

Cross 2007

Use of Technology Usability HIT Satisfaction

Adherence: overall weekly adherence 

with self-tests was 91% +11 with only 

3 pts (12%) completing less than 80% the self-tests during the 6 month period

91% thought the self-testing was not compolicated and that it took very little time86% said that the testing did not interefere with their normal activities64% said they could do the selftesting more than once a week

81% said that the initial training session was sufficient to use H9% thought that it was too limituse HAT91% of pts would consider usinin the future90% of pts were happy with hereceived from HAT at 6 mos vsinitially

AH-273
Appendix H. Data Table

Author Year

Cross 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Baseline vs. 6 monthsHBI3.8 + 4.3 vs 2.2 +3.6

p = 0.09Sedimentation Rate25.7 + 34.4 vs. 16.1 + 17.9

p = 0.06C-reactive Protein11.3 + 25.9 vs. 3 + 4

p = 0.1386% of pts reported receiving excellent care at 6 mos, vs. 65% at baseline57% of pts had their needs met at baseline, 71% after implementaiton of HAT

NR NR

AH-274
Appendix H. Data Table

Author Year

Cross 2007

Quality of Life Support Withdrawals

SIBDQ Questionnaire Scores Baseline vs. 6 months49.2 + 15.1 vs. 55.6 + 14.5

p = 0.06

NR N=34

5 did not comply with installatioHAT in home2 pts did not have telphone at 1 pt withdrew after 1st month1 pt had extended hospital stay

AH-275
Appendix H. Data Table

Author Year

Cross 2007

Adverse Effects Other

NR NS

AH-276
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Cruz-Correia 2007 Outpatient clinic Immunoallergy clinic Homes Randomized cross-over 

study

AH-277
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 2 mos *Age 16 to 65

*Diagnosis of asthma for 6+ months*Use inhaled budesonide/formoterol*Pre-bronchodilator FEV1 >50% predicted

Severe psychiatric, neurological, oncologic or immunologic disease.Unable to access Internet during study period.

NR

AH-278
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ASTHMA NR N = 29% Mean: 29

AH-279
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-280
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR P'ASMA

AH-281
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Web-based monitoring and decision support tool, tailored action plan, for patients and physician, vs. paper-based monitoring

Paper ® Internet (4 wks each)Internet ® Paper (4 wks each)

Peak flow and symptom diary

NR

AH-282
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Staff Training Subject Training co-interventions Physiolo

NR Subjects given instruction 

on instruments and peakflow monitoring during each 4-week phase of the study

NR NR

AH-283
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-284
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Use of Technology Usability HIT Satisfaction

Median time to complete diaries was 3 minutes for paper and Internet (p=.675)Median time to complete peak flow monitoring was 2 minutes (same for both)

Paper vs. Internet vs. Piko-1 (peak flow):Easy to record data: 93% vs. 93% vs. 93%Easy to view data: 81% vs. 93% vs. 67%Like to use it: 80% vs. 93% vs. 100%Takes too long to use: 31% vs. 33% vs. 37%

NR

AH-285
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Mean % adherence to self-mo

Piko-1 during paper: 45%Piko-1 during Internet: 46%Paper diary: 92%Internet diary: 41%

AH-286
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Quality of Life Support Withdrawals

NR NR NR

AH-287
Appendix H. Data Table

Author Year

Cruz-Correia 2007

Adverse Effects Other

NR Paper vs. Internet vs. Piko1 (peak 

flow):Easy to forget: 44% vs. 47% vs. 40%It may be useful: 87% vs. 100% vs. 93%It may help improve asthma control a lot: 50% vs. 73% vs. 50%It may help improve treatment adherence a lot: 53% vs. 64% vs. 58%

AH-288
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Delgado 2003 NR (author says that 

patients attending the university heart failure clinic but does not give any otherdetails)

Heart failure clinic at Toronto General hospital

Homes Cohort 

AH-289
Appendix H. Data Table

Author Year

Delgado 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 months *Confirmed diagnosis of heart failure

*Internet access at home or workplace*Pts who did not have access to the internet but who had a caregiver who could access the internet were also invited to participate

Expected survival rate of less than 6 months based on the patient's clinical status and prognostic functional tests (e.g. cardiopulmonary tests ordered by the cardiologist)

NR

AH-290
Appendix H. Data Table

Author Year

Delgado 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

Unclear HEART FAILURE NR N = 10 pts (62.5%) Mean (SD): 5

(+12.5)

Range: 32-83

AH-291
Appendix H. Data Table

Author Year

Delgado 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR Unclear/NR Ishaemic dise

ptsIdiopathic dilacardiomyopatOther causesNYHA classifiClass 2: 7 ptsClass 3: 9 ptsMedication prACEI: 14 pts Beta blockers(81.2%)Spironolacton(62.5%)Digoxin: 13 pt(81.2%)Furosemide: (87.5%)ARB: 3 pts (1Hydralazine/npt (0.6%)

AH-292
Appendix H. Data Table

Author Year

Delgado 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR www.heartfun

AH-293
Appendix H. Data Table

Author Year

Delgado 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Internet communication website was designed to be an informative resource for the patient and a daily communication method between patients and heart failure clinic at Toronto general hospital.

16 patients recruited in the cohort study. The website allowed for quick trending of weights, checkingof medication, tracking goals of therapy and easy communication of non urgent cases. Pt were asked not to use the system for urgent communications. The website provided updated information of the most relevant clinical trials in heart failure, updates made every 3 months to the homepage, facilitated links to sites like AHA, WHO. Pts instructed to enter their first morning weight, heart rate and blood pressure and any specific comments or questions, reply sent through the system by research nurse, responses included increasing or decreasing the dose of diuretic, obtain blood work, info about test results, etc. 

Computer Daily logs of weight, 

communication with research nurse, educalinks to other sites

AH-294
Appendix H. Data Table

Author Year

Delgado 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-295
Appendix H. Data Table

Author Year

Delgado 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-296
Appendix H. Data Table

Author Year

Delgado 2003

Use of Technology Usability HIT Satisfaction

For pts who completed the study questionnaire within 3 months, the average of logins was 73Drop off in frequency of site hits by a small group of patients over time, but majority continued usageover long periods (over 1 year)

NR Questionnaire on patient satisf

Mean score resultsThe links at the site were usefuI was able to enter information 5.0I would reliably receive a respofrom the clinic: 4.56I found the clinic website usefuCommunication using the webimproved my quality of care: 4

AH-297
Appendix H. Data Table

Author Year

Delgado 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-298
Appendix H. Data Table

Author Year

Delgado 2003

Quality of Life Support Withdrawals

Mean MLHFQ scoreBaseline: 59.75 (+1.5)

3 months: 49.87 (+1.3)

3 month scores vs. baseline scoresRelationship with friends and family 3.062 vs. 2.06,p =0.03Working around the house and yard3.68 vs. 2.81, p=0.008Side effects of medication2.81 vs. 1.93, p=0.048

NR NR

AH-299
Appendix H. Data Table

Author Year

Delgado 2003

Adverse Effects Other

NR Qualitative responses for patient 

satisfaction given, not listed here

AH-300
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Durso 2003 NR Physician clinic (The 

author's practice)

Homes Cohort 

AH-301
Appendix H. Data Table

Author Year

Durso 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 months Not Specified

*Type2 diabetes pts *Over 60 yrs of age *Independent in selfcare*Able to use the phone

NR NR

AH-302
Appendix H. Data Table

Author Year

Durso 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

Unclear TYPE 2 DIABETES NR N = 3 (42.86%) Mean (SD): 7

(+9.91)

AH-303
Appendix H. Data Table

Author Year

Durso 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR 6 pts had some 

college or higher level of education

Unclear Living alone in 

continuing caretirement com2 ptsLiving with soLiving with spptsMedicationTaking 9 or mmedicines/dayManaged with 3 ptsTreated with odiabetics- othCellular telephused by 1 pt bstudyFor the studyCellular telephused by 3 ptsHome phones 2 ptsUsed both - 2 

AH-304
Appendix H. Data Table

Author Year

Durso 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR personal diab

management (PDMS)

AH-305
Appendix H. Data Table

Author Year

Durso 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Web based interface into the automated telephone call system used to look into older patient's ability to use the PDMS and its impact on diabetes health behaviors and patient and provider communications

10 patients recruited. At times predetermined by patients and provider, the PDMS prompts patients with interactive recorded human voice messages, these include performing self care activities (e.g. check feet, check blood glucose, take medication),calling the provider for medical advice, providing health education, recording personal health data.Messages customized to meet individual needs. Pts record data by entering values on the key pad.data then transmitted to the secure web site and reviewed by health care provider at his/her discretion.

Telephone Data logs, customized 

reminders, performing care activities, providinhealtheducation

AH-306
Appendix H. Data Table

Author Year

Durso 2003

Staff Training Subject Training co-interventions Physiolo

NR Each pts was provided with 

individualized diabetes education. Pts also givenAmerican Diabetes Association (ADA) book Diabetes A to Z. The nurse practitionertaught each pt how to use the cellular phone to receive messages and record glucosevalues and weight.The PDMS prompted pts to call nurse practitioner when pt experienced diabetic symptoms, medication side effects or any problems with the care plan.

NR Glycosylated hemoglo

subject - Before vs. af1001 - 8.6 vs. 7.31002- 7.4 vs. 7.01004- 6.7 vs. 6.81005- 6.0 vs. 6.11007- 8.6 vs. 7.61008- 7.8 vs. 8.01011- 8.3 vs. 5.9BMIsubject - Before vs. Af1001- 30 vs. 321002- 24 vs. 231004- 33 vs. 331005- 29 vs. 281007- 29 vs. 271008- 28 vs. 291011- 35 vs. 33

AH-307
Appendix H. Data Table

Author Year

Durso 2003

Health Service Utilization Health Care Costs Knowledge

NR NR Diabetes Knowledge test score

Subject- Before vs. After1001: 86 vs. 861002: 79 vs. 1001004: 57 vs. 861005: 71 vs. 711007: 79 vs. 1001008: 79 vs. 711011: 64 vs. 57

AH-308
Appendix H. Data Table

Author Year

Durso 2003

Use of Technology Usability HIT Satisfaction

NR PDMS study usability questions (1-5 

scale, 5 is the best), Mean (SD)Convenience of cellular phones: 3.71 (+1.7)

Convenience of home phones: 4.17 (+1.60)Quality of voice on phone: 4.71 (+0.76)Your ability to understand message: 4.71 (+0.76)

Ease of cell phone use: 2.57 (+1.40)

Ease of home phone use: 4.33 (+1.63)

Ease of recording glucose, weight, and exercise: 3.86 (+1.46)

Value in management of diabetes: 4.28 (+0.95)

Aid to communication with health provider: 4.28 (+1.50)

NR

AH-309
Appendix H. Data Table

Author Year

Durso 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Frequency: % (n) (Before) vs. 

(After)ActivityNever: 42.9% (3) vs. 01-2 X week: 14.3%(1) vs. 14.33-4 X week: 14.3% (1) vs. 28.65-6 X week: 28.6% (2) vs. 14.3Everyday: 0 vs. 42.9% (3)Monitor glucoseNever: 28.6% (2) vs. 14.3% (14-6 X week: 14.3% (1) vs. 14.3Once/day: 28.6% (2) vs. 28.6% More than once/day: 28.6%(2) 42.9% (3)Miss medicationNever: 85% (6) vs. 85% (6)1-2 X month: 0 vs. 14.3 % (1)1-3 X week: 14.3% (1) vs. 0Check feetDaily: 100 % (7) vs. 100% (7)Symptoms of hypoglycemiaNever: 42.9% (3) vs. 14.3% (11-2 X month: 57.1 % (4) vs. 7(5)1-3 X week: 0 vs. 14.3 % (1)Symptoms of hyperglycemia

AH-310
Appendix H. Data Table

Author Year

Durso 2003

Quality of Life Support Withdrawals

NR NR 3 did not complete the study d

acute illness and hospitalizatio

AH-311
Appendix H. Data Table

Author Year

Durso 2003

Adverse Effects Other

NR Qualitative response in the form of 

summary of participants comments given, not listed here

AH-312
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Earnest 2004 Waiting room of clinic CHF clinic at U of Colorado 

Hospital at Denver 

Homes RCT 

AH-313
Appendix H. Data Table

Author Year

Earnest 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

JanuaryDecember 2002

12 mos *Followed up in the clinic, *Spoke 

English*18+ yrs old*Have used a web browser (not necessary to have home internet)

NR NR

AH-314
Appendix H. Data Table

Author Year

Earnest 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: 10% Minority CHF NR 77% (CI 68-84) Mean: 54

Range: 54-58

AH-315
Appendix H. Data Table

Author Year

Earnest 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 90% >$45k/yr: 53% (CI 42-

63)

49% college graduates (CI 39-59)

NR 100% previou

experience usinternet, 95% access to a hcomputer

AH-316
Appendix H. Data Table

Author Year

Earnest 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

19% (CI 12-27) in Safety Net insurance program for needy patients

NR English NR SPARRO, Sy

Providing Patto Records O

AH-317
Appendix H. Data Table

Author Year

Earnest 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Secure web access to medical records, guide to heart failure and a messaging system

Group 1: InterventionGroup 2: Control

Secure website Offer patients access 

records, lab tests and educational materials

AH-318
Appendix H. Data Table

Author Year

Earnest 2004

Staff Training Subject Training co-interventions Physiolo

NR Written user guide NR Exercise

EnduranceGrp 1 vs. Grp 2: 0.74Pooled: 0.25StrengtheningGrp 1 vs. Grp 2: 0.40Pooled: 0.95MOS SF-36PhysicalGrp 1 vs. Grp 2: 0.75Pooled: 1.0Physical role functioniGrp 1 vs. Grp 2: 0.60Pooled: 0.14

AH-319
Appendix H. Data Table

Author Year

Earnest 2004

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-320
Appendix H. Data Table

Author Year

Earnest 2004

Use of Technology Usability HIT Satisfaction

Demographic Factors Associate with use of SPARROAge: p=0.55Gender (%male): p= 0.63Race: p= 0.06Education (%college grad): p= 0.54HH income >$45000: p= 0.25Baseline KCCQ symptom (Mean): p= 0.07Number of clinic visits (Mean): p= 0.10

NR Baseline Expectations of Patie

and PhysiciansPatient empowerment: p= 0.01More questions: p= 0.11Worry more: p<0.01reports confusing: p= 0.01Notes offensive: p= 0.03Notes confusing: p= 0.35

AH-321
Appendix H. Data Table

Author Year

Earnest 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

General Health PerceptionGrp 1 vs. Grp 2: 0.71Pooled: 0.09

NR NR

AH-322
Appendix H. Data Table

Author Year

Earnest 2004

Quality of Life Support Withdrawals

NR MOS Social Support

OfflineGrp 1 vs. Grp 2: 0.95Pooled: 0.25OnlineGrp 1 vs. Grp 2: 0.08Pooled: 0.37

N = 26

AH-323
Appendix H. Data Table

Author Year

Earnest 2004

Adverse Effects Other

NR Limitations include a highly educated 

patient population, more computer literate and high staff/patient ratio in this clinic

AH-324
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Estabrooks 2005 Urban as well as rural NR

 Look at Glasgow 2004, Amthauer 2003 has more info on physicianand patient recruiting

Doctor's office Intervention arm of RCT 

AH-325
Appendix H. Data Table

Author Year

Estabrooks 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Age equal to or above 25 yrs 

*Language: English *Presence of Type 2 Diabetes.

Pts who chose smoking cessation as target behavior were excluded

NR

AH-326
Appendix H. Data Table

Author Year

Estabrooks 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES TYPE 2 DIABETES NR Reduce fat: 52.7%

Increase fruit and veggies: 49%Increase physical activity: 45%

Mean (SD)Reduce fat: 6(+13.37)

Increase fruits vegetables: 6(+11.86)Increase physactivity: 63 (+

AH-327
Appendix H. Data Table

Author Year

Estabrooks 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Grp 1 (Reduce fat)Caucasian: 86.5%Hispanic: 10.8%Other: 2.7%Grp 2 (Increase fruits and veggies)Caucasian: 81%Hispanic: 10.9%Other: 8%Grp 3 (Increase physical activity)Caucasia: 83.8%Hispanic: 11%Other: 5.2%

IncomeGrp 1 (Reduce fat)< $ 10,000: 10.2%$10,000-29,999: 25.9%$30,000-49,999: 33.3%>$ 50,000: 30.6%

Grp 2 (Increase fruits and veggies)< $ 10,000: 13.5%$10,000-29,999: 30.2%$ 30,000-49,999: 22.9%>$ 50,000: 33.3%Grp 3 (Increase physical activity)< $ 10,000: 11.2%$10,000-29,999: 24.9%$ 30,000-49,999: 27.4%>$50,000: 36.5%

Grp 1 (Reduce fat)<HS: 12.5%High school graduate: 22.3%College 1-3 yrs: 29.5%College/grad school: 35.7%Grp 2 (Increase fruits and veggies)<HS: 14%High school graduate: 30%College1-3 yrs: 34%College/grad school: 22%Grp 3 (Increase physical activity)<HS: 11%High school grad: 27.1%College1-3 yrs: 33.8%College/grad school: 28.1%

Urban as well as rural Married %

Grp 1: 70.5Grp 2: 68Grp 3: 68.1Median numbmorbid diseasGrp 1: 2Grp 2: 2Grp 3: 2DietFat consumptMean (SD), pGrp 1: 22.27 (

Grp 2: 19.18 (Grp 3: 20.49 (Fruit and vegeconsumption: (SD), p < 0.00Grp 1: 13.5 (+

Grp 2: 10.65 (

Grp 3: 12.40 (Physical activDays of vigoroactivity, p< 0.0Grp 1: 0.42Grp 2: 0.34Grp 3: 0.29

AH-328
Appendix H. Data Table

Author Year

Estabrooks 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Diabetes prio

AH-329
Appendix H. Data Table

Author Year

Estabrooks 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Clinic based, interactive, self management CD-ROM that allowed pts to select a behavioral goal and receive mail and telephone supportfor the initial 6 months followed by additional behavioral assessments.

Goal setting component targeted development of tailored action plan based on the pts needs;Pts completed brief assessment of diet and physical activity; Pts selected a behavioral change goal in the area of diet or exercise; interactive session facilitated pts in selecting specific activities to support the goal, identifying barriers and choosing strategies to overcome these barriers; program generated hard copy, minimal coaching involved, Pt then completed brief counseling session with care manager of that practice to provide goal related feedback

Computer (CD-ROM) Goal setting, Tailored 

action plan, assessmeof dietary intake and physical activity, hard of pts personal action 

AH-330
Appendix H. Data Table

Author Year

Estabrooks 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR Fat consumption, Mea

Baseline vs. 6 monthsGrp 1: 22.27 (+7.78) v

(+7.99); p < 0.0001

Grp 2: 19.18 (+8.99) v

(+8.75), p=0.0029

Grp 3: 20.49 (+8.49) v

(+7.80), p= 0.0058

Overall comparison= 0Fruit and veggie consu(SD)Baseline vs. 6 monthsGrp 1: 13.5 (+4.94) vs

; p=0.0967Grp 2: 10.65 (+3.59) v

(+4.11) , p<0.0001

Grp 3: 12.4 (+4.61) vs

(+5.20), p= 0.0587

Overall comparison= 0Vigorous activityBaseline vs. 6 monthsGrp 1: 0.42 vs. 0.46 ; Grp 2: 0.34 vs. 0.36 , Grp 3: 0.29 vs. 0.37 , Overall comparison= 0Moderate activityBaseline to 6 monthsGrp 1: 0.49 vs. 0.55; pGrp 2: 0.45 vs. 0.48 , 

AH-331
Appendix H. Data Table

Author Year

Estabrooks 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-332
Appendix H. Data Table

Author Year

Estabrooks 2005

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-333
Appendix H. Data Table

Author Year

Estabrooks 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-334
Appendix H. Data Table

Author Year

Estabrooks 2005

Quality of Life Support Withdrawals

NR NR Grp 1 (N)

Baseline vs. 6 months112 vs. 92 (No reasons given)Grp 2 (N)Baseline vs. 6 months100 vs. 90 (No reasons given)Grp 3 (N)Baseline vs. 6 months210 vs. 169 (no reasons given

AH-335
Appendix H. Data Table

Author Year

Estabrooks 2005

Adverse Effects Other

NR No

AH-336
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Ewald 2006 27 clinical practices 27 clinical practices Homes/Clinic Cohort Ferrer-Roca 2004b NR Island town Home or Work Cohort 

AH-337
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

3 months None *Age >18 years with essential 

hypertension requiring antihypertensive therapy

NR NR

September 2002-June 2003

9 mos *Dx of DM

*Aged: 18-75 years*Internet access at home or work, or mobile phone with WAP services*Basic knowledge of the IT

NR NR

AH-338
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HYPERTENSION NR 55% Mean Age: 57

Range: 33-90

YES: rural islanders DM NR NR Mean (Range

68) 

AH-339
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

German NR NR NR Mean (Range

BMI: 28.1 (19Duration of HT6.4 (0-23)

NR NR NR Rural, islanders NR

AH-340
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR German International 

guidelines for the management of HTN have acknowledged the need to look for alternative methods of BP measurement in order to quantify more precisely the contribution of HTN to CV risk.

TensioPhone telemonitoring 

NR NR NR NR Website with 

doctor interfacinputting data advice

AH-341
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Device stores BP measurement data. Device is an upper-arm oscillometric device with a built-in modem for data teletransmission via standard telephone lines.

All patients used the device 

for BPSM

BPSM device To record data, transm

data, and store data

Group 1: those who entered the trial to test the systemGroup 2: those that did not meet study criteria to enter the trial, but reviewed the website only

NR Website Education, data input, 

advice sending

AH-342
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Staff Training Subject Training co-interventions Physiolo

NR Received instruction on 

how to operate the BPSM device

HTN medication (lowers BP): olmesartan medoxomil

Office Measurements 

Systolic (SD)/Diastolic Baseline (visit 1):162.6 (+18.7)/96.3 (+1

Week 12 (visit 2):133.5 (+7.7)/79.8 (+5.

Systolic diff. v. baselin

Diastolic diff. v. baseli

BPSM

Baseline: 147.8 (+16.1

Week 9: 138.6 (+13.2

Systolic diff. v. baselinDiastolic diff. v. baseli

NR NR NR NR

AH-343
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Health Service Utilization Health Care Costs Knowledge

NR Assuming none since Germany has a 

universal health care system

NR

NR NR NR

AH-344
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Use of Technology Usability HIT Satisfaction

Reduction of BP>2 BPSMs/day and 100% compliant: -

16.6/-8.0mmHg1 BPSM/day and 75% compliant: -0.2/-3.3.mmHg

NR NR

NR 86% of Grp1 and 40% of Grp 2 would 

recommend the site (P<0.01)71 % of Grp1 and 92% of Grp 2 thought the system was easy to use (P<0.05)33% of Grp 1 and 12% of Grp 2 did not find the system useful and efficient (P<0.05)50% of Grp 1 and 88% of Grp 2 had difficulties in accessing the system(P<0.01)

Responses were only from Grpresults are ambiguous, with onsubjects completing questions satisfaction

AH-345
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-346
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Quality of Life Support Withdrawals

NR NR NRNR NR NR

AH-347
Appendix H. Data Table

Author Year

Ewald 2006Ferrer-Roca 2004b

Adverse Effects Other

NR NRNR NR

AH-348
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Ferrer-Roca 2004a NR NR Homes Cohort Finkelstein 1996 Lung transplant recipients Tertiary care academic 

center

Home-monitoring Cohort 

AH-349
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 8 months *Pts with diabetes

*Access to their own personal mobile phone or access to onebelonging to their relative

NR NR

1993-1994 12 mos *Alive and willing Transplant or training 

prior to full implementation

NR

AH-350
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR MR Range 18-75

0-24 yrs: 19 p25-49 yrs: 1 p50-75 yrs: 2 p>75: 1 pt

NR LUNG TRANSPLANT NR N = 41.46% (17/41) Mean (SD): 4

(±11.7)Range: 15-66

AH-351
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NRNR NR NR NR NR

AH-352
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Mobile phone 

messaging

NR NR NR NR Electronic spi

AH-353
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Pts used SMS to transmit data (glucose levels and body weight) to a server which automatically answered with an acknowledgement message. A monthly calculatedglycosylated hemoglobin result was also automatically sent to the pt by SMS.

Web based server capable of receiving and displaying patient data. Pts entered their blood glucose or other values such as body weight using their mobile phone.The server automatically answered via SMS with a pre recorded acknowledgement when a pt entered a blood glucose value, or with specific help or warning messages when data were wrong or out of range for the individual patient.Each month the system calculated a mean blood glucose value for each pt. Each pt's calculated mean HbA1c value was sent to the pts mobile phone via SMS and stored in the database. Moreover, 4 messages containing advice, automatic reminders or warnings were also sent.

Mobile phone Data input , SMS, 

acknowledgement message, monthly glycosylated Hb result through SMS, warningand reminders

Daily home monitoring of symptoms and vital signs in electronic diary, plus automatic recording of spirometer data, with weekly transmission to clinic

All patients received home monitoring diary instrument, plus initial training, weekly phone calls, quarterly newsletter, a hotline, plus usual care

Home spirometer with electronic diary and automatic weekly modem transmission of data

Tracking symptoms ansigns

AH-354
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Staff Training Subject Training co-interventions Physiolo

NR NR NR NRNR Initial training, weekly 

phone calls, quarterly newsletter, a hotline, 

Weekly phone calls, quarterly newsletter, a hotline

NR

AH-355
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Health Service Utilization Health Care Costs Knowledge

NR The monthly cost based on pt 

sending one text message/day for 5 days/wkThe std cost of text message was US $ 0.18The cost / pt was $ 3.6/monthA total of 25 messages /month including the monthly report, education, warnings and remindersCost to the diabetes manager was $4.5.These were compared with hypothetical use of premium SMS calls which cost almost $1.2/message.Thus, cost to the pt would be $24 /month, but the diabetes service would make a profit of $ 8.4/month

NR

NR Estimate 4.5-5 minutes per patient 

per week staff time, over usual care

 NR

AH-356
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Use of Technology Usability HIT Satisfaction

Average of 33 SMS server messages 

/ month from the 23 pts.Reduction in reported activity during aholiday period.

Elderly people had difficulty typing theSMS messages and this was instead done by relatives

People criticized the fact that sdid not allow data from previouto be entered.Average scores on pt satisfactsurveyQuestion- SMS users (n=6)

Are automatic messages of int

4.2Are doctors' messages useful?Is your diabetes better controllSMS?: 3.0Do you like the SMS diabetes system?: 4.2Is your glycosylated Hb level ointerest to you?: 4.2Do you prefer to manage diabeyourself?: 4.2Do you prefer doctor to managdiabetes?: 3.7What is your level of satisfactiothe SMS system?: 4.3

8941 transmissions by 41 subjects in 52 weeks; initial 6.2/wk, stable over 3.9/wk; 98% complete symptoms, vital signs, 100% complete spirometry

NR NR

AH-357
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-358
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Quality of Life Support Withdrawals

NR NR NRNR NR None (121 screened; 4 refused

unstable; 33 deaths, 37 before intervention ready = 41)

AH-359
Appendix H. Data Table

Author Year

Ferrer-Roca 2004aFinkelstein 1996

Adverse Effects Other

NR NSNR NR

AH-360
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Franklin 2006 NR Pediatric Clinics, Tayside Homes RCT 

AH-361
Appendix H. Data Table

Author Year

Franklin 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

October 2002-February 2003

NR *Type I Diabetes for > 1 year

*Tx with insulin therapy (2 or 3 injections per day)

Serious social problems, severe learning disabilities, needle phobia

NR

AH-362
Appendix H. Data Table

Author Year

Franklin 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 1 DIABETES NR Grp 1: 63%

Grp 2: 45%Grp 3: 55%

MeanGrp 1: 12.7Grp 2: 14.1Grp 3: 12.6

AH-363
Appendix H. Data Table

Author Year

Franklin 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Not CaucasianGrp 1: 4 ptsGrp 2: 3 ptsGrp 3: 3 pts

NR NR NR NR

AH-364
Appendix H. Data Table

Author Year

Franklin 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Sweet Talk

AH-365
Appendix H. Data Table

Author Year

Franklin 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Automated mobile phone text-messaging support system, goal setting

Group 1: Conventional insulin therapy (CIT)Group 2: CIT plus Sweet TalkGroup 3: Basal bolus or pump therapy, Sweet Talk, nutrition counselingAll patients received 3-4 month clinic visits and access to emergency hotline

Phone Text messages, remin

AH-366
Appendix H. Data Table

Author Year

Franklin 2006

Staff Training Subject Training co-interventions Physiolo

Goal setting training for diabetes team by team psychologist; details NR

NR NR HbA1c: 

Group 1 vs. Group 2: 

CI -0.7, +0.7, P= 0.99Group 2 vs. Group 3: 

CI -1.9, -0.5, P <0.001Diabetic ketoacidosis episodesGroup 1 vs. Group 2: 0.58Group 2 vs. Group 3: Severe hypoglycemiaGroup 1 vs. Group 2: 0.63Group 2 vs. Group 3: 

AH-367
Appendix H. Data Table

Author Year

Franklin 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-368
Appendix H. Data Table

Author Year

Franklin 2006

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-369
Appendix H. Data Table

Author Year

Franklin 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Self-efficacy for diabetes (SED) 

score:Group 1 vs. Group 256.0+ 13.7 vs. 62.1+6.6, 95% CI +2.6, + 7.5, P=0.003Group 2 vs. Group 362.1+6.6 vs. 63.1+ 7.2, 95% CI -2.1,+4.2, P=0.50

Adherence score (Visual analoscale- self-report)Group 1 vs. Group 2:70.4 (+20.0) vs. 77.2 (+16.1), 9

+0.4, +17.4, P=0.042Group 2 vs. Group 3: 77.2 (+16.1) vs. 78.8 (+16.2), 9-7.0, +8.0, P=0.90

AH-370
Appendix H. Data Table

Author Year

Franklin 2006

Quality of Life Support Withdrawals

NR Diabetes social support interview 

(DSSI):Family and friends:Group 1 vs. Group 2: NS, data not providedGroup 2 vs. Group 3: NS, data not provided

NR

AH-371
Appendix H. Data Table

Author Year

Franklin 2006

Adverse Effects Other

NR Diabetes social support received 

from diabetes teamGroup 1 vs. Group 2: Group 2 vs. Group 3: 

AH-372
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Frenn 2005 Urban public middle school NA Computer lab Nonrandomized between 

group

AH-373
Appendix H. Data Table

Author Year

Frenn 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 1 month NR NR NR

AH-374
Appendix H. Data Table

Author Year

Frenn 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES NR NR IG

DietMore than 3 sessions: 30%Less than 3 sessions of diet: 33.3%ActivityMore than 2 sessions: 26.3%Less than 2 sessions: 33.3%CGDiet: 44.9%Activity: 50%

12 yrsIGDietMore than 3 s45%Less than 3 sof diet: 50%ActivityMore than 2 s51.2%%Less than 2 s25%CGDiet: 51%Activity: 46.7%13 yrsIGDietMore than 3 s42.5%Less than 3 sof diet: 16.7%ActivityMore than 2 s37.2%Less than 2 s50%

AH-375
Appendix H. Data Table

Author Year

Frenn 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

AsianIGDietMore than 3 sessions: 0%Less than 3 sessions of diet: 0%ActivityMore than 2 sessions: 0%Less than 2 sessions: 0%CGDiet: 4.1%Activity: 5%BlackIGDietMore than 3 sessions: 20%Less than 3 sessions of diet: 50%ActivityMore than 2 sessions: 20.9%Less than 2 sessions: 33.3%

Free lunchIGDietMore than 3 sessions: 75%Less than 3 sessions of diet: 66.7%ActivityMore than 2 sessions: 72.1%Less than 2 sessions: 75%CGDiet: 71.4%Activity: 70%ReducedIGDietMore than 3 sessions: 12.5%Less than 3 sessions of diet: 0%ActivityMore than 2 sessions: 14%Less than 2 sessions: 8.3%

NR Urban public middle 

school

NR

AH-376
Appendix H. Data Table

Author Year

Frenn 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Internet/video 

low fat diet int

AH-377
Appendix H. Data Table

Author Year

Frenn 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Eight session health promotion/transtheoretical model internet/video delivered intervention to increase physical activity and reduce dietary fat among low income, culturally diverse, seventh grade students.

IG:Eight session blackboard platform delivered internet approach with 4 two to three min videos. Focus of the intervention on strategies appropriate for all stages of change, particularly for those in precontemplation and contemplation stages. (concepts included consciousness raising, self reevaluation, improve access to healthy foods and physical activity, decisional balance including reduce barriers to healthy foods and physical activity and emphasize benefits for healthy foods and physical activity).Computer generated tailored feedback provided for physical activity and dietary fat; 10 discussion board answers for which individualized e mailed feedback provided; structured workbook provided to record notes.

Computer Education, tailored 

feedback, emails, discussion board

AH-378
Appendix H. Data Table

Author Year

Frenn 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR Exercise:

IG students who compthan half the sessions moderate/vigorous exaverage of 22 mins codecrease of 46 mins fogroupt (103)= - 1.99, p=0.05Those who completed (n=39) increased activDietary fat:IG students participatihalf of the sessions dedietary fat from 30.7 % t(87)= 2.73, p=0.008CG students had 31.5pretest and 31.6% dietest.

AH-379
Appendix H. Data Table

Author Year

Frenn 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-380
Appendix H. Data Table

Author Year

Frenn 2005

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-381
Appendix H. Data Table

Author Year

Frenn 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-382
Appendix H. Data Table

Author Year

Frenn 2005

Quality of Life Support Withdrawals

NR NR IG students who did not compl

the exercises and not included analysis=12IG students who participated in than half the sessions (dietary and removed from analysis = 6

AH-383
Appendix H. Data Table

Author Year

Frenn 2005

Adverse Effects Other

NR Change in activity of IG students who 

did not complete half the exercises not significantly different from the control students, t (13)= 1.53, p=0.15IG students participating in less than half the sessions on dietary fat were not significantly different than students in the control group, t (16.6)= -1.843, p=0.08Pre and post test activity and dietary fat for subjects in each stage of change shown in graphic formGraphs depicting comparative subgroup changes in dietary fat and exercise given but very difficult to obtain precise valuesSubgroup analysis Activity; For IG students participating in more than half the activity sessions, moderate and vigorous activity were increased for all racial groupFor CG students, activity decreased in each racial grpDietary fat; For IG students participating in more than half the sessions, decreased for all racial groups 

AH-384
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Gega 2004 NR Computer aided self-help 

clinic

Fearfighter: mostly in clinic, occasionally at a free internet café or a medical center, some could access it at home or elsewhere linked to the internet after it became available on the webCope and BTSteps: self help booklets given and free phone calls made by pts from home to the IVR system.Balance: accessed system by PC with a CDROM drive at the clinic, home, free internet cafe or a physician's office.

Case Study; Cohort 

AH-385
Appendix H. Data Table

Author Year

Gega 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

12 months Fearfighter, cope 

and BT steps=12 weeksBalance=4 weeks

Presence of an anxiety and depressive disorder, motivation to do self help, and no substance abuse, psychosis or active suicidal plans

NR NR

AH-386
Appendix H. Data Table

Author Year

Gega 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ANXIETY or 

DEPRESSIVE DISORDER

NR Slightly under half 

were men (no raw data available)

NR

AH-387
Appendix H. Data Table

Author Year

Gega 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR A third were 

unemployed or students (no raw data )

NR Mean problem 

duration: 8 yrs moderately seproblems39% had givework/long term leaveAlmost 1/2 wehaving currentreatment from physician or a health profess1/2 were on psychotropic medication (ndata)20% had CBT previously35% used commost days at Broad diagnoDepression: 7Phobia/panic 60Generalized adisorder: 35Obsessive codisorder: 35

AH-388
Appendix H. Data Table

Author Year

Gega 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Computer aid

cognitive beh(CCBT) (FearBalance and B

AH-389
Appendix H. Data Table

Author Year

Gega 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Systems of cognitive behavior therapy accessible at home included fearfighter for phobia/panic; Cope for nonsuicidal depression; BTSteps for Obsessive compulsive disorder; Balance for generalized anxiety/mild depression.

CCBT systems help the pts make most of the decisions about how to devise, execute and complete CBT including appropriate homework and relapse prevention. The therapist's role was restricted to briefly screening the patient and offering live advice (by phone) if pt got stuck during CCBT.The clinic's pt accessed Cope and BT Steps by phoning a computer on an interactive voice response system (IVR) system after reading a manual. Computer faxed to the clinic weekly reports of phone calls, duration, modules accessed and for Cope pts, suicide riskBalance and Fearfighter could be accessed via internet and Balance had a CD-ROM form tooPts told to use the system as much as possible; pts advised to use fearfighter cope or BTSteps 

Computer, phone Cognitive behavior the

IVR 

AH-390
Appendix H. Data Table

Author Year

Gega 2004

Staff Training Subject Training co-interventions Physiolo

NR Pts were given manuals to 

read before calling the Cope and BTSteps system

NR NR

AH-391
Appendix H. Data Table

Author Year

Gega 2004

Health Service Utilization Health Care Costs Knowledge

NR Assuming administrative costs and 

15% overheads, the estimated per pt cost advantage of CCBT over face to face CBT would rise from 15% /pt for 350 pts a yr to 41% per pt for 1,350 pts a yrThe total cost of CCBT nationally might rise if many users who were previously untreated sought CCBT to offset savings from lower per patient cost

NR

AH-392
Appendix H. Data Table

Author Year

Gega 2004

Use of Technology Usability HIT Satisfaction

Mean of 58 days from pts starting CCBT to ending it.In this period, mean of 64 mins support from the clinicianThe clinic' pts who accessed computer by phone spent similar total times calling the computer as in previous studies- 2 hrs on Cope calls and 4 hrs on BT Steps calls.

NR Pts fairly satisfied with their CC

system and satisfied even morlive support and self help clinic whole.Marginal preference for therapcomputer guidance seensatisfaction and preference ratwere similar among users of hdifferent systems.

AH-393
Appendix H. Data Table

Author Year

Gega 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Significant improvement on 

work/social adjustment for fearfighter, cope and balance users.Significant improvement from pre to post treatment on measures specific to their problem for completers of each self helpClinically meaningful effect size of 0.8 or more exceeded by fearfighter users on the FQ's global phobia and anxiety/depression scores; by cope users on depression and on work/social adjustment; and by BTSteps users on obsessive compulsive total and sub scores.Completers improved comparably to completers in other studies that used the CCBT system and measures.

NR

AH-394
Appendix H. Data Table

Author Year

Gega 2004

Quality of Life Support Withdrawals

NR NR Over 12 months intake, screen

questionnaires received=355unsuitable on the questionnairscreening interview with a clinioffered to= 327attended=266suitable=210refused CCBT= 42 (20%)dropped out early or gave no ptreatment data= 60 (29%)

AH-395
Appendix H. Data Table

Author Year

Gega 2004

Adverse Effects Other

NR 3 case illustrations discussed.

AH-396
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Gerber 2007 Inner city Inner city clinics and 

Childhood diabetes registry

Homes Cohort 

Gerbert 2003 3 primary care clinics San Francisco Bay Area, 

CA

Clinic RCT 

AH-397
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months NR NR NRNR NR *Ages 18-65

*Smokes tobacco and/or drinks alcohol

Outside age range, does not smoke tobacco or drink

NR

AH-398
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: inner city DIABETES: Types 1 

and 2

NR Total: 32% Mean age: 22

(19-26)

YES: recruited from clinic that primarily serves low-income individuals on MediCal

YES: SMOKING and/or DRINKING ALCOHOL

NR 48.00% Range: 18-65

AH-399
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

African-American: 84%

NR Finished high school: 

74%

Urban Had received 

education pre74%Had diabetes complication: Out of 13 womhad ay least oComputer at h21%Current use ocomputer at wschool: 47%Very comfortacomputer: 84%Somewhat comfortable: 3

African-American: 48%Caucasian: 23%Latino: 12%Asian: 10%Other: 8%

NR NR NR Current Smok

31%Drinking at rishazardous levCombination: 

AH-400
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

Public aid: 53%No health insurance: 21%Unemployed or disabled: 37%

NR NR NR STYLE- Self m

training in youeffectiveness

NR NR NR NR Project Choic

AH-401
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Internet based transition support program consisting of background information on diabetes, goal setting exercises with individualized feedback, role playing, group discussions, empowerment activities and communication skills training designed to improve interactions with health professionals.

Internet based program on commercial course management software.Each week, a diabetes educator introduced a new module with an activity and feedback evaluation form and monitored the discussion board. This board provided encouragement, discussion of clinical and logistic problems and prevented sharing of misinformation. Three " ask an expert" segments allowed pts to consult with psychologist, patient advocacy expert and a social worker with experience in diabetes related health care access.

Computer Information on diabete

goal setting exercises individualized feedbacrole playing, group discussions,

This "video doctor" is an interactive, multimedia video doctor, that helps reduce primary care pts smoking and alcohol use by: a) a patient-centered advice message and b) a brief motivational intervention tailored to the information input by the pt

CG: 16Video doctor-advice message: 18Video doctor-advice message + brief intervention: 18

Interactive multimedia software

To provide suggestionabout how to quit smoking/drinking to pt

AH-402
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR 83% stated that they e

greater control of their following completion o

NR NR NR NR

AH-403
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-404
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Use of Technology Usability HIT Satisfaction

STYLE accessed a total of 4445 times25.8% of all hits were between 9 PM11 PMSTYLE used maximum in the first 2 months (1460 and 1417 hits respectively)Use declined 262 times the 6th month.Discussion board had the heaviest use (4-576 per participant, median 57, SD=148.5)50% completed modules within 30 mins, additional 30% spent 30-60 mins on the modules

50% felt that the modules were very easy or easy to complete with the remainder describing them as somewhat hard.67% reported trouble completing module activities on time often due to work, school and family commitments

Overall, every one of the pts inthat the module was very helpf

NR 7 point scale

Mean: 6.79

7 point scaleMean: 5.69

AH-405
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR 7 point scale intention to chang

behaviorSmokers: 5.94Alcohol Users: 4.42

AH-406
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Quality of Life Support Withdrawals

NR 50% received encouragement from 

family members often or very often to log in to STYLE.

NR

NR NR NR

AH-407
Appendix H. Data Table

Author Year

Gerber 2007Gerbert 2003

Adverse Effects Other

NR Qualitative results on the discussion 

board reported, not listed here

NR NR

AH-408
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Glasgow 2000 Outpatients Primary care physicianssmall group practices Center for healthy living, centralized location for 

most pts

RCT 

AH-409
Appendix H. Data Table

Author Year

Glasgow 2000

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Meeting the Welborn criteria for Type 2 

diabetes on the basis of age at diagnosis, body mass index, and when insulin was begun *Age >40 yrs*Living independently*Have a telephone*Not planningto move out of the area during the next year

NR NR

AH-410
Appendix H. Data Table

Author Year

Glasgow 2000

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR TF, CR: 43.6%

TF, no CR: 43%No TF, CR: 52.6%No TF, no CR: 33.7%

Mean (SD):(TF, CR) vs. (CR) vs. (No Tvs. (No TF, C57.4 (+ 9.4) v

(+9.6) vs. 60.5 

vs. 60.6 (+9.5

AH-411
Appendix H. Data Table

Author Year

Glasgow 2000

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

CaucasianTF, CR: 91.4%TF, no CR: 88.6%No TF, CR: 90.9%No TF, CR: 90.0%

NR Some college or more

TF, CR: 58.0%TF, no CR: 63.0%No TF, CR: 59.7%No TF, CR: 46.3%

Unclear Retired, %

(TF, CR) vs. (CR) vs. (No Tvs. (No TF, C35.8 vs. 31.6 vs. 45.0Live alone, %(TF, CR) vs. (CR) vs. (No Tvs. (No TF, C 64.2 vs. 44.3 vs. 51.2Years diagnomean(SD)(TF, CR) vs. (CR) vs. (No Tvs. (No TF, C6.4 (5.9) vs. 7 6.8 (7.6) vs. 5On insulin, % (TF, CR) vs. (CR) vs. (No Tvs. (No TF, C14.8 vs. 16.5 vs. 15.0Previous diab

AH-412
Appendix H. Data Table

Author Year

Glasgow 2000

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Choosing We

AH-413
Appendix H. Data Table

Author Year

Glasgow 2000

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Interactive multimedia touch screen , computer assessment and feedback sessionThe computer interaction assessed, immediately analyzed and provide feedback on the patient's dietary patterns, barriers to and support for dietary self management and preferences for different intervention strategies. The session concluded with a tailored dietary fat reduction goal based on the participant's eating patterns and preferences and a 1 page goals printout summarizing this info.Telephone FU- consisted of brief, structured calls to provide ongoing support and reinforcements to pts via continued contact with interventionist who met pts during the office visit. also provided personalized problem solving training based on barriers to dietary self care identified via 

Grp 1: no Telephone (TF), no Community resources(CR) Basic intervention pts received the above and a general pamphlet on low fat eatingGrp 2: No Telephone Follow up but community resources providedGrp 3: Telephone follow up, no community resourcesGrp 4: Telephone FU and community resources provided

Computer Assessment, educatio

analysis and tailored feedback

AH-414
Appendix H. Data Table

Author Year

Glasgow 2000

Staff Training Subject Training co-interventions Physiolo

NR NR NR HBA1c, p=NS, n=267

(No TF, no CR) vs. (no (TF, no CR) vs. (TF, CBaseline- 7.6(1.2) vs. (1.9) vs. 7.6 (1.8)3 month FU- 7.6 (1.4) 7.6 (2.1) vs. 7.5 (1.7)6 month FU- 7.4 (1.2) 7.4 (1.4) vs. 7.5 (1.7)Total Cholesterol, p=0(No TF, no CR) vs. (no (TF, no CR) vs. (TF, CBaseline-210 (40) vs. 202 (38) vs. 205 (35) 3 month FU-201(34) v198(37) vs. 201 (31)6 month FU- 206(39) v202(39) vs. 201 (30)Weight, NS, n=265(No TF, no CR) vs. (no (TF, no CR) vs. (TF, CBaseline- 199(36) vs. 219949) vs. 221(52)3 month FU- 198(37) v217(47) vs. 218(49)6 month FU- 197(37) v217(48) vs. 219 (51)Lipid ratio: total/HDL, (No TF, no CR) vs. (no 

AH-415
Appendix H. Data Table

Author Year

Glasgow 2000

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-416
Appendix H. Data Table

Author Year

Glasgow 2000

Use of Technology Usability HIT Satisfaction

NR NR Satisfaction with the program

3 month Follow Up, p=NS, n=2No TF, no CR: 36 (+5.5)

No TF, CR: 35 (+5.5)TF, no CR: 36 (+4.7)

TF, CR: 37 (+4.3)

AH-417
Appendix H. Data Table

Author Year

Glasgow 2000

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Block fat screener, p-NS, n=26

(No TF, no CR) vs. (no TF, CR(TF, no CR) vs. (TF, CR)Baseline: 48.6 (+28.5) vs. 49.0 

(+27.2) vs. 48.8 (+30.6) vs. 54(+33.8)

3 month FU: 26.2 (+17.1) vs. 2

(+15.2) vs. 26.8 (+19.0) vs. 26

(+23.2)

6 month FU: 24.7 (+17.5) vs. 2

(+14.4) vs. 25.7 (+19.2) vs. 22

(+17.4)

Kristal FFB fat composite, p=0n=265(No TF, no CR) vs. (no TF, CR(TF, no CR) vs. (TF, CR)Baseline: 1.9 (+0.5) vs. 1.9 (+0

2.0 (+0.4) vs. 1.9 (+0.5)

3 month FU: 1.9 (+0.5) vs. 1.9 

vs. 1.9 (+0.4) vs. 1.8 (+0.5)

6 month FU: 1.6 (+0.4) vs. 1.8 

vs. 1.7 (+0.4) vs. 1.6 (+0.4)

Kristal FFB fruit and vegetable p= 0.045, n=265(No TF, no CR) vs. (no TF, CR(TF, no CR) vs. (TF, CR)Baseline: 1.9 (+0.8) vs. 2.0 (+0

2.2 (+0.8) vs. 2.0 (+0.8) 

3 month FU: 1.8 (+0.8) vs. 1.9 

vs. 2.1 (+0.8) vs. 1.9 (+0.9)

AH-418
Appendix H. Data Table

Author Year

Glasgow 2000

Quality of Life Support Withdrawals

Diabetes intrusiveness, p= 0.014, n=267(No TF, no CR) vs. (no TF, CR) vs. (TF, no CR) vs. (TF, CR)Baseline: 25.7(11.1) vs. 29.2 (15.2) vs. 28.6 (12.0) vs. 30.8( 15.7)3 month FU: 31.0 (15.6) vs. 30.6( 15.0) vs. 32.4(13.0) vs. 31.4(13.3)6 month FU: 26.0(12.7) vs. 29.6(14.9) vs. 28.2( 12.40 vs. 29.2(14.0)

NR Attempt to contact: 706 pts

Ineligible: 164 (Type 1 DM, not area, no telephone)Never reached: 72 (incorrect pnumbers)Asked not to be contacted: 51Declined participation: 99 (not interested in dietary change, nperceiving a problem, too ill, prtime too consuming)Participants: 320At the 6 month FU, 43 failed to provide data5-13 per conditionANOVAs failed to reveal any meffect or interactions involving dropout statusfurther attrition analysis not performed

AH-419
Appendix H. Data Table

Author Year

Glasgow 2000

Adverse Effects Other

NR A series of ANOVAs conducted to 

determine whether there are any differences in demographic or dietary history/severity variables between pts.Table on intervention implementation by condition given, not listed here

AH-420
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Glasgow 2003 NR Primary care medial office Homes RCT Gomez 2002a NR Immune Dev. TrustLondon, The Lambeth, 

Southwark and Lewisham Health Authority - London and Apoyo Positivo - Madrid.

NR NR 

AH-421
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 10 months *Pts taking insulin met the Welborn 

criteria based on age at diagnosis, BMI and age ofinsulin initiation*Pts were living independently*Have a telephone*Read and write English *Diagnosed with Type 2 diabetes for at least 1 year*Not planning to move out of the area during the next year 

NR NR

NR NR *Patient has HIV/AIDS NR NR

AH-422
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 2 DIABETES 

MELLITUS

NR N = 150 pts (47%) Mean (SD): 5

NR HIV/AIDS NR NR NR

AH-423
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NRNR NR NR NR NR

AH-424
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Diabetes netwNR NR English, Spanish NR SEAHORSE 

AH-425
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Internet based self management project evaluating the incremental effects of adding tailored self management (TSM) training or peer support components to a basic internet based, information focused comparisonThe TSM intervention addressed self efficacy through internet-mediated modeling, accomplishment of graduated goals and development of individual and situation specific problem solving strategies. Pts had access to professional "coach" (could access twice per week) and resources to reach their dietary goals tailored to their needs. Pts also had access to online " dietitian question and answer" conference covering specific topics. They could enter and receive graphical feedback on their pattern of blood glucose levels for different periods of the day. 

Information only group: Pts had access to extensive number of articles on topics of medical, nutritional, and lifestyle aspects of diabetes. These provided info only and not individually tailored recommendations. Pts completed assessments online and received automated dietary change goals based on their current levels.TSM Grp: same as abovePeer support Grp: same as above

Computer Live chat, logs, databa

information, forums

Web based self monitoring system for HIV/AIDS patient care

NR Web interface Self-monitoring person

diary, remote doctor support, data analysisallows user to post comments and articles

AH-426
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Staff Training Subject Training co-interventions Physiolo

NR Pts received in home 

training in use of computer of approximately two to three visits of 1-2 hrs each

NR Dietary outcomes

Kristal totalNo peer support ( No support (PS)Baseline mean(SD)2.22 (0.41) vs. 2.19(0.10 month adjusted me2.00(0.38) vs. 1.96(0.3Group differences- 0.0MANCOVA/univariate No TSM vs. TSMBaseline mean(SD) 2.22(0.45) vs. 2.19(0.410 month adjusted me2.03 (0.38) vs. 1.93(0.grp differences- 0.10, MANCOVA/univariate Estimated gms of daily No PS vs. PSBaseline mean(SD) 41.3(26.4)vs. 44.0(3110 month adjusted me29.8(14.3) vs. 27.9(14Group differences-1.8MANCOVA/univariate No TSM vs. TSMBaseline mean(SD) 44.4(33.8) vs. 40.8(23

NR NR NR NR

AH-427
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-428
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Use of Technology Usability HIT Satisfaction

Average logons per participant per monthPS vs. No PSMonth 1-3: 18.7 vs. 9.4Month 4-6: 13.2 vs. 5.1Months 7-10: 6.7 vs. 3.6TSM vs. No TSMMonths 1-3: 16.7 vs. 11.4Months 4-6: 9.8 vs. 8.5Months 7-10: 5.3 vs. 5.0

NR NR

NR NR NR

AH-429
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Psychosocial outcomes

CES-D TotalNo PS vs. PSBaseline mean(SD)17.8(10.08) vs. 18.1(10.51)10 month adjusted mean(SD) 14.06(9.12) vs. 12.59(9.13)Group differences-1.47, MANCOVA/univariate p level-0.219No TSM vs. TSMBaseline mean(SD) 17.9(10.56)vs. 18.0(10.02)10 month adjusted mean 12.93(9.11) vs. 3.72(9.12) grp differences= -0.79 , MANCOVA/univariate p level-0.507

NR

NR NR NR

AH-430
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Quality of Life Support Withdrawals

NR No PS vs. PS

Baseline Mean (SD)4.23 (+1.23) vs. 4.05 (+1.28)

10 month adjusted Mean (SD) 4.71 (+1.12) vs. 5.22 (+1.11) 

Group differences= -0.51, MANCOVA/univariate p level-0.001No TSM vs. TSMBaseline Mean (SD)  4.14 (+1.32) vs. 4.14 (+1.20) 10 month adjusted mean 4.96 (+1.12) vs. 4.97 (+1.12)

Grp differences= -0.01 , MANCOVA/univariate p level-0.934

Data were collected on 82% of randomized participantsAnalyses of those present and not present done and did not ssignificant effects

NR NR NR

AH-431
Appendix H. Data Table

Author Year

Glasgow 2003Gomez 2002a

Adverse Effects Other

NR Overall change from baseline to10 

month follow-up given but not listed here

NR NR

AH-432
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Green 2008 10 Medical Centers within 

Group Health

Washington and Idaho Homes RCT 

AH-433
Appendix H. Data Table

Author Year

Green 2008

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

June 2005-December 2007

12 mos *Ages 25-75

*HTN diagnosis and taking HTN medications*Ability to use a computer*Internet access*E-mail address*Willingness to attend screening sessions at a clinic*Diastolic BP 90-109 mmHg*Systolic BP 140-190 mmHg*Written informed consent

Posessing diseases other than HTN such as diabetes, cardiovascular or renal disease, unable to use a computer, lack of access to the internet, no e-mail address, unwilling to attend screening sesions

NR

AH-434
Appendix H. Data Table

Author Year

Green 2008

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HTN NR Overall: 47.8%

UC: 55.3%IVR: 54.1%IVR + pharmacist care: 44.1%

Overall: 59.1 

UC: 56.8 (+8.

IVR: 59.5 (+8

IVR + PC: 59

AH-435
Appendix H. Data Table

Author Year

Green 2008

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Overall (%)Caucasian: 82.8African-American: 7.8Asian: 3.7Other: 5.7UCCaucasian: 82.9African-American: 8.5Asian: 3.1Other: 5.4IVR + PCCaucasian: 86.1African-American: 6.9Asian: 3.5Other: 3.5IVR + PCCaucasian: 79.3African-American: 8.0Asian: 4.6Other: 8.0

NR Overall (%)

<12 yrs or GED: 8.0Some Post-HS: 41.64 yr College Degree: 25.1Graduate School: 25.3UC<12 yrs or GED: 8.5Some Post-HS: 45.34 yr College Degree: 18.6Graduate School: 27.5IVR<12 yrs or GED: 7.3Some Post-HS: 42.54 yr College Degree: 27.8Graduate School: 22.4IVR + PC<12 yrs or GED: 8.0Some Post-HS: 37.24 yr College Degree: 28.7Graduate School: 26.1

NR Antihypertens

Medication ClOverall (%)0: 3.61: 47.02: 33.5>3: 15.8UC (%)0: 5.01: 49.22: 34.5>3: 11.2IVR (%)0: 1.91: 46.32: 33.2>3: 18.5IVR + PC (%)0: 3.81: 45.62: 33.0>3: 17.6BP, Mean (SDOverall

AH-436
Appendix H. Data Table

Author Year

Green 2008

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR NR

AH-437
Appendix H. Data Table

Author Year

Green 2008

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Access to Group Health's secure patient Web services (e-mail access, refilling prescriptions, health library, links to behavioral and lifestyle changes, etc), Group Health's HTN pamphlet, home blood pressure monitoring cuff

UC (n=258): Usual HTN care from physiciansIVR (n=259): access to Group Health's web services and home blood pressure monitoring systemIVR + PC (n=261): acces to Group Health's web services and home blood pressure monitoring system and phamacist intervention to help lower BP

Webserver and home BP monitoring cuff

Education, data input, collection, tailored feedback

AH-438
Appendix H. Data Table

Author Year

Green 2008

Staff Training Subject Training co-interventions Physiolo

NR Subject training on home 

blood pressure monitoring kit

Pharmacist assisted intervention

Subanalysis of Pts witBaseline > 160 mmHg Systolic BP (95% CI) UmeanUC (N=51): 152.4 (148IVR (N=47): 151.0 (14IVR + PC (N=52): 139144.0)Overall P Value: < 0.0UC: 0.64UC vs IVR + PC: <0.0IVR vs IVR + PC: <0.0Adjusted mean changUC: -14.4 (-18.6 to -10IVR: -17.8 (-22.2 to -1IVR + PC: -27.6 (-31.8 Overall P Value: < 0.0UC: 0.30UC vs IVR + PC: <0.0IVR vs IVR + PC: 0.00Diastolic BP (95% CI) meanUC: 84.4 (81.6 6o 87.2IVR: 83.8 (80.9 to 86.7IVR + PC: 81.0 (78.2 tOverall P Value: 0.21UC: 0.78UC vs IVR + PC: 0.10IVR vs IVR + PC: 0.10

AH-439
Appendix H. Data Table

Author Year

Green 2008

Health Service Utilization Health Care Costs Knowledge

NS NR NR

AH-440
Appendix H. Data Table

Author Year

Green 2008

Use of Technology Usability HIT Satisfaction

At 12 mos after randomizationMean (SD) number of message threadsUC: 2.4 (+4.6)IVR: 3.3 (+7.4)

IVR + PC: 22.3 (+10.2)

Mean (SD) number of patient initiated threadsUC: 1.8 (+4.2), p = 0.01

IVR: 2.7 (+7.1), p <0.01

IVR + PC: 4.2 (+6.0), p <0.01

Mean (SD) number of telephone encountersUC: 4.0 (+4.8), p<0.001

IVR: 3.8 (+5.0), p<0.001

IVR + PC: 7.5 (+9.3)

NR NR

AH-441
Appendix H. Data Table

Author Year

Green 2008

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Consumer Assessment of Healthcare Providers and Systems (Scale 0-10)Missing Data: 65Baseline: 7.9 (+1.5)

UC: 8.1 (+1.5)

IVR: 8.1 (+1.5)

IVR + PC: 8.3 (+1.4)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: 0 (-0.3 to 0.3)UC vs IVR + PC: 0.2 (-0.1 to 0.5)IVR vs IVR + PC: 0.2 (0 to 0.5)

NR NR

AH-442
Appendix H. Data Table

Author Year

Green 2008

Quality of Life Support Withdrawals

Secondary Outcomes at 12 mos for all pts completing f/u in the electronic communications and home blood pressure monitoring trial; Mean (SD)QoL (1-100 scale)General healthMissing Data: 38Baseline: 67.1 (+20.4)

UC: 66.7 (+20.4)

IVR: 66.6 (+20.9)

IVR + PC: 66.6 (+22.2)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: -0.1 (-4.0 to 3.7)UC vs IVR + PC: -0.1 (-4.0 to 3.8)IVR vs IVR + PC: 0 (-3.9 to 3.9)Physical healthMissing Data: 44Baseline: 80.6 (+27.0)

UC: 78.1 (+27.7)

IVR: 77.7 (+30.3)

IVR + PC: 81.0 (+26.5)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: -0.4 (-5.6 to 4.7)UC vs IVR + PC: 2.8 (-2.3 to 8.0)IVR vs IVR + PC: 3.3 (-1.9 to 8.5)

NR UC (n=11)

4 withdrew1 refused2 could not be contacted3 moved1 otherIVR (n=13)8 withdrew1 could not be contacted2 missed visit2 diedIVR + PC (n=24)8 withdrew5 could not be contacted4 missed visit4 left health plan2 too ill1 died

AH-443
Appendix H. Data Table

Author Year

Green 2008

Adverse Effects Other

NR Secondary Outcomes at 12 mos for 

all pts completing f/u in the electronic communications and home blood pressure monitoring trial; Mean (SD)# of antihypertensive medication classesMissing Data: 0Baseline: 1.64 (+0.85)

UC: 1.69 (+0.91)

IVR: 1.94 (+0.91)

IVR + PC: 2.16 (+0.93)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: 0.3 (0.1 to 0.4)UC vs IVR + PC: 0.5 (0.3 to 0.6)IVR vs IVR + PC: 0.2 (0.1 to 0.4)Aspirin use, No. (%)Missing Data: 38Baseline: 338 (+48.8)

UC: 124 (+53.0)

IVR: 131 (+56.0)

IVR + PC: 149 (+66.5)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: 1.1 (0.9 to 1.2)UC vs IVR + PC: 1.3 (1.1 to 1.5)IVR vs IVR + PC: 1.2 (1.0 to 1.4)

AH-444
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Guendelman 2002 Children's hospital Children's hospital Homes RCT Gustafson 1999 HIV clinics and 

organizations in Wisconsin

NR Homes Randomized trial 

Gustafson 1994 HIV & AIDS outpatients (no 

recruitment details)

NR Homes RCT 

AH-445
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

April 1999-July 2000

3 mos *Ages 8-16 years old

*English speaking caregivertelephone at home*Diagnosed with persistent asthma following NHLBI clinical practice guidelines

Involved in another asthma or drug efficacy trialInvolved in research requiring behavioral modificationIf they had mental or physical challenges that made it difficult to use the systemCo morbid conditions affecting quality of life

NR

NR 3 to 6 months 

implementation with 2-3 month follow-up (total 5 to 9 months)

*Pt has HIV Dementia, living with 

subject in experimental condition, living situation prohibiting computer installation, disappearance

NR

1992-1993 5 months of use, 3 

months post use for subset 30 INT/28 CTRL

NR NR NR

AH-446
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: children, mostly Medicaid population

ASTHMA NR Grp 1: 61%

Grp 2: 54%

Mean (Range16)

NR HIV NR Exp: 90.9%

CG: 89%

Mean (SD)Exp: 34.8CG: 34.1

NR HIV/AIDS NR NR NR

AH-447
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Grp 1Caucasian: 8%African-American: 79%Grp 2Caucasian: 12%African-American: 74%

NR Of the primary 

caregiver<HS

Grp 1: 39 %Grp 2: 51%Some CollegeGrp 1: 61%Grp 2: 49%

NR NR

CaucasianExp Grp: 81.2%CG: 86.7%

NR Mean years

Exp Grp: 14.3CG: 14.7

NR Employed

Exp Grp: 53.1CG: 44.4%

"Minorities:" 39/204 (19.1%)

NR NR NR NR

AH-448
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

Public Health InsuranceGrp 1: 92% Grp 2: 93% 

English speaking caregiver

NR NR Health Buddy

InsuredExp Grp: 75.8%CG: 80.5%

NR NR NR CHESS

NR NR NR NR CHESS

AH-449
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Interactive communication device with question and response tasks. Questions provided by the nurse to the patient for their response

Group 1: Intervention group that used Health BuddyGroup 2: Control group

Website Education, information 

sharing, advice sendin

Computerized information, decision support, expert

NR Computer and Internet Online library, referral 

directory, assessmentdecision aid, action pladiscussion group, ask expert, personal storie

9 services for information, support, problem solving

NR PC w/ modem Information, social and 

emotional support, prosolving, in 9 modules

AH-450
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Staff Training Subject Training co-interventions Physiolo

NR Those assigned to the 

Health Buddy were given a demonstration on how to use the device and explained how to install it at home

NR NR

NR NR NR NRNR NR NR NR

AH-451
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Health Service Utilization Health Care Costs Knowledge

Any urgent calls (Baseline vs. 12 week follow-up) - (P=0.05)Grp 1: 32% vs. 10%Grp 2: 22% vs. 15%ED visit (baseline vs. 12 week followup) - (P=0.21)Grp 1: 27% vs. 10%Grp 2: 28% vs. 18%Any hospitalizations (baseline vs. 12 week follow-up) - (P=0.96)Grp 1: 14% vs. 6%Grp 2: 13% vs. 2%

NR NR

Similar ambulatory and emergency visits; higher phone calls in Exp groupLower hospitalization in Exp group: 0.14 per subjectControl: 0.21 per subject (p=.02)

NR NR

Self report visits same; calls to clinic higher; post study self report visits fewer; visit time less; admissions problem 16% v 42% ctrl (NS); LOS 61% higher ctrl, 29% lower interest

NR NR

AH-452
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Use of Technology Usability HIT Satisfaction

NR NR NRNR NR NR15966 uses/116 subjects, ~39 hrs/138 uses each; 73% in Discussion Group, 17% in Info Svc, 2% in Problem Solving

NR NR

AH-453
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NRNR NR No change in risky behavior, 

improved attitude about reportstatus

AH-454
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Quality of Life Support Withdrawals

Limitation in activity (baseline vs. 12 week follow-up) - (P=0.03)Grp 1: 67% vs. 32%Grp 2: 72% vs. 47% Trouble sleeping (baseline vs. 12 week follow-up) - (P=0.83)Grp 1: 62% vs. 34%Grp 2: 59% vs. 27%Missed school (baseline vs. 12 weeks follow-up) - (P=0.41)Grp 1: 52% vs. 15%Grp 2: 44% vs. 22%

NR 12 participants (4 in Grp 1 and 

Grp 2) did not complete the 12 follow-upReasons included: moving or lcrisis events within the family, lost to follow-up

Exp group self-reported significantly improved cognitive function, negative emotions, active life and social supportImprovements persisted in social support and participation in healthcare 3 months after subjects had 6 months of CHESS

NR NR

Improved 5 of 8 dimensions: social support, cognitive fxn, active life

NR NR

AH-455
Appendix H. Data Table

Author Year

Guendelman 2002Gustafson 1999Gustafson 1994

Adverse Effects Other

NR NRNR NRNR NR

AH-456
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Gustafson 2001 Urban, small city and rural 

areas

2 Teaching hospitals; 2 Non-teaching hospitals and a cancer resource center

Homes RCT 

AH-457
Appendix H. Data Table

Author Year

Gustafson 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

2 yrs (April 1995-May 1997)

5 months *Pts were within 6 months of diagnosis

*Age <=60*Not homeless*Not active illegal drug users*Able to give informed consent*Able to understand and answer sample questions from the pre-test survey

Patients treated in small, rural general surgeries were excluded

NR

AH-458
Appendix H. Data Table

Author Year

Gustafson 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: the two non teaching hospitals and the cancer resource center were cateringto underserved population

BREAST CANCER NR N = 0% Mean (SD):

Grp 1: 44.3 (+

Grp 2: 44.4 (+

AH-459
Appendix H. Data Table

Author Year

Gustafson 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

CaucasianGrp 1: 76% Grp 2: 72%

Income (annual: >$ 

40,000)Grp 1: 58.1%Grp 2: 50.8%

Bachelor's degreeGrp 1: 45.8%Grp 2: 40.2%

Urban, small city and rural areas

Living with pa(%)Grp 1 vs. Grp71.9 vs. 72.6Days since diAt pretest; Me; Grp 1 vs. Gr55.0 (+36.3) v

(+32.6)

Stage : Early- (%); Grp 1 vs81.9 vs. 78.9Surgery: Mas%); Grp 1v s G55.4 vs. 60.0ChemotherapGrp 1 vs. Grp vs. 51.2Radiation, (%vs. Grp 2; 38.40.8

AH-460
Appendix H. Data Table

Author Year

Gustafson 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

Private insuranceGrp 1: 86%Grp 2: 84.7%

NR NR NR CHESS

AH-461
Appendix H. Data Table

Author Year

Gustafson 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Computer based patient support system providing information, decisionmaking and emotional support

Grp 2 (Control): Pts were given a copy of Dr. Susan Love's Breast BookGrp 1 (CHESS): had use of this for 6 months, were trained briefly and had a user's manual.CHESS description*Information system had short answers to frequently asked breast cancer questions, instant library with articles from scientific and popular press, consumer guide with description of health services, identifying good providers and the like, Referral directory with descriptions of and ways to contact local and national breast cancer servicesSupport services had discussion groups (limited access, facilitated bulletin boardsfor sharing information and support), ask an expert with confidential responses,and personal stories of 

Computer Forums, question and 

answers, library, expecommunication, action 

AH-462
Appendix H. Data Table

Author Year

Gustafson 2001

Staff Training Subject Training co-interventions Physiolo

NR Grp 1 pts were briefly 

trained on how to use CHESS

NR NR

AH-463
Appendix H. Data Table

Author Year

Gustafson 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-464
Appendix H. Data Table

Author Year

Gustafson 2001

Use of Technology Usability HIT Satisfaction

NR NR Participation- Level of comfort

Grp 1vs Grp2 Means (covariate adjusted); Difference between means ; CControl (95% CI)2 months- 80.7 vs. 74.3 ; 6.4 (10.7), p < 0.015 month-79.1 vs. 76.5; 2.6 ( -16.7)Confidence in doctorsGrp 1vs Grp2 Means (covariate adjusted); Difference between means ; CControl (95% CI)2 month - 83.0 vs. 77.3, 5.7 (111.3), p < 0.055 month- 82.8 vs. 79.0, 3.8 ( -29.8) Information competenceGrp 1vs Grp2 Means (covariate adjusted); Difference between means ; CControl (95% CI)  2 months- 70.4 vs. 65.6 ; 4.8 (8.1), p < 0.01 5 month - 69.3 vs. 65.8; 3.5 (06.9), p= 0.05Unmet information need

AH-465
Appendix H. Data Table

Author Year

Gustafson 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Participation- behavioral involv

Grp 1vs Grp 2 Means (covariate adjusted); Difference between means; CHControl (95% CI)  2 months: 75.6 vs. 73.1; 2.5 ( 6.1) 5 month: 74.5 vs. 72.8; 1.7 ( -5.6)

AH-466
Appendix H. Data Table

Author Year

Gustafson 2001

Quality of Life Support Withdrawals

Social/Family well beingGrp 1vs grp2 Means (covariate adjusted); Difference between means ; CHESS -Control (95% CI)  2 months- 79.3 vs. 78.2; 1.1 ( -3.0 to 5.4) 5 month - 75.8 vs. 74.7; 1.1 ( - 3.3 to 5.5)Emotional well beingGrp 1vs grp2 Means (covariate adjusted); Difference between means ; CHESS -Control (95% CI)  2 months- 73.9 vs. 72.8 ; 1.1 ( - 3.1 to 5.2) 5 month - 76.3 vs. 75.3; 1.0 ( -3.0 to 5.0)Functional well beingGrp 1vs grp2 Means (covariate adjusted); Difference between means ; CHESS -Control (95% CI)  2 months- 62.2 vs. 63.0 ; - 0.8 ( - 5.3 to 3.6) 5 month - 70.4 vs. 69.9 ; 0.5 ( -3.5 to 4.4)Breast cancer concernsGrp 1vs grp2 

Social supportGrp 1 vs. Grp 2 Means (covariate adjusted); Difference between means ; CHESS -Control (95% CI) 

2 months: 80.8 vs. 78.4 ; 2.4 (- 1.2 to 

5.9) 5 month: 84.2 vs. 79.3; 4.9 (1.4 to 8.4)

Patient's recruited: 296Withdrew before randomizationRandomized: 295Control: 148, CHESS: 147Out of control groupWithdrew before start of studyParticipated: 132Out of 132 that participated, Dropped from study: 7Completed trial, five month sur125Out of CHESS groupWithdrew before start of studyDied before start of study: 1Participated: 133Out of 133 that participated, Dropped from study: 12 (Died study=3, Lost to follow up=9)Completed trial, five month sur121

AH-467
Appendix H. Data Table

Author Year

Gustafson 2001

Adverse Effects Other

NR Recruitment rates in the different 

places given, not listed hereReasons for refusal to participate among patients contacted also givenMeasurement issues discussed, not listed hereThe author mentions that their sample size of 246 subjects at an alpha= 0.05 provided a 0.80 power to detect a 0.36 SD difference between the CHESS and control groups.The author has also discussed interactions with indicators of medical under service, not listed here

AH-468
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Gustafson 1998 5 Wisconsin counties Surgeons at 12 clinics, 10 

hospitals, 7 HMOs

Homes NR 

Hoch 1999 NR NR NR (Homes) Descriptive 

AH-469
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

1997 10 weeks *Elderly women with breast cancer

*Medicare eligible*Diagnosis w/in 4 months *Able to read and provide informed consent*No dementia

NR YE

1995-1998 NR NR NR NR

AH-470
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR BREAST CANCER NR N = 0% Mean (SD): 7

(+6.06)

NR EPILEPSY NR NR NR

AH-471
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR Potentially eligible 

urban: 28pts (76%)Rural: 10 pts (71%)

63% living alohad co morbid5.3% had metbreast cancerhad computer experience

NR NR NR NR NR

AH-472
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR CHESSNR NR NR NR MGH Neurolo

Web forum

AH-473
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

NR NR Website Provides patient educ

through a home PC, garticles, tutorials abouservices, anonymous access to experts, mohealth status, help madecisions

Open web forum NR Computer Forum

AH-474
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Staff Training Subject Training co-interventions Physiolo

NR NR NR NRNR NR NR NR

AH-475
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-476
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Use of Technology Usability HIT Satisfaction

Service, Number/% of uses, Number/% of patients using serviceDiscussion group: 743/33.9%, 38/100%Ask an expert: 374/17.1%, 38/100%Questions and answers: 287/13.1%, 38/100%Health Charts/profiles: 214/9.7%, 38/100%Instant Library: 161/7.3%, 32/85%Personal stories: 138/6.3%, 36/95%Consumer guide/referral: 75/3.4%, 29/76%Decision Analysis: 97/4.4%, 30/79%Stress Management: 52/2.4%, 18/48%Action Plan: 51/2.4%, 20/52%

NR NR

QuestionsCaregivers: 42%Patients: 35%Providers: 2%Unidentified: 21%AnswersCaregivers: 34%Patients: 38%Providers: 6%Unidentified: 22%

NR NR

AH-477
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-478
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Quality of Life Support Withdrawals

NR NR NRNR NR NR

AH-479
Appendix H. Data Table

Author Year

Gustafson 1998Hoch 1999

Adverse Effects Other

NR NR6% inaccurate responses NR

AH-480
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Holman 1996 Diabetic patients Diabetes clinic Homes Randomized cross-over 

design

Jan 2007 Specialty clinic Specialty clinic, university 

medical center

Homes RCT 

AH-481
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NA *Diabetic patients NR NR2004 3 months *Internet access

*"Persistent asthma"

Chronic co morbidity such as bronchopulmonary dysplasia

NR

AH-482
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR N = 67% Mean (SD): 3

(+11.7)

NR ASTHMA NR IVR: 35%

CG: 28%

Mean (SD)IVR: 10.9 (±2CG: 9.9 (±3.2

AH-483
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Duration of di

years: 12.7 (+

HbA1: 9.3%

Chinese NR NR Urban NR

AH-484
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR Patient-orient

regimen optim

NR NR NR NR Blue Angel fo

AH-485
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Hand held computer to record prep radial glucose level, expected meal size, expected exercise, health level and hypoglycemic episodes, and to recommend correct insulin dose

Cross-over design: each participant had two consecutive three week study periods, with and without the computerized insulin dose advice switched on, in a randomly allocated order.Training: one week lead in period of recording glucose levels. Patients were given written instructions and a 24 hour contact number

Hand-held computer Provide recommended 

insulin dose based on patient entered glucoslevel, size of meal, exercise, health and severity of hypoglycemepisodes

Education, monitoring, selfmanagement advice-not entirely clear how tailored

CG: symptom and spirometry diary, guideline based self management educationIVR: internet based symptom and spirometry diary, guideline based self management education, physician advice by email or phone

Computer Education, monitoring

management advice - entirely clear how tailo

AH-486
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Staff Training Subject Training co-interventions Physiolo

NR NR NR Advice On vs. Advice 

Pre-prandial glucose: 

7.5 (+0.4); p=.015

Fructosamine: 492 (+3

(+34); NS

HbA1: 9.2 (+0.5) vs. 9

NR Family instructed in use of 

website

NR PEF: NS (better mean 

median CTRL)

AH-487
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR Asthma knowledge

IVR: 93%CG: 70%

AH-488
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Use of Technology Usability HIT Satisfaction

NR NR Data NR. All the patients said t

they had found POIRO easy aconvenient to use

NR NR INT satisfied w/ program, contr

asked

AH-489
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Data NR. Reported text says most 

patients felt more confident about changing their insulin dose

NR

NR NR Monitoring adherence fell 96 to 

IVR vs. 93% to 53% CGChange in peak flow technique Spacer use NSSteroid use fell 83% to 63% IV82% to 42 CG

AH-490
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Quality of Life Support Withdrawals

NR NR Total: 1 patient. 16%

Primary reason: domestic issuunrelated to the trialAdditional factor: disappointmePOIRO "failed to prevent a hyp

Asthma control 'well controlled' 52 IVR: 52% to 62%CG: 40% to 42%QoL no difference

NR Withdrew on request or tech fa

IVR: 9 (9%)CG: 6 (7%)Did not return at 12 wkIVR: 6CG: 5

AH-491
Appendix H. Data Table

Author Year

Holman 1996Jan 2007

Adverse Effects Other

NR Ultralente insulin dose, soluble insulin 

dose, number of patients with hypoglycemia, number of hypoglycemic episodes

NR Symptoms: improved, but floor effect 

w/ CNTRLs

AH-492
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Japuntich 2006 General population via 

billboards, bus posters, flyers, TV ads and press releases

Center for Tobacco Research and Intervention

Homes RCT 

Jones 1999 Referred for radiotherapy 

of breast, prostate, larynx or cervical cancer

Radiotherapy clinic In Clinic RCT 

AH-493
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

October 2001 - July 2002

12 mos *Age >18 yrs

*Smoking at least 10 cigarettes per day*Having a traditional telephone line*Literate in English

Current depression, current use of psychiatric medication, medical conditions contraindicating bupropion SR use (e.g., history of seizure disorder), current use of a smoking cessation product or treatment, or being pregnant or likely to become pregnant during the treatment phase of the study

NR

1996-1997 3 months *Referred for radiotherapy of breast, 

prostate, larynx or cervical cancer

Severe symptoms, palliative care, unaware of dx, cognitive or visual disability

Yeex

AH-494
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SMOKING NR CG: 45.1%

CHESS SCRP: 45%

Mean (SD)CG: 41.0 (+11

CHESS SCRP(+12.4)

YES: "deprivation category" by external reference

CANCER NR NR NR

AH-495
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Not CaucasianCG: 17.4%CHESS SCRP: 24.6%

NR Control Group:

Less than high school: 2.8%High school or GED: 27.8%Some college or tech school: 47.2%College or graduate school: 21.5%CHESS SCRP:Less than high school: 3.6%High school or GED: 29.5%Some college or tech school: 51.8%College or graduate school: 15.1% 

NR # of cigarettes 

day: Mean (SControl: 22.1 

CHESS SCRP(+9.5)

Years smokin(SD):Control: 23.3 

CHESS SCRP(+12.1)

Number of quattempts: MeaControl: 6.1 (+

CHESS SCRP(+12.5)

NR NR NR NR NR

AH-496
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate English NR Comprehensi

Enhancement System for SmCessation and Prevention (C

NR NR NR NR Personalized 

based informa

AH-497
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Intensive internet intervention to provide information, emotional support and problemsolving assistance. Four sections. First section provides information about quitting smoking. Second section is a support center that provided chat programs as well as a cognitive behavioral therapy intervention for negative emotions. Third section was an information repository. Fourth section allowed participants to search for information within CHESS SCRP, a list of recommended websites, and tips for evaluating other websites.

Control Group: 9 weeks of twice daily buproprion SR (150mg), three brief individual counseling sessions, and five follow-up visitsCHESS SCRP: all of the above + 12 weeks of access to the CHESS SCRP websiteParticipant training: inhome training session for use of computers and the specific website.

CHESS SCRP website Information on quitting 

smoking, chat/discussgroups, ask-an-expert service, self-therapy fobad moods, journal (structured and unstructured), documestorage, additional recommended websiteadvice for evaluating owebsites

Cancer information tailored by data in EHR vs. not tailored vs. printed booklets

Tailored (PINF) vs. nontailored (GINF) vs. choice of 143 printed booklets (BKLT)

Computer Education

AH-498
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Staff Training Subject Training co-interventions Physiolo

Staff explained basic computer used if necessary. Staff demonstrated all of the CHESS SCRP services and showed where they were located within the website. 

NR 9 weeks of twice daily 

buproprion SR (150mg); 3 individual counseling sessions, five follow-up visits

None

NR NR NR NS change depression 

adjustment scores; 84anxiety scores, especespecially but also BKexamined

AH-499
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR Physician assessed 'above avg 

knowledge' 35% GINF, 25% P20% BKLT. [note that in generdocs assessed pt knowledge baverage]

AH-500
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Use of Technology Usability HIT Satisfaction

Over 12 week intervention period:Number of log-insMean: 33.6 (+30.8), Median: 24

Minutes logged onMean: 486.4 (+638.9), Median: 202.9

Most popular servicesSupport tools: Mean: 43.29 min (+109.81)

Reading discussion group postings: Mean: 24.70 min (+116.13)

Least popular servicesInformation tools: Mean: 7.97 min (+18.41)

Use not correlated with gender, race, educationUse was correlated with age: Mean (SD) logins per week:18-35: 2.02 (+1.53)

34-45: 2.18 (+2.54)≥ 46: 3.35 (+2.57)

NR NR

12 min (1-44) 1st use; 71% no repeat use: 20/169 PINF, 4/155 GINF

NR 80% would prefer 10 min w/ 

professional (71% PINF, 80% 90% BKLT)Overall 40% satisfiedSatisfaction with information hiPINF

AH-501
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Center for Epidemiological Studies 

Depression Scale (CES-D): measure used, Data NR

Fagerstrom Test for Nicotine Dependence (FTND) collectedNRLogistic Regression: use prediand 6 month abstinence levels transformed into 4 discreet categories of logins per week)3 month: O R: 1.79, 95%CI 1.26 month: O R: 1.59, 95%CI 1.0When controlling for # of past qattempts, longest period of abstinence in the past, how musuccess the participant expectthis quit attempt, age, and dependence as measured by tFTND: still significant:3 month: O R: 2.10 95%CI 1.366 month: O R: 2.13 95%CI 1.25Cessation rates: ns by gendereducation, race/ethnicity. Diffeage at 3 months (OR= 1.026, 9

1.002-1.05), ns at 6 monthsRelapse prevention: access to CHESS SCRP to predict abstinat 3 or 6 months: ns trend:3 month: O R: 1.07, 95%CI .54

6 month: O R: 1.66, 95%CI .76

NR NR NR

AH-502
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Quality of Life Support Withdrawals

NR NR All dropouts considered smoke

analyses.CHESS SCRP: 13.6% due to mappointments; 15% withdrew byear follow-up (reason NR); 19lost to follow-up.Control: 22.2% withdrew beforyear follow-up (reason NR); 20lost to follow-up.

NR NR NR

AH-503
Appendix H. Data Table

Author Year

Japuntich 2006Jones 1999

Adverse Effects Other

NR NRNR Cost of intervention calculated less 

compared to printed booklets at £7 per patient

AH-504
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Jones 2001 Community practice NR Home, Community 

resource center

RCT 

AH-505
Appendix H. Data Table

Author Year

Jones 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NA 3 months *ICD-10 diagnosis of schizophrenia

*Living in southern Glasgow

Age > 65 yrs, had an uncertain diagnosis, judged acutely ill, chronic problems restricting participation, recent participation in an educational program.

NR

AH-506
Appendix H. Data Table

Author Year

Jones 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SCHIZOPHRENIA NR N = 67% Range: 18 to 

AH-507
Appendix H. Data Table

Author Year

Jones 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-508
Appendix H. Data Table

Author Year

Jones 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-509
Appendix H. Data Table

Author Year

Jones 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Computer: 5 web-based sessions giving general information, personal information from medical record, feedback displays;Psych nurse: 5 in-person sessions (1 hour);Both: computer plus psych nurseComputer program sessionsPsychiatric nurse sessionsBoth computer and nurse sessions

NR WWW NR

AH-510
Appendix H. Data Table

Author Year

Jones 2001

Staff Training Subject Training co-interventions Physiolo

Researcher trained subjects on computer use

NR NR NR

AH-511
Appendix H. Data Table

Author Year

Jones 2001

Health Service Utilization Health Care Costs Knowledge

NR Costs (£) of each intervention arm 

were estimated (ITT analysis), Computer vs. Nurse vs. Both (includes time + travel):Staff costs: 138.4, 107.9, 139.8;Patient costs: 14.2-32, 12-28, 17-38

NR

AH-512
Appendix H. Data Table

Author Year

Jones 2001

Use of Technology Usability HIT Satisfaction

Time on computer sessions: Median: 69 minutesRange: 34-143

Of 39 subjects completing 1st sessionTouch screens easy to use: 27Touch screens OK to use: 9Touch screens difficult to use: 3One-third were uncertain about what to do following a display.

Computer vs. Nurse vs. BothSessions useful: 67% vs. 75% 60%Information relevant: 75% vs. 1vs. 60%Enjoy sessions: 72% vs. 92% v75%Enough information: 63% vs. 875%Illness description understanda59% vs. 58% vs. 60%Wanted information but not ob12% vs. 33% vs. 35%Got information not wanted: 3858% vs. 55%Mean satisfaction (0-10): 5.5 vvs. 6.2

AH-513
Appendix H. Data Table

Author Year

Jones 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-514
Appendix H. Data Table

Author Year

Jones 2001

Quality of Life Support Withdrawals

NR NR Randomized vs. Complete Dat

Computer: 56 vs. 34;Nurse: 28 vs. 20;Both: 28 vs. 13

AH-515
Appendix H. Data Table

Author Year

Jones 2001

Adverse Effects Other

NR Psychological Measures: Computer 

vs. Nurse vs. Both: Multiple measures used (BPRS, ITAQ, GAF, KISS) showed no difference across groups.% of patients with improved KISS scores (Knowledge and information about schizophrenia schedule), 1st to 3rd (3 month) interviews: 39%, 21%, 50%, p=.08 

AH-516
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Joseph 2007 Urban high schools NR Schools RCT 

AH-517
Appendix H. Data Table

Author Year

Joseph 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 12 months *Met study criteria for current asthma 

(defined asreport of ever having a physician diagnosis of asthma accompanied by one or more of the following-daytime and/or nighttime symptoms in the past 30 days-use of medication symptoms for asthma in the past 30 days,-medical care use for asthma in the past yearone or more refills of B-agonists in the past year)*Not reporting a physician diagnosis but answered positively to items from ISAAC and reported symptom frequencies similar to those used in EPR 2 for classification of mild intermittent asthma

NR NR

AH-518
Appendix H. Data Table

Author Year

Joseph 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: urban AfricanAmerican youth ASTHMA NR N = 36.6% Mean (SD): 1

(+1.0)

AH-519
Appendix H. Data Table

Author Year

Joseph 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

African American: 98%

NR High school students Urban high schools 52% qualities 

federal schooprogram. Demographic information ofnon- participagiven, not liste

AH-520
Appendix H. Data Table

Author Year

Joseph 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

49% Medicaid enrollees

NR NR NR Puff city

AH-521
Appendix H. Data Table

Author Year

Joseph 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

web based program focusing on controller medication adherence, rescue inhaler availability,smoking cessation/reduction. Consists of 4 consecutive educational sessions making use of normative and impassive behavior. Voiced over messages ( accommodates low literacy). Participant specific information obtained at baseline and 4 sessions and used for tailoringTheory based health messages and info on asthma control presented in reference to he 3 core behaviors mentioned above. e.g. info on basic asthma pathophysiology, trigger avoidance, correct use of metered dose inhaler and other devices.

IG- web based tailored program as described above.CG- students in this grp were directed to existing generic asthma educational websites.Students restricted to these sites and could not access links for outside programs or generalinterest sites. 4 computer sessions just like IG each of 30 minsFor both IG and CG, a program module including names and illustrations of asthma medications were used to facilitate identification of current usage.A health care referral coordinator proactively contacted the students in the treatment group depending upon the risk assessment report that had key questions indicating 

Computer Education, answering 

questions

AH-522
Appendix H. Data Table

Author Year

Joseph 2007

Staff Training Subject Training co-interventions Physiolo

NR NR NR Grp 1 vs. Grp 2 

Adjusted RR (95% CI)Symptom days(2 wks) 2.1 (+3.0) vs. 2.8 (+3.4

0.5 (+0.4-0.8); p= 0.00

Symptom nights/2 wks0.9 (+2.3) vs. 1.5 (+2.5

0.4 (+0.2-0.8); p= 0.00

School days missed/30.4 (+1.2) vs. 1.2 (+3.3

0.3 (+0.1-0.7), p= 0.00

Days restricted activity1.3 (+2.2) vs. 2.3 (+3.4

0.5 (+0.3-0.8), p= 0.02

Days had to change p0.4 (+1.2) vs. 0.6 (+1.5

0.5 (+0.3-1.2), p= 0.17

AH-523
Appendix H. Data Table

Author Year

Joseph 2007

Health Service Utilization Health Care Costs Knowledge

Grp 1vs Grp 2 Adjusted RR (95% CI), pHospitalizations/12 months0.2 (0.6) vs. 0.6 (2.0)0.2 (0.2-0.9); 0.01ED visits/12 months0.5 (2.0) vs. 0.8 (1.9)0.5 (0.3-1.3); 0.08

Estimated labor costs for the referral coordinator was $ 6.66/treatment student($8.05 per treatment student referred, $11.73 per student contacted)

NR

AH-524
Appendix H. Data Table

Author Year

Joseph 2007

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-525
Appendix H. Data Table

Author Year

Joseph 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Controller medication adheren

(n), Positive behaviorGrp 1 vs. Grp 220.4 (31) vs. 12.6 (18), p-- 0.09No change in negative behavioGrp 1 vs. Grp 262.5 (95) vs. 63.6 (91), p--?Negative change in behaviorGrp 1 vs. Grp 217.1 (26) vs. 23.8 (34), p--?Rescue inhaler activity, % (n)Positive behaviorGrp 1 vs. Grp 238.8 (59) vs. 32.2 (46), p--0.01 no change in negative behavioGrp 1 vs. Grp 248.7(74) vs. 43.3(62)Negative change in behaviorGrp 1 vs. Grp 212.5(19) vs. 24.5(35)Smoking cessation/reduction, Positive behaviorGrp 1 vs. Grp 295.0 (132) vs. 94.1 (111), p= 0

AH-526
Appendix H. Data Table

Author Year

Joseph 2007

Quality of Life Support Withdrawals

Cumulative scoreGrp 1 vs. Grp 25.3 (+1.3) vs. 5.0 (+1.5)

RR (95% CI)1.2 (0.9-1.6), p=0.35?Activity domainGrp 1 vs. Grp 25.3 (+1.7) vs. 5.0 (+1.7)RR (95% CI)1.4 (1.0-2.0), p= 0.16Emotional domainGrp 1 vs. Grp 25.7 (+1.5) vs. 5.3 (+1.6)RR (95% CI)1.2 (0.9-1.7), p-0.38Symptom domainGrp 1 vs. Grp 25.3 (+1.6) vs. 4.9 (+1.6)RR (95% CI)1.4 (1.0-1.9), p-- 0.07

NR Out of 350 pts who returned co

forms, Baseline incomplete: 36Out of this 36Transferred: 14Moved: 312th grade: 1Other reasons: 18

AH-527
Appendix H. Data Table

Author Year

Joseph 2007

Adverse Effects Other

NR Flowchart of study participation and 

randomization givenComparison of eligible students by study participation using results of the lung health survey given, not listed hereStudy compliance, status of core behavior, report of controller and rescue medication at session 1 by randomization grp given, not listed hereReasons for contact with referral coordinator and the no of students meeting the coordinator in both grps given, not listed here

AH-528
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kashem 2006 NR HF practice at Temple 

University medical center

Homes RCT 

AH-529
Appendix H. Data Table

Author Year

Kashem 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 8 months *Pts with NYHA class 2,3 or 4 Heart 

failure (HF) and at least 1 HF admission within the past 6 months*All pts required to have telephone and internet access*Be able to read and write*Know basic skills of computer use and internet navigation

NR NR

AH-530
Appendix H. Data Table

Author Year

Kashem 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CHF NR Control: 66.7%

Telemedicine: 72.2%

Control: 55.1 

Telemedicine(+10.6)

AH-531
Appendix H. Data Table

Author Year

Kashem 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Control vs. telemedicine, (%)Caucasian: 66.7 vs. 72.2African American: 27.8 vs. 27.8Latino/Hispanic: 5.6 vs. 0

NR NR NR Grp C vs. Grp 

Weight (poun206.1 (+66.5) 202.4 (+47.7)BMI:32 (+12.6) vs

(+6.9)

Systolic BP:119.1 (+17.7) 

(+7.2) 

Diastolic BP:70.5 (+7.5) vs

(+3.4)

Ejection fracti26.6 (+16.4) v

(+17.6)

NYHA (%):Class 2: 42.9 Class 3: 52.4 Class 4: 4.8 vCardiomyopaetiology (%)Ischemic: 44.4 Dilated: 38.9 vOther: 16.7 vs

AH-532
Appendix H. Data Table

Author Year

Kashem 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Insight Telehe

AH-533
Appendix H. Data Table

Author Year

Kashem 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Evaluation of web based internet telemedicine management system for reducing care encounters in heart failure patients

Grp T: pts reported three times weekly via a secure internet site for telemedicine intervention; received present standard care provided by the advanced HF and cardiomyopathy team.Pt use of the system: The pt can enter data regarding weight, steps per day with pedometer, blood pressure and heart rate (daily or several days' data can be entered in one login session); the pt can review their data by trend charts and numerical tables displayed on the computer screen; medication screen allows pt or provider to enter medication (each record includes dose, administration schedule, ordering physician, and date of prescription expiration); pt can review his/her medication, dose, frequency, physician and prescription information.

Computer Data entry, review of 

charts, numerical tableprescription informatiomessages between pt health care provider

AH-534
Appendix H. Data Table

Author Year

Kashem 2006

Staff Training Subject Training co-interventions Physiolo

NR All pts given 

sphygmomanometer and pedometer and instructed how to use them, pt made first time data entry with coaching by a research nurse

NR NR

AH-535
Appendix H. Data Table

Author Year

Kashem 2006

Health Service Utilization Health Care Costs Knowledge

Control vs. Telemedicine Clinic phone calls:21 vs. 39, p= 0.025Scheduled clinic visits:7 vs. 11, NSUnscheduled clinic visits:5 vs. 3, NSHospitalizations (number):18 vs. 9, p= 0.025Hospital days:133 vs. 44, p= 0.030

NR NR

AH-536
Appendix H. Data Table

Author Year

Kashem 2006

Use of Technology Usability HIT Satisfaction

InteractionsData sent by the patients: 1,253Text messages sent by the patient: 362Generic messages sent by the provider: 836Tailored text messages sent by the provider: 416Total number of internet interactions: 2,867

NR NR

AH-537
Appendix H. Data Table

Author Year

Kashem 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-538
Appendix H. Data Table

Author Year

Kashem 2006

Quality of Life Support Withdrawals

NR NR NR

AH-539
Appendix H. Data Table

Author Year

Kashem 2006

Adverse Effects Other

NR NR

AH-540
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kaufman 2003 Diabetic outpatients Medical center Homes Usability cognitive 

walkthrough and field observation

AH-541
Appendix H. Data Table

Author Year

Kaufman 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NA *Patient volunteers

*Selected segments based on log files*Contrasted 2 cases at extremes

NR YE

AH-542
Appendix H. Data Table

Author Year

Kaufman 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: rural upstate NY and Hispanic NYC

DM NR NR Mean (SD)

NYC: 69.6 (±6Upstate: 73.7 

AH-543
Appendix H. Data Table

Author Year

Kaufman 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Hispanic: 14ptsCaucasian: 11pts

NR Mean years (SD)

NYC: 8.5 (±5)Upstate NY: 12.1 (±3)

NR NR

AH-544
Appendix H. Data Table

Author Year

Kaufman 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR Spanish: 12

English: 13

NR IDEATel

AH-545
Appendix H. Data Table

Author Year

Kaufman 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Videoconferencing, BP & BG uploading and review, education, email

Novice users of system Computer Education, monitoring

videoconferencing,

AH-546
Appendix H. Data Table

Author Year

Kaufman 2003

Staff Training Subject Training co-interventions Physiolo

NR Ongoing NR NR

AH-547
Appendix H. Data Table

Author Year

Kaufman 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-548
Appendix H. Data Table

Author Year

Kaufman 2003

Use of Technology Usability HIT Satisfaction

NR System barriers: problematic widgets, 

small fonts, bad spacing, complex tasks, screen transitions, system stabilityCognitive/Skill barriers: mousekeyboard skill, mental model, literacy, numeracy. Also anxiety, self-efficacy, motivation

NR

AH-549
Appendix H. Data Table

Author Year

Kaufman 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-550
Appendix H. Data Table

Author Year

Kaufman 2003

Quality of Life Support Withdrawals

NR NR NR

AH-551
Appendix H. Data Table

Author Year

Kaufman 2003

Adverse Effects Other

NR NR

AH-552
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kenwright 2005 OCD patients BTSteps clinic Homes Randomized trial 

AH-553
Appendix H. Data Table

Author Year

Kenwright 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 17 weeks *Diagnosed primary obsessive 

compulsive disorder (OCD) for at least 2 years

Schizophrenia, bipolar disorder, other psychosis, primary major depression, suicidal plans, alcohol or substance abuse.

NR

AH-554
Appendix H. Data Table

Author Year

Kenwright 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR OCD NR N = 47.7% Mean: 40

Groups NR

AH-555
Appendix H. Data Table

Author Year

Kenwright 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR Unemployed: 45% NR NR NR

AH-556
Appendix H. Data Table

Author Year

Kenwright 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-557
Appendix H. Data Table

Author Year

Kenwright 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Live help-line support during office hours

Scheduled support: 9 therapist initiated phone calls at set appointments over the 17 weeksRequested support: patientinitiated calls when help was wanted

Phone Help-line support

AH-558
Appendix H. Data Table

Author Year

Kenwright 2005

Staff Training Subject Training co-interventions Physiolo

NR NR BT Steps: Phone system 

with user manual to guide individually tailored selfhelp for OCD. Uses Behavioral exposure therapy with ritual prevention (ERP). BTSteps guides traditional ERP in 9 steps. Steps 1-3 concern education and selfassessment, steps 4-9 guide daily self-exposure to triggers of rituals, obsessions, and discomfort, followed by selfimposed ritual prevention. IVR system helps users to identify triggers for rituals and obsessions and to set homework tasks that last at least 1 hour a day until discomfort falls for each trigger. 

NR

AH-559
Appendix H. Data Table

Author Year

Kenwright 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-560
Appendix H. Data Table

Author Year

Kenwright 2005

Use of Technology Usability HIT Satisfaction

Therapist-Support Phone Time: Mean per patient duration of support calls:Scheduled: Mean: 13 minutes (Range 5-35 minutes)Requested: Mean: 11 minutes (Range 3-25 minutes)Mean total support time per patient:Scheduled: Mean: 76 minutes (+78); 

Mean # calls: 7.5 (+3.7)

Requested: Mean: 16 minutes (+36); 

Mean # calls: 1.5 (+2.8)

Mean per patient time spent calling BTSteps's IVR system:Scheduled: 232 minutes (+162)

Requested: 178 minutes (+149)

NR NR

AH-561
Appendix H. Data Table

Author Year

Kenwright 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Yale-Brown Obsessive Compulsive 

Scale (YBOCS):Scheduled patients: significant improvements on YBOCS Total (t= 4.8, 95% CI= 4.6-113.6, effect size=1.2, p<.001,), YBOCS Obsessions (t= 4.8, 95%CI= 2.2-5.6, effect size, 1.0, p<.001), YBOCS Compulsions (t= 4.4, 95%CI= 2.2-6.1, effect size=1.1, p=.04)Requested patients: sig. improvement on YBOCS Compulsions (t= 2.0, 95%CI=.00-2.9, effect size=0.6, p=.04)Improvement significantly greater in Scheduled than Requested patients on YBOCS Total (F= 7.0, p=.01) and YBOCS Compulsions (F= 11.6, p=.001). ns for YBOCS Obsessions.Treated Target Triggers: discomfort 

during patients first two ERP homework tasks: Scheduled and Requested patients improved significantly, but difference ns (F=.917, p=.346)Work and Social Adjustment: 

Improvement greater for scheduled than requested patients on WSAS Total (F=3.2, df=1, p=.05)

NR

AH-562
Appendix H. Data Table

Author Year

Kenwright 2005

Quality of Life Support Withdrawals

NR NR Scheduled: 13.6%

Requested: 59.1% (2-tailed Fisher's exact test= 0

AH-563
Appendix H. Data Table

Author Year

Kenwright 2005

Adverse Effects Other

NR NR

AH-564
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kenwright 2004 NR Computer aided self-help 

clinic

Homes/clinic Cohort 

AH-565
Appendix H. Data Table

Author Year

Kenwright 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

Dec 2000 to Dec 2001

1 month *Presence of phobia or panic disorder 

(based on an interview checklist of ICD10 diagnostic criteria; WHO, 1992)*Motivation to try self- help*Absence of substance misuse, psychosis or active suicidal plan

NR NR

AH-566
Appendix H. Data Table

Author Year

Kenwright 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR PHOBIA/PANIC 

DISORDER

NR Home internet vs. 

London clinic60% vs. 52.9%

Home interneLondon clinic37 yrs vs. 36 

AH-567
Appendix H. Data Table

Author Year

Kenwright 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Condition; 

Home interneAgoraphobia panic= 6Social phobiaInsect phobia ClaustrophobCo morbid conditions=5 (depression=3Generalized adisorder=2)London clinicSpecific phobAgoraphobia panic= 5Social phobiaGeneralized adisorder=2Obsessive codisorder= 1Mean problem severityHome interneLondon clinic= 5.4Employed full 

AH-568
Appendix H. Data Table

Author Year

Kenwright 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR FearFighter

AH-569
Appendix H. Data Table

Author Year

Kenwright 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Computer sided exposure self help system at home on the internet with brief therapist support by phone

Home internet- unlimited access to fearfighter on the internet for 12 weeks, never saw or attended the clinic. Therapist scheduled 7 live 0 min support session by telephone at weeks 1,2, 4, 6, 8, 10 and 12London clinic-given appointments to use fearfighter on a stand alone computer in the clinic at weeks 1,2,4,6,8,10 and 12. Therapist gave support at start and end of each session, for a total of 10 min per session.

Computer Fearfighter interventio

(details of the intervennot available)

AH-570
Appendix H. Data Table

Author Year

Kenwright 2004

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-571
Appendix H. Data Table

Author Year

Kenwright 2004

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-572
Appendix H. Data Table

Author Year

Kenwright 2004

Use of Technology Usability HIT Satisfaction

Home internet usersMean use= 16 times (+11), over 66 

days (+25)

London clinicMean total of 237 min (+57) at the 

clinic

NR Internet users generally satisfie

felt comfortable using the syste(Further info available on requ3 pts said they would prefer faface guided self help to interneguided self help.

AH-573
Appendix H. Data Table

Author Year

Kenwright 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Week 0 vs. Week 12 vs. Week 16 vs. 

Change vs. Effect sizeFQ Global phobia (0-8), Mean (SD)Home internet:6.0 (+1.2) vs. 3.4 (+1.3) vs. 2.8 (+1.7) 

vs. 53 (p<0.001) vs. 1.5London clinic: 5.4 (+2) vs. 3.2 (+1.8) vs. 3.2 (+1.6) 

vs. 41 (p<0.001) vs. 0.4FQ total phobia (0-120), Mean (SD)Home internet: 46 (+27) vs. 32 (+24) vs. 35 (+23) vs. 

24 (p< 0.001) vs. 0.4London clinic: 49 (+27) vs. 32 (+23) vs. 33 (+27) vs. 33 (p<0.001) vs. 0.6FQ depression (0-8)Home internet: 4.1 (+1.9) vs. 3.2 (+4) vs. 2 (+1.5) vs. 

51 (p<0.05) vs. 0.6London clinic:4.3 (+2.4) vs. 4.3 (+7.1) vs. 2.9 (+2.1) 

vs. 33 (p<0.05) vs. 0.8FQ Anxiety/depression (0-48)Home internet: 19.4 (+6.6) vs. 19 (+4.8) vs. 7 (+4.1) 

vs. 64 (p<0.001) vs. 1.8London clinic:

NR

AH-574
Appendix H. Data Table

Author Year

Kenwright 2004

Quality of Life Support Withdrawals

NR NR 266 referred to clinic

56 met criteria for treatment wifearfighter154 met criteria for clinic's othecomputer aided self help systedepression, general anxiety anobsessive compulsive disorder56 more were unsuitable for computer aided treatmentOut of 56 pts in the fearfighter 13 refused self help43 began using intervention, 1dropped out before completion treatment (all in the clinic grp)

AH-575
Appendix H. Data Table

Author Year

Kenwright 2004

Adverse Effects Other

NR Internet users had slightly more 

therapist support than stand alone computer users113 min (+28.1) vs. 99 min (+11.4), 

t= -1.42, p=0.16

AH-576
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kim 2006a Outpatient diabetes clinic University hospital Homes RCT 

AH-577
Appendix H. Data Table

Author Year

Kim 2006a

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 mos *Age 20 years and older

*FBS less than 240 mg/dL and/or *Diabetes mellitus history less than 20 years*No chronic complications of severe retinopathy, nephropathy, or neuropathy*No evidence of heart disease, musculoskeletal disorders, or other disabling diseases that could restrict physical activity*No insulin administration

NR NR

AH-578
Appendix H. Data Table

Author Year

Kim 2006a

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DM NR N = 53.4% Mean: 55

AH-579
Appendix H. Data Table

Author Year

Kim 2006a

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Korean NR NR NR NR

AH-580
Appendix H. Data Table

Author Year

Kim 2006a

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR Korean Korean NR NR

AH-581
Appendix H. Data Table

Author Year

Kim 2006a

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

WB= Web-based intervention, included interactive and animated features, stage-based personalized sections on goal setting, activity planning, determining target heart rates, and psychological and physical readiness questionnaires. Assist sequential movement through relevant stage-based information.

Group 1: WB (web-based intervention group)Group 2: PM (print material intervention group)Group 2: UC (usual care group)

Web-based intervention Education, assessmen

advice sending

AH-582
Appendix H. Data Table

Author Year

Kim 2006a

Staff Training Subject Training co-interventions Physiolo

Health care providers had to be fully trained for the constructs and meaning of TTM and use of the webbased intervention

NR NR FBS:

Grp 1 and Grp 2 both significantly, however significantlyGrp 1 and Grp 2 decresignificantly more than both)HbA1C:Grp 1 and Grp 2 both significantly, however significantlyGrp 1 and Grp 2 decresignificantly more than and P<0.05, respectiv

AH-583
Appendix H. Data Table

Author Year

Kim 2006a

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-584
Appendix H. Data Table

Author Year

Kim 2006a

Use of Technology Usability HIT Satisfaction

NR NR 82.9% of Grp 1 reported satisf

with the intervention

AH-585
Appendix H. Data Table

Author Year

Kim 2006a

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-586
Appendix H. Data Table

Author Year

Kim 2006a

Quality of Life Support Withdrawals

Physical Activity:Both Grp 1 and Grp 2 increased significantly (P<0.001), however the 2 groups did not differ significantlyGrp 1 and Grp 2 increased significantly more than Grp 3 (P<0.01 and P<0.001, respectively)

NR NR

AH-587
Appendix H. Data Table

Author Year

Kim 2006a

Adverse Effects Other

NR NR

AH-588
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kim 2007 NR Endocrinology outpatient 

department of tertiary care hospital in South Korea

Homes Cohort 

AH-589
Appendix H. Data Table

Author Year

Kim 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

Jan 2003 to Aug 2004

12 weeks *Pts should be able to perform blood 

glucose testing and self injection of medication*Access the website *Have their own cellular phone

Clinical history of an important illness, renal insufficiency with a creatinine level> 0.08 mmol/l, or had been using insulin pumps.

NR

AH-590
Appendix H. Data Table

Author Year

Kim 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR CG: 42.3%

IG: 44%

CG: 47.5 (9.1IG: 46.8 (8.8)

AH-591
Appendix H. Data Table

Author Year

Kim 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR BMI (kg/m2), 

IG23.4 (+2.5) vs

(+2.9)

Diabetes dura(yrs) CG : IG:8 (+4.9) : 5.2 

Treatment meCG : IGInsulin:8 (+30.8) : 8 (

OHA:18 (+69.2) : 1

Glucosylated hemoglobin (%7.59 (+1.09) : 

(+1.72)

Fasting plasmglucose (mmo7.9 (+1.3) vs. 

(+1.4)

2HPMG (mmo12.9 (+4.3) vs

(+4.5)

AH-592
Appendix H. Data Table

Author Year

Kim 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Nurse short m

service

AH-593
Appendix H. Data Table

Author Year

Kim 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

To investigate the effectiveness of an educational intervention that used both cellular phone and the internet to provide SMS relating to plasma glucose levels.

CG: pts met the endocrinologist once or twice during the study period and were given recommendations about medication, medication dosage and lifestyle modification by the specialistIG: pts logged in website and sent their self monitored blood glucose levels and drug information, including the kinds and dosages of insulin and oral anti diabetic medications; data transported to the internet server system and automatically displayed on individual electronic chart in the homepage and pts able to see the recommendations as well as data on the website(After integrating the information, the researcher sent optimal recommendations back to each pt, weekly, by a SMS by a cellular phone or wired internet)

Cellular phone, computer Data collection, 

recommendations by S

AH-594
Appendix H. Data Table

Author Year

Kim 2007

Staff Training Subject Training co-interventions Physiolo

NR Pt trained on entering data 

on the website for 30 mins by a researcher

Glycosylated hemoglobin (%); CG vs. IGBaseline:7.59 (+1.09) vs. 8.09 

(+1.72)

3 months:7.66 (+0.91) vs. 6.94 

(+1.04)

Group:0.22 (+0.644)

Time:7.46 (+0.008)Group X Time:8.35 (+0.005)Fasting plasma glucose (mmol/l)Baseline:7.9 (+1.3) vs. 8.4 (+1.4)

3 months:8.2 (+2.3) vs. 8.0 (+1.9)Group:0.07 (+0.787)

Time:0.40 (+0.532)Group X Time:3.03 (+0.088)2 HPMG (mmol/l)Baseline:12.9 (+4.3) vs. 14.2 (+4.5)

3 months:

NR

AH-595
Appendix H. Data Table

Author Year

Kim 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-596
Appendix H. Data Table

Author Year

Kim 2007

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-597
Appendix H. Data Table

Author Year

Kim 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-598
Appendix H. Data Table

Author Year

Kim 2007

Quality of Life Support Withdrawals

NR NR IG: 5 subjects did not record th

glucose levels for more than 4 on the web siteCG: 4 subjects were lost beforcompleting post test , 1 moved another city and three decided out of the program before comthe post test

AH-599
Appendix H. Data Table

Author Year

Kim 2007

Adverse Effects Other

NR NS

AH-600
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kim 2006b Endocrinology outpatient 

department of tertiary care hospitcal located in an urban area

South Korea Homes Quasi-experimental, 

cohort, 1-group, pretest and posttest

AH-601
Appendix H. Data Table

Author Year

Kim 2006b

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

January 2003 to March 2004

12 weeks *30 years of age or older

*Access to internet in the home*Diagnosed diabetes*Able to perform perform blood glucose self-testing and self-injection of medication*Able to input data at the web site*Have their own cell phone

Not following the inclusion criteria, clinical history of heart failure, hepatic dysfunction, renal insufficiency with a creatinine level higher than 1.5 mg/dL, or had been using insulin pumps

NR

AH-602
Appendix H. Data Table

Author Year

Kim 2006b

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DM NR 42% 43.5

AH-603
Appendix H. Data Table

Author Year

Kim 2006b

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Mean

BMI: 24.3Systolic BP: 1mmHgDiastolic BP: mmHgDuration of di5.6 yrsHbA1c: 8.1%Fasting blood 164.9 mg/dL

AH-604
Appendix H. Data Table

Author Year

Kim 2006b

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR Korean NR NR

AH-605
Appendix H. Data Table

Author Year

Kim 2006b

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Pts were asked to input blood glucose levels every day via internet or cell phone. Optimal recommendations were sent weekly to each pt via SMS

NR Website and text 

messages

Data input and data receiver

AH-606
Appendix H. Data Table

Author Year

Kim 2006b

Staff Training Subject Training co-interventions Physiolo

NR 30 minute instruction on 

how to input data at the website

NR Glycosylated hemoglo

Pretest: 8.1 + 2.1

Posttest: 7.0 + 1.1

Difference: -1.1 + 2.1

t: 2.89P: 0.006AdherenceDiabetic diet, d/wkPretest: 3.8 + 2.8

Posttest: 3.0 + 2.7

Difference: -0.8 + 1.8

t: 1.44P: 0.18230-minute physical exPretest: 2.5 + 2.2

Posttest: 3.4 + 2.2

Difference: 0.9 + 2.0

t: 2.22P: 0.036Diabetic medication taPretest: 4.8 + 2.6

Posttest: 5.9 + 1.9

Difference: 1.1 + 1.9

t: 2.36P: 0.32Foot care, d/wkPretest: 4.9 + 3.0

AH-607
Appendix H. Data Table

Author Year

Kim 2006b

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-608
Appendix H. Data Table

Author Year

Kim 2006b

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-609
Appendix H. Data Table

Author Year

Kim 2006b

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-610
Appendix H. Data Table

Author Year

Kim 2006b

Quality of Life Support Withdrawals

NR NR 12 pts did not record their gluc

levels for more than 4 weeks owebsite

AH-611
Appendix H. Data Table

Author Year

Kim 2006b

Adverse Effects Other

NR Secondary Outcomes at 12 mos for 

all pts completing f/u in the electronic communications and home blood pressure monitoring trial; Mean (SD)# of antihypertensive medication classesMissing Data: 0Baseline: 1.64 (+0.85)

UC: 1.69 (+0.91)

IVR: 1.94 (+0.91)

IVR + PC: 2.16 (+0.93)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: 0.3 (0.1 to 0.4)UC vs IVR + PC: 0.5 (0.3 to 0.6)IVR vs IVR + PC: 0.2 (0.1 to 0.4)Aspirin use, No. (%)Missing Data: 38Baseline: 338 (+48.8)

UC: 124 (+53.0)

IVR: 131 (+56.0)

IVR + PC: 149 (+66.5)

Differences between IVR Groups, Mean Difference or RR (95% CI)UC vs IVR: 1.1 (0.9 to 1.2)UC vs IVR + PC: 1.3 (1.1 to 1.5)IVR vs IVR + PC: 1.2 (1.0 to 1.4)

AH-612
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kosma 2005 Rehabilitation centers, 

hospitals, colleges, and disability newsletters

NR Homes Pilot Study; RCT 

AH-613
Appendix H. Data Table

Author Year

Kosma 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 4 weeks *Female age: 18-54

*Male age: 18-44*Inclusion criteria based on health status, stages of change level, age range, disability type, internet access, but is NR what specifically was used to include/exclude pts

Unclear NR

AH-614
Appendix H. Data Table

Author Year

Kosma 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR NR Physical Disability

Amputation: 5.3%Cerebral palsy: 18.7%Muscular dystrophy: 4%Spinal Cord injury: 33.3%Multiple Sclerosis: 21.3%Cerebral Palsy: 18.7%Other: 17.3%

28% 38.73 yrs (+8

AH-615
Appendix H. Data Table

Author Year

Kosma 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 89.3%African-Americans: 5.3%

NR College Grad: 49.3%

Graduate degree: 29.3%HS grad: 4%Some college: 17.3%

NR Stages of Cha

Precontempla10.7%ContemplatioPreparation: 3Action: 9.3%Maintenance: 

AH-616
Appendix H. Data Table

Author Year

Kosma 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-617
Appendix H. Data Table

Author Year

Kosma 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Web-based physical activity motivational program with and without discussion, weekly motivational messages

IVR (n=46): 4 week webbased physical activity motivational program with a different lesson plan for each linkCG (n=29): no intervention

Website and emails Motivational message

discussion

AH-618
Appendix H. Data Table

Author Year

Kosma 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR Posttest

CG: more active 9.3 MIVR: 6.1 MET hrs/day

AH-619
Appendix H. Data Table

Author Year

Kosma 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-620
Appendix H. Data Table

Author Year

Kosma 2005

Use of Technology Usability HIT Satisfaction

NR 75% found the program appropriate 

for national distribution among adults with physical disabilities, easy to use scored 4.5 out of a 5 pt scale

Out of a 5 pt scale (5 highest, lowest)Helpfulness: 3.4 (+1.4)

Ability to change physical activ(+1.4)

Ability to maintain physical act3.3 (+1.2)

AH-621
Appendix H. Data Table

Author Year

Kosma 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Stages of change Pretest, n

Preconemplation: 8Conemplation: 28Preparation: 25Action: 7Maintenance: 7Stages of change Posttest, nPrecontemplation: 7Conemplation: 21Preparation: 22Action: 13Maintenance: 12

AH-622
Appendix H. Data Table

Author Year

Kosma 2005

Quality of Life Support Withdrawals

NR NR NR

AH-623
Appendix H. Data Table

Author Year

Kosma 2005

Adverse Effects Other

NR NR

AH-624
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kressig 2002 Community dwellers VA rehabilitation center and 

a local senior center

Unclear Cohort 

AH-625
Appendix H. Data Table

Author Year

Kressig 2002

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NR *60 yrs of age and older without medical 

contraindication for exercise*Intact manual dexterity (ability to dial a phone number)*Intact near vision (ability to read a newspaper)*System included prescreening procedures to ensure pts are at minimal risk for falls and demonstrate normal cognitive function and normal mood

NR YE

AH-626
Appendix H. Data Table

Author Year

Kressig 2002

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR NR NR 50% Mean: 7.04 (+

Range: 60-87

AH-627
Appendix H. Data Table

Author Year

Kressig 2002

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR $10,000 to $39,000: 

11$40,000 or higher: 17Did not want to say: 6

Some college education or more: 33

NR No or very litt

computer experience:15Cognitive funcmeasured by ranged from 0 with 30 pts (out of scoring betwe2

AH-628
Appendix H. Data Table

Author Year

Kressig 2002

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Exercise expe

AH-629
Appendix H. Data Table

Author Year

Kressig 2002

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

System is a computerized health promotion tool whose ease of use and acceptability of the exercise recommendations were rated by pts. Time and assistance requirements and ability to successfully interface with the software were also evaluated.

Exercise expert system: software generates individualized exercise prescriptions for older healthy individuals based on responses given by clients and their practitioners to three different questionnaires, questionnaires pertain to medical history, mood, functional status, mental status, and specific preferences known to influence exercise behavior and maximize subsequent adherence.

Computer Tailored exercise 

prescriptions

AH-630
Appendix H. Data Table

Author Year

Kressig 2002

Staff Training Subject Training co-interventions Physiolo

NR System training manual: 

illustrated training manual with elementary mouse training and general instructions for interaction with the system.Pts were seated in front of computer and left alone with the manuals until they were ready to start the computerized questionnaire. Pts encouraged to ask questions after reviewing the manual.

NR NR

AH-631
Appendix H. Data Table

Author Year

Kressig 2002

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-632
Appendix H. Data Table

Author Year

Kressig 2002

Use of Technology Usability HIT Satisfaction

Mean (SD), Min to Max in minutesTraining time: 3.76 (+1.50), 1-7

Completion time: 32.58 (+15.57), 12-

79

Mean (SD), min-maxEase of use score (0-5)4.58 (+0.66), 3-5Requests for assistanceMouse related: 0.67 (+1.16), 0-5

Non mouse related: 2.36 (+1.90), 0-8

Mean (SD), min- maxPrescription acceptability score 21.45 (+2.24), 15-24

AH-633
Appendix H. Data Table

Author Year

Kressig 2002

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-634
Appendix H. Data Table

Author Year

Kressig 2002

Quality of Life Support Withdrawals

NR NR 33 pts completed the protocol,

1 male pt unable to complete because of fatigue

AH-635
Appendix H. Data Table

Author Year

Kressig 2002

Adverse Effects Other

NR Pearson correlation (p values) for 

objective performance measuresMeasures; Total time vs. total assistance vs. mouse related vs. non mouse relatedAge: 0.314 (0.750) vs. 0.532 (0.001) vs. 0.338 (0.054) vs. 0.491 (0.004)Computer experience: - 0.456 (0.008) vs. - 0.389 (0.025) vs. - 0.303 (0.086) vs. - 0.325 (0.065)Education: 0.049 (0.785) vs. - 0.116 (0.521) vs. - 0.227 (0.203) vs. - 0.013 (0.944)OMCT score:0.218 (0.222) vs. 0.081 (0.654) vs. -0.089 (0.622) vs. 0.161 (0.371)Income: - 0.248 (0.164) vs. - 0.204 (0.256) vs. - 0.139 (0.442) vs. - 0.182 (0.310)Ease of use score:- 0.481 (0.005) vs. - 0.427 (0.013) vs. - 0.311 (0.079) vs. - 0.370 (0.034)Prescription acceptability score:- 0.008 (0.967) vs. - 0.057 (0.754) vs. 

AH-636
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Kwon 2004 Outpatient clinic Kangnam St. Mary's 

Hospital Diabetes Center

Homes RCT 

Labiris 2004 Internet Urban areas of USA, EU, 

Canada, and Australia

Homes Cohort 

AH-637
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

May-August 2001

3 mos *Type 2 diabetes for >1 year

*Internet access*>30 years old

Significant diseases that were likely to affect the outcome and compliance of this study

Any who had participated 

in other programs that provided any information or education for diabetes management

NR

8 yrs NR NR Need for physical 

examination and insufficient provision of medical data were the main reasons for failure to answer all requests

NR

AH-638
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 2 DM NR Grp 1: 58%

Grp 2: 64%

MeanGp 1: 54 Gp 2: 53 

YES YES: Orthopedic 

related illnesses

NR 60.50% Men: 43 (18-7

Women: 55 (3

AH-639
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Korean NR NR NR NRNR NR NR YES: urban areas of 

EU, USA, Canada, and Australia

NR

AH-640
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR Korean Korean NR Web-based d

management 

NR NR NR NR NR

AH-641
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

NR Group 1: Intervention group

Group 2: Control group

NR NR

Internet-based counseling: patients would send emails to panel of physicians and orthopedic surgeons, and GPs, and these professionals would respond

Number of RequestsSystemic diseases: 764Pediatric orthopedics: 154Sports Injuries: 143Hand: 81Fracture complications: 64Misc: 28

E-mail To provide online 

counseling to pts

AH-642
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Staff Training Subject Training co-interventions Physiolo

NR NR NR At 12 week follow-up H

for Grp 1 decreased frbaseline to 6.94%, (P<decreased significantly 

up than Grp 2 (P<0.05HDL for Grp 2 change50.6 mg/dl (P<0.05)No other significant ch

NR NR NR NR

AH-643
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-644
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Use of Technology Usability HIT Satisfaction

NR NR NRNR NR NR

AH-645
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-646
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Quality of Life Support Withdrawals

NR NR N = 9

Grp1: 2 for not following the stprotocol, 2 for lost to follow-upGrp 2: 5 for lost to follow-up

NR NR NR

AH-647
Appendix H. Data Table

Author Year

Kwon 2004Labiris 2004

Adverse Effects Other

NR NRNR NR

AH-648
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

LaFramboise 2003 NR NR Homes RCT 

AH-649
Appendix H. Data Table

Author Year

LaFramboise 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 2 months *Inclusion criteria were oriented to 

person, place and time*No visual impairment*Ability to speak and read English*Having an active phone service.

NR NR

AH-650
Appendix H. Data Table

Author Year

LaFramboise 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CHF NR Telephonic: 16.7%

Home visit: 7.8%Health buddy: 13.3%Home visit/health buddy: 12.2%Total: 50.0%

Mean (RangeTelephonic: 686)Home visit: 7385)Health buddy91)Home visit/hebuddy: 74.4 (5Total: 70.3 (3

AH-651
Appendix H. Data Table

Author Year

LaFramboise 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Telephonic vs. Home visit vs. Health buddy vs. Home visit+health buddy vs. TotalCaucasian:25.6 % vs. 20.0% vs. 21.1% vs. 17.8% vs. 83.3%African American:3.3% vs. 4.4% v 2.2% vs. 3.3% vs. 13.3%Other: 0.0% vs. 1.1% vs. 0.0% vs. 1.1% vs. 2.2%

Mean (Range)Telephonic: $16,646 (5837-32,000)Home visit: $ 15,179 (5584-32,000)Health buddy: $ 20,101 (5700-50000)Home visit+health buddy: $ 21,680 (5800-

65000)Total: $ 18,380 (5584-65,000)

Mean (Range)Telephonic vs. Home visit vs. Health buddy vs. Home visit+health buddy vs. Total11.4 (7-14) vs. 10.6 (3-

16) vs. 11.9 (6-18) vs. 11.9 (8-16) vs. 11.4 (3-18)

NR Living arrange

(%)Telephonic vsvisit vs. Healtvs. Home visibuddy vs. TotAlone: 5.6 vs. 8.9 vs. 8.9 vsWith spouse/signifiother: 17.8 vs12.2 vs. 11.1 With other relvs. 5.6 vs. 2.2 vs. 15.6In home assisassisted living1.1 vs. 0 vs. 0 

AH-652
Appendix H. Data Table

Author Year

LaFramboise 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Health buddy

AH-653
Appendix H. Data Table

Author Year

LaFramboise 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Simple, user friendly, readily visible, desktop telehealth communication device with daily interventions with recovery assessment, strategies to manage problems, education and positive reinforcement

Telephonic: Baseline focused physical assessment; HF disease management program (nurse provides assessment and education thru 5 weekly phone calls); HFP patient education notebook, non emergency phone number provided to call with questions; 2-month focused physical assessmentHome visit: Everything same as above except that pts received 5 weekly home visits instead of phone callsHealth buddy: everything similar to telephonic grp except Assessment and educational components of the HFP provided daily through Health buddy for up to 6 monthswith phone calls to pts as needed for follow up of high risk responsesHome visits+health buddy: 

Computer Education, communica

response to questions 

AH-654
Appendix H. Data Table

Author Year

LaFramboise 2003

Staff Training Subject Training co-interventions Physiolo

NR NR NR Functional status, Mea

Telephonic vs. Home buddy vs. Home visit+Baseline: 574.3 (373.6(266.6) vs. 682.4 (465(281.25)2 month: 633.16 (434.(319.9) vs. 723.4 (462(264.2)F (p)= 8.4 (0.005)Role-physical, mean (Telephonic vs. Home buddy vs. Home visit+Baseline: 52.9 (44.9) v31 (36.1) vs. 41.7(40.62 mo: 62.5 (44.9) vs. 558.3 (48.3) vs. 59.6 (4F (p)= 12.7 (0.001)Bodily pain, mean (SDTelephonic vs. Home buddy vs. Home visit+Baseline: 53.7 (26.7) v

AH-655
Appendix H. Data Table

Author Year

LaFramboise 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-656
Appendix H. Data Table

Author Year

LaFramboise 2003

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-657
Appendix H. Data Table

Author Year

LaFramboise 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Mental status, Mean (SD)

Telephonic vs. Home visit vs. Health buddy vs. Home visit+health buddyBaseline: 70.6 (21.8) vs. 73.9(21.1) vs. 76.2 (20.7) vs. 76.6 (17.4)2 mo: 72.9(16.2) vs. 80(14.5) vs. 78.3 (20.8) vs. 83.2 (11.8)F (p)= 7.1 (0.009)VitalityTelephonic vs. Home visit vs. Health buddy vs. Home visit+health buddyBaseline: 39.4 (24) vs. 40.9(23.1) v 35 (22) vs. 37(22.3)2 mo: 39 (27) vs. 44.8(29) vs. 41.7(23.1) vs. 42.4 (19.5)F (p) = 7.6 (0.007)

NR

AH-658
Appendix H. Data Table

Author Year

LaFramboise 2003

Quality of Life Support Withdrawals

NR NR Out of 66 originally assigned to 

health buddy and grp health buddy/home visit20 (30%) were not able to use buddy and not eligible for inclucontinuationOf these, 4 were not interested being inconvenienced by the technology and 2 were weak obecause of other conditions.6 retirement homes and had no available outlet for health buddelectrical cord.

AH-659
Appendix H. Data Table

Author Year

LaFramboise 2003

Adverse Effects Other

NR NR

AH-660
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Lahm 1996 Preschool special 

education class2 classes in special schools, 4 classes in rural schools, 5 in medium-sized urban school districts, 7 in large metropolitan districts

Virginia Classroom Convenience Cohort 

Lee 2007 Metabolism Center NR Homes RCT 

AH-661
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

9 months NR *Ages 3-5

*Placement in a non-categorical preschool special education class

Not chosen by the teacher to participate in the study

NR

September 2003-May 2004

6 mos *No severe complications that would 

affect the patients' ability to follow physician's orders*Normal level of consciousnessAbility to read and communicate with spoken language*Have internet access

Invalid test results on one of the laboratory tests

YE

AH-662
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR NR YES: 

physical/mental/emotional disability

79.00% Range: 3-5

NR TYPE 2 DM NR Grp 1: 57%

Grp 2: 46%

Range<29

Grp 1: 7.4%Grp 2: 2.1%30-39Grp 1: 7.5%Grp 2: 2.1%40-49Grp 1: 15.7%Grp 2: 9.3%50-59Grp 1: 34.2%Grp 2: 29.4%60-69Grp 1: 18%Grp 2: 27.9%70-79Grp 1: 14.2%Grp 2: 26.4%>80Grp 1: 3%Grp 2: 2.8%

AH-663
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Preschool class YES: classes recruited 

from urban and rural school districts

NR

NR NR Illiterate

Grp 1: 9.8%Grp 2: 12.1%Elementary school/Junior high schoolGrp 1: 32%Grp 2: 27.2%High SchoolGrp 1: 15.7%Grp 2: 20%College or UniversityGrp 1: 38.8%Grp 2: 38.6%Masters or PhDGrp 1: 3.7%Grp 2: 2.1%

NR NR

AH-664
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NRNR NR NR NR POEM

AH-665
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Computer programs designed to engage preschool age children with developmental, physical, or mental disabilities

IVR: 48 Interactive multimedia 

software

Goal of the study was identify software desigfeatures that enhance engagement of young children with disabilitiewith instructional softw

Web-based system for managing patients' care of their diabetes

Group 1: Intervention group that used POEMGroup 2: Control group

Web-based intervention Education, medical da

retrieval, reminders

AH-666
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Staff Training Subject Training co-interventions Physiolo

1 full-day workshop on a single-subject research and alternating treatments design

NR NR NR

NR Intervention group received 

training on accessing the internet, using the POEM system, and reading their cell phone messages

NR Fasting blood glucose 

Grp 1: 125.14 to 108.8Grp 2: 137.69 to 125.9P=0.001HbA1C (%)Grp 1: 8.56 to 6.74 vsGrp 2: 8.90 to 7.42P=0.001Total cholesterol level Grp 1: 180.01 to 160.9Grp 2: 189.56 to 174.6P=0.012

AH-667
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-668
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Use of Technology Usability HIT Satisfaction

Twice a day for 5 minutes for a maximum of 15 days

NR NR

Education DifferencesThose in the education category of "College or University" logged into the POEM system more often than those in the education category of "Illiterate" or "Elementary school/Junior high school" (P=0.007 and P=0.012, respectively)Age DifferencesThose in the age category of <29 years logged into the POEM system more often than those in the age category of 50-59, 60-69, and 70-79 (P=0.011, P=0.000, P=0.000, respectively)Those in the age category of 40-49 years logged into the POEM system more often than those in the age category of 60-69 and 70-79 (P=0.012 and P=0.040, respectively)

NR NR

AH-669
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-670
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Quality of Life Support Withdrawals

NR NR 19 students had incomplete daNR NR NR

AH-671
Appendix H. Data Table

Author Year

Lahm 1996Lee 2007

Adverse Effects Other

NR NRNR NR

AH-672
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Leu 2005 University of Washington 

Physician's network clinics. (no other details given)

20 Primary care providers and 2 endocrinologist clinics

Homes RCT 

AH-673
Appendix H. Data Table

Author Year

Leu 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

Enrollment occurred from November 6, 2001 through July 8, 2002, The last patient deenrolled on December 20, 2002

3-6 months *Pts with HbA1c values between 8.0%-

9.4% at the time of recruitment with either Type 1 or Type 2 Diabetes.

NR NR

AH-674
Appendix H. Data Table

Author Year

Leu 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR NR Mean: 51

AH-675
Appendix H. Data Table

Author Year

Leu 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Predominantly Caucasian

NR NR Unclear Type 2 Diabe

ptsType 1 DiabePts with Type Diabetes were likely to particthe study;1 in participatedPts with Type Diabetes were younger (averis 46 yrs)The rate of participation bpts referred bor an Endocriwas the same19 pts had Hbchecked withimonths prior tstudy enrollm

AH-676
Appendix H. Data Table

Author Year

Leu 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-677
Appendix H. Data Table

Author Year

Leu 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Wireless two way pager based automated messaging system offering appointment reminders, medication reminders, blood glucose testing reminders, exercise reinforcement, dietary reinforcement, meal time reinforcement, and lab result reporting were offered along with custom reminders of time and birthdays, messages sent to pts via email, pt information stored in the diabetes registry. Responses processed by a constantly running program that forwarded unexpected responses to investigator

Group 1 (Pager): all of the above, training on the use of pager provider, investigator could be reached anytime. Pts had contact numbers and also the number of the UWPN clinic and instructions to dial 911 for emergencies.Group 2 (No pager): Pts in pager group could interact with the health care team by sending and receiving paper messagesPts in control group could page the investigator

Pager Send and receive sho

messages, Pt could respond to a message a canned response or typing in a small rudimentary keyboard

AH-678
Appendix H. Data Table

Author Year

Leu 2005

Staff Training Subject Training co-interventions Physiolo

NR Pts in the pager group 

received instructions on how to use the pager.

NR HbA1c 

No pager (n= 19) vs. pPrior to enrollment: 8.5Preliminary interview: Exit interview: 7.9% vsDifference (Mean +/- S1.12% vs. - 0.13 +/-0.9Blood pressure (% hypNo pager (n= 19) vs. pPreliminary interview68% (17/25) vs. 64% (Preliminary (stayed en71%(15/21) vs. 62%(1Exit interview76%(16/21) vs. 38% (

AH-679
Appendix H. Data Table

Author Year

Leu 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-680
Appendix H. Data Table

Author Year

Leu 2005

Use of Technology Usability HIT Satisfaction

Messaging system sent 21,764 messages out of which 12,025 sent to the patient.1,676 replies, out of which 114 were non-canned2 pts utilized the system by replying to as many messages as they can i.e. 770 of 884 (87%) and 708 of 830 (85%). System statistics in detail given in tabular form, not listed here.

NR Attitudes towards technology: 

(post)MessagesAnnoying to comforting: 7.7Unhelpful to helpful: 7.0Inconvenient to convenient: 8.5Not useful to useful: 7.8Patient felt not cared for to carby the health care team: 8.5

AH-681
Appendix H. Data Table

Author Year

Leu 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Attitude towards the health care teamPager vs. No PagerOverall satisfaction with diabetes related carePre: 8.5 vs. 8.5 ; Post: 8.8 vs. 7.9Clear treatment planPre: 8.6 vs. 7.8; Post: 8.8 vs. 8.3Possible side effects of medications are clearPre: 8.2 vs. 8.5; Post: 8.5 vs. 7.8Questions concerning diabetes answeredPre: 7.8 vs. 8.4; Post: 8.5 vs. 8.1

NR Attitude towards diabetes

Pager vs. No pagerPlan for hypoglycemiaPre: 8.4 vs. 7.3; Post: 8.3 vs. 7Health: poor to excellentPre: 6.2 vs. 6.5; Post: 6.8 vs. 7Diabetes.- unmanageable to manageablePre: 6.2 vs. 6.4; Post: 7.2 vs. 7Glucose monitoringUnimportant to importantPre: 8.2 vs. 8.3; Post: 8.0 vs. 8Not done to regularlyPre: 7.8 vs. 6.8; Post: 8.0 to 7.Difficult to easyPre: 8.4 vs. 7.3; Post:8.6 vs. 7Treatment planRealistic to UnrealisticPre: 7.5 vs. 8.2; Post: 8.1 vs. 8Health care better at end of stuto yesNo pager (post) vs. pager(post5.8 vs. 7.2

AH-682
Appendix H. Data Table

Author Year

Leu 2005

Quality of Life Support Withdrawals

NR Poor to good family support:

Pager group (post)= 8.0

Pager group: 3 withdrew (1 did use pager, 1 lost the pager, 1 stopped because he felt he rectoo many pages) and 1 patient passed away from complicatiostroke.Experimental group: 3 withdrew 1 was lost to follow up.

AH-683
Appendix H. Data Table

Author Year

Leu 2005

Adverse Effects Other

NR The different message types 

requested are given, not listed hereThere is also a table with the message system statisticsMessages were annoying: 44% (8/18)Messages were from my physician: 13%(2/15)Messages were not applicable: 12% (2/17)Pager worked (no problems): 71% (15/21)Enjoyed using the pager: 79% (15/19)Want to continue program now that study is over: 68% (13/19)

AH-684
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Levetan 2002 American Diabetes 

Association program 3mos prior to enrollment

Penn Medical Laboratory, Washington D.C.

Homes RCT 

AH-685
Appendix H. Data Table

Author Year

Levetan 2002

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

6 mos NR *Completing an ADA recognized 

diabetes education program during the 3mos period before study enrollment

*History of renal insufficiency with Creatinine level > 1.5mg/d*Women who were pregnant at the time or planning a pregnancy during the study period*Pts using insulin pumps*Illiterate pts*Pts receiving blood transfusion within past 30 days*Pts with underlying illness affecting chances of survival during study period

NR

AH-686
Appendix H. Data Table

Author Year

Levetan 2002

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR CG: 30

IVR: 35p=0.71

CG: 60IVR: 57p=0.25

AH-687
Appendix H. Data Table

Author Year

Levetan 2002

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

African AmericanCG: 83IVR: 89p=0.32

NR Education (% no 

college)CG: 44IVR: 47p=0.62

NR Systolic BP (m

CG: 143IVR: 142p=0.85Diastolic BP (CG: 83IVR: 83p=0.80Duration of di(years)CG: 3IVR: 5.5p=0.10HDL (mg/dl)CG: 41IVR: 42p=0.57LDL (mg/dl)CG: 116IVR: 115p=0.98HbA1c (%)CG: 8.39 (+2.

IVR: 8.85 (+2

p=0.25Microalbumin

AH-688
Appendix H. Data Table

Author Year

Levetan 2002

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR DiaLOG (Diab

Goals)

AH-689
Appendix H. Data Table

Author Year

Levetan 2002

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Computer-generated customized report presented as an 11"x17" laminated color poster with magnets, with a bulleted list of personalized goals and recommended steps for achieving the goals

IVR: 75Received computergenerated poster as well as 1 phone call from a health educator discussing the poster and the planPts also received a color report that was designed for the pts' medical record; in addition to graphic display of HbA1c, information included pts' BP, lipid, and microalbumin status, and date for pts' next dilated retinal examination

Computer-generated report (poster)

Education, goal settingencouragement, know

AH-690
Appendix H. Data Table

Author Year

Levetan 2002

Staff Training Subject Training co-interventions Physiolo

NR NR NR Changes from baselin

Weight (lb)CG: +1.0IVR: +1.54Systolic BP (mmHg)CG: -4IVR: -4Diastolic BP (mmHg)CG: -5IVR: -4HDL (mg/dl)CG: +3IVR: +3LDLCG: -7IVR: -5Changes in HbA1c froBaseline (%)CG: 8.39 (+2.03)

IVR: 8.85 (+2.48)

End of Study (%)CG: 7.79 (+1.91)

IVR: 7.78 (+2.22)

Change from Baseline 

AH-691
Appendix H. Data Table

Author Year

Levetan 2002

Health Service Utilization Health Care Costs Knowledge

NR NR Pt Characteristics at Baseline

Heard of an "A-One-C" test (%CG: 52IVR: 42p=0.47Changes from baseline at FolloHeard of a "A-One-C" test (%)CG: -5.1IVR: +14.3

AH-692
Appendix H. Data Table

Author Year

Levetan 2002

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-693
Appendix H. Data Table

Author Year

Levetan 2002

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-694
Appendix H. Data Table

Author Year

Levetan 2002

Quality of Life Support Withdrawals

NR NR CG

1 death3 pts developed debilitating syndromes and dropped7 pts declined F/uIVR1 death2 lost laboratory specimens7 pts declined F/u1 developed debilitating illnessFinal # of pts: 128

AH-695
Appendix H. Data Table

Author Year

Levetan 2002

Adverse Effects Other

NR NR

AH-696
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Lieberman 2003 WWW NA WWW Cross-sectional cohort 

study

Lorig 2006 Website postings NR Homes RCT 

AH-697
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

March 2001 - April 2003

NA *Internet connection

*Registration with alcoholcheckup.com*Informed consent

Unable to provide consent or "understand text" (NR how this was measured)

NR

18 mos 12 mos *Age >18 yrs

*Physician's diagnosis of heart disease, chronic lung disease or type 2 diabetes*Access to a computer with internet and e-mail capabilities*Agree to 1-2 hours per week of log on time spread over at least 3 sessions/week for 6 weeks*Able to complete online questionnaire

Active treatment of cancer within 1 year; participated in the smallgroup Chronic Disease Self-Management Program

NR

AH-698
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ALCOHOL 

DEPENDENCE/USE

NR Non-abuse: 54.5%

Alc. Abuse: 52.8%

MeanNon-abuse: 3Alc. Abuse: 34

NR HEART DISEASE, 

HYPERTENSION, LUNG DIASEASE, DIABETES, ARTHRITIS

NR Usual Care: 28.4%; 

Intervention: 28.8%

Mean (SD)Usual Care: 5(+11.3)

IVR: 57.4 (+1

AH-699
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Mean drinks/w

Non-abuse: 9Alc abuse: 36(p<.001);Age first drinkNon-abuse: 1Alc abuse: 16 (p<.001);

CaucasianUsual Care: 88.7%; Intervention: 87.3%

NR Mean years (SD)

CG: 15.8 (+3.16)

IVR 15.4 (+3.00)

NR NR

AH-700
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR alcoholscheckNR NR NR NR Internet-base

Disease Self- Program (CD

AH-701
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Information tailored to data entered for 5 questionnaires; feedback scores on alcohol screening instruments, drinking compared to national averages

NR WWW Tailored information

Interactive web-based English-language instruction, web-based bulletin board discussion groups, and the book Living a Healthy Life with Chronic Conditions . Book contains all program content, as well discussions of the major types of chronic diseases, medication, and also has illustrations of suggested exercises.Each week for 6 weeks, participants are asked to log on at least 3 times for a total of 1 to 2 hours. Participation includes reading the week's content on web pages, posting an action plan on the bulletin board, checking in with a buddy via e-mail, and participating in any selftests and activities.

Usual Care: received usual care (whatever care they had been receiving or chose to receive)Intervention group: usual care + the intervention above.Online moderator training: participation in workshop and then co-leading a workshop with a previously trained moderator. Moderators also have online manual that gives daily and weekly instructions as to moderator duties.

Website Provide content on we

pages, bulletin board, buddy email, self-tests activities

AH-702
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Staff Training Subject Training co-interventions Physiolo

NR NR NR NRModerator Training; see intervention

NR NR NR

AH-703
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Health Service Utilization Health Care Costs Knowledge

NR NR Asked in information was new 

"unexpected" to themNon-abuse: 8.3Alc abuse 9.4: (p<.001);Correlation between unexpecteinformation and helpfulness: 0(p<.001)

Mean (SD) Changes at 1 year:Physician Visits (during past 6 months): NSEmergency Visits (during past 6 months): SignificantUsual Care: -0.144 (+1.82), 

Intervention:-0.00 (+1.06) (p=.030). 

no longer significant when 6 month data is included (p=0.141).Days in hospital (during past 6 months): NA

NR NR

AH-704
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Use of Technology Usability HIT Satisfaction

NR NR Perceived helpfulness overall:

Non-abuse 8.9Alc. Abuse 10.4 (p<.001);Among alc. Abusers, helpfulnecomparative drinking rates 3.8SOCRATES screening tool fee3.3., AUDIT screening feedbac

Average log in: 26.5 times (+22.8) 

Range: 0-177

NR NR

AH-705
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Higher perceived helpfulness 

correlated with higher SOCATEscores (Pearson corr 0.295, p<ability to recognize the problem (Pearson 0.3, p<.001), ambivaabout need to make a change (Pearson 0.3, p<.001), and havtaken steps to change (Pearso0.156, p<.001).

NR Online/ usual care NS (trend p=.06)

All subjects: baseline self-efficacy and increases at 6 months were associated with pain, fatigue, disability, illness intrusiveness, health distress, and global general health at 1 year (p< .001) and shortness of breath (p = .007)M (SD) Changes at 1 year:

Self- efficacy: trendUsual Care: 0.200 (1.82) Intervention: 0.406 (1.98) (p=0.051) (effect size= .096)

Mean (SD) Changes in Health 

Behaviors at 1 year:Aerobic exercise: NSUsual Care: SignificantStretch/strength exercise: CG: 1.16 (+49.6)

IVR: 11.9 (+60.4) (p=0.032) (e

size=0.308)Communication with physicianPractice stress management: N

AH-706
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Quality of Life Support Withdrawals

NR NR NRM (SD) Changes in Health Indicators at 1 yearHealth distress: SignificantUsual Care: -0.193 (1.07)Intervention: -0.377 (1.11) (p=.013) (effect size=.160)Self-reported global health: NSIllness intrusiveness: NSDisability: NSFatigue: SignificantUsual Care: -0.358 (2.09)Intervention: -0.720 (2.14) (p=.011) (effect size= 0.151)Pain: SignificantUsual Care: -0.047 (2.46)Intervention: -0.367 (2.72) (p= .008) (effect size= 0.106)Shortness of Breath:Usual Care: -0.216(2.40)Intervention: -0.537 (2.41) (p= .020) (effect size =0.097)

NR UC: 15%,

IVR: 22.5%Reasons NR

AH-707
Appendix H. Data Table

Author Year

Lieberman 2003Lorig 2006

Adverse Effects Other

NR NRNR Exploratory comparison of 

intervention group to original study of small group CDSMP. Health indicators, health behaviors, selfefficacy and health utilization are compared 

AH-708
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Ma 2006 Diabetes Center, The 

Queen Elizabeth Hospital

Specialty outpatient clinic Homes Phase I: Use case study

Phase II: Cohort

McClure 2006 2 Health plans, Group 

Health Cooperative and Henry Ford Health System

Recruitment through the health plans (letters sent to smokers identified in EHR, and health plan newsletters)

WWW Cohort study (recruitmen

study)

McDaniel 2002 Inner-city community 

health center

Indianapolis, Indiana Clinic Cohort 

AH-709
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR Phase II: 3 months Patients:

*Diagnosed with diabetes in the past year in the hospital database*If no home internet, willing to use library Internet with assistanceProviders: *Involved in diabetes management

NR NR

Sept 2004 - July 2005 (recruitment)

NR *GHC or HFHS member

*Smoked 100 lifetime cigarettes*Current 10+ cig/day*Ready to quit next 30 days *Age 21-70*Home/work access to Internet*E-mail account used 2x/week or more

Enrolled in smoking treatment program; medical contraindication to nicotine therapy

NR

NR 1 week *Female

*Smokers*Attend inner-city community health center

NR NR

AH-710
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR Phase I, Patients: 10 

of 11 had TYPE 2 DIABETES, 1 had TYPE 1 DMPhase II, Patients: All TYPE 2 DM

NR Phase I, Patients: 7 of 

12 (58.3%)

RangePhase I, Patie62 Phase II, Pati65

NR SMOKING NR N = 40.5% Mean: 46.3NR YES: SMOKING NR 0.00% 41.5 (+12.4)

Range: 18-71

AH-711
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NRCaucasian: 79.6% NR HS or less: 24.2%

Vocational/tech: 11.9%Some College: 56.3%

NR NR

African-American: 23%Caucasian: 68%

NR 35% did NOT have 

HS diploma

YES: attending innercity community clinic NR

AH-712
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Violet TechnoInsured with participating health plan

NR NR NR Project Quit

NR NR NR NR A New Beginn

AH-713
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Diabetes Information Profile (DIP): info tailoring and priority algorithms; Quiz Service: quiz tailoring and priority algorithms; Agenda Service: agenda algorithms.

NR Web portal Patient profile data 

collection, information quizzes tailored to pro(boolean filtering baseprofile data) and prioriweights (filtering using rules based on patient data, knowledge [leveor 3], frequency of information review, anreported information preferences); quizzes filtered based on patiedata 

Recruitment study, not an intervention study; Project Quit is a tailored interactive tobacco treatment website plus nicotine patch therapy.

NR Website Web-based cognitive 

behavioral counselingtailored interactive site

Computer program designed to give tailored smoking cessation messages in a format that would be relevant and acceptable to the users based upon the information entered by the pt

IVR: 100 Computer program Smoking cessation

AH-714
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Staff Training Subject Training co-interventions Physiolo

Available NR NR NANR NR NR NANR NR NR NR

AH-715
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Health Service Utilization Health Care Costs Knowledge

NA NA NANA NA NANR NR NR

AH-716
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Use of Technology Usability HIT Satisfaction

Patients, Phase II: Login times N=82, Individuals 2 to 15 (Median: 6)Use of InfoService all 12 patients: 82 out of 257 information items visited for 10 sec or greater;Use of QuizService, 5 of 12 pts: individuals did 5 to 110 quiz questions, (Mean: 61.8)Use of AgendaService, 1 of 12 pts

Phase II, patients N=9 (agree with the statements below):Easy to use: 7Relevant to DM: 7Tailored info meets needs: 6Tailored info useful: 7Tailored agenda useful: 7Tailored agenda easy to follow: 6Would recommend to others: 6Would continue to use: 5

Focus Group, patients: Experiepatients found the tailored infoless useful because they knew Focus Group, professionals: levaluable for more experienced patients or those who have diain control; should add HgA1c aweight tracking; provide summfeedback on quiz results.

3256 visited project website1866 enrolled in studyRecruitment of site visitors: 69% from mailed letters, 21% from newsletter, 9% from other (provider referral, etc.)Estimated 1.6% and 2.5% of smokers enrolled in study (from health plan 1 and 2)

NR NR

NR Initially, non-applicable information 

would be presented to certain ptAvg. time to complete program: 13.9 minutes (Range 7.2-88.8 minutes)

Satisfaction Range of 14-70Mean: 60.2 (+6.3)

AH-717
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNA NA NANR NR Cut down on cigarettes: 52%

Quit Smoking for 24 hrs: 15%Called a smoking cessation pr6%Talked to a doctor about quittin15%Read information about quittinDiscussed quitting with friends40%

AH-718
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Quality of Life Support Withdrawals

NR NR Phase 1: 1 of 12NA NA NANR NR N=10

AH-719
Appendix H. Data Table

Author Year

Ma 2006McClure 2006McDaniel 2002

Adverse Effects Other

NR Health professional feedback on 

program:

NA NRNR NR

AH-720
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

McKay 2001 Pts recruited by e-mail 

postings to diabetes specific UseNet groups, list serves, web sites and online community groupsinviting them to participate in an internet based Physical activity(PA) program

NA Homes RCT 

AH-721
Appendix H. Data Table

Author Year

McKay 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

6 week recruitment period

2 mos *Diagnosis of Type 2 Diabetes

*Age > 40 years

*Physical activity level below the current minimumrecommendation by the American college of sports medicine (ACSM) and CDC (i.e. 30 mins of cumulative moderate intensity activity for 5 or more days/week)

NR NR

AH-722
Appendix H. Data Table

Author Year

McKay 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 2 DIABETES NR N = 47% Mean: 52.3

AH-723
Appendix H. Data Table

Author Year

McKay 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 82% NR Completed college: 

50%

NR Employed full 

62%Taking insulinDiagnosed widiabetes for ma year: 83%Had one or mmorbid condit

AH-724
Appendix H. Data Table

Author Year

McKay 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR D-net active li

intervention

AH-725
Appendix H. Data Table

Author Year

McKay 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Multi-level, socio ecological model of diabetes self management and follow up support for behavioral change.The pts in the intervention group received goal setting and personalized feedback, identified and developed strategies to overcome barriers, received and could post messages to an on line personal coach and were involved in peer groupsupport activities.

Control group- received graphic feedback comparing baseline PA level to ACSM recommendations for moderate level PA-received PA safety precautions-Access to information only websiteIntervention group- received graphic feedback comparing baseline PA level to ACSM recommendations for moderate level PA- received 5 steps to action personalized PA plan that consisted of Benefits of PA (i.e. personal motivators), Goal setting, Activity exploration, Schedule of weekly activity, Barriers to PA and tips to overcome barriers.- received support from personal coach that consisted of 5 tailored messages over 8 week period, correspondenceas required by pts, referral 

Computer Live chat, links to 

resources sites, messgraphic feedback of Plevels, communicationwith personal coach

AH-726
Appendix H. Data Table

Author Year

McKay 2001

Staff Training Subject Training co-interventions Physiolo

NR NR NR Physical activity summ

Moderate to vigorous (min/day)Intervention vs. controUnadjusted; Baseline- 7.3 +/- 6.2Unadjusted; follow up- vs. 18.0 +/- 17.3Log transformed; Base0.47 vs. 0.75 +/- 0.45Log transformed; follo0.57 vs. 1.12 +/- 0.42Time---- F (1,64)= 29.5Group X time-------F (1(p=0.938); n2= 0.000Walking (min/day)Intervention vs. ControUnadjusted; baseline- 8.4 +/- 8.4Unadjusted; follow up- vs. 16.8 +/- 22.8Log transformed; base0.45 vs. 0.78 +/- 0.46Log transforms; follow 0.48 vs. 1.01 +/- 0.49Time---------------- F (1(p<0.001)Group X time--------F = (p=0.781), n2=0.001

AH-727
Appendix H. Data Table

Author Year

McKay 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-728
Appendix H. Data Table

Author Year

McKay 2001

Use of Technology Usability HIT Satisfaction

IVR vs. CGWeb site activity analysis: 341 logons (1.1 logons/pt/week) vs. 105 logons (0.3 logons/pt/week)Total web pages viewed: 9962 vs. 1811Pts average session duration: 3 mins vs. 11 minsUsage during first 2 weeks and last 2 weeksIVR: 204 logons (2.7/pt) and 40 logons (0.5/pt)CG: 77 logons (0.9/pt) and 7 logons (0.09/pt)IVR pts had 5938 page views (156.3/pt) within the behavior change focused active lives area and 310 page views (8.2/pt) within the diabetes specific information library area.IVR pts posted 80 messages (2.1/pt) to personal coach and 42 messages(1.1/pt) to peer support grp area

NR Intervention vs. control

Satisfaction: 4.9 (+1.4) vs. 3.7 

Student's t value= 2.07, p=0.04Program's relevance to pts nee5.1 (+1.2) vs. 4.1 (+1.2), t= 1.9

p=0.058Ease of use, helpfulness of PA section- results did not differ significantlyIn the intervention group, 88% personal coach helpful and 35found peer support helpful

AH-729
Appendix H. Data Table

Author Year

McKay 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Depressive symptoms (CES-D)

Intervention vs. controlBaseline; 16.9 +/- 11.6 vs. 17.6 +/- 10.4Follow up; 14.9 +/- 12.5 vs. 19.9 +/- 14.2Time------- F (1,64)= 0.06 (p=0.809)Group X time--------F(1,64) = 2.77 (p=0.101), n2=0.042

NR

AH-730
Appendix H. Data Table

Author Year

McKay 2001

Quality of Life Support Withdrawals

NR NR Out of 40 control pts, only 33 (

completed the post assessmensurveyOut of 38 intervention pts, only (92.1%) completed the survey

AH-731
Appendix H. Data Table

Author Year

McKay 2001

Adverse Effects Other

NR The total number of logons was used 

to determine the association between program use and the three outcome measures. ANOVA results given for the same, not listed here

AH-732
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

McMahon 2005 Volunteer (solicited by mail 

and telephone)

VA Boston Healthcare System (4 hospital-based clinics and 10 communitybased outpatient clinics)

Homes RCT 

AH-733
Appendix H. Data Table

Author Year

McMahon 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

October 2001-April 2003

None *HbA1C(>=) 9.0%

*>18 yrs old*Ability to understand written and spoken English*"Willingness to use glucose-and blood pressure-monitoring devices"*Required to have a VA-based PCP*Access to a telephone

Lacking any/all of the stated criteria above

M

AH-734
Appendix H. Data Table

Author Year

McMahon 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR CG: 100%

Intervention: 99%

CG: 63(+) 7

Intervention: 6

AH-735
Appendix H. Data Table

Author Year

McMahon 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR CG:

College or Above: 67%High school graduate: 19%Some HS: 6%Below HS: 2%Intervention:College or above: 59%HS graduate: 22%Some HS: 12%Below HS: 6%

NR NR

AH-736
Appendix H. Data Table

Author Year

McMahon 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English Believe that care 

management has been studied in diabetic patients, but that results have been mixed; believe that with increased use of internet in chronically ill, Internet may help improve QoL and quality of condition

MyCareTeam

AH-737
Appendix H. Data Table

Author Year

McMahon 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Website displayed blood pressure and glucose monitoring outcomes in table and graphical form for patients and care management providers, contained web-enabled diabetes educational modules, provided links to other diabetes resource websites, and allowed secure chat/e-mail between patient and care provider. Information submitted was also reviewed by a certified diabetes educator and advanced practice nurse (made recommendations to patient and PCP).

CG: usual care with PCPIntervention: usual care + notebook computer, glucose meter, blood pressure monitor, and access to MyCareTeam website*Intervention group received computer training and support by the study staff (mean 2.3hrs per subject). Allowed to send emails to PCP and other 

experts

Internet, Telephone Education and selfmanagement skills, bl

pressure monitoring wrecommended at least times weekly, and glucmonitoring was individualized to each patient

AH-738
Appendix H. Data Table

Author Year

McMahon 2005

Staff Training Subject Training co-interventions Physiolo

NR Training time was 

dependent on comfort with computers/internet and devices provided

Additional education provided at regularly scheduled clinic visits

BEFORE STUDY: Mean A1C (%)CG: 9.9 (+-)0.8Intervention: 10.0 (+-) Blood Pressure (mmHDiastolic:CG: 139(+-)20, 80(+-)Intervention: 141(+-)2Lipids (mg/dl):LDL cholesterolCG: 97(+-)21Intervention: 100(+-)35HDL cholesterolCG: 40(+-)8Intervention: 43(+-)14TriglyceridesCG: 204 (+-)140Intervention: 178(+-)1

AH-739
Appendix H. Data Table

Author Year

McMahon 2005

Health Service Utilization Health Care Costs Knowledge

Clinic visits at 0, 3, 6, 9, and 12 months for both groupsIntervention Uploads:Medians: 1, 18, 31Persistent User: Log-in every 3 months (n = 30)Intermittent User: Log-in > 3mos (n = 22)

NR NR

AH-740
Appendix H. Data Table

Author Year

McMahon 2005

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-741
Appendix H. Data Table

Author Year

McMahon 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR AFTER STUDY

Overall: P < 0.001Changes in A1C at 12mosCG: -1.2(+)1.4%

Intervention: -1.6(+)1.4%

P < 0.05Persistent Users: -1.9(+)1.2 vs

Intermittent: -1.2(+)1.4

P = 0.051CG: P <0.05Persistent Uploaders: -2.1(+)1

Lowest: -1.1(+)1.7

P < 0.05Changes in BP at 12mosHypertensive PatientsSystolic BPCG (n = 35): -7(+)21 mmHg

Intervention: (n = 37): -10(+)17

P < 0.01Diastolic BPCG: -6(+)11

P = 0.058 v. baselineIntervention: -5(+)13

P = 0.053 v. baselineHypertensive Patients at the ethe studyCG: n = 37Intervention: n = 28Changes in Lipid Profiles

AH-742
Appendix H. Data Table

Author Year

McMahon 2005

Quality of Life Support Withdrawals

NR NR N = 20

CG:Lost to follow-up: 11Withdrew Consent: 1Intervention:Lost to follow-up: 7Discontinued intervention: 1

AH-743
Appendix H. Data Table

Author Year

McMahon 2005

Adverse Effects Other

NR Hypoglycemic episodes

CG: 33 events in 11 participants (Median: 2/pt)Intervention: 46 events in 13 participants (Median: 3/pt)

AH-744
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

McPherson 2006 Pediatric outpatient 

respiratory clinics

3 United Kingdom hospitals Homes RCT 

McTavish 1994 Inner city Chicago Cook county hospital Homes Pilot study 

AH-745
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

1 years 6 months *Had scheduled follow-up appointment 

at 1 of 3 clinics in the study period*Parental consent and child assent

Did not have a diagnosis of asthma, were not currently receiving treatment for asthma, or were assessed by parents or clinician to have significant learning or behavioral difficulties

NR

Oct 1, 1993- Jan 15, 1994

15 weeks *All African American women were 

stage 1 or stage 2 cancer pts

NR No

AH-746
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR YES: ASTHMA NR 54.00% Median, Mode

CG: 9, 7IVR: 11, 8

Inner city Chicago African American women

BREAST CANCER NR All women Range: 36-66 

6 of the 8 womwere 55 yrs oyounger

AH-747
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

WhiteCG: 86.3%IVR: 90%

NR NR NR Mean duration 

asthmaCG: 6.64 (+3.

IVR: 6.91 (+3

3mo to 13

African American All women were on 

public assistance

Half never completed high school and 3 had prior computer experience

Urban (inner city) Five had 

lumpectomies had mastectowithin the pas(5 had surgerthe past 6 moOnly 1 women CHESS prior deciding what to have.

AH-748
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR The Asthma FNR NR NR NR CHESS

AH-749
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Interactive computer game with a secret-agent theme; as "secret agents" users are encouraged to explore all of the sections, finding out as much about selfmanagement as possible by listening to voiceovers, completing quizzes, and engaging with interactive problem solving tasks

CG: 51IVR: 50

Computer game To provide education 

asthma to children

CHESS: web based information, discussion group, ask an expert, and interactive services (tailored action plan, decision aides and health tracking)

Pilot study group with CHESS intervention

Computer Forum, question and 

answers, library, expecommunication, action plan.

AH-750
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Staff Training Subject Training co-interventions Physiolo

NR NR NR 6 month Follow-Up

Courses of Steroids, MIVR: -2.22, p=0.026Days off school, MeanIVR: 2.1, p=0.034

NR A CHESS staff member 

gave approx. 1 hour of instruction and orientation

NR NR

AH-751
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Health Service Utilization Health Care Costs Knowledge

NR NR Mean change in Asthma Know

Assessment scoreCG: 1.55 (0.65-2.48)IVR: 3.97 (3.02-4.92)

NR NR NR

AH-752
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Use of Technology Usability HIT Satisfaction

1-10 times NR 35 of 37 Felt that the game wa

good way to learn about asthmthe majority (31/37) believed thhad learned more about their afrom using the package.

CHESS was used 886 times within the 15 wk periodOn avg, each woman used CHESS 7 times/ wk and was on the system for more than 1 hour/wkUse of social support components: 55%Information components: 41%Problem solving components: 4%Service: number of uses (minutes)Discussion groups: 442 (5046)Ask an expert: 227 (1487)Questions and Answers: 83 (617)Personal stories: 43 (504)Instant library: 48(341)Decisions and conflicts: 20 (208)Getting help/support: 18 (76)Action Plan: 5 (48)

Overall value of CHESS: 7.00 with three services rated below 7.00AP (action plan) rated lowest at 6.50Overall CHESS for ease of use: 5.80 and 6 of the services rated above 6.00 in ease of useAP rated lowest in terms of ease of use: 4.80DG (disc. groups)was the most used despite the lower ease of use coreCHESS is extremely user friendly and lack of computer experience is not a barrier

Women felt acceptance, understanding and relief while CHESS, 

AH-753
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR Experienced low levels of negative 

emotions such as stress, boredom, fear, sadness, indifference, helplessness, and anger,(all less than 3.0) elation and empowerment were low (2.25 and 2.2) while anxiety was high (4.5)

NR

AH-754
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Quality of Life Support Withdrawals

NR NR 11 lost to follow-upNR NR NR

AH-755
Appendix H. Data Table

Author Year

McPherson 2006McTavish 1994

Adverse Effects Other

NR NRNR Comparison table given- use of 

CHESS components by percent of total uses in two study population.

AH-756
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Meigs 2003 NR Hospital based staff 

resident practice. (The adult Medicine clinic)

Unclear RCT 

Nakagawa 2000 Waitlist of OCD patients 

wanting BT

NR Homes Cohort with historical 

control

AH-757
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

May 1997-April 1999

NR NR NR NR

NR 3 weeks NR NR NR

AH-758
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR TYPE 2 DIABETES NR Patients with type 2 

DiabetesGrp 1 vs. Grp 2: 44.9% vs. 50.5%

Mean (SD)Grp 1: 68 (+1

Grp 2: 67 (+1

NR OCD NR Gp 1: 49%

Gp 2: 55%

MeanGrp 1: 28.6Grp 2: 27.9

AH-759
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Grp 1Caucasian: 71%Black: 19.2%Other: 9.8%Grp 2Caucasian: 71.1%Black: 18.9%Other: 10.0%

NR NR NR Mean duration 

diabetes (SD)Grp 1 vs. Grp (+5.5) vs. 9.7 

p= 0.8Diabetes therGrp 1 vs. Grp Insulin; 30.6 vp= 0.1Oral hypoglycmedications; 63.2, p= 0.6Diet and exer17.9 vs. 10.0, 0.005Any lipid lowemedication; 338.1 , p= 0.06Any antihypermedication; 779, p= 1.0Hyperlipidemivs. 59.8, p=0.Hypertension76.0, p= 0.03Smoking; 15.6 14.8, p= 0.8Any micro vascomplication; 50.2, p= 0.2Any cardiovas

NR NR NR NR NR

AH-760
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

PayorCommercial < 65Grp 1: 12.4%Grp 2: 11.0%, p= 0.2IIMO < 65Grp 1: 17.6%Grp 2: 19.6%MedicareGrp 1: 60.9%Grp 2: 54.3%Medicaid or otherGrp 1: 9.2%Grp 2: 15.1%

NR NR NR Diabetes Dise

management (DMA)

NR NR NR NR BT STEPS

AH-761
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Web based decision support tool, displaying interactive patient specific clinical data, treatment advice, and links to other web based care resources

Grp 1: received the intervention- DMA provides a single screen view of patient specific information, enabling decision support at the time of patient contact. It displays trended and tabular real time electronic laboratory data interactively linked to evidence based treatment recommendations, facilities to aid encounter work flow, and links to additional patient and provider care resources.Grp 2: not given the intervention- no additional details provided

Computer Decision support syste

links to other resourceLab data in trended antabulated form

Computer driven, telephone interview system using interactive voice response

Group 1: BT STEPS intervention Group 2: Matched waiting list controls

Computer/internet + telephone

Four step self-assessmand five step selfintervention

AH-762
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Staff Training Subject Training co-interventions Physiolo

Staff members were systematically exposed to the intervention, receiving formal pre intervention training and two interactive feedback sessions during the intervention year

NR NR Glycemic control outco

Grp 1 vs. Grp 2at least one HbA1c tesmonthsPreintervention: 264/8256/88%Change: +1.6 % vs. - Mean number of HbA1Preintervention: 1.7 (0(+0.1)Change: + 0.3 vs. - 0HbA1c< 7%Preintervention: 51/2161/26.6%Change: +1.7% vs. - Mean HbA1c (% hemoPreintervention: 8.4 (0Change: - 0.23 vs. + 0Cholesterol control ouGrp 1 vs. Grp 2At least 1 LDL chol tesmonthsPreintervention: 177/5167/57.4%Change: +7.2% vs. + Mean number of LDL Preintervention: 0.8 (0

NR 5 minutes with coordinator 

explaining how to use system

NR NR

AH-763
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Health Service Utilization Health Care Costs Knowledge

NR NR NRGrp 1 completed fewer clinician guided ERP sessions than Grp 2 (8 versus 22)

NR NR

AH-764
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Use of Technology Usability HIT Satisfaction

NR NR NRNR NR 9 patients in Grp 1 reported be

more satisfied with the clinician guided ERP versus the BT ST(P<0.001)

AH-765
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR NR

AH-766
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Quality of Life Support Withdrawals

NR NR Preintervention year- diabetic p

N= 1595Pts exclusionsNot in study group: 621Study patients not seen during intervention year: 225Clinic chart not found: 36Patient ineligible: 115

NR NR 3 from Grp 1

2 prior to starting, due to being off waiting list and 1 during triabecause of marked improveme

AH-767
Appendix H. Data Table

Author Year

Meigs 2003Nakagawa 2000

Adverse Effects Other

NR Demographics related to staff 

providers given, not listed hereIntracorrelation coefficients to assess inter abstractor variability given, not listed hereThe author also discusses power issues, not given here

NR NR

AH-768
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Nguyen 2003 NR NR Homes Cohort Nguyen 2005 Grp 1: was recruited from 

a pool of individuals who previously participated in a face-to-face DSMP, which would allow testing of a "booster” effect. Grp 2: recruited from all other sources (pulmonary practices, Better Breather groups, pulmonary rehabilitation programs, and online COPD groups)

NR Homes Nonrandomized between 

group

AH-769
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 months *Subjects in Grp 1: recruited from a 

database of patients who participated in face-to-face Dyspnoes Self management program (DSMP)*Subjects in Grp 2: recruited from all other sources and had no relationship with the investigators

NR Ye

NR 3 months *Active use of a computer and 

Internet/web*Diagnosis of COPD (bronchitis emphysema, irreversible asthma)*ADL limited bydyspnea*Approval from a health provider to start an exercise program*Ability to speak and write English*Resident of the San Francisco Bay area or southern California.

NR Ye

AH-770
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR YES NR NR Mean (Range

Grp 2: 69.1 (5

YES COPD NR N = 50% Mean (SD)

69.1 (+7.0)

Range: 55-82

AH-771
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NRAfrican-American: 1pt (6%)Caucasian: 13pts (81%)Native American: 1pt (6%)Other: 1pt (6%)

NR High school: 4pts 

(25%)Some college: 4pts (25%)College or greater: 8pts (50%)

NR 81% retired o

disabled

AH-772
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR iDSMPNR NR English NR Dyspnea Self

Program

AH-773
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

*Nurse facilitated and peer supported internet based dyspnoea self management program*Weekly structures self management education via live text chat*Exercise monitoring, goal setting and feedback*Pulmonary function and symptom monitoring(pts monitor lung function with air watch and data uploaded into the site*Peer and professional communication via chats, bulletin boards, email

Grp 1: pts from database who participated in Face to Face DSMP allowing a test of booster effectGrp 2: iDSMP - no relationship with investigator in order to evaluate the program as primary interventionHome visits conducted and process metrics tracked

Computer Communication, chat, 

emails, symptom monitoring, self management educatio

Website to help individuals with COPD better manage their condition by being informed of activities that help or exacerbate their condition

Group 1: "boosters" had previously participated in some DSMPGroup 2: Primary intervention, no prior DSMP

Dyspnea Self-Management Program

Logon to website at letimes/week, 2 weekly sessions, online bulletboard and email suppindividualized exercise plans

AH-774
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR FEV1/FVC: 41 +/- 7%

Improvements noted f(effect size=0.86)Endurance exercise 0overall sample

NR 1-2 hours of in home 

training on web site and spirometry device

AirWatch spirometer P-values

CRQ DyspneaGrp 1 vs. Grp 2: 0.86Pooled sample: 0.005Dyspnea DistressGrp 1 vs. Grp 2: 0.25Pooled:0 .48Dyspnea impactGrp 1 vs. Grp 2: 0.43Pooled: 0.48

AH-775
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-776
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Use of Technology Usability HIT Satisfaction

First month registered most logins (330) compared to 104 in the final month

NR NR

NR NR NR

AH-777
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Improvement in self efficacy for 

managing dyspnoea = 0.94

NR

NR Self efficacy for managing dyspnea 1 

vs. 2: .38pooled: .009CRQ Mastery1 vs. 2: .08pooled: .14

Exercise, self-report, MeasureMedical Outcomes Study SF-3

AH-778
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Quality of Life Support Withdrawals

NR Support: 0.32 NRNR NR N=8

Group 1: 2 were too busy,1 overwhelmed, 3 sporadic participationGroup 2: 1 dropped due to comfailure, 1 died of pneumonia

AH-779
Appendix H. Data Table

Author Year

Nguyen 2003Nguyen 2005

Adverse Effects Other

NR NRComputer issues included hand trembles for 3 individuals and paralysis for one, all of whom were able to accommodate their limitations (e.g., using a different mouse). Stiff necks. Patients were sent to a reputable site for computer stretches. 

NR

AH-780
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Noel 2004 NR VA system Homes single blind single site 

randomized trial

AH-781
Appendix H. Data Table

Author Year

Noel 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6-12 months *Documented high use of healthcare 

resources and barriers to accessing healthcare services due to geographic, economic, physical, linguistic, technological, and/or cultural factors*Pts met these criteria and had been actively receiving nurse case management for at least 6 months preceding the study and during the study

NR NR

AH-782
Appendix H. Data Table

Author Year

Noel 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DM, CHRONIC LUNG 

DISEASE, COMPLEX CHF

NR Total= 97%

IG: 42%CG: 55%

MeanTotal= 71IG: 72CG: 70

AH-783
Appendix H. Data Table

Author Year

Noel 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR One morbidity 

Total vs. IG vCHF: 57 vs. 3COPD: 35 vs17DM: 56 vs. 24 Two co morbi(%), Total vs. IG vCHF+COPD: 12 vs.10CHF+DM: 34 vs. 11COPD+DM: 1vs. 7Three co morTotal vs. IG vCHF+COPD+vs. 6 vs. 5

AH-784
Appendix H. Data Table

Author Year

Noel 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Home telehea

AH-785
Appendix H. Data Table

Author Year

Noel 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Home telehealth units used standard phone lines to communicate with the hospital. FDA approved peripheral devices monitored vital signs and valid questionnaires were used to evaluate quality of life outcomes with out of range data triggering alerts.

The CG received usual home health care services plus nurse case managementIG received home telehealth plus nurse case managementHome telehealth- user friendly devices with a touch screen interface and step by step instructions using graphics, large text and audio. FDA approved peripheral devices plug into telehealth unit and collect data for temperature, blood pressure, pulse, blood glucose, 3 lead ECG, stethoscope for heart and lung sounds, pulse oximetry and weight, pain level is self reported; data transmitted through telephone lines to VACT's web based intranet system and directly into the facility's electronic database; digital camera used to monitor wound 

Home telehealth device Data input, alerts, dise

specific education modules, on screen assessment surveys

AH-786
Appendix H. Data Table

Author Year

Noel 2004

Staff Training Subject Training co-interventions Physiolo

NR NR NR Diabetic A1C levels; M

Baseline IG: 8.30 (+1.61)

CG: 7.03 (+1.08)

Between groups; t=+3z=0.0046 monthsIG: 7.3 (+1.47) , t= + 4

p=0.0001, z=0.001CG: 7.83 (+1.65), - 3.

z=0.006Between groups; t= - z=0.260

AH-787
Appendix H. Data Table

Author Year

Noel 2004

Health Service Utilization Health Care Costs Knowledge

Bed days of care (BDOC):Total (n=64) vs. IG (n=26) vs. CG (n=38)Pre: 842 vs. 317 vs. 525Post: 243 vs. 49 vs. 194Total visits:Total (n=102) vs. IG (n= 46) vs. CG (n=56)Pre: 1613 vs. 682 vs. 931Post: 1520 vs. 682 vs. 838Urgent visits:Total (n=98) vs. IG (n=44) vs. CG (n=54)Pre: 622 vs. 320 vs. 302Post: 544 vs. 237 vs. 307Coumadin visits:Total (n=26) vs. IG (n=11) vs. CG (n= 15)Pre: 187 vs. 79 vs. 108Post: 146 vs. 82 vs. 64RN home visits:Total (n= 21) vs. IG (n=12) vs. CG (n=9)Pre: 223 vs. 119 vs. 104Post= 197 vs. 94 vs. 103Cost variables, Mean (SD): VA and non VA BDOC

Cost item ($): IG vs. CGRN/home visits Pre study: 11, 067 vs. 9,672Post study: 8,742 vs. 9,579BDOCPrestudy: 273,918 vs. 567,126Post study: 46,296 vs. 204,474CoumadinPre study: 1,185 vs. 1,620Post study: 1,230 vs. 960Total visitsPre study: 34,100 vs. 46,550Post study: 34,100 vs. 41,900Urgent visitsPre study: 16,000 vs. 15,100Post study: 11,850 vs. 15,350Transport (VA costs)Prestudy: 35,811 vs. 44,816Post study: 32,258 vs. 40,538Transport, cost patientPrestudy: 18,186 vs. 22,759Post study: 16,381 vs. 20,586Telehealth unitIG: 78,302

NR

AH-788
Appendix H. Data Table

Author Year

Noel 2004

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-789
Appendix H. Data Table

Author Year

Noel 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

QoL-self rated health status, M (SD)Baseline, IG (n)= 47, CG (n)=57Between grps; p= 0.207, z= 0.208IG: 81.32 (+13.07)

CG: 84.86 (+15.33)

3 months, IG (n)= 47, CG (n)= 57Between groups; p= 0.755, z= 0.590IG: 81.34 (+13.71), p= 0.984, z= 

0.728CG: 82.25 (+15.83), p= 0.070, 

z=0.1296 months, IG (n)= 47, CG (n)= 57Between groups; p= 0.353, z= 0.296IG: 82.47 (+12.89) , p= 0.447, z= 

0.392CG: 85.14 (+16.28), p= 0.885, 

z=0.9449 months, IG (n)=15, CG (n)= 27Between groups; p= 0.596, z= 0.763IG: 84.40 (+13.23), p= 0.980, z= 

0.776CG: 82.11 (+13.34), p= 0.757, 

z=0.89312 months, IG (n)=8 , CG (n)= 14Between groups: p= 0.506, z= 0.616IG: 88.00 (+22.16), p= 0.110, z= 

0.159CG: 82.21 (+10.81), p= 0.150, 

NR NR

AH-790
Appendix H. Data Table

Author Year

Noel 2004

Quality of Life Support Withdrawals

QoL -Cognitive status, M (SD)Baseline, IG (n)= 47, CG (n)=57Between grps: p= 0.751, z= 0.844IG: 19.31 (+1.71); 

CG: 19.42 (+1.51)

3 months, IG (n)= 47, CG (n)= 57Between groups: p= 0.578, z= 0.440IG: 19.62 (+1.06), p= 0.227, z= 0.123

CG: 19.46 (+0.169), p= 0.699, 

z=0.3416 months, IG (n)= 47, CG (n)= 57Between groups: p= 0.921, z= 0.157IG: 19.70 (+1.06) , p= 0.115, z= 0.056

CG: 19.68 (+0.69), p= 0.171, z=0.135

9 months, IG (n)=15, CG (n)= 27Between groups: p= 0.234, z= 0.441IG: 19.80 (+0.414), p= 0.028, z= 

0.034CG: 19.56 (+0.892), p= 0.032, 

z=0.07412 months, IG (n)=8 , CG (n)= 14Between groups: p= 0.006, z= 0.019IG= 20 (+0.00), p= 0.095, z= 0.102

CG= 19.43 (+0.646), p= 0.635, 

z=0.763QoL -Functional, M (SD)Baseline, IG (n)= 47, CG (n)=57

NR The author says that there was 

diminishing participation beyonmonths but no more details giv

AH-791
Appendix H. Data Table

Author Year

Noel 2004

Adverse Effects Other

NR Cognitive status showed significant 

improvement at 12 months for the few remaining participants in the IGTelehealth added $ 1666 to outcome costs during the 6 months of studyHealthcare costs decreased by 58% for the telehealth grp and 47% for the CG.

AH-792
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Owen 2005 NR Hematology/Oncology 

outpatient clinic at a large medical center in the US

Homes Randomized wait-list 

control

AH-793
Appendix H. Data Table

Author Year

Owen 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 mos At the outset: 

*Women with histological confirmed clinical stage 1 or 2 breast cancerAfter randomization:*Small number of pts were stage 0 or stage 3 and because of their strong desire to participate, were included in the study

Recruitment efforts are described in detail in another study (Owen, Klapow, Roth)

NR

AH-794
Appendix H. Data Table

Author Year

Owen 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR BREAST CANCER NR N = 0% Mean (SD)

CG: 51.3 (+10

IG: 52.5 (+8.6

AH-795
Appendix H. Data Table

Author Year

Owen 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

CaucasianCG: 100%IG: 95.8%

Mean Annual Income (SD)CG vs. IGCG: $65,000 (+$24,407)

IG: $65,000 (+$114,148)

Mean years (SD)CG: 15.3 (+2)

IG: 15.8 (+2.2) 

NR Marital status 

Married)CG vs. IG77.8 vs. 87.5Distance to cl(miles)- Mean CG vs. IG55.2 (66.3) vs(301.5)Employment s(%) Employed full CG vs. IG----- 42.9Employed parCG vs. IG-----14.3Not employedIG--------36.6 Time since di(months) meaCG vs. IG--- 31.8(37.6) vs(28.4)Breast consersurgery (%)CG vs. IG----- 27.8

AH-796
Appendix H. Data Table

Author Year

Owen 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Survive webs

AH-797
Appendix H. Data Table

Author Year

Owen 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Self guided, internet based coping skills training given to pts in the intervention group

IG: provided with bulletin board for asynchronous group discussions, a dictionary of medical terminology, a database of breast cancer resources and websites, information and coping advice for management of common physical symptoms, a forum for sharing artwork and poetry, and six structured coping skills exercises.They were identification of passive and active coping styles; communication with family and friends; identification of relationships among stress, emotions and behavior; stress management training; assertiveness training; structured problem solving trainingThey also received 39 prompts sent by email summarizing a coping skill exercise and suggesting that the pts post a 

Computer Discussion groups, bu

boards, forum, email

AH-798
Appendix H. Data Table

Author Year

Owen 2005

Staff Training Subject Training co-interventions Physiolo

NR Pts were given brief 

instructions on using the website

NR NR

AH-799
Appendix H. Data Table

Author Year

Owen 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-800
Appendix H. Data Table

Author Year

Owen 2005

Use of Technology Usability HIT Satisfaction

Average total log ins to the website: 35.5Hits to the bulletin board: 52.2Postings sent to the bulletin board: 9.5Uses of the coping exercises: 73.4Hrs spent logged in to the website: 3.4Mins per session over the course of 12 week intervention: 8.2

NR Coping exercises found most h

68.2%Coping group/bulletin board: 5Information about coping with symptoms: 57.2%Either the coping exercises or discussion group was helpful: 

AH-801
Appendix H. Data Table

Author Year

Owen 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-802
Appendix H. Data Table

Author Year

Owen 2005

Quality of Life Support Withdrawals

Health related quality of lifeFACT-Total measure CG vs. IGPre M (SD) = 81(14.3) vs. 78.3(15.7)post M (SD) = 85.3(11.8) vs. 88.7(10.8)F value- grpX baseline interaction effect- 0.33 (1,45)Treatment effect size- 0.30EQ-5D thermometerCG vs. IGPre M (SD) = 78.4 (17.2) vs. 75.6(20.2)post M (SD) = 83.5(17.1)vs. 85.4 (9.9)F value- grpX baseline interaction effect- 16.40 (1,39), p<0.001Treatment effect size- 0.13Psychological wellbeingFACT- EWBCG vs. IGPre M (SD) = 15.4(4.0) vs. 14.8(3.9) post M (SD) = 15.3 (3.6) vs. 16.5(2.6)F value- grpX baseline interaction effect- 1.00 (1,45)Treatment effect size- 0.38, p <0.10

NR Pts expressing interest in the s

154Excluded- 59 (not interested amore, lack of familiarity with computer, participatingin a competing trial, could not breached by phone)Did not complete baseline assessment- 33Randomized- 62, IG=30 (Received intervention-3not participate in intervention, logged in- 5)Loss to follow up=6 (could not reached or unwilling to complefollow up assessment)

Analyzed=26 (excluded from a

due to incomplete follow up-6)CG= 30 Loss to follow up= 3 (could not reached or unwilling to complefollow up assessment)Analyzed= 27 (excluded from analysis due to incomplete folldata=3)Cross over into intervention gr18declined to participate= 11

AH-803
Appendix H. Data Table

Author Year

Owen 2005

Adverse Effects Other

NR Pts motivation for enrolling in the 

study given, not listed hereThere is a graph showing use of each component of the intervention over timeA table showing Pearson correlation coefficients between linguistic variables associated with messages and change scores on dependent variables given, not listed here

AH-804
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Piette 1997 NR PAVA Diabetes care clinic, 

Endocrinology clinic, General Internal Medicine clinic

Homes Cohort 

AH-805
Appendix H. Data Table

Author Year

Piette 1997

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NR *Pts with diagnosis of IDDM or NIDDM

*Proficient in English

Pts excluded if psychotic, demented or blind as determined by the diagnosis in the medical record, residedin a congregate care facility, or did not have a working touch tone telephone in their home.

NR

AH-806
Appendix H. Data Table

Author Year

Piette 1997

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DIABETES NR N = 62 (92.4%) Range

<55: 15 pts

55-64: 20 pts>65: 30 pts

AH-807
Appendix H. Data Table

Author Year

Piette 1997

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Total

Caucasian: 61.5%Black: 9.2%Hispanic: 18.5%Asian/Pacific Islander: 10.8% Low income % in the groups<55 yrsCaucasian: 66.7% Black: 6.7%Hispanic: 26.7% Hispanic55- 64 yrsCaucasian: 50%Black: 20%Hispanic: 20%Asian/Pacific Islander: 10% >65+ yrsCaucasian: 66.7%Black: 3.3%Hispanic: 13.3%Asian/Pacific Islander: 16.7 % Pts taking insulinCaucasian: 55.6%Black: 7.4%Hispanic: 33.3%Asian/Pacific Islander: 

VA eligible due to low income: 46.9%Low income:<55 yrs: 43%55-64 yrs: 38.9%65< yrs: 53.1%

Taking insulin: 46.1%Not taking insulin: 47.4%

NR NR Marital status

Married , 45.married, 1.6% applicable Marital status groupsLess than 55 married, 42.9married, 7.1% applicable55-64 yrs- 50married, 50% married,65 + yrs- 56.3married, 43.7marriedTaking insulinmarried, 50.0married, 3.8% applicableNot taking ins57.9% marrienot marriedEmployment s10.9% full timemployed, 67unemployed/r21.9% otherEmployment sthe groupsLess than 55 

AH-808
Appendix H. Data Table

Author Year

Piette 1997

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Automated vo

messaging sy

AH-809
Appendix H. Data Table

Author Year

Piette 1997

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

AVM system using specialized computer technology to telephone patients, communicate messages, and collect information

n= 65 ptsAVM calling protocol designed:*to inquire about diabetic symptoms that may be prognostic of poor glucose control and adverse health outcomes*to inquire about pts problems with glucose monitoring and foot care,*to inquire about adherence to diet and medication schedule and *to give pts the option of listening to health promotion messages regarding diabetic eye care, foot care, cardiovascular health and management of sick days.Pts reporting serious problems reached within 48 hrs. At the end of each AVM call, pts instructed to call the PAVA Out patient clinic about health or health care (contact number provided)

Telephones Messages, 

information/educationstorage of data.

AH-810
Appendix H. Data Table

Author Year

Piette 1997

Staff Training Subject Training co-interventions Physiolo

NR NR NR Reported health probl

Chest Pain< 55 yrs= 20%, 55-64 = 26.7%, Taking insultaking insulin= 23.7%, Foot problems< 55 yrs= 33.3%, 55-665+ = 10%, Taking insNot taking insulin= 1518.5%Poor glucose control< 55 yrs= 33.3%, 55-665+ = 13.3%, Taking iNot taking insulin= 1318.5%Hypoglycemic sympto< 55 yrs= 66.7%, 55-665+ = 26.7%, Taking iNot taking insulin= 2638.5%Hyperglycemic sympto< 55 yrs= 60%, 55-64 = 36.7%, Taking insultaking insulin= 36.8%, Poor health < 55 yrs= 20%, 55-64 = 10%, Taking insulin= taking insulin= 7.9%, TReported self care proGlucose monitoring< 55 yrs= 16.7%, 55-6

AH-811
Appendix H. Data Table

Author Year

Piette 1997

Health Service Utilization Health Care Costs Knowledge

NR Human calling system-generate 

390,000 calls/yr, Assuming 10 mins/call, 26 full time staff required at more than $ 1.2 million /yr.AVM system with multiple phone lines-(costing around $ 90,000) and one all time nurse (costing around $ 22,500) for a total cost of $112,500. (around 10% of human calling system)Assuming system paid for in the first year, program cost for 2nd year would be $ 22,500 ($3/pt) or 2% of the cost of the human calling program

NR

AH-812
Appendix H. Data Table

Author Year

Piette 1997

Use of Technology Usability HIT Satisfaction

NR NR Listened to 2+ preventive mes

< 55 yrs: 37.5%55-64 yrs: 74.4%>65: 85.6%

Taking insulin: 71.5%Not taking insulin: 70%Total: 70.6%Reported AVM was helpful< 55 yrs: 92.9%55-64 yrs: 100%>65: 100%Taking insulin: 94.7%Not taking insulin: 100%Total: 98.1%No difficulty responding< 55 yrs: 100%55-64 yrs: 100%>65: 95.7%Taking insulin: 95.2%Not taking insulin: 100%Total: 98.1%Would like to receive AVM< 55 yrs: 100%55-64 yrs: 82.4%>65: 87.5%Taking insulin: 95%Not taking insulin: 84.4%Total: 88.5%

AH-813
Appendix H. Data Table

Author Year

Piette 1997

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-814
Appendix H. Data Table

Author Year

Piette 1997

Quality of Life Support Withdrawals

NR NR Potential eligible pts: 74

Ineligible: 4Difficulty understanding EnglisHad memory deficit: 1Asked to withdraw because he dissatisfied with the AVM callsFinal sample: 65 pts(2 not accounted for by the aut

AH-815
Appendix H. Data Table

Author Year

Piette 1997

Adverse Effects Other

NR Pts ability to respond to AVM calls 

given, not reported here

AH-816
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Pike KJ 2007 American Cancer Society 

website

NA WWW Randomized trial 

Rami 2006 NR NR Homes Randomized cross over 

trial

AH-817
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

October 2004 - May 2005

3 months *Visitors to ACS website responding to 

online recruitment for study*English speaking*Smoke daily*U.S. residents*Complete online informed consent and intake questions

NR NR

NR 6 months *HbA1c >8%

*Duration of disease >1 year*Age between 10-19 yrs*Willingness and consent to participate in the study*Willingness to attend follow up visits every 3 months

NR NR

AH-818
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SMOKING NR N = 30% Mean: 41NR DIABETES-Type 1 NR TM-PD Grp: 50%

PD-TM Grp: 61%

TM-PD: 14.5 19.3), NSPD-TM: 16.2 19.0), NS

AH-819
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 87% NR Some college: 75% NR 21% use the 

daily and 66% Internet more once/day

NR NR NR NR TM-PD vs. PD

Duration of di(yrs)6.4 (1.0-12.8) (2.9-11.1), NSBMI19.0 (15.9-3217.8 (18.4-28HbA1c at star9.1 (8.0-11.3) (8.3 - 11.6), N

AH-820
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate NR NR Control: pdf fi

tailored preserole models; Ptailored feedbof change; Qpersonalized group supportClinic: structuassessment wprevention; CAddiction & Mtailored contesupport with Rdiscussion

NR NR NR NR VIE-DIAB sys

AH-821
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Subjects randomized to use one of 6 websites described above

NR NR NA

Feasibility study of a telemedicine support system (VIE-DIAB)and its effect on glycemic control in adolescents with TIDMVIE-DIAB collects and processes data on glycemic control received from mobile phone services (SMS, GPRS, WAP) and from password protected internet connection to the server, daily serum glucose concentrations glyphs displays, once a week automatically generated SMS or personalized message with advice.

TMVIE-DIAB collects and processes data on glycemic control received from mobile phone services (SMS, GPRS, WAP) and from password protected internet connection to the server such as date and time, serum glucose value, insulin dosage, carbohydrate intake, and additional notes. daily serum glucose concentrations glyphs displays, once a week automatically generated SMS or personalized message with advicePDRoutine scheme with a daily written protocol

Mobile phones SMS, data log, free te

serum glucose levels, screen tailored glyphs

AH-822
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Staff Training Subject Training co-interventions Physiolo

NA NR NR NRNR Pts instructed to measure 4 

blood glucose values/dayAll pts informed about technical features of the program, of SMS communications, and all pts received a manual containing these instructions.

NR Glycemic control impr

stage, p<0.05

At the end of the study

reached the same endTM-PD vs. PD-TM; me9.2 (7.4-12.6) vs. 8.85 

AH-823
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR Telecommunication costs was low NR

AH-824
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Use of Technology Usability HIT Satisfaction

"Wide variation" in individual website use; 3 sites had higher use (20% or more subjects making 5 or more visits) and 3 had lower use (<10% make 5 or more visits)High vs. low site useNon-ITT quitting: 26% vs. 22.1% 

NR NR

NR NR Overall satisfaction good

Pts mainly criticized the additioworkload and GPRS access problems- because of these problems, pts rated the system consuming and only half of thewanted to continue with the syTable showing results of pt satisfaction survey given, not lhere

AH-825
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Self-reported, 7-day point prev

of quitting at 3 months: CG: 22controlExp Grp: 23.5%Intent-to-treat (54% response; assume non-responders smokCG: 10.9%Exp Grp: 11%

NR NR NR

AH-826
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Quality of Life Support Withdrawals

NR NR 46% non-response to follow-up 

survey (online with phone f/u)

NR NR NR

AH-827
Appendix H. Data Table

Author Year

Pike KJ 2007Rami 2006

Adverse Effects Other

NR NRNR NR

AH-828
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Rasmussen 2005 Catchment area of H:S 

Bispebjerg university hospital

Asthma specialist clinic, GP 

clinic

Homes Prospective randomized 

comparative design(prospective RCT)

AH-829
Appendix H. Data Table

Author Year

Rasmussen 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Combination of respiratory symptoms 

and at least 1 objective measure of asthma viz*Airway hyper responsiveness to inhaledmethacholineless than or =4 μmol*Peak expiratory flow variability (PEF) of equal to or more than 20% and/or minimum of 15% (300mL) increase in FEV1 after bronchodilation 

NR NR

AH-830
Appendix H. Data Table

Author Year

Rasmussen 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ASTHMA NR Grp 1: 27%

Grp 2: 30%Grp 3: 30%Withdrawals: 14% 

Mean (RangeGrp 1 : 28 (18Grp 2 : 30 (19Grp 3 : 30 (20

AH-831
Appendix H. Data Table

Author Year

Rasmussen 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR Catchment area NR

AH-832
Appendix H. Data Table

Author Year

Rasmussen 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Physician ma

interactive astmonitoring too

AH-833
Appendix H. Data Table

Author Year

Rasmussen 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Electronic diary, action plan for patients, decision support system for physicianPts given a peak flow meter, pts record symptoms, need for rescue medication and PEF values.3 colored warning system with written action planPts fill diary daily and follow instructions given by computer and physicianCheck online repository at www.mosby.com/jaci

Grp 1: Internet groupGrp 2 : Specialist groupGrp 3 : GP groupGrp 1: details given above, also check onlineGrp 2: Pts treated acc to severity and taught how to adjust medication,pts given peak flow meter and written action plan that had a 3 color warning system based on symptom score and PEF valuesGrp 3: Pts asked to contact GP with letter describing study and test results. GP to assess symptoms and tests and decide need for pharmaceutical treatment. pts did not receive treatment or info from study physician

Computer, push-button telephone

Electronic asthma diardaily recordings of symptoms, PEF valueinstructions from physdecision support for physicians-check onlinjournal

AH-834
Appendix H. Data Table

Author Year

Rasmussen 2005

Staff Training Subject Training co-interventions Physiolo

None specified as suchbut all GPs received a circular about asthma and GINA guidelines by local authority

Pts instructed in the use of internet diary, pts in SP group taught medication adjustment

NR Improved symptoms -

(95% CI)Grp 1 vs. Grp 2:2.64 (1.43-4.88), p= 0Grp 1 vs. Grp 3:3.26 (1.71-6.19), p < 0Grp 2 vs. Grp 3: 1.23 (0.66-2.30), p= NImproved AQLQ scorequality of life questionOdds ratio (95% CI)Grp 1 vs. Grp 2:2.21 (1.09-4.47), p= 0Grp 1 vs. Grp 3: 2.10 (1.02-4.31), p = 0Grp 2 vs. Grp 3:0.95 (0.43-2.07) , p= NImproved FEV1 more to 300 mLOdds ratio (95% CI)Grp 1 vs. Grp 2:3.26 (1.50-7.11) , p= 0Grp 1 vs. Grp 3:4.86 (1.97-11.94), p <Grp 2 vs. Grp 3:1.49 (0.55-4.05) , p= NImproved AHR (airway responsiveness)Odds ratio (95% CI)

AH-835
Appendix H. Data Table

Author Year

Rasmussen 2005

Health Service Utilization Health Care Costs Knowledge

Acute unscheduled visits by % pts on a monthly basisGrp1 vs. Grp 2 vs. Grp 33.7% vs. 2.1% vs. 1.3% (P= 0.05, chi sq)GP group visited physician on avg once (95th percentile, 1-3)Internet grp: used tool on avg 4 times in 2 week block (range, 1-6)Sp group: 1 pt hospitalized due to exacerbationmore data on the website

NR NR

AH-836
Appendix H. Data Table

Author Year

Rasmussen 2005

Use of Technology Usability HIT Satisfaction

Pts in Internet group used tool on average 4 times in 2-week blocks Range: 1-6

NR NR

AH-837
Appendix H. Data Table

Author Year

Rasmussen 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR No asthma Medication

Grp 1 (baseline: FU, McnemarGrp 2 (baseline: FU, McnemarGrp 3 (baseline : FU, Mcnema44%:0%, <0.001 vs. 57% :1<0.001 vs. 53% : 26%, <0.0Take Inhaled corticosteroidsGrp 1 (baseline: FU, McnemarGrp 2 (baseline: FU, McnemarGrp 3 (baseline : FU, Mcnema21% : 91%, <0.001 vs. 20% < 0.001 vs. 17% : 29%, 0.Good ComplianceGrp 1 (baseline: FU, McnemarGrp 2 (baseline: FU, McnemarGrp 3 (baseline : FU, Mcnema32% :87%, <0.001 vs. 25% <0.001, 36%:54%, <0.001Use of action planGrp 1 (baseline: FU, McnemarGrp 2 (baseline: FU, McnemarGrp 3 (baseline : FU, Mcnema+E42% :88%, <0.001 vs. 3%<0.001 vs. 0% : 6%, NS

AH-838
Appendix H. Data Table

Author Year

Rasmussen 2005

Quality of Life Support Withdrawals

NR NR N = 47

Grp 1: 15Grp 2: 12Grp 3: 20no significant difference in the dropout rate, P= 0.26, chi sq

AH-839
Appendix H. Data Table

Author Year

Rasmussen 2005

Adverse Effects Other

Grp1 vs. Grp 2 vs. Grp 3Dysphonia: 17% vs. 4% vs. 9%Oropharyngeal candidiasus: 18% vs. 3% vs. 4%

Pts characteristics at baseline given, not listed hereViz- symptoms gradingAQLQ scoresFEV1, % predictedAHR

AH-840
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Reid RD 2007 University of Ottawa Heart 

Centre

Hospital to Home Homes RCT 

AH-841
Appendix H. Data Table

Author Year

Reid RD 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

November 2004 - May 2005

12 months *Smoke 5 or more cig/day, *Age 18+

*Hospitalized for acute coronary syndrome or catheterization or percutaneous coronary intervention (PCI)

Live 1 hour or more away

NR

AH-842
Appendix H. Data Table

Author Year

Reid RD 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SMOKING NR IVR: 61%

CG: 74%

MeanIVR: 54CG: 53.9

AH-843
Appendix H. Data Table

Author Year

Reid RD 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR High school

IVR: 91%CG: 70%More than HSIVR: 9%CG: 30%

NR NR

AH-844
Appendix H. Data Table

Author Year

Reid RD 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-845
Appendix H. Data Table

Author Year

Reid RD 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

All subjects: inpatient bedside counseling by nurse specialist and nicotine therapy, if needed. Experimental: IVR contact post-discharge on day 3, 14 and 30. IVR queries on smoking status, confidence in quitting, use of pharmacotherapy, use of self-help; If subject wanted to quit or had confidence <7 (out of 10), nurse counselor called 3 times over 8 week period. 

IVR or Usual Care Telephone Subject responses to 

flag a counselor to cal

AH-846
Appendix H. Data Table

Author Year

Reid RD 2007

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-847
Appendix H. Data Table

Author Year

Reid RD 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-848
Appendix H. Data Table

Author Year

Reid RD 2007

Use of Technology Usability HIT Satisfaction

IVR contact3 days: 35/5014 days: 36/5030 days: 34/50 

NR NR

AH-849
Appendix H. Data Table

Author Year

Reid RD 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Self-report not smoking at 52 w

unadjusted: IVR: 46%CG: 34.7% (OR 1.6, CI 0.7-3.6, p=0.25)Adjusted for age, education, diagnosis, prior quitting: OR 2.0.92-5.92, p=.07)

AH-850
Appendix H. Data Table

Author Year

Reid RD 2007

Quality of Life Support Withdrawals

NR NR 1 control subject died

AH-851
Appendix H. Data Table

Author Year

Reid RD 2007

Adverse Effects Other

NR NR

AH-852
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Robertson 2006 NR One public hospital and 

public and private clinics 

Homes Cohort 

AH-853
Appendix H. Data Table

Author Year

Robertson 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 12 mos *Currently receiving treatment for 

depression under the care of a mental health professional*Previously or currently satisfying the diagnostic criteria for a major depressive episode*Access to a computer and the internet*Willing to use the internet as a part of the treatment*Willing to provide informed consent*Sufficient cognitive functioning*Proficiency in English*Age > 18 yrs

NR NR

AH-854
Appendix H. Data Table

Author Year

Robertson 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DEPRESSION NR N = 37.5% Mean (Range

64) 

AH-855
Appendix H. Data Table

Author Year

Robertson 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-856
Appendix H. Data Table

Author Year

Robertson 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Recovery Roa

AH-857
Appendix H. Data Table

Author Year

Robertson 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

E-health system , to be used as an adjunct to treatment by pts and clinicians. The system facilitates self management and provides patients with a secure online consultation system, automated patient administered progress monitoring questionnaireswith feedback, systematic psychoeducation, and evidence based therapy.

Patient side12 e0sessions for 12 months, e- progress monitoring and progress report, e-psychological therapy-psychoeducation, econsultation system, emedical record, e-diary, pt 

controlled privacy settings, adherence reminders to complete recovery road sessions and to take medicines.Clinician sidePatient progress monitoring outcomes, access to online questionnaires, flags to identify at risk pts, patient brochures, e-consultations, e-medical record. Later on in the study, automated reminders and case management system added.

Computer progress reports, onlin

consultation, therapy, 

AH-858
Appendix H. Data Table

Author Year

Robertson 2006

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-859
Appendix H. Data Table

Author Year

Robertson 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-860
Appendix H. Data Table

Author Year

Robertson 2006

Use of Technology Usability HIT Satisfaction

NR NR The author mentions that satis

ratings were obtained from pts public and private sectors usinonline questionnaire majority of pts surveyed were satisfied and said that it increatheir knowledge of depression enhanced their relationships wcliniciansAll clinicians reported that they satisfied with the system, 83% reported that it helped the relationship with the patient, 10reported that it helped the pt to manage their condition

AH-861
Appendix H. Data Table

Author Year

Robertson 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Mean DSS was severe at session 1 

and declined to mild by session 8, a large reduction in depression severity ( d (within-subject)= 1.03)58% of pts had subclinical levels of depressive symptomatology at session 8.

The rate of adherence decline greater for pts who received onautomated reminders than thoreceived case management.By session 8, adherence for camanagement was 34% higher automatic reminders.A Cox regression survival analrevealed a significant hazard r2.43 (95% CI=1.01-5.84) for automated reminders relative tmanagement when controlling baseline depression severity. Medication adherence at each session ranged from 91% at se1 to 100% at session 6.

AH-862
Appendix H. Data Table

Author Year

Robertson 2006

Quality of Life Support Withdrawals

NR NR Of the 144 pts referred, 7 withd

prior to using the system and 2not complete the session, data pts were lost due to technical difficulties

AH-863
Appendix H. Data Table

Author Year

Robertson 2006

Adverse Effects Other

NR Graphs depicting adherence to 

recovery road across sessions and depression severityof the patient given.

AH-864
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Robertson 2005 NR Private psychiatric 

practices, public psychiatry services, and general practice

Homes Cohort 

AH-865
Appendix H. Data Table

Author Year

Robertson 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 12 months *Previously or currently satisfying the 

diagnostic criteria for a major depressive episode*Access to a computer and internet*Willing to use the internet as part of treatment*Willing to provide informed consent*Sufficient cognitive functioning*Proficiency in English*Age > 18 yrs

NR NR

AH-866
Appendix H. Data Table

Author Year

Robertson 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DEPRESSION NR N = 43.87% Range: 19-64

AH-867
Appendix H. Data Table

Author Year

Robertson 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR NR

AH-868
Appendix H. Data Table

Author Year

Robertson 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Recovery Roa

AH-869
Appendix H. Data Table

Author Year

Robertson 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Pts provided with education, progress monitoring, e- consultation, e- diary and online evidence based therapy.

12 month progressive monitoring and treatment over 12 months, pts received progressmonitoring questionnaires, progress graphs and summary progress reports.Additional online features included online evidence based therapy, systematic education,a secure e-consultation system, an e diary and a record of currently and previously prescribed medications.Grp 1: had standard automated e mail reminders for all sessions and in response to nonadherenceGrp 2: case management consisting of standard automated e mail reminders for all sessions and email and telephone follow up by a case manager in response to non-adherence

Computer Progress reports, onlin

consultation, therapy, 

AH-870
Appendix H. Data Table

Author Year

Robertson 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-871
Appendix H. Data Table

Author Year

Robertson 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-872
Appendix H. Data Table

Author Year

Robertson 2005

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-873
Appendix H. Data Table

Author Year

Robertson 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Adherence 

After first 8 sessions, Grp 1: 55%Grp 2: 84%For both grps adherence tendedecline over timeCox regression survival analysnon adherence to Recovery Rocontrolling for baseline depresrevealed a significant hazard r2.4 (95% CI, 1.0-5.8), alpha < for automated reminders relativcase management

AH-874
Appendix H. Data Table

Author Year

Robertson 2005

Quality of Life Support Withdrawals

NR NR NR

AH-875
Appendix H. Data Table

Author Year

Robertson 2005

Adverse Effects Other

NR NR

AH-876
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Rodgers A 2005 Posters, websites, media 

articles, email and text messaging mailing lists, in N.Z.

NA Mobile phone text 

messaging

RCT 

AH-877
Appendix H. Data Table

Author Year

Rodgers A 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Age 16+

*Smoke daily*Ready to quit in 30 days*Able to send/receive text messages*Vodafone owner*Speak English*Informed consent

NR NR

AH-878
Appendix H. Data Table

Author Year

Rodgers A 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SMOKING NR CG: 41.7%

Exp: 41.3%

Mean: 22

AH-879
Appendix H. Data Table

Author Year

Rodgers A 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

EuropeanCG: 63.8%Exp Grp: 62.2%MaoriCG: 21%Exp Grp: 20.7%PacificCG: 3.4%Exp Grp: 3.6%

($New Zealand): < 15000CG: 25.2%Exp Grp: 25.9%15000-30000CG: 40.9%Exp Grp: 42.7%>30000CG: 32.1%Exp Grp: 30.1%

NR NR Cig/day Mean

groups

AH-880
Appendix H. Data Table

Author Year

Rodgers A 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate English NR NR

AH-881
Appendix H. Data Table

Author Year

Rodgers A 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Text messages tailored to preferences, smoking history, barriers to quitting, set around a quit date

Exp Group: 5 per day for 1 week before QD and 4 weeks after, then 3 per week for 26 weeks; 1 free month of text msg for 1 month; Text quit buddy (similar subjects put in touch with each other) CG: text message thanking them and providing contact information every 2 weeks, 1 free month of messaging if completing follow-up. 

Mobile phone text messaging

Text messaging, TXT TXT quizzes, TXT bud

AH-882
Appendix H. Data Table

Author Year

Rodgers A 2005

Staff Training Subject Training co-interventions Physiolo

NR NR NR Salivary cotinine on ra

of 100 subjects

AH-883
Appendix H. Data Table

Author Year

Rodgers A 2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-884
Appendix H. Data Table

Author Year

Rodgers A 2005

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-885
Appendix H. Data Table

Author Year

Rodgers A 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Confidence in staying quit: Control 

20%, Exp 33% (high confidence at 26 weeks);

6-week quit ratesCG: 13%Exp Grp: 28% Relative risk 2.2 (1.79 to 2.70, p<0.0001)

AH-886
Appendix H. Data Table

Author Year

Rodgers A 2005

Quality of Life Support Withdrawals

NR NR N = 1705

Data available6 wks: 1624, 95% (withdrawal 6 mos: 1265, 74% (withdrawal 

AH-887
Appendix H. Data Table

Author Year

Rodgers A 2005

Adverse Effects Other

Rate of finger painControl: 48 (5.6%)Exp Grp: 52 (6.1%)Motor Vehicle AccidentControl: 43 (5%)Exp Grp: 38 (4.5%)

NR

AH-888
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Rogers 2001 NR New York Upstate 

university in SyracuseInternal medicine outpatient practices

Homes RCT 

AH-889
Appendix H. Data Table

Author Year

Rogers 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

May 1999 to April 2000

NR *Adults with previous diagnosis of 

essential hypertension and under evaluation for a change in antihypertensive therapy because of 1) elevated blood pressure (systolic equal to or more than 140 or diastolic equal to or more than 90 mm Hg) despite current therapy, 2) undesirable side effects of current antihypertensive medications3) office systolic pressure of at least 180 mm Hg or diastolic pressure of at least 110 mm Hg with no current use of antihypertensive medications*For pts with DM, heart disease, stroke, nephropathy, peripheral arterial disease, or hypertensive retinopathy;*An office systolic BP of at least 130 mm Hg or an office diastolic of at least 85 mm Hg

Pts younger than 18 yrs, pregnant, had secondary hypertension, or did not have the mental or physical capacity to monitor BP at home

NR

AH-890
Appendix H. Data Table

Author Year

Rogers 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HTN NR Home service: 43.3%

Usual care: 55.7%

Mean (SD); Hservice vs. us62.6 (+10) vs

(+11.9)

AH-891
Appendix H. Data Table

Author Year

Rogers 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Home service vs. usual care (%)White: 80.7 vs. 91.2African American:12.3 vs. 7.0Other: 7 vs. 1.8

NR NR NR Cigarette smo

Current: 7.3 vFormer: 41.8 Never: 50.9 vMedical historCardiovasculadisease: 13 vStroke or carosurgery: 9.3 vChronic broncemphysema ochronic obstrupulmonary disvs. 14.6Diabetes: 22.26.3Mean body mindex (SD), K31.5 (+7.6) vs

(+5.2)

Mean total phactivity score7.32 (+1.27) v

(+1.52)

Mean intake osaturated fatty 15.1 (+7.1) vs

(+7.2)

AH-892
Appendix H. Data Table

Author Year

Rogers 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

Private insurance plans or Medicare

NR NR NR Home monito

AH-893
Appendix H. Data Table

Author Year

Rogers 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

A home service consisting of automatic transmission of blood pressure data over telephone lines, computerized conversion of the information into report forms, and weekly electronic transmission of the report forms to physicians and patients

Home service:automatic BP recording at home, central processing and electronic reports weekly to pts and physicians; pts instructed to take BP 3 times in the morning before eating or drinking and 3 times in the evening before going to bed , 3 days per week for 8 weeks (could take additional readings if desired)After each reading, device dialed service support center and transmitted data.Results displayed in report form and faxed to the physician, both pt and physician received weekly reports and a summary report at the end.(physicians adjusted medications thru phone calls/office visits or both if required)Usual care:Pts assigned to usual care were treated for 

Home monitoring service Data input, data 

transmission, data in rformat

AH-894
Appendix H. Data Table

Author Year

Rogers 2001

Staff Training Subject Training co-interventions Physiolo

NR NR NR Mean systolic BP

Home service: decreaHg (95% CI: - 1.61 to - 8.Usual care: 0.1 mm H(95% CI: - 3.43 to 3.1Mean diastolic BPHome service: decreaHg (CI: 0.14 to - 4.04, p= Usual care: 2.1 mm h(CI: - 0.21 to 4.37, p=0Average mean arteriaHome service= decreaHg(CI: - 0.59 to - 5.05 mm Usual care: 1.3 mmHg (CI: - 1.01 to 3.67 mm Statistically significant seen inMean diastolic pressu4.1 mm Hg (CI: 0.93 to p= 0.012)Mean systolic pressur4.8 mm Hg (CI:0.10 to p=0.047)Mean arterial pressure 4.1 mm Hg (CI:0.91 to 

AH-895
Appendix H. Data Table

Author Year

Rogers 2001

Health Service Utilization Health Care Costs Knowledge

One office visit during the trialHome service: 73.2%Usual care: 60%Significant difference seen in median length of follow up timeHome service: 79 daysUsual care: 72 days

NR NR

AH-896
Appendix H. Data Table

Author Year

Rogers 2001

Use of Technology Usability HIT Satisfaction

NR NR Pts satisfied with their medical 

In both grps, 94% pts said thaagreed with the statement " I feam receiving good medical car96% of home service and 89% usual care strongly agreed to tstatement that "I feel my doctoall the information necessary to diagnose or treat me"

AH-897
Appendix H. Data Table

Author Year

Rogers 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-898
Appendix H. Data Table

Author Year

Rogers 2001

Quality of Life Support Withdrawals

NR NR Pts identified as possible cand

167cancelled appointment=9, did keep scheduled appointment=found to be ineligible=2Pts approached by study mana148declined=17Pts fitted with device=131declined to continue=2, cannot tolerate device=8Pts randomly assigned=121Home service=60completed=56Did not complete=4 (withdrew=discontinued coz of health problems=1)Usual care= 61completed trial=55, did not complete=6 (withdrew=discontinued coz of health problems=1, lost to follow up=

AH-899
Appendix H. Data Table

Author Year

Rogers 2001

Adverse Effects Other

NR The decrease in BP with home 

service evident in both men and women, in younger and older patients and in each ethnic group (graph given, values difficult to pinpoint)Medication dose changeHome service: 33.3%Usual care: 6.6%p<0.001Type of antihypertensive medication changed inHome service: 40%Usual care: 27.9%p= 0.159Significant main outcomes obtained when only pts who participated in the trial for 4 months were includedDifference between the two study groups forMean arterial: p=0.023Diastolic: p= 0.024systolic-p= 0.047

AH-900
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Ross SE 2004 Outpatient clinic Specialty clinic Homes RCT 

AH-901
Appendix H. Data Table

Author Year

Ross SE 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

September 2001-September 2002

12 mos *Heart failure patients

*Ages 18+*Speak English*Have used Web browser in the past

Patients who were physicians, nurses or physician assistants

NR

AH-902
Appendix H. Data Table

Author Year

Ross SE 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: Safety Net InsuranceExp Grp: 19%CG: 19%

CHF NR Exp: 80%

CG: 74%

MeanExp Grp: 57CG: 55

AH-903
Appendix H. Data Table

Author Year

Ross SE 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

CaucasianExp Grp: 92%CG: 88%

<45,000/yearExp Grp: 56%CG: 50%

College GraduateExp Grp: 53%CG: 44%

NR Home compu

Exp Grp: 96%CG: 94%

AH-904
Appendix H. Data Table

Author Year

Ross SE 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

Safety Net InsuranceExp Grp: 19%CG: 19%

NR NR NR System Provi

to Records O(SPPARO)

AH-905
Appendix H. Data Table

Author Year

Ross SE 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Web access to medical record, educational guide, secure messaging

Exp: Access to SPPARO;Control: Usual care

WWW PHR: clinical notes, la

test results

AH-906
Appendix H. Data Table

Author Year

Ross SE 2004

Staff Training Subject Training co-interventions Physiolo

NR Exp: Written user guide 

provided;Con: None. Offered SPPARO after study was completed

NR Mortality

Exp Grp: 6pts (11%)CG: 6pts (11%)

AH-907
Appendix H. Data Table

Author Year

Ross SE 2004

Health Service Utilization Health Care Costs Knowledge

Utilization during Study Year 2002:% Patients HospitalizedExp Grp: 20%CG: 23%N HospitalizationsExp Grp: 22CG: 21% going to ERExp Grp: 20%CG: 13%N ER visitsExp Grp: 20CG: 8N Heart Failure Clinic VisitsExp Grp: 324CG: 325

NR NR

AH-908
Appendix H. Data Table

Author Year

Ross SE 2004

Use of Technology Usability HIT Satisfaction

SPARRO use highest first 3 months, then averaged 24% of exp. subjects used the program each monthsN Subjects reviewing record components (cumulative): Notes 41, Labs 37, Radiology 21, Educational guide 27, e-messages 41. Use of e-messaging plus phone messagingExp Grp: 63 plus 287CG: 267 phoneTotal messages first 6 months by groupExp Grp: 150CG: 88 (p=.05);Total messages second 6 months by groupExp Grp: 109CG: 103 (p=.66);Message categoriesAppt schedule: 20%Refill meds: 15%Question on meds: 14%Test results: 12%Feeling ill: 8%Help interpreting tests: 3%.

NR NR

AH-909
Appendix H. Data Table

Author Year

Ross SE 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Satisfaction ratings 0 to 5:How well doctors understand your problemsBaseline: 4.56 MonthIVR vs. CG: 4.4 vs. 4.4 (Diff 0, CI -0.3, 0.2);12 MonthIVR vs. CG: 4.6 vs. 4.2 (Diff +0.4, CI 0.1, 0.6) p=0.02;How well doctors explain what they are doing and why? Baseline: 4.26 MonthIVR vs. CG: 4.5 - 4.1 (Diff +0.4, CI 0.1, 0.7);12 MonthIVR vs. CG: 4.5 - 4.1(Diff +0.4, CI 0.1, 0.7) p=0.02;

Scored 0 to 100: Baseline 85;6 month Exp-Control: 88 - 84 (Diff +4, CI -3, 9)12 Month Exp-Control 91 - 85 (Diff + 6, CI -1, 11) p=0.08

Medication Adherence (0 to 4)Baseline 3.4;6 Month Exp-Control: 3.5 - 3.4 +0.1, CI -0.2, 0.4;12 Month Exp-Control: 3.6 - 3.+0.2, CI -0.1, 0.6), p=0.15'General Adherence (0 to 100): Baseline 82:6 Month Exp-Control: 81 - 78 (+2.3, CI -3.7, 8.3);12 Month Exp-Cont: 85 - 78 (D+6.4, CI 1.8, 10.9), p=0.01

AH-910
Appendix H. Data Table

Author Year

Ross SE 2004

Quality of Life Support Withdrawals

Scored 0 to 100Baseline: 566 monthExp Grp: 64CG: 59(Diff +5, CI -5, 13)12 monthExp Grp: 64CG: 62 (Diff +2, CI -7, 11), p=0.63

NR NR

AH-911
Appendix H. Data Table

Author Year

Ross SE 2004

Adverse Effects Other

None reported;1 subject disagreed with medical record relating to alcohol use and requested amendment be made.

NR

AH-912
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Rotondi AJ 2005 Rehabilitation hospitals NA Homes Cohort 

AH-913
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months TBI patient (traumatic brain injury):

*Age 18+ *Was inpatient in rehab hospital*Lives with a female significant other*Speaks English well enough to answer data collection questionsFSO (female significant other) of patient: *Age 18 +*Lives as significant other*Speaks English well enough to answer data questions

Excluded if not possessing above requirements

NR

AH-914
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR NR TBI Patient: 100%

FSO: 0%

Mean (RangePatient: 50.2 FSO: 46.4 (24

AH-915
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR Family income 

<$30,000: 61.8%

Less than High schoolPatient: 45.5%FSO: 22.7%

NA FSO with no p

computer exp23.5%

AH-916
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate NR NR WE CARE (w

caregiver accresources and 

AH-917
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Home computer with Internet access, support group, education and information libraries 

NR Computer + Internet Support groups (patie

only, caregiver only, bask an expert; Q&A LiReference library; community resources library and event sche

AH-918
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Staff Training Subject Training co-interventions Physiolo

Help modules; no stated staff training discussed; technical support available

NR NR NA

AH-919
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Health Service Utilization Health Care Costs Knowledge

NA NR NR

AH-920
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Use of Technology Usability HIT Satisfaction

Evaluation of FSO/Caregiver:Proportion of "hits" to site functions (hits = page view for 1 minute or more)Support group: 68.6%Community services library: 20.1%Q&A library: 5.2%Number of "hits" for 17 users: 838 (+1015)

Range: 26-3679Median: 454.

FSO reports of Ease of Use (1 to 5 point scale, 5=extremely):Overall: 4.1Compared to other websites: 4.2Support group: 3.8Reference library: 3.7Community resources: 3.5Ask expert: 3.7Q&A library: 3.7Event calendar 3.6.FSO report of Value (1 to 5 scale, 5=extremely valuable):Overall 3.9Compared to other sites: 3.9Support group: 3.8Reference library: 3.4Community resources: 3.3Ask expert: 3.4Q&A library: 3.5Events calendar: 2.5.

FSO reaction to site:

Helpful: 75% extremely, 18.8% Satisfying: 43.8% extremely, 3veryWonderful: 100% extremelyDull: 0% extremely, 0% very, 6moderatelyPSO Emotions while using site

Supportive: 7.7% extremely, 7veryMotivation: 7.7% extremely, 38very, 15.4% moderatelyUnderstanding: 26.7% extreme33.3% very, 26.7% moderately

AH-921
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-922
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Quality of Life Support Withdrawals

NR Categories (frequencies) of Support 

Messages by FSOSelf-disclosure: 32.3%Provide emotional support: 29.1%Request information or clarification: 6.8%Provide advice/offer solution: 6.2%Interpretation: 5.2%Request for feedback: 5.1%Agreement: 4.4%Shared norms/group process: 4.1%Validation/normalization: 3.3%Provide information: 2.3%Identify with group: 1.1%Negative/flaming: 0%.

4 dropped out before computeprovided2 received computer but had ninterest in the intervention.

AH-923
Appendix H. Data Table

Author Year

Rotondi AJ 2005

Adverse Effects Other

NR NR

AH-924
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Ruland 2003 Cancer patients attending 

outpatient department

Outpatient clinic Outpatient clinic Randomized trial (pilot 

study)

AH-925
Appendix H. Data Table

Author Year

Ruland 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

2 mos Same Day *Age >21 yrs

*Able to read, write and speak English*No cognitive impairment*Able to provide informed consent*Not too fatigued*Participation approved by patient's physician

New patients coming for their first visit

NR

AH-926
Appendix H. Data Table

Author Year

Ruland 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CANCER NR N = 61% Mean (SD)

56.3 (+11.3)Range: 23-77

AH-927
Appendix H. Data Table

Author Year

Ruland 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Mean years (SD): 12.8 

(+2.38) Range: 4-20

NR Clinician sam

Assigned to GAssigned to GClinicians wermean of 3.5 p

Range: 1-9

AH-928
Appendix H. Data Table

Author Year

Ruland 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR CHOICEs

AH-929
Appendix H. Data Table

Author Year

Ruland 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Comprehensive patient assessment tool for cancer specific symptoms, functional problems, and preferences along physical, psychosocial, emotional and spiritual dimensionsSDM/ care planning component that highlights for clinicians which symptoms patients areexperiencing, severity, degree of bother and importance to the patient

Grp 1: study explained and informed consent taken, use of touch screen computer explained pts completed CHOICEs assessment on the computer with or without help of interview assistant, pts completed the ease of use questionnaire assessment summaries printed and given to pts and clinicians in subsequent consultationsGrp 2: same as above except assessment summaries were neither printed nor given to patient or clinician at any time

Computer Questions, CHOICEs 

assessment

AH-930
Appendix H. Data Table

Author Year

Ruland 2003

Staff Training Subject Training co-interventions Physiolo

NR Pts were explained the use 

of the touch screen computer

NR Pts Mean score for pe

health (SD): 6.17 (+2.5

Range: 0-10Mean selected sympto15.10 (+13.96)

Range: 1-50Number of reported syand CG: No significanOut of 16 problem catCHOICEsEnergy problems with symptoms: 63.5%Pain: 53.8%Worries/concern: 50.0Sleep/rest and mood/fGrp differences in conreported symptoms anaddressedin the pt consultationCongruence Mean (SDGrp1 vs. Grp 22.84 (+2.98) vs. 7.63 (

df- 50 (1), p < 0.01Importance weighted cMean (SD)Grp1 vs. Grp 212.8 (+15.7) vs. 33.0 (df- 50 (1), p< 0.01

AH-931
Appendix H. Data Table

Author Year

Ruland 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-932
Appendix H. Data Table

Author Year

Ruland 2003

Use of Technology Usability HIT Satisfaction

Time reqd median of 9 mins (+10.35)

Range: 0.5 to 49 mins)25% of sample used 5 mins or less

Completed assessment on touch pad comp without assistance: 80%Had assistance (reasons- too weak, had disabilities, out of convenience): 20%Reliability of instrument: Cronbach's alpha 0.98Personal comments of pts added: not reported hereEase of use correlated with previous comp experience: ( r=0.42, p<0.01)

No significant grp differences isatisfactiont= 0.75p=0.45 (pt satisfaction scores skewed)

AH-933
Appendix H. Data Table

Author Year

Ruland 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-934
Appendix H. Data Table

Author Year

Ruland 2003

Quality of Life Support Withdrawals

Perceived HRQoL: 6.59 (+2.44)

Range: 0-10

NR 73 approached; 14 did not wis

participate; 59 consented; 3 wias received bad news and did wish to complete questionnairewere not well; 2 did not like computers

AH-935
Appendix H. Data Table

Author Year

Ruland 2003

Adverse Effects Other

NR Number of symptoms reported by pts 

having influence on number addressed in the consultgiven- ANCOVA results not reported here

AH-936
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Runge 2006 Volunteer General Practitioner (GP) 

offices and outpatient facilities

36 study centers (includes GP offices and outpatient facilities)

Nonrandomized between group

Safren 2002 NR NR Home/with patient Cohort Sciamanna 2004 Public health clinic waiting 

room

NR Computer in clinic Cohort study 

AH-937
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

12 months None *Ages 8-16 years

*Presence of confirmed asthma diagnosis for at least 1 year*No other asthma program participation prior to study entry*Sufficient knowledge of German language was assumed*Obtain informed consent

Outside age range, no asthma, prior participation in an asthma program, not speaking German, no informed consent

NR

3 months NR NR NR NROctober 2001-February 2002

NA *1 provider at 10 clinic sites

*Patient with same day visit with study provider

Non-English speaking NR

AH-938
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ASTHMA NR CG: 79.2%

SPMP: 54.7% SPMP + IEP: 65.9%

Ages 8-16 Mean Ages: CG = 11.5 (+)

SPMP = 11.1 

SPMP + IEP: (+)2.2

NR HIV/AIDS NR NR NRYES: all 95 (63.3%) NR N = 32.43% Range

<25: 22 pts (1

25-34: 24 pts 35-44: 31 pts 45-54: 32 pts >55: 41 pts (2

AH-939
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

German NR NR NR NRAmerican NR NR NR NoneCaucasian: 123 pts (83.1%)Black: 6 pts (4.1%)Hispanic: 17 pts (11.4%)Other: 2 pts (1.4%)

NR <HS: 32 (21.3%)

HS: 47 (31.3%)HS+: 71 (47.4%)

NR Exercise <150 

51%

AH-940
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

Cost-benefit ratio for each intervention strategy was analyzed from the perspective of health insurance (German)

NR German Believe in the positive 

impact of patient education, but unhappy that only less than 1% of all children in Germany are instructed in a systematic manner per year

Standardized Management Internet based program (asth

NR NR English NR MediMomNR NR English NR NR

AH-941
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

SPMP: educational sessions about asthma knowledge and asthma selfmanagement skill, IEP: 

internet educational module with asthma-related quizzes and interactive adventure games incorporating asthmarelated situations, schedule chats with asthma experts, on-line peak flow protocol, chat rooms

CG (n=48): baseline clinical visit, second clinical visit after 6mos SPMP (n=86): baseline clinical visit, second clinical visit after 6mos, final clinical visit after 12mos + educational instruction SPMP + IEP (n=44): baseline clinical visit, second clinical visit after 6mos, final clinical visit after 12mos + educational instruction + interneteducation program access

Classroom, Internet Education and selfmanagement skills, al

cost-benefit analysis

Pager system: a clinician or investigator input a patient's medication regimen into an Internet computer program; the patient carried a beeper that had a display window, and when it was time for the patient to prepare or take a pill, the beeper notified the patient with instructions

IVR GrpControl Grp

Pager Medication reminder

Computer assessment with tailored printed feedback

NA Computer Computerized assess

on smoking and physiactivity, 4-5 page repotailored feedback, 1-pprovider report

AH-942
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Staff Training Subject Training co-interventions Physiolo

NR NR NR Lung Function Tests 

CG: Baseline PEF = 3First Visit = 341+110* SPMP: Baseline PEF First Visit = 366+101* Second Visit = 376 + 9SPMP + IEP: Baseline 86* First Visit = 363 + 89* Second Visit = 368 + 9*P>0.05 v. baseline vitest) 

NR NR NR NRNR None, research associate 

available for help if needed

31.3% patients used Internet

NA

AH-943
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Health Service Utilization Health Care Costs Knowledge

Clinic visits at 0, 6, and 12 month periods, patient outcomes under usual care was also noted, scheduled chats with asthma experts (IEP only)

*See actual paper for Data (not relevant to list since cost is in Euros and based on universal healthcare)

NR

NR NR NRNA NR NR

AH-944
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Use of Technology Usability HIT Satisfaction

Average 2hours/mo Peak flow protocol section, patient 

chat room, and asthma adventure game were the most used of the site

NR

AdherenceIVR Grp2 weeks: 70%12 weeks: 64%CG2 weeks: 56%12 weeks: 52%

Reported that younger employees tended to use the e-mail as a communication tool for health counseling more than older employees, and there are different levels of skill and acceptability for email among employees in administrative and manufacturing divisions

NR

NR Asked for help using computer: 12%

Felt computer easy to use: 81.1%

NR

AH-945
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR See Quality of Life ratings below NRNR NR NRSatisfaction with provider visit significantly related to having a chronic condition, being a nonsmoker, and having a provider who reviewed the report with the patient

NR NR

AH-946
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Quality of Life Support Withdrawals

KINDL Questionnaire: 75.5+11.78CG: T0 = 76.5

T1 = 74.9SPMP: T0 = 75.0T1 = 77.2

T2 = 79.3SPMP + IEP: T0 = 75.3

T1 = 77.3

T2 = 80.5

Regular physician check-ups, scheduled chats with asthma experts (IEP only), chat rooms with peers (IEP only), teacher-classmates (SPMP and/or IEP only)

N = 124 (34.6%) Relocation of patients: 6 Withdrawal of Caregiver conseNonattendance of scheduled foup visits: 11 No returning of mail questionn41 Relocation of personnel in two center: 41 Reasons not documented: 41

NR NR NRNR NR NR

AH-947
Appendix H. Data Table

Author Year

Runge 2006Safren 2002Sciamanna 2004

Adverse Effects Other

NR *Outcomes not abstracted: costbenefit ratio due to reasons listed 

below

NR Adherence was assessed through a 

pill bottle system that recorded each time a patient opened the bottle to take out a pill. Could underreport if patient took out more pills, like to go on vacation

Patients: 266/208/150 24.8% patients had privacy concerns 

using computer

AH-948
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Sciamanna 2005 Secure website NR WWW Randomized Group 

AH-949
Appendix H. Data Table

Author Year

Sciamanna 2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR NA *List of people with self-reported OA 

from a marketing organization and had knee pain 3+ months, saw doctor for knee pain, and told diagnosis of osteoarthritis

NR NR

AH-950
Appendix H. Data Table

Author Year

Sciamanna 2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR OSTEOARTHRITIS 

OF THE KNEE

NR N = 23.1% Mean: 47.8

AH-951
Appendix H. Data Table

Author Year

Sciamanna 2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 86.8%Black: 6.6%Native American: 2.5%Hispanic: 3.3%Other: 5%

NR >College: 25.6% NR NR

AH-952
Appendix H. Data Table

Author Year

Sciamanna 2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR myexpertdoct

AH-953
Appendix H. Data Table

Author Year

Sciamanna 2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Computer assessment with tailored feedback about OA and OA care

Group 1: satisfaction with OA care before using website;Grp 2: satisfaction with OA care after using website

Secure website Computer assessmen

OA, satisfaction with ccomputer personalized feedback about OA quof care and treatment recommendations

AH-954
Appendix H. Data Table

Author Year

Sciamanna 2005

Staff Training Subject Training co-interventions Physiolo

NR NR Uses Internet for health at 

least several times per month: 65.5%

NA

AH-955
Appendix H. Data Table

Author Year

Sciamanna 2005

Health Service Utilization Health Care Costs Knowledge

NA NR NA

AH-956
Appendix H. Data Table

Author Year

Sciamanna 2005

Use of Technology Usability HIT Satisfaction

NR Easy to use: 97.5% Website ratings: 23.1% excelle

52.9% very good, 24% good;Website could:*help get better care: 77.7%*help manage OA: 86.8%*help talk to doctor: 81.8%*better understand OA: 90.1%

AH-957
Appendix H. Data Table

Author Year

Sciamanna 2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Overall satisfaction with medical care:Grp 1: 67.9%Grp 2: 69.5%No difference in satisfaction in technical quality, physician manner, doctor-patient communication, or time spent with doctor

NR NR

AH-958
Appendix H. Data Table

Author Year

Sciamanna 2005

Quality of Life Support Withdrawals

NR NR NR

AH-959
Appendix H. Data Table

Author Year

Sciamanna 2005

Adverse Effects Other

NR NR

AH-960
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Shapiro 2007 NR NR Homes Randomized wait-list 

control

AH-961
Appendix H. Data Table

Author Year

Shapiro 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 2 month *Age range between 18-60

*Current diagnosis of DSM IV BED based on the SCID broadened to include individuals with sub threshold BED who acknowledged engaging in at least two objective binge eating episodes/month*BMI equal to or more than 27 Kg/m2

*Regular access to IBM compatible 

computer.

Current AN or BN; current severe depression or active suicidal intent; any developmental or learning disorder or cognitive impairment contraindicating CBT or the effective use of a computer and currently taking medication for the treatment of obesity. Individuals currently taking psychoactive medication were not excluded provided their medication dose was stable and their eating behaviors had remained stable during the course of medication treatment.

NR

AH-962
Appendix H. Data Table

Author Year

Shapiro 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR EATING DISORDER NR CBT: 9.09%

CD-ROM: 9.09%WL: 4.55%Total: 7.58%

Mean (SD)CBT: 39.14 (+

CD-ROM: 40.(+10.61)

WL: 39.09 (+1

Total: 39.55 (

AH-963
Appendix H. Data Table

Author Year

Shapiro 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Black (not Hispanic)CBT: 31.82%CD-ROM: 27.27%WL: 22.73%Total: 27.27%White (not Hispanic)CBT: 68.18%CD-ROM: 63.64%WL: 77.27%Total: 69.7-%AsianCBT: 0CD-ROM: 4.55%WL: 0Total: 1.52%OtherCBT: 0CD-ROM: 4.55%WL: 0Total: 1.52%

NR NR NR Never married

CBT: 36.36%CD-ROM: 33.WL: 22.73%Total: 30.77%MarriedCBT: 31.82%CD-ROM: 28.WL: 27.27%Total: 29.23%Monogamous relationship awith partnerCBT: 31.82%CD-ROM: 38.WL: 50%Total: 40%BMI Mean (SDCBT: 37.72 (+

CD-ROM: 39.(+7.88) 

WL: 34.70 (+6

Total: 37.35 (

No BEDCBT: 28.57%CD-ROM: 28.WL: 33.33%Total: 30.16%

AH-964
Appendix H. Data Table

Author Year

Shapiro 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR CD-ROM bas

behavioral tre

AH-965
Appendix H. Data Table

Author Year

Shapiro 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

CD-ROM based cognitive behavioral treatment for binge eating disorder focusing on balanced nutrition, regular exercise, psychoeducation, individualized interactive CBT exercises, tracking system applicable to a broad range of adult men and women from all races and background.

Grp 1= Group CBT; 10 weeks of group treatment for weekly 90 min sessions in small grps.Facilitated by PhD level psychologist and consisted of psychoeducation on healthy nutrition, daily food and behavior diaries, identifying thoughts, feelings and behaviors related to binge eating episodes, challenging and restructuring thoughts and techniques for avoiding unhealthy behavior.Grp 2= CD-ROM based cognitive behavioral program for treatment of obesity that focuses on balanced nutrition, regular exercise, psychoeducation, basic concepts , techniques and examples of CBT, individualized interactive CBT exercises, helpful guidelines for slip control and relapse prevention, tracking system to monitor utilization and applicable to a broad range of adult men and women from all races 

Computer individualized interacti

cognitive behavior exercises, education

AH-966
Appendix H. Data Table

Author Year

Shapiro 2007

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-967
Appendix H. Data Table

Author Year

Shapiro 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-968
Appendix H. Data Table

Author Year

Shapiro 2007

Use of Technology Usability HIT Satisfaction

At the follow up, 66.7% of those in the CD-Rom condition reported that they had used the same at least once or twice after completing the program

NR NR

AH-969
Appendix H. Data Table

Author Year

Shapiro 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR At baseline, individuals in the C

ROM reported more binge eatidays and fast food consumptioalso had a higher weight and Bthan those in the WL conditionAt post treatment, no significandifference between the groupsIn terms of abstinence, in CD Rgrp, 13.3% and 12.5% were bifree at post treatment and follorespectively.In the group CBT, 7.7% and 22were binge free at post treatmefollow p respectively.In the WL, no pt was binge free post treatment.Table 2 gives values of outcomvariables summarized above

AH-970
Appendix H. Data Table

Author Year

Shapiro 2007

Quality of Life Support Withdrawals

NR NR Drop out rates prior to the end 

weeks,CD-ROM= 31.5%, Group CBT40.9%, WL= 9%Overall difference was statisticsignificant(p<0.05) with significmore dropouts in the group cothan the WL (p<0.03) and no difference between either CD-and group or CD-ROM and W

AH-971
Appendix H. Data Table

Author Year

Shapiro 2007

Adverse Effects Other

NR Changes in binge days over time 

(CDROM vs. Group CBT vs. WL)Baseline LS means (SD)2.85 (0.21) vs. 2.76(0.23) vs. 2.77 (0.18)Post treatment LS means (SD)2.42 (0.18) vs. 2.12(0.18) vs. 2.71( 0.13)Two month follow up LS means (SD)2.00 (0.30) vs. 1.49(0.31) vs. 2.65(0.18)No pair wise p values were significant but the interaction term for treatment group by visit was significant(p<0.04)75% of those who completed the WL condition chose to receive the CDROM over group treatment

AH-972
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Shaw 2007 (5) NCI's Cancer 

Information Service, hospitals, clinics, public health departments, and Medicaid program.

NR Homes RCT 

AH-973
Appendix H. Data Table

Author Year

Shaw 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

Detroit: June 2001 - April 2003Wisconsin: May 2001 - April 2003

4 months *Poverty Level <= 250% of the federal 

poverty level*Within 1 year of diagnosis or had metastatic breast cancer*Not homeless*Able to read and understand an informed consent letter

Homeless NR

AH-974
Appendix H. Data Table

Author Year

Shaw 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: Low-Income: <=250% of the federal poverty level

METASTIC BREAST CANCER

NR N = 0% Mean (SD): 5

(+11.81)

AH-975
Appendix H. Data Table

Author Year

Shaw 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

N = 231Caucasian: 144 pts (62.3%) African–American: 83pts (35.9%) Other: (1.7%)

NR N = 231

Some junior high: 2pts (0.9%) Some high school: 24pts (10.4%) High school degree: 72pts (31.2%) Some college: 69pts (29.9%) Associate or technical degree: 28pts (12.1%) Bachelor’s degree: 28pts (12.1%) Graduate degree: 8pts (3.5%)

Detroit cohort: UnknownWisconsin cohort: Rural

On average, whad been diagmonths before start of the DDstudy with motwo-thirds of tbeing in the reearly stages o

AH-976
Appendix H. Data Table

Author Year

Shaw 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR Must be able to read 

and understand an informed consent letter

NR NR CHESS

AH-977
Appendix H. Data Table

Author Year

Shaw 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

CHESS: a browser which automatically collects data on an individual keystroke level as participants used the system. This capability enabled us to track each user’s code name, data, time spent and URL of every Web page requested. The key variables of interest are four measures of CHESS service use: information, discussion group, ask an expert, and interactive service.

Group 1: 231 subjects all received same treatment. After submitting their pretest, all study participants were loaned a computer and given Internet access for 4 months. They also received personal training to learn how to use the computer and the Internet, but the majority of time was spent on learning how to use CHESS. Interactive Services, one of four services offered by CHESS; There may be prompts from the computer, but the computer is not, for the most part, guiding the user.

Home computer + Internet To serve as platform f

study intervention (CHand

AH-978
Appendix H. Data Table

Author Year

Shaw 2007

Staff Training Subject Training co-interventions Physiolo

NR They also received 

personal training to learn how to use the computer and the Internet, but the majority of time was spent on learning how to use CHESS.

NR NR

AH-979
Appendix H. Data Table

Author Year

Shaw 2007

Health Service Utilization Health Care Costs Knowledge

NR NR In the current study, the chang

information competence from tto post-test was statistically significantPre- test Mean: 2.37 (+0.69)

Post-test Mean: 2.83 (+0.64), |

10.21, P < 0.001)Effect size calculated via Cohe0.69Pre-test reliability a = 0.75Post-test reliability a = 0.79As expected, pre-test level of tdependent variable had a stronsignificant relationship with the dependent variable (health information competence) (b = 0or 463, P < 0.01)Interaction between informatiointeractive services was signifi= 0.206, P < 0.01). Interaction between information services adiscussion group use (b = 0.160.01) was also significantThe first and second interactioblocks explained additional sigvariances (2.1%) and(2.6%), respectively, in information competence

AH-980
Appendix H. Data Table

Author Year

Shaw 2007

Use of Technology Usability HIT Satisfaction

Key variables of interest are four measures of CHESS service use: InformationMean: 35.46 (+56.78)

Median: 16.89Range: 475.4Discussion groupMean: 506.18 (+1369.35)

Median = 62.59Range: 12743.8Ask an expertMean: 27.96 (+51.64)

Median: 6.46Range: 432.0Interactive serviceMean: 27.85 (+52.86)Median: 9.88Range: 500.7

NR NR

AH-981
Appendix H. Data Table

Author Year

Shaw 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-982
Appendix H. Data Table

Author Year

Shaw 2007

Quality of Life Support Withdrawals

NR NR 286 recruited

231 (81%) completed both preand post-test surveys. Withdra55, or 19%.

AH-983
Appendix H. Data Table

Author Year

Shaw 2007

Adverse Effects Other

NR NR

AH-984
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Shegog 2001 6 clinics and 7 schools in a 

large urban area

Houston, TX Clinic RCT 

AH-985
Appendix H. Data Table

Author Year

Shegog 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

1 year NR *Been diagnosed as having asthma by 

their health care provider*Age 7-13

Did not have a diagnosis of asthma or was outside of the age range

NR

AH-986
Appendix H. Data Table

Author Year

Shegog 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR YES: ASTHMA NR 65.00% Mean: 10.7

Range: 8-13

AH-987
Appendix H. Data Table

Author Year

Shegog 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

White non-Hispanic: 47.9%African American: 40.8%Hispanic: 7.0%Other: 2.8%Asian: 2.6%

NR School Grades

As&Bs: 73.2%Bs&Cs: 23.9%Cs&Ds: 2.8%

Children recruited from schools and clinics in a "large urban area"

Asthma SeveLow: 15.5%Mild: 38.0%Moderate: 35Severe: 11.3%

AH-988
Appendix H. Data Table

Author Year

Shegog 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Watch, Discov

and Act (WDT

AH-989
Appendix H. Data Table

Author Year

Shegog 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Theory-based application of CD-ROM educational technology for pediatric asthma self-management education

IVR: 38CG: 33

Computer game To provide education 

asthma to children

AH-990
Appendix H. Data Table

Author Year

Shegog 2001

Staff Training Subject Training co-interventions Physiolo

NR NR NR NR

AH-991
Appendix H. Data Table

Author Year

Shegog 2001

Health Service Utilization Health Care Costs Knowledge

NR NR Knowledge questionnaire

Pretest Mean (SD)CG: 15.7 (+5.8)IVR: 18.6 (+5.1)Posttest Mean (SD)CG: 17.8 (+6.3)

IVR: 21.1 (+5.4)

Posttest ScoresSelf-regulationCG: 0.06 (+0.3)

IVR: 3.3 (+1.3)

PreventionCG: 1.8 (+1.0)

IVR: 2.7 (+1.1)

TreatmentCG: 1.5 (+0.8)

IVR: 2.7 (+1.4)

AH-992
Appendix H. Data Table

Author Year

Shegog 2001

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-993
Appendix H. Data Table

Author Year

Shegog 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Pretest

CG: 51.6 (+9.7)

IVR: 53.4 (+9.7)

PosttestCG: 51.5 (+10.7)

IVR: 56.5 (+9.8)

NR

AH-994
Appendix H. Data Table

Author Year

Shegog 2001

Quality of Life Support Withdrawals

NR NR Lack of availability for follow-up

Refusal to participate further: 1

AH-995
Appendix H. Data Table

Author Year

Shegog 2001

Adverse Effects Other

NR NR

AH-996
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Skeels 2006 HIV clinic and Community 

based HIV organization

Outpatient clinics Clinic Cohort 

AH-997
Appendix H. Data Table

Author Year

Skeels 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 1 week NR NR NR

AH-998
Appendix H. Data Table

Author Year

Skeels 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HIV NR NR NR

AH-999
Appendix H. Data Table

Author Year

Skeels 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Middle school /High 

school: 10 ptsSome college: 15 ptsCollege graduate: 4 pts>Masters Degree: 1 pt

NR Used comput

times or neveExperienced upts

AH-1000
Appendix H. Data Table

Author Year

Skeels 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Computer ass

treatment edu+(CARE+)

AH-1001
Appendix H. Data Table

Author Year

Skeels 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Goal - to improve ART adherenceand to lower risk of secondary infectionsPts answer tailored risk assessment questions about social support, sexual behavior etc., CARE+ provides personalized feedback (identifies areas of trouble and areas where they are doing well), skill building videos, risk reduction plan focused on ART adherence or reducing risk of secondary infections. Used in clinic prior to meeting provider, printout of plan received to keep or discuss with provider. 

Pts explained on how to navigate CARE+Questionnaire to collect demographic and background info, use of CARE+ observed. Semi structured interview to identify usability issues and assess views on computer counseling

Tablet PC Tailored assessment, 

personalized plan

AH-1002
Appendix H. Data Table

Author Year

Skeels 2006

Staff Training Subject Training co-interventions Physiolo

NR Explained as to how to 

navigate CARE+

NR NR

AH-1003
Appendix H. Data Table

Author Year

Skeels 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1004
Appendix H. Data Table

Author Year

Skeels 2006

Use of Technology Usability HIT Satisfaction

NR All 30 completed the CARE+, 5 had 

experience using Tablet PC beforeMany participants had positive comments- not listed hereThree main issues identified by observation and interview*Seeing the screen (eyesight impairment due to HIV medication, lighting probs)*Target acquisition (difficulty with selecting buttons in CARE+ screens)*Difficulties tappingMore details (pts responses) given but not listed here

NR

AH-1005
Appendix H. Data Table

Author Year

Skeels 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-1006
Appendix H. Data Table

Author Year

Skeels 2006

Quality of Life Support Withdrawals

NR NR NR

AH-1007
Appendix H. Data Table

Author Year

Skeels 2006

Adverse Effects Other

NR Attitudes toward CARE

Issues discussed in the interviewHonesty, judgment, embarrassment, compassion, trust, privacy, accessibility convenience, sympathy, importance of human dialogue and interaction miscommunications , preference for computer counseling or person to person counseling. (Detailed response

The author also mentions that the follo

AH-1008
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Smith 2000 Rural-at least 25 miles 

away from 6 major cities of Montana

Certified diabetes educator centers

Homes RCT 

AH-1009
Appendix H. Data Table

Author Year

Smith 2000

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 10 months *Female

*Diabetes- type 1 or 2*Ages 35-60 yrs*Able to read and write English*Possess the sight and dexterity to use a computer (not necessarily be computer literate)*Have a telephone in theirhome*Live at least 25 miles out of 6 major cities in Montana

NR NR

AH-1010
Appendix H. Data Table

Author Year

Smith 2000

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES: rural DIABETES NR N = 0% Mean: 46.7

AH-1011
Appendix H. Data Table

Author Year

Smith 2000

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR Rural: at least 25 

miles away from 6 major cities of Montana

Married: 80%Employees: 6

AH-1012
Appendix H. Data Table

Author Year

Smith 2000

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Women to wo

project

AH-1013
Appendix H. Data Table

Author Year

Smith 2000

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

5 month computer intervention involved installing comp/software, providing each women with a health info notebook on general health and specific diabetes info.4 components(1) Conversation area- pts encouraged to converse with each other about anything, comments open and available, monitored by CDE, CDE did not actively participate unless invited by pts or to redirect inappropriate participation(2) Mailbox- pts speak privately with each other or to the nurse monitor.(3) Health chat areastructured, electronic classroom, open for all, questions specific to dealing with diabetes encouraged and articles from health info notebook discussed, active role by CDE

Computer Group: all of the aboveNon computer group: hard copy of all materials computer group was given (health info notebook and computer-generated info)

Computer Chats, questions, post 

messages, mailbox

AH-1014
Appendix H. Data Table

Author Year

Smith 2000

Staff Training Subject Training co-interventions Physiolo

NR Computer group trained in 

the use of software and how to log in and connect to the systemAt the first visit, confidentiality and support group participation were reviewed and protocol of the project explainedA toll free line available to contact the study team (both groups)

NR NR

AH-1015
Appendix H. Data Table

Author Year

Smith 2000

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1016
Appendix H. Data Table

Author Year

Smith 2000

Use of Technology Usability HIT Satisfaction

Mean minutes logged on per monthMonth 1: 129Month 2: 68Month 3: 78Month 4: 44Month 5: 37 Conversation area: each woman used 111 times over the course of 5 monthsMailbox: each woman used around 40 times over the course of 5 monthsHealth chat: accessed 38 times by each

NR NR

AH-1017
Appendix H. Data Table

Author Year

Smith 2000

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Psychosocial adjustment to illness 

scaleComputer group vs. non computer group77.79 vs. 80.24

NR

AH-1018
Appendix H. Data Table

Author Year

Smith 2000

Quality of Life Support Withdrawals

Raw mean scoresQuality of lifeComputer group: 17.1755Non-Computer group: 17.9014

Raw mean scoresPersonal resource questionnaireComputer Grp: 121.76Non-Computer Grp: 128.53

NR

AH-1019
Appendix H. Data Table

Author Year

Smith 2000

Adverse Effects Other

NR Supportive/informational conversation 

given but not reported hereImportance of components of the intervention as perceived by pts given, not listed hereAttitudes of the pts toward the intervention describes (qualitative responses recorded) not listed here

AH-1020
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Southard 2003 Hospital and community 

physician referral

Cardiac rehabilitation centers

Homes RCT 

AH-1021
Appendix H. Data Table

Author Year

Southard 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Coronary heart disease

*CHF*Possessing both

No a priori criteria reported; excluded subjects including a relative of a subject, lack of MD referral, failure to complete blood tests

Ye

AH-1022
Appendix H. Data Table

Author Year

Southard 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CARDIOVASCULAR 

DISEASE

NR N = 75% Mean

Exp Grp: 61.8Usual Care: 6

AH-1023
Appendix H. Data Table

Author Year

Southard 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Not CaucasianExp Grp: 1.9% UC: 3.9%

Exp Grp<20000: 1.34%20000-40000: 19.2%40000-60000: 13.5%60000-80000: 17.3%80000-100000: 9.6%>100000: 5.8%Usual Care<20000: 5.9%20000-40000: 25.5%40000-60000: 17.6%60000-80000: 17.3%80000-100000: 9.6%>100000: 7.8%

Exp GrpHS: 26.4%Trade school: 28.3%>HS: 43.4%CGHS: 25.5%Trade school: 15.7%>HS: 51%

NR NR

AH-1024
Appendix H. Data Table

Author Year

Southard 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Online case m

tailored feedbgroup

AH-1025
Appendix H. Data Table

Author Year

Southard 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Online case management (once/week for 30 minutes)

NR Website Self-monitoring, secur

messaging with case manager and dieticianeducation modules witgraphic feedback, onligroup

AH-1026
Appendix H. Data Table

Author Year

Southard 2003

Staff Training Subject Training co-interventions Physiolo

NR NR Ad hoc telephone Mean values, baseline 

WeightCG: 196.2 to 196.7Exp Grp: 202.8 to 199BMICG: 29.2 to 29.3Exp Grp: 30.9 to 30.3 Systolic BPCG: 130.9 to 128.8Exp Grp: 131.7 to 129Diastolic BPCG: 72.1 to 71.8Exp Grp: 74.4 to 72.5 CholesterolCG: 181.8 to 175.1Exp Grp: 183.3 to 178LDLCG: 109.3 to 107.9Exp Grp: 108.4 to 103HDLCG: 41.5 to 39.7Exp Grp: 39.5 to 37.7 

AH-1027
Appendix H. Data Table

Author Year

Southard 2003

Health Service Utilization Health Care Costs Knowledge

Major cardiovascular event rateCG: 15.7%Exp Grp: 4.1% (Fisher's p = .053)

Estimates for major CVD events: CG: $104,684Exp Grp: $31,110

NR

AH-1028
Appendix H. Data Table

Author Year

Southard 2003

Use of Technology Usability HIT Satisfaction

1-week time analysis with 31 experimental subjects: estimated 10.8 hours of health professional time per 6-month period

NR NR

AH-1029
Appendix H. Data Table

Author Year

Southard 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Minutes of exercise (self-repor

week, baseline and follow-up: 142.4 to 165Exp Grp: 150.2 to 208.4 (NS)

AH-1030
Appendix H. Data Table

Author Year

Southard 2003

Quality of Life Support Withdrawals

NR NR NR

AH-1031
Appendix H. Data Table

Author Year

Southard 2003

Adverse Effects Other

NR NR

AH-1032
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Stinson 2006 Purposive sample Rheumatology clinic, 

university-affiliated pediatric tertiary care center

Homes Cohort 

AH-1033
Appendix H. Data Table

Author Year

Stinson 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

15 days None *Between ages 9-18

*Diagnosed with JIA by rheumatologist*Had experienced pain over past 24 hours*Able to speak and read English

Having known major cognitive or psychiatric disorders that might interfere with ability complete self-reported daily pain ratings, having other known major medical disorders (i.e. Crohn's disease) that would be a source of chronic pain, known severe vision problems that would interfere with ability to see e-Ouch screen, known hand deformities that would prevent use of the electronic diary

NR

AH-1034
Appendix H. Data Table

Author Year

Stinson 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR Juvenile Idiopathic 

Arthritis [JIA]

NR Phase I: 20%

Phase II: 20%

Mean Age: 13(+)2.68

Phase I: 13.6 

Phase II: 13.5 

AH-1035
Appendix H. Data Table

Author Year

Stinson 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR School Grade

Phase I:Public School (n = 6): 60%HS (n=4): 40%Phase II:Public School (n=6): 60%HS (n=4): 40%

NR NR

AH-1036
Appendix H. Data Table

Author Year

Stinson 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR Believe that 

assessment in the form of documentation is the best way to tackle managing pain and effectiveness of therapies

e-Ouch ElectrDiary

AH-1037
Appendix H. Data Table

Author Year

Stinson 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Obtains 3 daily pain ratings (upon waking, after school, before bed), uses scroll bar slide (No Pain--Very Much Pain) as well as a slide with front and back view of human body asking viewer to indicate "parts of the body picture" where they hurt at that moment

Phase I: usual care + eOuch Pain DiaryPhase II: usual care + eOuch Pain Diary

Internet, e-Ouch Pain Diary is a PDA

Keep track of severity pain, medication usedof pain, and other factinhibited by pain (sociacademic, etc.)

AH-1038
Appendix H. Data Table

Author Year

Stinson 2006

Staff Training Subject Training co-interventions Physiolo

NR Initial introduction to pain 

diary, otherwise NR

NR *See table below

AH-1039
Appendix H. Data Table

Author Year

Stinson 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1040
Appendix H. Data Table

Author Year

Stinson 2006

Use of Technology Usability HIT Satisfaction

NR Quotes: "very easy to learn"

 "very easy to use" 

Quotes: "really cool""better than just getting a book blank paper and writing down ayour pain each day"

AH-1041
Appendix H. Data Table

Author Year

Stinson 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-1042
Appendix H. Data Table

Author Year

Stinson 2006

Quality of Life Support Withdrawals

*See table below NR NR

AH-1043
Appendix H. Data Table

Author Year

Stinson 2006

Adverse Effects Other

NR Various changes to the software to 

improve usability and make the program more efficient with fewer errors*Study also includes table that lists the types of arthritis the pts were experiencing

AH-1044
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Strecher 2006 NR NR Homes RCT 

AH-1045
Appendix H. Data Table

Author Year

Strecher 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 12 months *Target quit date that was within 7 days 

of the enrollment date*Provided a valid e-mail 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 /day*Had purchased NiQuitin CQ 21 mg*Agreed to be contacted for follow up email and web based questionnaires at 6 and 12 weeks*Age > 18 yrs

NR NR

AH-1046
Appendix H. Data Table

Author Year

Strecher 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR SMOKING NR N = 43.5%

Grp 1 vs. Grp 2:57.9% vs. 50%

Mean (SD): 3(+10.2)

>40 yrsGrp 1: 58.4 Grp 2: 48.6 <40

Grp 1: 50.4Grp 2: 40.3

AH-1047
Appendix H. Data Table

Author Year

Strecher 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Subjects smo

average of 23cigarettes/day 

Pts had smokcigarettes for average of 20(+10.3)

Subjects who their first cigathe day within of waking: 88

AH-1048
Appendix H. Data Table

Author Year

Strecher 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR CQ PLAN (co

quitters stop s

AH-1049
Appendix H. Data Table

Author Year

Strecher 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Tailored web based smoking cessation materials

Grp 1: web based committed quitters stop smoking plan- Info collected in the enrollment questionnaire used to tailor CQ PLAN materials including demographics, smoking history, motives for quitting, expected difficulties quitting, ans situations that were expected to present challenges. Pts also identified a supportive person that would receive an email with tailored advice for supporting the subject. Initial web based cessation guide, three sequential tailored newsletters delivered via the web, and behavioral support messages delivered via internet for a 10 week period.Grp 2: web based control condition- web based materials had a graphic design and navigational structure very similar to the CQ PLAN, cognitive 

Computer Tailored web based 

smoking cessation materials, e mail messages, newsletter

AH-1050
Appendix H. Data Table

Author Year

Strecher 2006

Staff Training Subject Training co-interventions Physiolo

NR NR NR Tobacco related illnes

Yes: Grp 1 vs. Grp 255.6 vs. 38.2 , OR (95(1.42-3.00), p<0.001No: Grp 1 vs. Grp 251.9 vs. 45.3 , OR(95% (1.02-1.66), p<0.05Interaction OR (95% C1.57), p<0.05

AH-1051
Appendix H. Data Table

Author Year

Strecher 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1052
Appendix H. Data Table

Author Year

Strecher 2006

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-1053
Appendix H. Data Table

Author Year

Strecher 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Self efficacy, 

Low ; Grp 1 vs. Grp 2; 49.9 vs. 42.2 , OR (95%CI)=1.36 (1.01-1.85) , p<0.05High; Grp 1 vs. Grp 2;56.8 vs. 44.8 , OR(95% CI)= 1.63 (1.22-2.19), p<0.001Interaction OR (95% CI)= 1.09 (0.89-1.35),Motivation to quithigh; Grp 1 vs. Grp 2; 54.6 vs. 44.8 , OR (95%CI)= 1.49(1.19-1.93) , p<0.001Low; Grp 1 vs. Grp 2; 48.4 vs. 38.9 , OR(95% CI)= 1.47 (1.00-2.20), p<0.10Interaction OR (95% CI)= 1.01 (0.80-1.27)Weight ConcernYes; Grp 1 vs. Grp 2; 55.8 vs. 41.8 , OR (95%CI)= 1.78 (1.18-2.17), p<0.01No; Grp 1 vs. Grp 2; 52.2 vs. 43.7 , OR(95% CI)= 1.41 (1.12-1.79) , p<0.01interaction OR (95% CI)= 1.12 (0.89-1.43)

No of cigarettes smokedMore than 20; Grp 1 vs. Grp 2OR (95%CI)= , p<0.001<20; Grp 1 vs. Grp 2; vs. , OR

CI)=, p<0.05Interaction OR (95% CI)=, p<0First cigarette of the day<5 mins; Grp 1 vs. Grp 2; 51.8 OR (95%CI)=1.32(0.96-1.82), No; Grp 1 vs. Grp 2; 53.9 vs. 4OR(95% CI) =1.63 (1.25-2.12),p<0.001Interaction OR (95% CI)= 1.11 1.36)

AH-1054
Appendix H. Data Table

Author Year

Strecher 2006

Quality of Life Support Withdrawals

NR NR Total= 3971

CQ Plan: 232 never logged onControl: 238 never logged onOut of the 3501 who logged onwithdrew CQ PLAN: 15Control: 16A total of 3470 received the 6 wFU survey1850 responded to this surveyBetween the 6 an 12 week surmore withdrewCQPLAN: 8Control: 7This left 3455 pts, out of which responded to the 12 week surv

AH-1055
Appendix H. Data Table

Author Year

Strecher 2006

Adverse Effects Other

NR Details about Non smoking children 

and adult smokers given, not listed hereFigure depicting mediation pathway of tailored treatment, perceived relevance and smokingcessation given

AH-1056
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Stuart 2003 30 primary care practices Primary care practices Home Randomzied three group 

AH-1057
Appendix H. Data Table

Author Year

Stuart 2003

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

1 year NR *Age >18 yrs

*English literate*Not currently taking an antidepressant medication*Newly prescribed an antidepressant medication by their primary care provider*Access to a touch-tone telephone*Willingness to participate in the study

Any criteria against the Inclusion criteria

NR

AH-1058
Appendix H. Data Table

Author Year

Stuart 2003

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR DEPRESSION NR NR NR

AH-1059
Appendix H. Data Table

Author Year

Stuart 2003

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

American NR NR NR None

AH-1060
Appendix H. Data Table

Author Year

Stuart 2003

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate English NR Interactive Vo

Intervention

AH-1061
Appendix H. Data Table

Author Year

Stuart 2003

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Automated telephone answering/reminding system

Grp 1: Education-treatment team education and patient self-care educationGrp 2: Education and Call: education listed above + 1 office nurse telephone call within 2 days of the visit when the antidepressant medication was prescribedGrp 3: Education + Call + IVR: education and call listed above + IVR program lasting for 3 months

Telephone Medication 

reminder/adherence/eragement/recording de

AH-1062
Appendix H. Data Table

Author Year

Stuart 2003

Staff Training Subject Training co-interventions Physiolo

NR NR Education and call from 

nurse within 2 days of antidepressant prescribed

NR

AH-1063
Appendix H. Data Table

Author Year

Stuart 2003

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1064
Appendix H. Data Table

Author Year

Stuart 2003

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-1065
Appendix H. Data Table

Author Year

Stuart 2003

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR No significant differences in pa

compliance with their antidepremedication among the 3 treatmgroups

AH-1066
Appendix H. Data Table

Author Year

Stuart 2003

Quality of Life Support Withdrawals

NR NR NR

AH-1067
Appendix H. Data Table

Author Year

Stuart 2003

Adverse Effects Other

NR NR

AH-1068
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Taylor 2006 Walter Reed Army Medical 

Center

Sleep disorders center Homes RCT 

Tsang 2001 Outpatient clinic Diabetes hospital clinic Homes Randomized cross-over 

study

AH-1069
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

October 2002 - March 2003

1 month *Diagnosed with obstructive sleep 

apnea syndrome (OSAS)*Prescribed CPAP (continuous positive airway pressure)

Current or previous treatment with CPAP or other therapies such as oral appliance or surgery for OSAS.

NR

NR 6 mos NR 

"selected 20 patients from diabetes clinic"

NR NR

AH-1070
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR OSA NR UC: 71%

Exp Grp: 66%

MeanCG: 44.6Exp Grp: 45.8

NR DIABETES NR DMS to Control: 50%

Control to DMS: 78%

MeanDMS to ContrControl to DM

AH-1071
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

CaucasianUC: 60%Exp Grp: 49%;African-AmericanUC: 40%Exp Grp: 42%;HispanicUC: 0%Exp Grp: 5%;AsianUC: 0%Exp Grp: 5%

0-24999CG: 20%Exp Grp: 11%;25000-49000CG: 21%Exp Grp: 19%;50000-74999CG: 25%Exp Grp: 30%;75000-99999CG: 20%Exp Grp: 27%;100000+CG: 14%Exp Grp: 13%

High schoolCG: 19%Exp Grp: 20%2-3 yrs of CollegeCG: 25%Exp Grp: 21%;Technical/Associate DegreeCG: 6%Exp Grp 7%BachelorsCG: 25%Exp Grp: 11%Masters DegreeCG: 25%Exp Grp: 37%Doctorate+CG: 0%Exp Grp: 4%

NR OSAS Severit

MildCG: 25% UCExp Grp: 31%ModerateCG: 27%Exp Grp: 35%Severe CG: 48%Exp Grp: 34%

NR NR NR NR DMS-Control 

Control-DMS:Duration of illnvs. 11.8 yrs (pMean BMI: 22HgA1c: 8.56 v

AH-1072
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR (all militaryconnected) NR NR NR The Health BuNR NR NR NR "Diabetes Mo

System" DMS

AH-1073
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

OSAS library with customized information; Daily questions on sleep and CPAP use, responses monitored and categorized into low/medium/high risk, sleep specialist call patient after 3 days of high-risk responses.High<4 h CPAP use;

Medium= 4+ CPAP use but poor sleep;Low=4+ CPAP use and good sleep.

Experimental group: daily patient-entered data on CPAP use, monitoring by sleep specialist.Usual care: clinic visit 1 month after CPAP started, routine access to clinic specialist as needed.

NR Computerized questio

condition-specific libra

DMS: Electronic food diary, blood glucose monitoring, email from and to health professionals;Control: usual communication with diabetes clinic

Group 1: used DMS for 12 weeks, then control for 12 wks;Group 2: had control period for 12 weeks, then DMS for 12 wks

Hand-held electronic diary with touch screen

Food and glucose monitoring with feedba

AH-1074
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Staff Training Subject Training co-interventions Physiolo

NR NR Usual care from clinic NRInstruction with 2-week trial period

NR NR Mean difference in Hg

treatment to control: 00.155-1.5)DMS-Control group Hfrom 8.56% to 7.55% Control-DMS group Hfrom 8.81% to 8.76 to 

AH-1075
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NRNR NR NR

AH-1076
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Use of Technology Usability HIT Satisfaction

NR Exp group

Found HealthBuddy very easy to use: 83%2 out of 3 reported it a positive experienceHad no difficulty using it: 83% Were likely or very likely to continue using it: 75%

Experimental groupSatisfied with Health Buddy: 69Satisfied with communication wclinicians: 71% 

For all 19 subjects:Frequency of use (transmission/week):<1: 31-2: 73-4: 45-6: 2>7: 3

For all 19 subjectsDMS is easy to use: 18Useful to assess eating: 12Would recommend: 11Would use again: 12

NR

AH-1077
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Mean hours of CPAP use (hrs)

CG: 4.22Exp Grp: 4.29, p=0.87;CPAP mean proportion (hrs):CG: 50.1%Exp Grp: 46.9%, p=0.61;

NR NR NR

AH-1078
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Quality of Life Support Withdrawals

Functional status, measured by Modified Functional Outcomes of Sleep Questionnaire (MFOSQ, 8 items, ranges from 0 to 32).UC: 2.27Exp group: 2.03, p=0.76

NR Usual care: 8 subjects did not 

complete assessments; 1 subjwas withdrawn b/c CPAP use wless than 20 days;Exp. Arm: 7 were withdrawn foactivating HealthBuddy, 1 withfor not using it for 30 day obseperiod, 1 withdrew b/c of choosalternative therapy.

NR NR N = 1

AH-1079
Appendix H. Data Table

Author Year

Taylor 2006Tsang 2001

Adverse Effects Other

NR NRNR NR

AH-1080
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Turnin 1992 150 Km radius of 

Toulouse, France. (no other details about setting given)

NR Homes RCT 

AH-1081
Appendix H. Data Table

Author Year

Turnin 1992

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 1 yr *Good command of French, spoken as 

well as written*Age >18 yrs of age.

NR NR

AH-1082
Appendix H. Data Table

Author Year

Turnin 1992

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR IDDM and NIDDM NR NR Grp A: 44.8 (+

Grp B: 45.2 (+

AH-1083
Appendix H. Data Table

Author Year

Turnin 1992

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Diabetes (%); 

vs. Grp BInsulin depen69 vs. 76Non insulin dependent:20 vs. 10Not insulin deinsulinated: 1Occupation (%Grp A vs. Grp Working: 74 vUnemployed: 25

AH-1084
Appendix H. Data Table

Author Year

Turnin 1992

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Diabeto

AH-1085
Appendix H. Data Table

Author Year

Turnin 1992

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

A computer assisted diet education system with energy requirement calculation, individualized meal analysis, specially adapted menus and recipes, consultation of the monitoring cardiovascular disease (MONICA) general dietary information, and electronic mail

For the first 6 months, Grp A used diabeto and Grp B were control subjectsFor the next 6 months, Group B used diabeto and this Grp was followed before and after use of diabeto

Computer Energy requirement 

calculation, individualimeal analysis, specialadapted menus and recipes, consultation omonitoring cardiovascdisease (MONICA) gedietary information, anelectronic mail

AH-1086
Appendix H. Data Table

Author Year

Turnin 1992

Staff Training Subject Training co-interventions Physiolo

NR 1 hr demonstration of the 

operation of the system and then an individualized demonstration of the operation of minitel when creating its dossier. A book of use is also given to the patient.

NR Body weight Change

Study 1:NSGrp A: 64.7 (+2.0) to 6

Grp B: 67.5 (+1.6) to 6For overweight pts, no change in body weightwo phases of the studGrp A: 75.8 (+3.2) and 

Grp B: 75.4 (+3.3) and Study 2; NS changeChange: 68.5 (+1.7) to 

For overweight pts, sigimprovement Change: 75.4 (+3.5) to p<0.01Biological variationsStudy 1: Total HbA1 CGrp A: 10.7 (+0.4) to 1

Grp B: 10.8 (+0.4) to 1FructosamineGrp A: 4.71 (+0.16) to 

Grp B: 4.82 (+0.13) to Study 2:

AH-1087
Appendix H. Data Table

Author Year

Turnin 1992

Health Service Utilization Health Care Costs Knowledge

NR NR Study 1:

Avg test grades significantly improved for Grp A compared Grp BThe average increased from 5

to 64 (+2) for Grp A, p< 0.0001

The average decreased from 5

to 54 (+2) for Grp B

Details grades of test 1,2, 3 angiven, not listed here.A figure showing that patients knowledge increased with the that they spent with the systemStudy 2:Avg grades obtained improved significantly from 56 (+1.5) to 6

(+2.5)

p<0.0001Detailed grades of test 1,2, 3 abefore and after use given, not here.

AH-1088
Appendix H. Data Table

Author Year

Turnin 1992

Use of Technology Usability HIT Satisfaction

Each pt used the system 6 times/month for an avg duration of 15 min/session for a total of 90 min/month9 pts never used the system4 pts used it more than 3 hrs/month

NR Interest in dietetics before use 

Very low: 2.5Low: 9.2Medium: 51.2High: 26.8Very high: 4.9Interest in advantages of diabeHome access 24 hrsVery low: 0Low: 2.4Medium: 4.9High: 46.3Very high: 39Free of chargeVery low: 0Low: 0Medium: 9.8High: 22Very high: 61IndividualizedVery low: 0Low: 0Medium: 34.7High: 29.3Very high: 26.8Two way dialogueVery low: 4.9Low: 19.5

AH-1089
Appendix H. Data Table

Author Year

Turnin 1992

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Study 1; Grp A vs. Grp B

Caloric excessBeginning of study, To:365 (+75) vs. 209 (+37)

6 months, T6:132 (+81) vs. 238 (+99)Carbohydrate (%)To: 42.2 (+0.8) vs. 40.7 (+0.8)

T6: 44.0 (+0.9) vs. 40.7 (+0.7)

Carbohydrate deficit(%)To: 39.7 (+0.7) vs. 39.1 (+0.7)T6: 42.9 (+0.9) vs. 40.3 (+0.7)

Fat (%)To: 38.3 (+1.0) vs. 38.8 (+0.8)T6: 36.0 (+0.9) vs. 38.5 (+0.8)Fat excess (%)41.9 (+0.9) vs. 41.4(+0.6)

37.4 (+1.1) vs. 40.0 (+0.7)

Study 2; Grp B (beginning vs. months)Caloric excess: 193 (+37) vs. 9

(+49), p<0.05

Carbohydrate (%): 40.3 (+0.9) 

42.0 (+1.0), NS

Carbohydrate deficit, (%), 38.9 

AH-1090
Appendix H. Data Table

Author Year

Turnin 1992

Quality of Life Support Withdrawals

NR NR First study

9 pts lost to study in minitel grp (never used the system, 2 for personal reasons, 2 for lack of moved and 4 could not be accfor), In the control grp 1 pt movand was lost to the studySecond study6 pts dropped out, (5 between and the 12th month)

AH-1091
Appendix H. Data Table

Author Year

Turnin 1992

Adverse Effects Other

NR The author says that factors favorably 

influencing the amount of use of the system were the patients interest in dietetics and their amount of free time. Retired people and those not employed made the most use of the systemThe author also says that the possibility of unlimited use with free access 24 h/day in their own homes was an important factor in motivating the pts to monitor their diet

AH-1092
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

van den Berg 2007 Rheumatology outpatient 

clinics

3 Teaching hospitals Homes RCT 

AH-1093
Appendix H. Data Table

Author Year

van den Berg 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

March 2003-August 2004

12 mos *Dx of RA

*Physical activity of at least 30 mins a day at least 5 days/week*Access to a computer with internet capabilities*Able to cycle on a bicycle ergometer*No cardiopulmonary conditions which would not allow moderately intensive physical activity

NR NR

AH-1094
Appendix H. Data Table

Author Year

van den Berg 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR RHEUMATOID 

ARTHRITIS

NR Grp 1: 24% Median at Ba

Grp 1: 49.5

AH-1095
Appendix H. Data Table

Author Year

van den Berg 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR Grp 1:

Low education level: 18%Medium education level: 61%High education level: 21%

NR NR

AH-1096
Appendix H. Data Table

Author Year

van den Berg 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NR

AH-1097
Appendix H. Data Table

Author Year

van den Berg 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Web-based system for reporting physical activity and receiving personalized exercise schedules

Group 1: Intervention group (only one mentioned in this paper)Group 2: General training group (data reported elsewhere)

Web-based intervention Education, personalize

exercise schedule, dainput and sending

AH-1098
Appendix H. Data Table

Author Year

van den Berg 2007

Staff Training Subject Training co-interventions Physiolo

Some Homes visits were offered to assist subjects

NR NR NR

AH-1099
Appendix H. Data Table

Author Year

van den Berg 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1100
Appendix H. Data Table

Author Year

van den Berg 2007

Use of Technology Usability HIT Satisfaction

Over the follow-up period, 70% of Grp 1 logged into the system at least once a week

NR The majority of individuals wer

satisfied or very satisfied with tsystem variation and clarity (8794%, respectively). The majority of individuals wersatisfied with the frequency of with their PT. 55/62 (89%) reported they did experience any miscommunicabetween themselves and their due to the use of email.

AH-1101
Appendix H. Data Table

Author Year

van den Berg 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Individuals who logged in more 

frequently participated in more moderate intensity and vigorouintensity physical activity than who logged in less frequently, month follow-up (P=0.036 and P=0.001, respectively)

AH-1102
Appendix H. Data Table

Author Year

van den Berg 2007

Quality of Life Support Withdrawals

RAQoL:Those that reported more often on their physical activity schedules reported significantly greater improvements than those that reported less often (P=0.01)

NR 5 from Grp 1 lost to follow-up

AH-1103
Appendix H. Data Table

Author Year

van den Berg 2007

Adverse Effects Other

NR NR

AH-1104
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Van den Brink

2005 NR Tertiary health care center Homes Cohort 

AH-1105
Appendix H. Data Table

Author Year

Van den Brink

2005

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

September 2000-January 2002

6 weeks *Pts were eligible if they had undergone 

a laryngectomy, a commando procedure(removal of a tumor in the mouth or throat by splitting the lower jaw) or a neck dissection (removal of the lymph nodes in the neck)*Pts also needed to be able to read and write Dutch*Have a phone at home.

NR NR

AH-1106
Appendix H. Data Table

Author Year

Van den Brink

2005

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR HEAD AND NECK 

CANCER

NR N = 26 pts (72.2%) Mean (Range

78)

AH-1107
Appendix H. Data Table

Author Year

Van den Brink

2005

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR No education: 0 pts

Elementary school: 6 ptsLower technical school: 14 ptsSecondary education: 6 ptsMiddle technical training: 2 ptsHigh school/College: 3 ptsHigher education: 3 ptsUniversity level: 2 ptsInformation about marital status given, not listed here

NR 20 pts (56%) 

experience wcomputers beparticipation

AH-1108
Appendix H. Data Table

Author Year

Van den Brink

2005

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Electronic hea

information su

AH-1109
Appendix H. Data Table

Author Year

Van den Brink

2005

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Electronic health information system with four different functions, namely, communication, information, contact with fellow sufferers and monitoring of discharged patients in head and neck cancer care.

The electronic support system consisted of the following featuresCommunication: Pts as well as health care providers able to create messages with the subject and type of message, stored centrally. Each pt had own PO box.Information: specially developed website containing general information on cancer treatment of H and N cancer, rehabilitation after treatment, and links to other relevant websitesContact with fellow sufferers: forum where pts could discuss any subject, either anonymously or with their own name.Monitoring: Pts completed electronic monitoring questionnaires on a regular basis after discharge.E-mail alerts: when an abnormal answer appeared in the questionnaire or pt classifies response as question, an email alert generated and sent to the 

Computer Email alerts, electronic 

monitoring questionnaforum, information

AH-1110
Appendix H. Data Table

Author Year

Van den Brink

2005

Staff Training Subject Training co-interventions Physiolo

Involved health care providers received information on how to use the systemand were provided with access passwords

Medical student trained and supervised pts while the patient practice use of the systemin the hospital 

NR NR

AH-1111
Appendix H. Data Table

Author Year

Van den Brink

2005

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1112
Appendix H. Data Table

Author Year

Van den Brink

2005

Use of Technology Usability HIT Satisfaction

Total number of patient sessions: 982Average: 27.3 (+18.4), Range: 4-69

On average, a pt session lasted 12 mins.16% took place after office hoursDetails about usage by health care provider also given, not listed here

Encountered difficulty (technical problems) while using the system: 9 pts (25%)Reasons given: system did not work anymore (3 times), too slow (3 times), password forgotten (once), handling the mouse was difficult (once), computer refused to switch off (once)Additional remarks made by pt quoted- not listed hereFelt that being able to contact care providers by means of the system provided a feeling of security: 83%Found that learning how to use the system was not difficult at all: 61%

Use and appreciation of the electronic health information syMonitoringPts using this functionality: 36 % of satisfied patient users: 94(31/33)Mean score (Range): 8.0 (2-10Ask questionsPts using this functionality: 23 % of satisfied patient users: 91 (21/23)mean score (Range): 7.8 (1-10Read messages Pts using this functionality: 27 % of satisfied patient users: 89 (24/27)Mean score (Range): 7.7 (4-10Contact with fellow sufferersPts using this functionality: 3 (8% of satisfied patient users: 67 Mean score (Range): 6.7 (6-8)InformationPts using this functionality: 22 % of satisfied patient users: 91 (20/22)Mean score (Range): 7.8 (1-10Overall score for the system as 

AH-1113
Appendix H. Data Table

Author Year

Van den Brink

2005

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-1114
Appendix H. Data Table

Author Year

Van den Brink

2005

Quality of Life Support Withdrawals

NR NR During the inclusion period, 59 

the criteria for inclusion20 refused to participate (inclu66%)Computer phobia: 9 timesTiredness/need rest: 4 timesDoes not like idea of filling papquestionnaires- 2 timesLooks after demented parent-1Moving house- 1Partner does not allow pt to haaccess to computer- 1No time- (company ownership)Afraid of theft of laptop computMoreover;3 stopped shortly after inclusio2 were too tired to participate1 died

AH-1115
Appendix H. Data Table

Author Year

Van den Brink

2005

Adverse Effects Other

NR A table detailing information about - 

subjects of questions generating email alerts given, not listed here.Additional remarks made by pts about the system quoted, not listed hereList of questions asked in the patient's questionnaire with patient answers given, results from the GP's questionnaire also given

AH-1116
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Van der Meer 2007 NR 19 general practices, (44 

GP's)Out patient clinic of department of pediatrics of Leiden University Medical Center, Netherlands

Homes Cohort 

AH-1117
Appendix H. Data Table

Author Year

Van der Meer 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 1 month follow up 

(monitoring) after which subjects invited to participate in focus groups

*Physician diagnosed asthma*Age 12-17 yrs*Use of inhaled corticosteroids for at least 3 months in the previous year*No serious co morbid conditions that interfered with asthma treatment*Access to internet at home*Ability to understand Dutch

NR NR

AH-1118
Appendix H. Data Table

Author Year

Van der Meer 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR ASTHMA NR Pts with well controlled 

asthma: 55.5%Pts with poorly controlled asthma: 41%p=0.51

Pts with well casthma (well)(+1.7)

Pts with poorlcontrolled ast(poor): 14.7 (+

p= 0.36

AH-1119
Appendix H. Data Table

Author Year

Van der Meer 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR NR NR Duration of as

Mean years, (Well: 7.4 (4.98.8 (5.1)Current prescinhaled corticosteroidWell: 17; PooDetails of care provider also 

AH-1120
Appendix H. Data Table

Author Year

Van der Meer 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Internet based 

management asthma contro

AH-1121
Appendix H. Data Table

Author Year

Van der Meer 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Adolescents with mild to moderate persistent asthma monitored their asthma control on a website with return message containing their FEV1 and peak expiratory flow values expressed as percentages. After 4 weeks of monitoring, pts invited to take part in eight focus groups.(more on Van der Meer et al 2006)

All pts received a hand held 

electronic spirometer and were trained to perform three maneuvers every morning before receiving medication, and to report FEV1 and peak expiratory flow values by entering them daily into a designated web application or via SMS during a 1 month period. Pts received return message with FEV1 and peak expiratory flow values expressed as a percentage of expected or best value. (Methods described in detail in Van der meer et al, 2006.Following the electronic monitoring study, eight focus group sessions lasting 1- 1.5 hrs were conducted where pts were stratified according to asthma control, gender and age.

Computer, cell phone Data log in, return 

messages.

AH-1122
Appendix H. Data Table

Author Year

Van der Meer 2007

Staff Training Subject Training co-interventions Physiolo

NR Trained to perform 

spirometry three times before medication

NR Well vs. Poor

ACQ score0.6 (+0.3) vs. 1.9 (+0.5

ATAQ control score0 (+0) vs. 2 (+1.8), p<

Prebronchodilator FEVPredicted, Mean (SD)100.7 (+20.9) vs. 90.7 

p=0.14

AH-1123
Appendix H. Data Table

Author Year

Van der Meer 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1124
Appendix H. Data Table

Author Year

Van der Meer 2007

Use of Technology Usability HIT Satisfaction

NR Majority of the participants felt that 

sending lung function values and symptom scores via the internet or SMS was easy and fast.

Barriers and benefits of internebased self management: Well Poor, n (%)Internet based monitoring: elecmonitoring is feasible: 15 (83) (88), p=1.00Recognize benefits of internet monitoring: 1 (6) vs. 4 (24) , p=Internet information: need for comprehensive information: 3 vs. 5 (29), p= 0.44Positive features of internet information: 11 (61) vs. 12 (71p=0.73Internet based action plan: ablready to use internet based acplan: 2(11) vs. 11 (65), p<0.01No need to use action plan at a(78) vs. 3 (18), p<0.01

AH-1125
Appendix H. Data Table

Author Year

Van der Meer 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR Well v. Poor

KASE-AQAttitude: 3.7 vs. 3.8, p=0.55Self-efficacy: 4.0 vs. 3.6,p=0.046Intrinsic barriers to current asthma managementNegative attitude towards asthma managementWell vs. Poor2 (11) vs. 3 (18), p=0.66Limited perceived ability to manage asthmaWell vs. Poor3 (17) vs. 11 (65),p<0.01Barriers and benefits of internet based self management:Internet based medical reviewPositive attitude towards electronic consultation: 8 (44) vs. 10 (59), p=0.51Negative attitude toward electronic consultation: 2 (11) vs. 2 (12), p=1.00

NR

AH-1126
Appendix H. Data Table

Author Year

Van der Meer 2007

Quality of Life Support Withdrawals

NR Intrinsic barriers to current asthma 

managementNegative social influencesWell vs. Poor; 0 (0) vs. 3 (18), p=0.11

Invited to participate in one mointernet based monitoring studn=180No informed consent= 79Withdrawal from monitoring stuComplete internet based monitstudy=97Contradictory scores on ACQ aATAQ control domain= 17Eligible for focus group discuss80

AH-1127
Appendix H. Data Table

Author Year

Van der Meer 2007

Adverse Effects Other

Expressions of acceptance of asthma symptoms given, not listed here

Barriers and drivers for HIT useAttitude towards asthma management: 3 pts with poorly controlled asthma were bothered by face to face medical reviews, since they learned to live with their symptoms and saw no need for regular consultations (driver for HIT use)Views on barriers and benefits of internet based asthma self management:Pts thought that internet based monitoring and reporting was feasible, not time consuming and did not interfere with their daily activities. Also sending lung function values and symptom scores was easy and fast.Pts in the well controlled group had fun doing the measurements but did not think that it is useful and did not observe any benefits from daily electronic monitoring and feedback as they did not have any symptoms atthe time.Pts with poorly controlled asthma reported the usefulness of daily monitoring stating that being able to react to changes in asthma were personal benefits of the system.All pts said that the internet is easy to 

AH-1128
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Weinert 2005 Clinics where patients with 

CI visited

Montana, Idaho, North Dakota, South Dakota, Wyoming

Unclear Three group 

Wilkie 2001 Radiation and medical 

oncology outpatient clinics.

Large medical center In clinical exams rooms 

at radiation and medical oncology outpatient clinics.

Cohort 

AH-1129
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

2 years NR *Live at least 25 miles outside of an 

urbanized area (city of >12,500)

*Live with a CI

*Live within 25 miles of urbanized area*No CI

M

NR 5 weeks *Cancer diagnosis

Have pain, fatigue, or both*English literate *Ages > 18 yrs

*Physically and cognitively able to complete the study

Blind patients and nonEnglish speaking patients were excluded from the study.

NR

AH-1130
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

YES Diabetes, Rheumatoid 

condition, Heart disease, Cancer, MS

NR 0 Mean (Range

54.1 (38-66)

NR CANCER DIAGNOSIS 

AND CHRONIC PAIN

NR N = 10 (24%) Mean (SD): 5

12.4)Range: 28-78

AH-1131
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

White: 96.8%Hispanic: 1.6%Native American: 1.6%

0-15,000: 20.6%15,000-24,999: 12.7%25,000-34,999: 11.1%35,000-44,999: 15.9%45,000-54,999: 19.0%>55,000: 18.9%

Mean (SD)14.5 (+1.95)

YES: lived at least 25 miles outside of urbanized areaNumber of miles to routine health care: 60.7 (+82.36)

None

Caucasian: 40 ptsAsian: 1 pt

NR HS: 23%

Vocational School: 8% Associate Degree: 25% Bachelor's Degree: 25%Master's Degree: 10% Doctoral Degree: 3% Other: 8%

NR Married: 69%

Single: 28% Widowed: 3%

AH-1132
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR NRNR NR NR NR SymptomCon

AH-1133
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Online health teaching units with expert facilitated discussions, internet access, and a self-help social support group

Grp 1: online teaching units + expert facilitated discussion + self-help social support groupGrp 2: online teaching unitsGrp 3: CG

Computer/internet lessening depression 

isolation and bolsterinsocial support, develocomputer skills, acquirinternet skills

NR All (41) completed 

SymptomReport. They then completed the SymptomConsult program. 19 were randomized to choose 1 of 3 computer games. 22 were randomized to view tailored educational materials; reporting pain, safe use of pain medicines, and managing fatigue.

Fujitsu 1600 pentab computer with Windows 98-based SymptomReport program. (touch screen computer)

The SymptomReport program was designed an interactive, touch-smethod for assessmenpain and fatigue. This electronic version of th1970 edition of the Mcpain questionnaire (Mthe pain barriers questionnaire (BQ), anSchwartz cancer fatiguscale (SCFS-6).

AH-1134
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Staff Training Subject Training co-interventions Physiolo

NR NR NR NRThe researcher instructed the patient to read and touch the computer screen to start. The researcher provided assistance in completing the computerized SymptomReport if asked by the patient and then documented the assistance provided.

The researcher instructed the patient to read and touch the computer screen to start. The researcher provided assistance in completing the computerized SymptomReport if asked by the patient and then documented the assistance provided.

NR NR

AH-1135
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Health Service Utilization Health Care Costs Knowledge

NR NR Self-rated computer skills at ba

IVR: 4.66 (+1.75)CG: 5.32 (+2.07)

Self-rated computer skills at 18IVR: 5.93 (+1.51)

CG: 5.26 (+1.94)

NR NR The Symptom Consult program 

seemed to increase patients’ understanding of the symptomtheir awareness of pain and faand enhance compliance with symptom management.

AH-1136
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Use of Technology Usability HIT Satisfaction

NR NR NRFewer patients completed the computer items the second time, usually because of time constraints or a desire to skip questions not relevant to their clinical situation.

Average time spent viewing SymptomConsult: 20 minutes. Fatigue intervention: 7 minutes. Each pain intervention component: 1-16 minutes.

The majority of participants (71indicated that the computer prowas easy, enjoyable, informatia good tool.

AH-1137
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR Intervention provided a chance 

think more and helped enrich communication with doctors.

AH-1138
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Quality of Life Support Withdrawals

NR NR NRNR NR NR

AH-1139
Appendix H. Data Table

Author Year

Weinert 2005Wilkie 2001

Adverse Effects Other

NR NRPatients complained of repetitive questions, especially if the pain questions didn’t personally apply.

NR

AH-1140
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Williamson 2006 NR NR Homes RCT 

AH-1141
Appendix H. Data Table

Author Year

Williamson 2006

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 2 years *African American girls between 11-15 

yrs*BMI above 85th percentile for age and gender*At least one obese biological parent as defined by BMI>30*One designated parent who was overweight BMI>27 and was willing to participate in the study*Adolescent's family was willing to pay $300 out of pocket expenses toward the purchase of a computer worth $1000*Family home had electricity and at least one functional telephone line

NR No

AH-1142
Appendix H. Data Table

Author Year

Williamson 2006

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

All African Americans Obesity NR 0 Adolescents: 

(+1.4)

Parents: 43.2 

AH-1143
Appendix H. Data Table

Author Year

Williamson 2006

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

African Americans NR NR NR Height (cm)

Adolescents: (+8.1) Parents

(+6.9)Weight (kg)Adolescents: (+22.5)Parents: 101.

BMIAdolescents: (+7.9)

Parents: 38.4 

Body fat DXA Adolescents: (+7.5)

Parents: 48.4 

BMI PercentilAdolescents: (+2.5)

AH-1144
Appendix H. Data Table

Author Year

Williamson 2006

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR HipTeens 

AH-1145
Appendix H. Data Table

Author Year

Williamson 2006

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Interactive Internet based lifestyle behavior modification program for African American girls over 2 year period. Web based program providing nutrition education and behavior modification by asynchronous email communication and links to culturally competentwebsites and interactive components such as weight and activity graphs, food intake monitoring with counselors providing feedback on the same.

Grp 1: ControlGrp 2: BehavioralBoth the groups had the following: 4 face to face counseling session during the first 4 weeks, Weight graph, email to study staff, links to African American health websites, links to women's health website. In the lessons to be read by adolescents and parents, the control grp had lessons for healthy lifestyle whereas the behavioral grp had lessons promoting dietary and behavioral exercise change. Apart from the above, the behavioral grp had physical activity graph, food monitoring with feedback using traffic light diet, rate your plate exercise for healthy eating habits, behavioral contracting with parent, adolescent, counselor; problem solving to overcome obstacles, 

Computer E mail, links to website

activity graphs, monitoof daily food intake anphysical activity

AH-1146
Appendix H. Data Table

Author Year

Williamson 2006

Staff Training Subject Training co-interventions Physiolo

The counselors were educated on culturally relevant dietary and physical activity issues, and they incorporated this information into face to face and internet counseling sessions.

Adolescents and parents were trained to set up their new computers at home, access and use the website, and use email to communicate with their counselor by the internet.

NR Grp 1 vs. Grp 2 - 24 m

Weight (kg)Adolescents: 6.3 (+1.6

Parents: -0.60 (+0.89) 

(+0.91)

BMIAdolescents: 1.2 (+0.6

(+0.66)

Parents: 0.04 (+0.34) 

(+0.34)

Body fat DXA (%)Adolescents: 0.84 (+0

(+0.71)

Parents: 0.51 (+0.46) 

BMI PercentileAdolescents: -0.001 (+

-0.004 (+0.003)

Body weight at baselinvariate for body weighparentsF (1, 54)= 27.2 , p < 0Baseline BMI percentivariate for changes in F (1, 54) = 10.89, p< 0significant overall effefor parental changes i

AH-1147
Appendix H. Data Table

Author Year

Williamson 2006

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1148
Appendix H. Data Table

Author Year

Williamson 2006

Use of Technology Usability HIT Satisfaction

Figure showing mean website hits, activity graph and weight graph hits given, difficult to get the exact numbersSignificant differences in website use between the 2 grps for both adolescents and parents in the first yearAdolescents: F (1,55)= 4.95 , p< 0.05Parents: F (1, 55)= 4.15, p<0.05Second year- difference diminish; almost equal in both grps (very reduced web usage)

NR NR

AH-1149
Appendix H. Data Table

Author Year

Williamson 2006

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Dieting and weight concerns o

adolescents, Time* treatment approached significance- F (3, 48)= 2.44 , pAdolescents (Control grp) decrdieting and weight concerns dufirst 6 months, bt scores returnbaseline levels during the next monthsAdolescents in both grps reporimprovements in exercise and eating as compared to baseline.(p<0.05)

Adol in behavioral grp reported 

less fattening foods as comparcontrolF (1, 48)= 2.08, p< 0.05Parents in behavioral grp repomore exercise than the controlF (1, 49)= 4.10, p< 0.05Parents in behavioral grp repogreater reductions in over eatinParents in both grps reported improvements in avoidance of fattening foods during 24 montin comparison with baseline.

AH-1150
Appendix H. Data Table

Author Year

Williamson 2006

Quality of Life Support Withdrawals

NR NR Seven families dropped out of 

(24%) and 10 dropped out of treatment (36%)Difference NS

AH-1151
Appendix H. Data Table

Author Year

Williamson 2006

Adverse Effects Other

NR Baseline characteristics of the 

sample including height, weight, BMI, Body fat DXA,BMI percentile given, not listed here.Figure showing mean website hits, weight graph hits and activity graph hits given, not listed hereFigure showing changes in weight loss behaviors from baseline for both parents and adolescents given, not listed hereNS results showing parents in behavioral grp reporting more comfort and interest with computers at baseline showed trends for more website hits and email use during 2nd yr of studyBehavioral correlates of changes in adiposity given, not listed here

AH-1152
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Winzelberg 2003 Public service 

announcements on local radio, in newspapers and flyers in oncology offices in SFBay and LA

NR Homes Randomized clinical trial 

Woodruff 2001 Rural alternative schools NA School computer Pre-post cohort study 

AH-1153
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 3 mos *Female

*Receiving a primary breast carcinoma diagnosis within 32 mos*Not suicidal*Living in CA*Communicate in written English

NR YE

NR 3 months *Age 13 to 18

*Obtain parental consent*Smoked 1 cigarette in past month

NR NR

AH-1154
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR BREAST CANCER NR N = 0% Mean (SD): 4

(+6.2)

Range: 30-69

NR SMOKING NR N = 66% Mean: 15

AH-1155
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasian: 80%African-American: 4%Asian: 4%Hispanic/Latina: 6% Other: 6%

NR Some graduate 

school: 36%Bachelor's Degree: 28% Some college: 28% Trade School: 1%<HS: 7%

NR Married: 68%

Single: 15%Divorced: 11%Widowed: 4%Separated 1%34% were in a cancer suppoor individual counseling at 

Caucasian: 55%Hispanic: 28%Other: 17% 

NR Alternative school Rural NR

AH-1156
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR English NR Bosom BuddiNR NR NR NR Breathing Roo

AH-1157
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

12 week, structured, web based support group moderated by a mental health professional

Group 1: InterventionGroup 2: Wait-list control

Website Support group, online 

read survivors stories, 

Counselor-facilitated chat roomAll users logged onto weekly, 1-hour sessions for 7 weeks 

NR Website Chatroom, facilitated b

tobacco counselor, usActive World (avatars)

AH-1158
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Staff Training Subject Training co-interventions Physiolo

NR Those who were given 

computers to use for the study were "instructed" on their use

NR NR

NR NR NR NA

AH-1159
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NRNA NA NR

AH-1160
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Use of Technology Usability HIT Satisfaction

NR NR NRNR NR NR

AH-1161
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NRNR NR 38.9% self-reported not smokin

AH-1162
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Quality of Life Support Withdrawals

NR Group experience evaluation (% 

reporting a lot or a great deal)Getting support and encouragement: 65Helping others: 56Learning that problems are not unique: 56Develop new friendships: 63Get advice: 55Express true feelings: 65Model self after group participants: 30Confront difficult probs. and fears: 44Discuss sexual concerns: 44

NR

NR NR NR

AH-1163
Appendix H. Data Table

Author Year

Winzelberg 2003Woodruff 2001

Adverse Effects Other

NR Psychological:

CES-D: F=6.00 (p<0.01)PCL-C: F=8.36 (p<0.01)PSS: F=3.88 (p<0.05)STAI: F=.78Cancer Behavior InventoryF scoresSeeking support: .59Seeking understanding: 0.03Coping: 0.19Affect Regulation: 0.07Positive Attitude: 0.40Activity/Independence: 0.00MiniMacHelpless/hopeless: 0.21Anxious/preoccupation: 0.90Fighting spirit: 0.95Cognitive avoidance: 1.02Fatalism: 0.45

NR NR

AH-1164
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Yon 2007 NR NR Homes Cohort with historical 

control

AH-1165
Appendix H. Data Table

Author Year

Yon 2007

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 24 months *Age > 18 yrs

*BMI > 25 and < 39 kg/m2 

*Regular access to a computer (not less than 3 yrs old with CD Rom drive, internet connection, at least 64 megabytes of RAM, 350 MHz processor speed and windows 98 or higher as a computer operating system)

Pts ineligible if they planned a pregnancy or to move from the area within the next 12 months, had a history of major medical or psychiatric problems, took medications that had implications for weight loss, were unable to participate in a mild to moderate exercise program, or unable to regularly attend weekly meetings.

NR

AH-1166
Appendix H. Data Table

Author Year

Yon 2007

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR OBESITY NR Grp 1: 8 (5%)

Grp 2: 16 (19%)

Mean age (SDGrp 1: 48.2 (+

Grp 2: 46.1 (+

AH-1167
Appendix H. Data Table

Author Year

Yon 2007

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

NR NR High 

school/Vocational schoolGrp 1: 10% Grp 2: 7%Some collegeGrp 1: 11%Grp 2: 30%College degreeGrp 1: 18%Grp 2: 34%Graduate/Professional degreeGrp 1: 22%Grp 2: 44%

NR Marital status 

0.001Grp 1 vs. Grp Married/civil uvs. 86Separated/divwidowed: 13 vNever marriedComputer abiGrp 1 vs. Grp Novice/basic 10 vs. 8Software + intvs. 77Hobbyist: 5 vsProfessional: 

AH-1168
Appendix H. Data Table

Author Year

Yon 2007

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Calorie King's 

AH-1169
Appendix H. Data Table

Author Year

Yon 2007

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Use of the Calorie king software to self monitor food intake and exercise installed in Palmzire 21

 Pts given printed lessons for review each week, facilitated by a master' s level grad student, pts instructed to reduce energy intake,individual calorie goals determined, graded goals for programmed physical activity was used, pts encouraged to increase lifestyle exercise, Grp 1: PDA grp-instructed to self monitor food and exercise with the software, pts electronically submitted their food and exercise diary weekly at group meeting by using the hotsync function, positive feedback self-monitoring behavior and support provided via weekly email messages from the group facilitator to each subject.Grp 2: small paper weekly diaries provided and a book with calorie listings of food. The diary was handed to the facilitator at weekly meetings and positive 

PDA Weight and exercise d

AH-1170
Appendix H. Data Table

Author Year

Yon 2007

Staff Training Subject Training co-interventions Physiolo

NR Initial orientation and 

consent, second orientation session for baseline data collection and technical support and assistance for the PDA and calorie King software provided.

NR Mean weight loss, kg 

Grp 1 vs. Grp 26.3 (+6.1) vs. 7.2 (+5.2

0.17, p= 0.68Mean % weight loss (SGrp 1 vs. Grp 27 (+6.5) vs. 8.3 (+5.8)

0.99, p=0.32All subjects Mean weight loss, kg Grp 1 vs. Grp 25.8 (+6.1) vs. 5.8 (+5.5

0.04, p=0.84

AH-1171
Appendix H. Data Table

Author Year

Yon 2007

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1172
Appendix H. Data Table

Author Year

Yon 2007

Use of Technology Usability HIT Satisfaction

Most pts reported making entries regularly throughout or at the end of the day (61%)Pts entered most or all of the food they consumed (80%) and most of all of the programmed exercise (71%)

The following aspects were liked by the ptsAbility to look up calorie and nutritional information: 65%Portability: 47%Convenience: 34%Ease of entering food and exercise data: 26%Disliked PDA as unable to find commonly eaten food and it was hard to see the screen: 44$

NR

AH-1173
Appendix H. Data Table

Author Year

Yon 2007

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR NR

AH-1174
Appendix H. Data Table

Author Year

Yon 2007

Quality of Life Support Withdrawals

NR NR PDA group: 57 completers (7 

withdrew)Control group: 93 completers (withdrew)

AH-1175
Appendix H. Data Table

Author Year

Yon 2007

Adverse Effects Other

NR Correlation between dietary self 

monitoring and weight loss (ANCOVA results) relationship between attendance and weight loss, relationship between compliance and calorie goals and weight loss and between exercise goals and weight loss given, not listed here

AH-1176
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Zabinski 2001 Public West coast 

university

NA Homes Cohort 

AH-1177
Appendix H. Data Table

Author Year

Zabinski 2001

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 10 weeks *Self reported high body image 

concerns*Failed to report substantial depressivesymptomatology*Did not report thoughts of harming themselves or others

Pts who self reported a past or current eating disorder diagnosis

NR

AH-1178
Appendix H. Data Table

Author Year

Zabinski 2001

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR NR NR N = 0% Mean (Range

(18-20)

AH-1179
Appendix H. Data Table

Author Year

Zabinski 2001

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

Caucasians: 2 pts (50%)Asian: 1 pt (25%)Hispanic/Latina: 1 pt (25%) 

NR Freshmen in College: 

3Sophomore in College: 1

Urban NR

AH-1180
Appendix H. Data Table

Author Year

Zabinski 2001

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR NR NR NR Internet Relay 

AH-1181
Appendix H. Data Table

Author Year

Zabinski 2001

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

Chatroom in a public site (yahoo), consisting of electronic newsgroup and a chat room. Forum for pts to discuss reactions to chat, readings and assignments via asynchronous postings moderated by the author

3 components to the treatment. Weekly readings, chat discussions, and summaries of each chat discussions. At the beginning of each week, pts received single page of reading, logged on to the chat room for an hour, moderator assigned homework, and allowed pts to express their feelings, moderator e mailed one page summary of the chat.

Computer Interactive chat room, 

asynchronous postingthe net

AH-1182
Appendix H. Data Table

Author Year

Zabinski 2001

Staff Training Subject Training co-interventions Physiolo

NR Instruction manual and 

contact number of the research assistant given

NR Measure - M (SD)

Baseline vs. Post Tx vfollow upEffect sizes Baseline to Post Tx ; Pfollow upEating disorder invent----bulimia subscale10.0 (1.4) vs. 10.5 ( 2.(7.4) -0.36 ; -0.63----Drive for thinness s19.5 (12.5) vs. 20.0 ( 16.2)-0.04 ; 0.28Eating disorders examquestionnaireGlobal1.5(1.2) vs. 1.1 (1.0) v0.33 ; 0.40Weight1.8(1.3) vs1.5(1.6) vs. 0.33 ; 0.40Shape2.2(1.3) vs. 1.3 (1.3) v0.69;0.31Eating0.7 (1.0) vs. 0.4(0.4) v0.30;0.25Restraint

AH-1183
Appendix H. Data Table

Author Year

Zabinski 2001

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1184
Appendix H. Data Table

Author Year

Zabinski 2001

Use of Technology Usability HIT Satisfaction

Members participated in 79% of the sessions. Pts who missed a chat session contacted moderator for update (contacts unprompted)

Easier for pts to be honest with the computer- acceptability was high.

Pts rated their satisfaction with program really highTable 2 lists specific responsethe participants with raw scorelisted here

AH-1185
Appendix H. Data Table

Author Year

Zabinski 2001

Health Satisfaction Self-efficacy Health Behavior

Outcomes

Pts reported that the intervention helped prevent negative attitudes about their weight and shape making them feel bad and to recognize the thoughts and situations that trigger negative feelings or behaviors (4 or higher on 7 point scale)3 pts also reported that the intervention helped them to challenge negative thought patterns (5 or higher on a 7 point scale). Table 3 gives ratings of perceived change with pt responses, not listed here

NR NR

AH-1186
Appendix H. Data Table

Author Year

Zabinski 2001

Quality of Life Support Withdrawals

NR NR NR

AH-1187
Appendix H. Data Table

Author Year

Zabinski 2001

Adverse Effects Other

NR Qualitative responses of pts given 

AH-1188
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

Zimmerman 2004 Large tertiary hospital 

where pts who were admitted for elective CABG over a 9 month period

NR Homes RCT 

AH-1189
Appendix H. Data Table

Author Year

Zimmerman 2004

Years of

Study

Length of follow-up 

(months) Inclusion Criteria Exclusion Criteria 

NR 6 months *Age > 65 years

*1st CABG surgery*Not visually/hearing impaired*English literate*No apparent physical impairments that would limit their physical functioning after surgery

Any criteria against the Inclusion criteria

YE

AH-1190
Appendix H. Data Table

Author Year

Zimmerman 2004

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

NR CABG NR 72% 71.22 (65-79)

AH-1191
Appendix H. Data Table

Author Year

Zimmerman 2004

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

100% Caucasian NR NR NR NR

AH-1192
Appendix H. Data Table

Author Year

Zimmerman 2004

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

NR English literate English NR Health Buddy

AH-1193
Appendix H. Data Table

Author Year

Zimmerman 2004

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

CABG patients access web for daily information on recovery, provider can observe patients' progress at home

IVR: 24UC: 21

Website Data input, receiving 

information

AH-1194
Appendix H. Data Table

Author Year

Zimmerman 2004

Staff Training Subject Training co-interventions Physiolo

NR NR NR Frequency of Evaluati

Symptoms by Group aweeksFatigue2 week, 4 week, 6 weeUC: 6, 11, 8IVR: 7, 13, 11Sleep2 week, 4 week, 6 weeUC: 11, 5, 8IVR: 12, 9, 7Swelling2 week, 4 week, 6 weeUC: 7, 3, 3IVR: 11, 8, 9Shortness of Breath2 week, 4 week, 6 weeUC: 6, 3, 2IVR: 7, 8, 5Incisional Pain2 week, 4 week, 6 weeUC: 6, 3, 2IVR: 8, 6, 6Appetite2 week, 4 week, 6 weeUC: 8, 6, 4

AH-1195
Appendix H. Data Table

Author Year

Zimmerman 2004

Health Service Utilization Health Care Costs Knowledge

NR NR NR

AH-1196
Appendix H. Data Table

Author Year

Zimmerman 2004

Use of Technology Usability HIT Satisfaction

NR NR NR

AH-1197
Appendix H. Data Table

Author Year

Zimmerman 2004

Health Satisfaction Self-efficacy Health Behavior

Outcomes

NR NR Mean Scores of Symptom 

interference on physical functioat 2, 4, and 6 weeksFatigue2 week, 4 week, 6 weekUC: 1, 1.36, 1.12IVR: 1.6, 1.4, 0.76Sleep2 week, 4 week, 6 weekUC: 0.84, 0.48, 0.64IVR: 0.2, 0.6, 0.28Swelling2 week, 4 week, 6 weekUC: 0.32, 0.12, 0.12IVR: 0.64, 0.44, 0.36Shortness of Breath2 week, 4 week, 6 weekUC: 0.52, 0.28, 0.08IVR: 0.6, 0.76, 0.2Incisional Pain2 week, 4 week, 6 weekUC: 1.04, 0.76, 0.36IVR: 0.72, 0.6, 0.56Appetite2 week, 4 week, 6 weekUC: 0.44, 0.88, 0.84

AH-1198
Appendix H. Data Table

Author Year

Zimmerman 2004

Quality of Life Support Withdrawals

Mean Scores of symptom interference on enjoyment of life at 2, 4, and 6 weeksFatigue2 week, 4 week, 6 weekUC: 1.12, 1.0, 1.08IVR: 1.32, 0.88, 0.68Sleep2 week, 4 week, 6 weekUC: 0.92, 0.68, 0.68IVR: 0.56, 0.6, 0.44Swelling2 week, 4 week, 6 weekUC: 0.28, 0.12, 0.12IVR: 0.52, 0.44, 0.36Shortness of Breath2 week, 4 week, 6 weekUC: 0.52, 0.12, 0.04IVR: 0.76, 0.56, 0.2Incisional Pain2 week, 4 week, 6 weekUC: 1.04, 0.76, 0.36IVR: 0.4, 0.36, 0.32Appetite2 week, 4 week, 6 weekUC: 0.24, 0.84, 0.76

NR N=7

3 were readmitted to a healthcfacility and were unable to obtahealthcare data for over a wee1 moved1 had a rotary telephone2 did not want to be contacted

AH-1199
Appendix H. Data Table

Author Year

Zimmerman 2004

Adverse Effects Other

NR NR

AH-1200
Appendix H. Data Table

Author Year

Patient Recruitment

Setting Clinical Practice Setting

Patient Technology 

Setting Study design 

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AH-1201
Appendix H. Data Table

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management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

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(months) Inclusion Criteria Exclusion Criteria 

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AH-1202
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Underserved Chronic Illness

Disability/

Impairment Gender (% Male) Age (ye

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AH-1203
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Ethnicity Income (US$) Education

Urban / Rural /

Frontier Othe

Patient Characteristics*

References, continued

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AH-1204
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Health Insurance 

Status Literacy Language(s) Health Beliefs Intervent

References, continued

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AH-1205
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Intervention Described Intervention Groups Type of Device* Functions*

Intervention*

References, continued

Kressig, R. W., & Echt, K. V. (2002). Exercise prescribing: computer application in older adults. Gerontologi42 (2), 273-277.Kwon, H.-S., Cho, J.-H., Kim, H.-S., Song, B.-R., Ko, S.-H., Lee, J.-M., et al. (2004). Establishment of blood glucose monitoring system using the internet. Diabetes Care, 27 (2), 478-483.

Kaufman, D. R., Patel, V. L., Hilliman, C., Morin, P. C., Pevzner, J., Weinstock, R. S., et al. (2003b). Usabilitthe real world: assessing medical information technologies in patients' homes. Journal of Biomedical Inform36 (1-2), 45-60.Kenwright, M., Marks, I., Graham, C., Franses, A., & Mataix-Cols, D. (2005). Brief scheduled phone support a clinician to enhance computer-aided self-help for obsessive-compulsive disorder: randomized controlled trJournal of Clinical Psychology, 61 (12), 1499-1508.Kenwright, M., & Marks, I. M. (2004). Computer-aided self-help for phobia/panic via internet at home: a pilot study. British Journal of Psychiatry, 184 , 448-449.Kim, C., & Kang, D. (2006a). Utility of a Web-based intervention for individuals with type 2 diabetes: the impon physical activity levels and glycemic control. CIN: Computers, Informatics, Nursing, 24 (6), 337-345.

Kim, H. (2007). A randomized controlled trial of a nurse short-message service by cellular phone for people diabetes. International Journal of Nursing Studies, 44(5), 687-692.

Kim, H., Kim, N., & Ahn, S. (2006b). Impact of a nurse short message service intervention for patients with diabetes. Journal of Nursing Care Quality, 21 (3), 266-271.

Kosma, M., Cardinal, B. J., & McCubbin, J. A. (2005). A pilot study of a web-based physical activity motivatiprogram for adults with physical disabilities. Disability and rehabilitation, 27(23), 1435-1442.

Joseph, C. L. M., Peterson, E., Havstad, S., Johnson, C. C., Hoerauf, S., Stringer, S., et al. (2007). A web-btailored asthma management program for urban African-American high school students. American Journal oRespiratory & Critical Care Medicine, 175(9), 888-895.Kashem, A., Droogan, M. T., Santamore, W. P., Wald, J. W., Marble, J. F., Cross, R. C., et al. (2006). Webbased Internet telemedicine management of patients with heart failure. Telemedicine journal and e-health : official journal of the American Telemedicine Association, 12(4), 439-447.

AH-1206
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Staff Training Subject Training co-interventions Physiolo

References, continued

McClure, J. B., Greene, S. M., Wiese, C., Johnson, K. E., Alexander, G., & Strecher, V. (2006). Interest in asmoking cessation program and effective recruitment strategies: results from Project Quit. Journal of MedicResearch, 8 (3), e14.McDaniel, A. M., Hutchison, S., Casper, G. R., Ford, R. T., Stratton, R., & Rembusch, M. (2002). Usability teoutcomes of an interactive computer program to promote smoking cessation in low income women. ProceeAnnual Symposium. , 509-513.Ma, C., Warren, J., Phillips, P., & Stanek, J. (2006). Empowering patients with essential information and comsupport in the context of diabetes. International Journal of Medical Informatics, 75(8), 577-596.Labiris, G., Papadopoulis, G., Lentaris, G., Kafentzis, M., & Krakakis, A. (2004). Internet based counseling torthopedic patients. Acta Orthopaedica Scandinavica, 75 (3), 366-367.

LaFramboise, L. M., Todero, C. M., Zimmerman, L., & Agrawal, S. (2003). Comparison of Health Buddy with approaches to heart failure management. Family and Community Health, 26(4), 275-288.

Lahm, E. A. (1996). Software That Engages Young Children with Disabilities: A Study of Design Features. [JReports - Research]. Focus on Autism and Other Developmental Disabilities, 11(2), 115-124.Lee, T.-I., Yeh, Y.-T., Liu, C.-T., & Chen, P.-L. (2007). Development and evaluation of a patient-oriented edufor diabetes management. International Journal of Medical Informatics, 76(9), 655-663.

Leu, M. G., Norris, T. E., Hummel, J., Isaac, M., & Brogan, M. W. (2005). A randomized, controlled trial of an wireless messaging system for diabetes. Diabetes Technology & Therapeutics, 7 (5), 710-718; discussion 7

Levetan, C. S., Dawn, K. R., Robbins, D. C., & Ratner, R. E. (2002). Impact of computer-generated personaHbA(1c). Diabetes Care, 25 (1), 2-8.Lorig, K. R., Ritter, P. L., Laurent, D. D., & Plant, K. (2006). Internet-based chronic disease self-managemenrandomized trial. Medical Care, 44 (11), 964-971.Lieberman, D. Z. (2003). Determinants of satisfaction with an automated alcohol evaluation program. CyberBehavior, 6 (6), 677-682.

AH-1207
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Health Service Utilization Health Care Costs Knowledge

References, continued

McTavish, F. M., Gustafson, D. H., Owens, B. H., Wise, M., Taylor, J. O., Apantaku, F. M., et al. (1994). CHAn interactive computer system for women with breast cancer piloted with an under-served population. Proceedings - the Annual Symposium on Computer Applications in Medical Care, 599-603.Meigs, J. B., Cagliero, E., Dubey, A., Murphy-Sheehy, P., Gildesgame, C., Chueh, H., et al. (2003). A controtrial of web-based diabetes disease management: the MGH diabetes primary care improvement project. Diacare, 26 (3), 750-757.Nakagawa, A., Marks, I. M., Park, J. M., Bachofen, M., Baer, L., Dottl, S. L., et al. (2000). Self-treatment of obsessive-compulsive disorder guided by manual and computer-conducted telephone interview. Journal of Telemedicine & Telecare, 6 (1), 22-26.Owen, J. E., Klapow, J. C., Roth, D. L., Shuster, J. L., Jr., Bellis, J., Meredith, R., et al. (2005). Randomized a self-guided internet coping group for women with early-stage breast cancer. Annals of Behavioral Medicin30 (1), 54-64.Piette, J. D., & Mah, C. A. (1997). The feasibility of automated voice messaging as an adjunct to diabetes outpatient care. Diabetes Care, 20 (1), 15-21.Nguyen, H. Q., Carrieri-Kohlman, V., Rankin, S. A., Slaughter, R., & Stulbarg, M. S. (2003). Pilot study of an dyspnea self-management program for COPD. AMIA, Annual Symposium Proceedings/AMIA Symposium., 

McKay, H. G., King, D., Eakin, E. G., Seeley, J. R., & Glasgow, R. E. (2001). The diabetes network internet-physical activity intervention: a randomized pilot study. Diabetes Care, 24 (8), 1328-1334.

McPherson, A. C., Glazebrook, C., Forster, D., James, C., & Smyth, A. (2006). A randomized, controlled triainteractive educational computer package for children with asthma. Pediatrics, 117 (4), 1046-1054.Nguyen, H. Q., Carrieri-Kohlman, V., Rankin, S. H., Slaughter, R., & Stulbarg, M. S. (2005). Is Internet-basesupport for dyspnea self-management in patients with chronic obstructive pulmonary disease possible? Resa pilot study. Heart & Lung, 34 (1), 51-62.Noel, H. C., Vogel, D. C., Erdos, J. J., Cornwall, D., & Levin, F. (2004). Home telehealth reduces healthcare Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 10(2), 170McMahon, G. T., Gomes, H. E., Hickson Hohne, S., Hu, T. M., Levine, B. A., & Conlin, P. R. (2005). Web-bacare management in patients with poorly controlled diabetes. Diabetes care, 28 (7), 1624-1629.

AH-1208
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Use of Technology Usability HIT Satisfaction

References, continued

Ruland, C. M., White, T., Stevens, M., Fanciullo, G., & Khilani, S. M. (2003). Effects of a computerized systesupport shared decision making in symptom management of cancer patients: preliminary results. Journal of American Medical Informatics Association, 10(6), 573-579.Robertson, L., Smith, M., Castle, D., & Tannenbaum, D. (2006). Using the Internet to enhance the treatmendepression. Australasian Psychiatry, 14 (4), 413-417.

Robertson, L., Smith, M., & Tannenbaum, D. (2005). Case management and adherence to an online diseasmanagement system. Journal of Telemedicine & Telecare, 11 Suppl 2, S73-75.Rasmussen, L. M., Phanareth, K., Nolte, H., & Backer, V. (2005). Internet-based monitoring of asthma: a lonterm, randomized clinical study of 300 asthmatic subjects. Journal of Allergy & Clinical Immunology, 115(6), 

1142.Reid, R. D., Pipe, A. L., Quinlan, B., & Oda, J. (2007). Interactive voice response telephony to promote smocessation in patients with heart disease: a pilot study. Patient Education & Counseling, 66(3), 319-326.

Rodgers, A., Corbett, T., Bramley, D., Riddell, T., Wills, M., Lin, R. B., et al. (2005). Do u smoke after txt? Reof a randomised trial of smoking cessation using mobile phone text messaging. Tobacco Control, 14 (4), 255

Rogers, M. A. M., Small, D., Buchan, D. A., Butch, C. A., Stewart, C. M., Krenzer, B. E., et al. (2001). Home monitoring service improves mean arterial pressure in patients with essential hypertension: a randomized, controlled trial. Annals of Internal Medicine, 134(11), 1024-1032, I1042.Pike, K. J., Rabius, V., McAlister, A., & Geiger, A. (2007). American Cancer Society's QuitLink: randomized Internet assistance. Nicotine & Tobacco Research, 9 (3), 415-420.

Rami, B., Popow, C., Horn, W., Waldhoer, T., & Schober, E. (2006). Telemedical support to improve glycemcontrol in adolescents with type 1 diabetes mellitus. European Journal of Pediatrics, 165 (10), 701-705.

Ross, S. E., Moore, L. A., Earnest, M. A., Wittevrongel, L., & Lin, C.-T. (2004). Providing a web-based online medical record with electronic communication capabilities to patients with congestive heart failure: randomiztrial. Journal of Medical Internet Research, 6(2), e12.Rotondi, A. J., Sinkule, J., & Spring, M. (2005). An interactive Web-based intervention for persons with TBI atheir families: use and evaluation by female significant others. Journal of Head Trauma Rehabilitation, 20(2)185.

AH-1209
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Health Satisfaction Self-efficacy Health Behavior

Outcomes

References, continued

Safren, S. (2002). Internet paging system helps improve adherence. Counseling is combined with reminders

Alert, 17 (9), 118-119.Sciamanna, C. N., Harrold, L. R., Manocchia, M., Walker, N. J., & Mui, S. (2005). The effect of web-based, personalized, osteoarthritis quality improvement feedback on patient satisfaction with osteoarthritis care. AmJournal of Medical Quality, 20(3), 127-137.Sciamanna, C. N., Novak, S. P., Houston, T. K., Gramling, R., & Marcus, B. H. (2004). Visit satisfaction and tailored health behavior communications in primary care. American Journal of Preventive Medicine, 26(5), 4

430.Shapiro, J. R., Reba-Harrelson, L., Dymek-Valentine, M., Woolson, S. L., Hamer, R. M., & Bulik, C. M. (2007Feasibility and acceptability of CD-ROM-based cognitive-behavioural treatment for binge-eating disorder. European Eating Disorders Review, 15 (3), 175-184.Southard, B. H., Southard, D. R., & Nuckolls, J. (2003). Clinical trial of an Internet-based case management system for secondary prevention of heart disease. Journal of Cardiopulmonary Rehabilitation, 23(5), 341-34Stinson, J. N., Petroz, G. C., Tait, G., Feldman, B. M., Streiner, D., McGrath, P. J., et al. (2006). e-Ouch: usatesting of an electronic chronic pain diary for adolescents with arthritis. Clinical Journal of Pain, 22(3), 295-3

Shaw, B. R., Han, J. Y., Baker, T., Witherly, J., Hawkins, R. P., McTavish, F., et al. (2007). How women with cancer learn using interactive cancer communication systems. Health Education Research, 22 (1), 108-119.

Shegog, R., Bartholomew, L. K., Parcel, G. S., Sockrider, M. M., Masse, L., & Abramson, S. L. (2001). Impacomputer-assisted education program on factors related to asthma self-management behavior. Journal of thAmerican Medical Informatics Association, 8(1), 49-61.Skeels, M. M., Kurth, A., Clausen, M., Severynen, A., & Garcia-Smith, H. (2006). CARE+ user study: usabiliattitudes towards a tablet pc computer counseling tool for HIV+ men and women. AMIA, Annual Symposium Proceedings/AMIA Symposium., 729-733.Smith, L., & Weinert, C. (2000). Telecommunication support for rural women with diabetes. Diabetes Educa26 (4), 645-655.Runge, C., Lecheler, J., Horn, M., Tews, J.-T., & Schaefer, M. (2006). Outcomes of a Web-based patient education program for asthmatic children and adolescents. Chest, 129 (3), 581-593.

AH-1210
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Quality of Life Support Withdrawals

References, continued

Wilkie, D. J., Huang, H. Y., Berry, D. L., Schwartz, A., Lin, Y. C., Ko, N. Y., et al. (2001). Cancer symptom cofeasibility of a tailored, interactive computerized program for patients. Family & Community Health, 24(3), 48

Taylor, Y., Eliasson, A., Andrada, T., Kristo, D., & Howard, R. (2006). The role of telemedicine in CPAP compliance for patients with obstructive sleep apnea syndrome. Sleep & Breathing, 10(3), 132-138.Tsang, M. W., Mok, M., Kam, G., Jung, M., Tang, A., Chan, U., et al. (2001). Improvement in diabetes contra monitoring system based on a hand-held, touch-screen electronic diary. Journal of Telemedicine & Teleca7 (1), 47-50.Turnin, M. C., Beddok, R. H., Clottes, J. P., Martini, P. F., Abadie, R. G., Buisson, J. C., et al. (1992). Telemexpert system Diabeto. New tool for diet self-monitoring for diabetic patients. Diabetes Care, 15 (2), 204-212

van den Berg, M. H., Ronday, H. K., Peeters, A. J., Voogt-van der Harst, E. M., Munneke, M., Breedveld, F. al. (2007). Engagement and satisfaction with an Internet-based physical activity intervention in patients with rheumatoid arthritis. Rheumatology, 46 (3), 545-552.

Strecher, V. J., Shiffman, S., & West, R. (2006). Moderators and mediators of a web-based computer-tailoresmoking cessation program among nicotine patch users. Nicotine & Tobacco Research, 8 Suppl 1 , S95-101

Stuart, G. W., Laraia, M. T., Ornstein, S. M., & Nietert, P. J. (2003). An interactive voice response system to enhance antidepressant medication compliance. Topics in Health Information Management, 24(1), 15-20.Williamson, D. A., Walden, H. M., White, M. A., York-Crowe, E., Newton, R. L., Jr., Alfonso, A., et al. (2006)Year Internet-Based Randomized Controlled Trial for Weight Loss in African-American Girls. Obesity, 14 (7)

1243.van den Brink, J. L., Moorman, P. W., de Boer, M. F., Pruyn, J. F. A., Verwoerd, C. D. A., & van Bemmel, J. (2005). Involving the patient: a prospective study on use, appreciation and effectiveness of an information syin head and neck cancer care. International Journal of Medical Informatics, 74(10), 839-849.

van der Meer, V., van Stel, H. F., Detmar, S. B., Otten, W., Sterk, P. J., & Sont, J. K. (2007). Internet-based management offers an opportunity to achieve better asthma control in adolescents. Chest, 132 (1), 112-119Weinert, C., Cudney, S., & Winters, C. (2005). Social support in cyberspace: the next generation. CIN: ComInformatics, Nursing, 23 (1), 7-15.

AH-1211
Appendix H. Data Table

Author Year

References

Armstrong, K., Weber

management decision

Artinian, N. T., Hardenmonitoring device for pAn, L. C., Perry, C. L.,intervention for collegeAndersson, G., Bergsttrial. British Journal ofAndersson, G., Carlbrand in vivo group expoAndersson, G., Lunds

361.

Andrade, A. S. A., McGadherence to highly acAndrewes, D. G., O'Codisorders. Australian &Araki, I., Hashimoto, Hdrinking behavior modAlbisser, A. M., Harris

self-care on outcomes

Anhoj, J., & Nielsen, L6 (3), e23.

Adverse Effects Other

References, continued

Winzelberg, A. J., Classen, C., Alpers, G. W., Roberts, H., Koopman, C., Adams, R. E., et al. (2003). Evaluaan internet support group for women with primary breast cancer. Cancer, 97 (5), 1164-1173.Woodruff, S. I., Edwards, C. C., Conway, T. L., & Elliott, S. P. (2001). Pilot test of an Internet virtual world chroom for rural teen smokers. Journal of Adolescent Health, 29(4), 239-243.

Yon, B. A., Johnson, R. K., Harvey-Berino, J., Gold, B. C., & Howard, A. B. (2007). Personal digital assistancomparable to traditional diaries for dietary self-monitoring during a weight loss program. Journal of BehavioMedicine, 30(2), 165-175.Zabinski, M. F., Wilfley, D. E., Pung, M. A., Winzelberg, A. J., Eldredge, K., & Taylor, C. B. (2001). An interainternet-based intervention for women at risk of eating disorders: a pilot study. International Journal of EatinDisorders, 30 (2), 129-137.Zimmerman, L., Barnason, S., Nieveen, J., & Schmaderer, M. (2004). Symptom management intervention in elderly coronary artery bypass graft patients. Outcomes Management, 8 (1), 5-12.

AH-1212
Appendix I: Study Quality Assessment Table with References

Randomized Controlled TrialsAuthor Year Random assignment?

Treatment allocation concealed? Groups similar at baseline?

Eligibility specified?

Andersson 2003 Unclear/NR Unclear/NR Yes YesAndersson 2006 Yes Unclear; NR Yes YesAndersson 2005 No No Yes Yes

Andrade 2005 Unclear No Yes YesAndrade 2001 Unclear NR NR No

Araki 2006 Unclear NR Yes Yes

Armstron

g 2005 Unclear/NR Yes Unclear Yes

Artinian 2003 Yes NR Yes Yes

Bachofen 1999 No No N

A No

Barnason 2006 No; use of lot drawing 

method

Unclear; NR Unclear Yes

Barnason 2003 No; drawin

g lot used Unclear; method NR Yes Yes

Barrera 2002 Unclear, implied NR Yes YesBlock 2004 Yes Unclear; NR No YesCarlbrin

g 2007 Yes No Yes Yes

Cawse

y 2000 NR NR NR Yes

Chan 2003 Yes; random numbers 

table

NR Yes Yes

Chen 2006 No N/

A Unclear Minimal

Cho 2006 Yes No Yes Yes

AI ‐ 1
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continued

Blinding? Author

Care provider blinded? Patient blinded? High withdrawal/attrition rate? 

Unclear; NR Andersson No No Yes; 14/44 (31.8%)NR Andersson N/A No No; 2/64 (3.1%)Unclear Andersson No No Yes; 32/117=27%NR

Andrade No No No

NR

Andrade No No NR

NR Araki N/A No No; 1 patient excluded from analys

(missin

g data on lab)

Yes

Armstron

g Yes Yes Yes; 5/32 (25%)

NR

Artinian No No NR

Unclear Bachofen No No No (0.09%)

Unclear Barnason No No Unclear; NRUnclear Barnason No No Unclear; NR

NR Barrera N/A No Yes; 37/160 (23%); but results of 

ANOVA showed that those who dropped out were comparable to those who were assessed at T2

NR Block N/

A No No; 10/491 (2%)

Unclear Carlbrin

g No No No

NR Cawse

y No No NR

No Chan No No NoNR Chen No No NRUnclear Cho No No NR

AI ‐ 2
Appendix I: Study Quality Assessment Table with References

Was the rate differential by group? 

Quality Rating

No; self-help+telephone=7/24 (29.2%), self-help alone=7/20 

(35.0%)

Fair

No; treatment=2/32 (6.2%), control=0

Fair

Yes; treatment=21/57 (36.8%) (main reason=treatment too demanding), control=11/60 (18.3%)

Poor

No Fair

NR PoorNo FairUnclear FairNR FairN

A Poor 

Unclear; NR PoorUnclear; NR Fair

No; 10/40 (25%) in the Support Group condition and 9/40 (22%) in each of the other three conditions

Fair

NR FairNo FairNR Fair-PoorNo GoodNR PoorNA Good

AI ‐ 3
Appendix I: Study Quality Assessment Table with References

Christensen 2004 Yes Yes No; significantly more years of 

eduction for one group compared to the others (15.0 vs 14.6 vs 14.4, P=0.04)

Yes

AI ‐ 

4
Appendix I: Study Quality Assessment Table with References

NR Christensen No No Yes; 111/525 (21.1%)

AI ‐ 5
Appendix I: Study Quality Assessment Table with References

Yes; Treatment group 1=29/165 (17.6%), Treatment group 2=61/121 (33.5%), control=21/178 (11.8%); but did an ITT

Fair

AI ‐ 

6
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continuedAuthor Year Random assignment?

Treatment allocation concealed? Groups similar at baseline?

Eligibility specified?

Cruz-Correia 2007 Yes NR NR, but crossover trial with patients 

serving as their own controls

Yes

Estabrooks 2005 No No Yes YesFranklin 2006 Yes NR Yes YesFrenn 2003 No N/A Yes for evaluable population, NR for 

initial population

No

Ge

ga 2004 N

A No Yes Yes

Glasgow 2000 Unclear Unclear; NR Yes YesGlasgow 2003 Unclear; NR Unclear; NR No; total cholesterol levels were 

si

gnificantl

y lower in the one 

group

Yes

Green 2008 Probably; NR No; serially-numbered 

envelopes

No: Significantly fewer females in the one 

group

Yes

Guendelman 2002 NR No Yes YesGustafson 1994 NR NR NR YesGustafson 2001 Unclear NR Yes YesGustafson 2001 Unclear Unclear; NR Unclear YesGustafson 1999 Yes Yes Yes No

AI ‐ 

7
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continued

Blinding? Author

Care provider blinded? Patient blinded? High withdrawal/attrition rate? 

NR Cruz-Correia N/A No No; 2/21 (9.5%)Unclear Estabrooks No No Yes

NR Franklin No No Somewhat; 14/92 (15.2%) were lo

follow-up or discontinued intervent

NR Frenn No No Yes; 34/137 (24.8%) for physical 

activity and 48/137 (35.0%) for diefat anal

yses

No Ge

ga No No Yes (29%)

No Glasgow No No No; 43/320 (13.4%)Unclear Glasgow No No Yes; 18%Yes Green No No No; 48/778 (6.2%)No Guendelman No No No; 9%NR Gustafson No No NRNo Gustafson NR NR Yes; 15.5 % in the control group (2

out of 148) and 17 % in the Intervention 

group (25 out of 147)

No Gustafson No No No; 19/265 (7.2%)NR Gustafson No No No; 21/219 (9.6%)

AI ‐ 

8
Appendix I: Study Quality Assessment Table with References

Was the rate differential by group? 

Quality Rating

Yes: Internet=2/9 (22.2%), Paper=0; but, crossover and analyzed combined treatments periods with noncompleters excluded from both groups

Fair

Yes Poor

Somewhat; Group 1=3/28 (10.7%), Group 2=6/33 (18.2%), Group3=5/31 (16.1%)

Fair

Unclear; distribution of dropouts NR

Poor

Unclear PoorYes; basic=13/80 (16.2%), basic+telephone=13/80 (16.2%), basic+community resources=5/80 (6.2%), combined=12/80 (15.0%)

Fair

No; differences described as "ns", but data N

R

Fair

No FairNo; health buddy gorup=4/66 (6.1%), asthma diary=8/68 

(11.8%)

Fair

NR Poor

No FairNo; control=7/132 (5.3%), experimental=12/133 (9.0%)

Fair

No; CHESS=13/107 (12.1%), control=8/97 (8.2%)

Fair

AI ‐ 

9
Appendix I: Study Quality Assessment Table with References

Holman 1996 Unclear Unclear/NR Unclear/NR No

AI ‐ 10
Appendix I: Study Quality Assessment Table with References

No Holman No No Yes; 16.7% (1 patient)

AI ‐ 11
Appendix I: Study Quality Assessment Table with References

Yes; 0% in on/off sequence, 33% in off/on sequence

Fair-Poor

AI ‐ 12
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continuedAuthor Year Random assignment?

Treatment allocation concealed? Groups similar at baseline?

Eligibility specified?

Jan 2007 NR No Unclear YesJapuntich 2006 Unclear; randomized- no 

details on approach

Unclear/NR Yes Yes

Jones 1999 NR NR NR YesJones 2001 Unclear NR Unclear; NR YesJoseph 2007 Yes; random number 

generator

Unclear Yes Yes

Kashem 2006 NR NR No; SBP and DBP were significantly 

higher in the control group

Yes

Kenwri

ght 2004 No No Yes Yes

Kenwright 2005 Yes; random numbers 

table

No; opaque envelopes contained numbers

Unclear Yes

Kim 2006 NR NR Unclear YesKim 2007 Yes NR Yes for completers (n=51); NR for all 

randomized participants (n=60)

Yes

Kosma 2005 Unclear/NR NR Yes YesKressi

g 2002 No No N

A Yes

Kwon 2004 Unclear NR Yes Yes

AI ‐ 13
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continued

Blinding? Author

Care provider blinded? Patient blinded? High withdrawal/attrition rate? 

NR Jan No No No; 20/184 (10.9%)No Japuntich No No Yes; Chess Group: 47.9%

Control Group: 43%

NR Jones No No No; 87/525 (16.6%)NR Jones No No Yes; 66/112 (58.9%); but conducte

ITT analysis using LOCF method

Unclear Joseph No No No; 13.0%NR Kashem No No NRNo Kenwri

ght No No Yes; (37% in London clinic)

No Kenwright No No Yes; Scheduled support- 13.6%

Requested support- 59%

NR Kim No No NR; but 100% included in analyses 

per Table 2

NR Kim No No No; 9/60 (15%)NR Kosma NR Somewhat Yes

50% non-completersMultiple disabilt

y

groups in stud

y

No Kressi

g No No No

NR Kwon No No No; 9/110 (8.2%)

AI ‐ 14
Appendix I: Study Quality Assessment Table with References

Was the rate differential by group? 

Quality Rating

Probably not; of 184 "eligible", 5 families "declined",then 15 more excluded due to "at the request of the participants" or "lack of data due to internet failure": intervention=9/97 (9.3%), control=6/82 (7.3%)

Fair

No Fair-PoorYes; personal computer=22/178 (12.3%), general computer=39/167 (23.3%), booklet information=26/180 

(14.4%)

Poor

No FairNo FairNR Fair-PoorUnclear Fair

Yes; 45.4% difference. 2-tailed Fisher's exact test score = .004

Fair- Poor

NR FairNo; intervention=5/30 (16.7%), control=4/30 (13.3%)

Fair

No PoorN

A Fair

No: intervention=4/55 (7.3%), control=5/55 (9.1%)

Fair

AI ‐ 15
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continuedAuthor Year Random assignment?

Treatment allocation concealed? Groups similar at baseline?

Eligibility specified?

LaFramboise 2003 NR NR Yes for completers (n=90); NR for all 

randomized participants (n=103)

Yes

Lahm 1996 No N/

A Yes No

Lee 2007 No No Probably, unclear YesLeu 2005 Yes Yes Unclear Yes

Levetan 2002 Yes NR Yes YesLorig 2006 Unclear Unclear- not reported Yes YesMcKay 2001 Unclear; NR Unclear Yes YesMcMahon 2005 Yes; random variables 

generator

Unclear Yes Yes

McPherson 2006 Unclear; method of 

sequence generation NR

Unclear No; computer group was older and 

had more knowledge at baseline

Yes

Meigs 2003 Unclear Yes Yes NoNoel 2004 Unclear NR More patients in intervention group 

had care

giver (23% vs 0)

Yes

Owen 2005 Yes NR No; participants in treatment group 

were significantly more likely than those assigned to the control group to have received some treatment with a chemotherapeutic agent

Yes

Pike 2007 NR NR Yes YesRami 2006 Unclear NR Yes Yes

Rasmussen 2005 Unclear/NR No Unclear Yes

AI ‐ 16
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continued

Blinding? Author

Care provider blinded? Patient blinded? High withdrawal/attrition rate? 

NR LaFramboise No No No; 12/103 (12.6%)No Lahm No No No; all 48 experiments completed NR Lee No No Yes; 25% to 28.5%, due to failing t

return or having invalid test resultsdiffered depending on outcome

No Leu No No Yes; 8/50 (16%)

NR Levetan No No No; 22/150 (14.7%)No Lorig No No Y: treatment: 22.5%; usual care: 1NR McKay No No No; 10/78 (12.8%)No McMahon No No Yes; 19.2%No McPherson No No No; 14/101 (13.9%)No Meigs No No UnclearUnclear Noel No No Unclear; attrition NRNo Owen No No No; 9/62 (14.5%)NR Pike No No Yes; 2951/4651 (45.7%); but also 

reported results of ITT anal

ysis

NR Rami No No No; 100% completedUnclear/NR Rasmussen No No Yes; 47/300 (15.7%)

AI ‐ 17
Appendix I: Study Quality Assessment Table with References

Was the rate differential by group? 

Quality Rating

Unclear; distribution of dropouts NR

Fair

N/

A Poor

No; intervention=36/144 (25%) to 42/144 (29.2%); control=35/144 (24.3%) to 42/144 (29.2%)

Poor

No Fair

No; 11/75 (14.7%) in each group

Fair

No FairYes; control=7/40 (17.5%) vs intervention=3/38 (7.9%)

Fair

Yes; Control=23.1%, Intervention=15.4%

Fair

No FairYes PoorUnclear; attrition NR FairNo; intervention=6/32 (18.7%) vs control=3/30 (10%)

Fair

No FairNo Fair

No; (15, 12, 20 subjects, respectivel

y; P= 0.26)

Fair

AI ‐ 18
Appendix I: Study Quality Assessment Table with References

Reid 2007 Yes Yes No; significantly more patients in the 

control group completed some postsecondar

y eduction (P=0.05)

Yes

AI ‐ 19
Appendix I: Study Quality Assessment Table with References

NR Reid No No No; 15/100 (15%)

AI ‐ 20
Appendix I: Study Quality Assessment Table with References

No; UC=8/50 (16%), IVR=7/50 (14%)

Fair

AI ‐ 21
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continuedAuthor Year Random assignment?

Treatment allocation concealed? Groups similar at baseline?

Eligibility specified?

Robertson 2006 NA No NA YesRobertson 2005 Unclear No Unclear Yes

Rodgers 2005 Unclear/NR Yes Yes YesRogers 2001 Probably Yes No; BMI significantly higher in home 

service group

Yes

Ross 2004 Yes Yes Yes YesRuland 2003 Unclear/NR Unclear/NR Yes YesSciamanna 2005 Unclear/NR Unclear/NR Yes YesShapiro 2007 Yes

Randomized block desi

gn

No No Yes

She

g

o

g 2001 Unclear Unclear; NR Yes No

Smith 2000 Unclear Unclear; NR Unclear; NR YesSouthard 2003 Yes Unclear/NR Yes YesStretcher 2006 Yes NR Yes YesStuart 2003 Unclear NR NR YesTaylor 2006 Yes NR No; significantly more single patients 

in one group (11% vs 0, p=0.01)

Yes

Tsang 2001 Unclear NR No; duration of illness significantly 

greater for one 

group

No

Turnin 1992 Unclear No Yes Yes

AI ‐ 22
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continued

Blinding? Author

Care provider blinded? Patient blinded? High withdrawal/attrition rate? 

Unclear Robertson No No Yes

22 % loss to follow up

Unclear Robertson No No Unclear

Yes Rodgers No No Yes; 440/1705 (25.8%), but 

performed ITT analysis with all patients with missin

g data

NR Rogers No No No; 10/121 (8.3%); dropouts includ

in analyses

NR Ross No No Yes for questionnaire returns at 12 

months: 26/107 (24.3%), but 15/26 (57.7%) of withdrawals were due tdeath and all deaths were included mortality and utilization of health services outcomes

NR Ruland No No No; 7/59 (11.9%)Yes Sciamanna No No N/A; all measurements and 

interventions were done during a sin

gle session

Yes Shapiro No No YesNR She

g

o

g No No No; 5/76 (6.6%)

Unclear; NR Smith No No Unclear/NRNo Southard No No No; 4/104=3.8%)Unclear Stretcher N

A Yes Yes

NR Stuart No No NRNR Taylor N/A No No; 7/121 (6%)No Tsang No No No=1/20 (5%)Unclear Turnin No No Yes; (16% in group A and 1.9% in 

group B in stud

y 1)

AI ‐ 23
Appendix I: Study Quality Assessment Table with References

Was the rate differential by group? 

Quality Rating

NA FairN

A Poor

No; control=179/853 (21.0%), active=261/852 (30.6%)

Good

No; home service=4/60 (6.7%), usual care=6/61 (9.8%)

Good

Yes: SPPARO=16/54 (29.6%), Control=10/53 (18.9%)

Good

Unclear; group assignment of withdrawals N

R

Fair

N/A FairYes GoodNR Fair

Unclear/NR PoorNo; SI=4/53 (7.5%), UC=0 FairNo FairNR PoorNo; TC=4/62 (6.4%), TM=3/59 (5.1%)

Fair

No: Group 1=0, Group 2=1/10 (10%)

Fair

Yes Fair

AI ‐ 24
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continuedAuthor Year Random assignment?

Treatment allocation concealed? Groups similar at baseline?

Eligibility specified?

Williamson 2006 Yes No Yes YesWinzelberg 2003 Unclear Unclear/NR Yes YesW

ylie- Rosett 2001 Yes Unclear Yes Yes

Zimmerman 2004 Unclear No NR Yes

AI ‐ 25
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trials, continued

Blinding? Author

Care provider blinded? Patient blinded? High withdrawal/attrition rate? 

No Williamson No No Yes; (24% in control and 36% 

in treatment)

Unclear/NR Winzelberg No No Yes; 14/72 (19.4%)Unclear W

ylie- Rosett No No Unclear

Unclear Zimmerman No No No

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Appendix I: Study Quality Assessment Table with References

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Appendix I: Study Quality Assessment Table with References

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omputer support on younger women with 

mpact of a computer-based support system for 

Medical Care, 604-608.

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cessation via the internet: a randomized clinical 

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trial of personalised computer based 

290), 835-840.

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9), 888-895.

AI ‐ 30
Appendix I: Study Quality Assessment Table with References

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Appendix I: Study Quality Assessment Table with References

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physical disabilities. 

using the internet. 

AI ‐ 32
Appendix I: Study Quality Assessment Table with References

Feasibility and acceptability of CD-ROM-based 

of a computer-assisted education program on 

8(1), 49-61.

onitoring service improves mean arterial 

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medical record with electronic communication 

2), e12.

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sonalized, osteoarthritis quality improvement 

f depression. Australasian Psychiatry, 14 (4), 

management system. Journal of Telemedicine & 

ults of a randomised trial of smoking cessation 

 2

6 (4), 645-655.

stem for secondary prevention of heart disease. 

smoking cessation program among nicotine 

with a monitoring system based on a hand-held, 

c expert system Diabeto. New tool for diet selfg cessation in patients with heart disease: a 

nhance antidepressant medication compliance. 

ance for patients with obstructive sleep apnea 

AI ‐ 33
Appendix I: Study Quality Assessment Table with References

Randomized Controlled Trial References, continued

Williamson, D. A., Walden, H. M., White, M. A., York-Crowe, E., Newton, R. L., Jr., Alfonso, A., et al. (2006). Two-Year Internet-Controlled Trial for Weight Loss in African-American Girls. Obesity, 14 (7), 1231-1243.

Zimmerman, L., Barnason, S., Nieveen, J., & Schmaderer, M. (2004). Symptom management intervention in elderly coronary ar

Outcomes Management, 8 (1), 5-12.Wylie-Rosett, J., Swencionis, C., Ginsberg, M., Cimino, C., Wassertheil-Smoller, S., Caban, A., et al. (2001). Computerized weioptimizes staff time: the clinical and cost results of a controlled clinical trial conducted in a managed care setting. Journal of the Association, 101 (10), 1155-1162; quiz 1163-1154.Winzelberg, A. J., Classen, C., Alpers, G. W., Roberts, H., Koopman, C., Adams, R. E., et al. (2003). Evaluation of an internet sprimary breast cancer. Cancer, 97 (5), 1164-1173.

AI ‐ 34
Appendix I: Study Quality Assessment Table with References

Randomized 

ass graft patients. 

ntervention 

n Dietetic 

roup for women with 

AI ‐ 35
Appendix I: Study Quality Assessment Table with References

AI ‐ 36
Appendix I: Study Quality Assessment Table with References

Cohort StudiesAuthor Year

Sufficient description of the groups?

Intervention/ treatment reliably ascertained? Groups comparable?

Adequate 

adjustmenconfoundfactors?

Alibisser 2001 No Unclear; usage metrics 

NR

Unclear; comparability of baseline characteristics NR, but possible differences in baseline HbA1c levels 

(9.5 vs 8.5)

No

An 2006 Yes No Yes for measured characteristics; 

unclear for unmeasured factors of motivation and readiness to quit

No

Gerber 2007 Yes Yes N

A

N

A

Kim 2006b Yes Yes Baseline characteristics provided for 

group; pre-post test one sample

NA

McClure 2006 Yes Yes No; newsletter recruitees were more 

likely to be female, caucasian, and older

No

McTavish 1995 Yes Yes N

A

N

A

Nakagawa 2000 Yes Yes Unclear NoNguyen 2005 Yes Yes Yes NARunge 2006 Yes No; 45.2% of ITT 

population did not use the intervention

No; significantly more males in control group (79.2% vs 54.7% vs 65.9%)

Unclear

Safren 2003 No No NR NRShaw 2007 Yes Yes Yes YesVan den Brink 2005 Yes Unclear N

A

N

A

Van der Meer 2007 Yes Yes Yes N

A

Weinert 2005 Yes Unclear; adherence NR Unclear; baseline characteristics 

reported only as combined across 

groups

No adjustmmade

AI ‐ 37
Appendix I: Study Quality Assessment Table with References

Cohort Studies, continued

Groups assembled at a similar point? Author

Blind to exposure status? Follow-up long enough? High withdrawal/attrition rate? 

Unclear Alibisser Unclear Yes; 1 year UnclearUnclear An Unclear Unclear No; 32/304 (10.5%)Yes Gerber Unclear Yes No

NA Kim NA No; 12 weeks 44 started;33 completed; 27% did 

complete (n=12 )

Unclear McClure Unclear Unclear; NR N/A; outcome measure is respons

rate

Yes McTavish Unclear Yes No

Yes Nakagawa No Unclear; 3 weeks Yes; 52.4% Unclear Nguyen Unclear Unclear; 3 months Yes; 7/23 (30.4%)Yes Runge NR Yes; 12 months Yes; 34.8%NR Safren NR Yes; 12 weeks NRYes Shaw Yes Unclear; 4 months Unclear/NRYes Van den Brink Unclear Yes NoNo Van der Meer Unclear Yes NoUnclear Weinert NR Yes; 18 months NR

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Appendix I: Study Quality Assessment Table with References

Rate differential by group? 

Quality Rating

Unclear PoorYes; for abstinance survey; beta=22/47 (46.8%), RealU=10/257 (3.9%)

Poor

N

A Fair

NA FairN/A; outcome measure is response rate

Fair

N

A Fair

N/A; historical controls selected, in part, based on availability of case notes and outcome data

Poor

Yes; booster group=5/12 (41.7%), primary=2/11 (18.2%)

Poor

Yes; control=20%, SPMP=63%, SPMP plus IEP=18.5%

Poor

NR PoorUnclear; NR FairNo FairN

A Fair

NR Poor

AI ‐ 39
Appendix I: Study Quality Assessment Table with References

Cohort Study References

Albisser, A. M., Harris, R. I., Albisser, J. B., & Sperlich, M. (2001). The impact of initiatives in education, self-management traininself-care on outcomes in diabetes disease management. Diabetes Technology & Therapeutics, 3 (4), 571-579.An, L. C., Perry, C. L., Lein, E. B., Klatt, C., Farley, D. M., Bliss, R. L., et al. (2006). Strategies for increasing adherence to an onintervention for college students. Nicotine & Tobacco Research, 8 Suppl 1 , S7-12.

Gerber, B. S., Solomon, M. C., Shaffer, T. L., Quinn, M. T., & Lipton, R. B. (2007). Evaluation of an internet diabetes self-manag

adolescents and young adults. Diabetes Technology & Therapeutics, 9 (1), 60-67.Shaw, B. R., Han, J. Y., Baker, T., Witherly, J., Hawkins, R. P., McTavish, F., et al. (2007). How women with breast cancer learn communication systems. Health Education Research, 22 (1), 108-119.

van den Brink, J. L., Moorman, P. W., de Boer, M. F., Pruyn, J. F. A., Verwoerd, C. D. A., & van Bemmel, J. H. (2005). Involving 

study on use, appreciation and effectiveness of an information system in head and neck cancer care. International Journal of M839-849.Kim, H., Kim, N., & Ahn, S. (2006b). Impact of a nurse short message service intervention for patients with diabetes. Journal of 266-271.McClure, J. B., Greene, S. M., Wiese, C., Johnson, K. E., Alexander, G., & Strecher, V. (2006). Interest in an online smoking cerecruitment strategies: results from Project Quit. Journal of Medical Internet Research, 8(3), e14.

McTavish, F. M., Gustafson, D. H., Owens, B. H., Wise, M., Taylor, J. O., Apantaku, F. M., et al. (1994). CHESS: An interactive 

with breast cancer piloted with an under-served population. Proceedings - the Annual Symposium on Computer Applications in Nakagawa, A., Marks, I. M., Park, J. M., Bachofen, M., Baer, L., Dottl, S. L., et al. (2000). Self-treatment of obsessive-compulsivand computer-conducted telephone interview. Journal of Telemedicine & Telecare, 6(1), 22-26.

Nguyen, H. Q., Carrieri-Kohlman, V., Rankin, S. H., Slaughter, R., & Stulbarg, M. S. (2005). Is Internet-based support for dyspn

patients with chronic obstructive pulmonary disease possible? Results of a pilot study. Heart & Lung, 34 (1), 51-62.Runge, C., Lecheler, J., Horn, M., Tews, J.-T., & Schaefer, M. (2006). Outcomes of a Web-based patient education program for adolescents. Chest, 129 (3), 581-593.

Weinert, C., Cudney, S., & Winters, C. (2005). Social support in cyberspace: the next generation. CIN: Computers, Informatics, 

Safren, S. A., Hendriksen, E. S., Desousa, N., Boswell, S. L., & Mayer, K. H. (2003). Use of an on-line pager system to increase medications. AIDS Care, 15 (6), 787-793.van der Meer, V., van Stel, H. F., Detmar, S. B., Otten, W., Sterk, P. J., & Sont, J. K. (2007). Internet-based self-management oachieve better asthma control in adolescents. Chest, 132 (1), 112-119.

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Appendix I: Study Quality Assessment Table with References

computer-assisted 

oking cessation 

raining program for 

nteractive cancer 

ent: a prospective 

formatics, 74(10), 

Care Quality, 21 (3), 

program and effective 

er system for women 

Care, 599-603.

er guided by manual 

management in 

tic children and 

2

3 (1), 7-15.

nce to antiretroviral 

opportunity to 

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Appendix I: Study Quality Assessment Table with References

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Appendix I: Study Quality Assessment Table with References

Case Series StudiesAuthor Year

Representative sample? Inclusion explicit?

Did individuals enter survey at a similar point?

Follow-up enough?

Brennan 2001 Unclear; selection 

methods NR

NR Unclear; medical history NR No; 1 wee

Cavan 2003 Unclear; how patients 

were selected

No;patients described as having Type I Diabetes

No; duration of diabetes ranged from 3 to 29 years

Unclear; 6 

Cross 2007 Yes Yes No; average duration of disease was 

5.8, with a SD of 5.1

Yes; 6 mo

Durso 2003 No; investigator selected No No; 40% took 

≥ 9 medications/day, 

30% managed with insulin, others treated with oral antidiabetics

Unclear; 3 

Ewald 2006 Yes Yes No; duration of hypertension ranged 

from 0 to 23 

years

Yes; 12 we

Ferrer-Roca 2004 Unclear Yes Unclear Yes

Finkelstein 1996 Yes Yes Yes Yes; 12 moGerber 2007 No Yes No Yes; 6 moGerbert 2003 No Yes NR Yes; single 

administra

Gustafson 1998 No Yes Yes Unclear; 1Labiris 2004 Unclear; recruitment 

methods NR

No Unclear; only age and gender reported Yes; 8 yea

Lieberman 2003 No; self-selected 

volunteers

Yes Unclear Yes

Ma 2006 No No Unclear; NR Yes; 3 moMcDaniel 2002 Unclear if selection was 

consecutive

Yes No No; 1 wee

Nguyen 2003 Unclear; selection criteria 

NR

N Unclear Unclear; 3 

Piette 1997 Yes Yes No Unclear; 4 

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Appendix I: Study Quality Assessment Table with References

Case Series Studies, continued

Outcomes assessed with objective criteria? Author

Quality Rating

Yes Brennan PoorNo Cavan PoorPartly; both laboratory and subjective measures used

Cross Fair

Yes Durso PoorYes Ewald FairYes Ferrer-Roca Poor

Yes Finkelstein GoodYes Gerber FairPartly Gerbert FairYes Gustafson FairUnclear; used "predetermined questionnaires, in Likert-scale forms

Labiris Poor

Yes Lieberman PoorYes Ma FairYes McDaniel FairParty Nguyen PoorYes Piette Fair

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Appendix I: Study Quality Assessment Table with References

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Appendix I: Study Quality Assessment Table with References

Case Series Studies, continuedAuthor Year

Representative sample? Inclusion explicit?

Did individuals enter survey at a similar point?

Follow-up enough?

Skeels 2006 Unclear No Unclear/NR No; single Stinson 2006 Yes Yes N/

A No; 1 sess

van den Berg 2007 No Yes No Yes; 12 movan der Meer 2007 Yes Yes N/

A No; 1 mon

Wilkie 2001 No Yes Unclear Unclear; 1 Woodruff 2001 No Yes Unclear Unclear; 3 Zabinski 2001 Unclear Yes Unclear Unclear; 1

AI ‐ 46
Appendix I: Study Quality Assessment Table with References

Case Series Studies, continued

Outcomes assessed with objective criteria? Author

Quality Rating

Yes Skeels PoorYes Stinson Fair

Party: Yes for technology usage, No for satisfaction

van den Berg Fair

Yes van der Meer FairYes Wilkie FairNo Woodruff PoorYes Zabinski Fair

Case Series Study References

Cross, R. K., & Finkelstein, J. (2007). Feasibility and acceptance of a home telemanagement system in papilot study. Digestive Diseases & Sciences, 52 (2), 357-364.

Durso, S. C., Wendel, I., Letzt, A. M., Lefkowitz, J., Kaseman, D. F., & Seifert, R. F. (2003). Older adults upilot study. MEDSURG Nursing, 12 (5), 313-317.

Ewald, S., vor dem Esche, J., Uen, S., Neikes, F., Vetter, H., & Mengden, T. (2006). Relationship between 

and blood pressure reduction with antihypertensive therapy : results of the OLMETEL (OLMEsartan TELemInvestigation, 26 (8), 439-446.Ferrer-Roca, O., Cardenas, A., Diaz-Cardama, A., & Pulido, P. (2004a). Mobile phone text messaging in thTelemedicine & Telecare, 10 (5), 282-285.Finkelstein, S. M., Snyder, M., Edin-Stibbe, C., Chlan, L., Prasad, B., Dutta, P., et al. (1996). Monitoring pradherence. Journal of Medical Engineering & Technology, 20(6), 203-210.Gerber, B. S., Solomon, M. C., Shaffer, T. L., Quinn, M. T., & Lipton, R. B. (2007). Evaluation of an interneadolescents and young adults. Diabetes Technology & Therapeutics, 9 (1), 60-67.Gerbert, B., Berg-Smith, S., Mancuso, M., Caspers, N., McPhee, S., Null, D., et al. (2003). Using innovativbrief smoking and alcohol intervention. Health Promotion Practice, 4 (3), 249-261.Cavan, D. A., Everett, J., Plougmann, S., & Hejlesen, O. K. (2003). Use of the Internet to optimize self-mawith DiasNet. Journal of Telemedicine & Telecare, 9 Suppl 1, S50-52.

Brennan, P. F., Moore, S. M., Bjornsdottir, G., Jones, J., Visovsky, C., & Rogers, M. (2001). HeartCare: an patient home recovery after coronary artery bypass graft (CABG) surgery. Journal of Advanced Nursing, 3

AI ‐ 47
Appendix I: Study Quality Assessment Table with References

s with inflammatory bowel disease: a 6-month 

cellular telephones for diabetes management: a 

frequency of blood pressure self-measurement 

toring blood pressure) study. Clinical Drug 

anagement of diabetes. Journal of 

ss after lung transplantation from home-patient 

betes self-management training program for 

deo doctor technology in primary care to deliver 

ement of type 1 diabetes: preliminary experience 

ernet-based information and support system for 

 699-708.

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Appendix I: Study Quality Assessment Table with References

Case Series Study References, continued

van der Meer, V., van Stel, H. F., Detmar, S. B., Otten, W., Sterk, P. J., & Sont, J. K. (2007). Internet-based self-management oachieve better asthma control in adolescents. Chest, 132 (1), 112-119.Wilkie, D. J., Huang, H. Y., Berry, D. L., Schwartz, A., Lin, Y. C., Ko, N. Y., et al. (2001). Cancer symptom control: feasibility of a computerized program for patients. Family & Community Health, 24(3), 48-62.Woodruff, S. I., Edwards, C. C., Conway, T. L., & Elliott, S. P. (2001). Pilot test of an Internet virtual world chat room for rural teeAdolescent Health, 29 (4), 239-243.Zabinski, M. F., Wilfley, D. E., Pung, M. A., Winzelberg, A. J., Eldredge, K., & Taylor, C. B. (2001). An interactive internet-based risk of eating disorders: a pilot study. International Journal of Eating Disorders, 30(2), 129-137.Piette, J. D., & Mah, C. A. (1997). The feasibility of automated voice messaging as an adjunct to diabetes outpatient care. DiabeSkeels, M. M., Kurth, A., Clausen, M., Severynen, A., & Garcia-Smith, H. (2006). CARE+ user study: usability and attitudes towacounseling tool for HIV+ men and women. AMIA, Annual Symposium Proceedings/AMIA Symposium., 729-733.Stinson, J. N., Petroz, G. C., Tait, G., Feldman, B. M., Streiner, D., McGrath, P. J., et al. (2006). e-Ouch: usability testing of an efor adolescents with arthritis. Clinical Journal of Pain, 22(3), 295-305.

van den Berg, M. H., Ronday, H. K., Peeters, A. J., Voogt-van der Harst, E. M., Munneke, M., Breedveld, F. C., et al. (2007). En

with an Internet-based physical activity intervention in patients with rheumatoid arthritis. Rheumatology, 46 (3), 545-552.

Lieberman, D. Z. (2003). Determinants of satisfaction with an automated alcohol evaluation program. CyberPsychology & Beha

Ma, C., Warren, J., Phillips, P., & Stanek, J. (2006). Empowering patients with essential information and communication supporInternational Journal of Medical Informatics, 75(8), 577-596.McDaniel, A. M., Hutchison, S., Casper, G. R., Ford, R. T., Stratton, R., & Rembusch, M. (2002). Usability testing and outcomes program to promote smoking cessation in low income women. Proceedings / AMIA, Annual Symposium., 509-513.Nguyen, H. Q., Carrieri-Kohlman, V., Rankin, S. A., Slaughter, R., & Stulbarg, M. S. (2003). Pilot study of an online dyspnea selCOPD. AMIA, Annual Symposium Proceedings/AMIA Symposium., 951.

Labiris, G., Papadopoulis, G., Lentaris, G., Kafentzis, M., & Krakakis, A. (2004). Internet based counseling to remote orthopedic 

Scandinavica, 75 (3), 366-367.Gustafson, D. H., McTavish, F., Hawkins, R., Pingree, S., Arora, N., Mendenhall, J., et al. (1998). Computer support for elderly wJAMA, 280 (15), 1305.

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Appendix I: Study Quality Assessment Table with References

Abbreviations for all Quality Assessments

opportunity to 

, interactive 

ers. Journal of 

ntion for women at 

e, 2

0 (1), 15-21.

blet pc computer 

c chronic pain diary 

nt and satisfaction 

), 677-682.

ontext of diabetes. 

teractive computer 

ement program for 

. Acta Orthopaedica 

with breast cancer. BMI = body mass index

CIT = conventional insulin therapyCTRL = controlIIT = individualized insulin therapyITT = Intention to treat analysisIVR = interactive voice responseJAMA = Journal of the American Medical AssociationNA=not applicableNR=not reportedNS=not significantRA = Rheumatoid ArthritisSD = standard deviationSDS = standard deviation scoresSI = special interventionUC = usual careWLC = weight list control

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Appendix I: Study Quality Assessment Table with References

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