JMIR MEDICAL INFORMATICS

Sawesi et al

Original Paper

The Impact of Information Technology on Patient Engagementand Health Behavior Change: A Systematic Review of the
Literature
Suhila Sawesi1, B Pharm, MPharmSc, PhD (c); Mohamed Rashrash2, B Pharm, MPharm, PhD; Kanitha Phalakornkule1,
MSc, PhD (c); Janet S Carpenter3, RN, PhD; Josette F Jones1, RN, PhD
1School of Informatics and Computing – Indianapolis, Department of BioHealth Informatics, IUPUI, Indianapolis, IN, United States

2

School of Pharmacy, Chapman University, Irvine, CA, United States

3

Indiana University School of Nursing, Science of Nursing Care Department, Indiana University, Indianapolis, IN, United States

Corresponding Author:
Suhila Sawesi, B Pharm, MPharmSc, PhD (c)School of Informatics and Computing – Indianapolis
Department of BioHealth Informatics
IUPUI
Walker Plaza (WK) 120
719 Indiana Avenue
Indianapolis, IN, 46202
United States
Phone: 1 317 274 8059
Fax: 1 317 274 8059
Email: ssawesi@umail.iu.edu

Abstract
Background: Advancements in information technology (IT) and its increasingly ubiquitous nature expand the ability to engage
patients in the health care process and motivate health behavior change.
Objective: Our aim was to systematically review the (1) impact of IT platforms used to promote patients’ engagement and toeffect change in health behaviors and health outcomes, (2) behavior theories or models applied as bases for developing theseinterventions and their impact on health outcomes, (3) different ways of measuring health outcomes, (4) usability, feasibility, andacceptability of these technologies among patients, and (5) challenges and research directions for implementing IT platforms tomeaningfully impact patient engagement and health outcomes.
Methods: PubMed, Web of Science, PsycINFO, and Google Scholar were searched for studies published from 2000 to December
2014. Two reviewers assessed the quality of the included papers, and potentially relevant studies were retrieved and assessed for
eligibility based on predetermined inclusion criteria.
Results: A total of 170 articles met the inclusion criteria and were reviewed in detail. Overall, 88.8% (151/170) of studies
showed positive impact on patient behavior and 82.9% (141/170) reported high levels of improvement in patient engagement.
Only 47.1% (80/170) referenced specific behavior theories and only 33.5% (57/170) assessed the usability of IT platforms. The
majority of studies used indirect ways to measure health outcomes (65.9%, 112/170).
Conclusions: In general, the review has shown that IT platforms can enhance patient engagement and improve health outcomes.
Few studies addressed usability of these interventions, and the reason for not using specific behavior theories remains unclear.
Further research is needed to clarify these important questions. In addition, an assessment of these types of interventions should
be conducted based on a common framework using a large variety of measurements; these measurements should include thoserelated to motivation for health behavior change, long-standing adherence, expenditure, satisfaction, and health outcomes.
(JMIR Med Inform 2016;4(1):e1) doi: 10.2196/medinform.4514
KEYWORDS
patient engagement; patient behavior; technology; Internet; web-based; cell phone; social media

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Introduction
Patient engagement is currently considered the cornerstone of
the health care system revolution for its positive impact on
health outcomes and health care costs [1,2]. A growing bodyof evidence demonstrates that lack of patient engagement is amajor contributor to preventable deaths. In fact, it is estimatedthat 40% of deaths in the United States are caused by modifiablebehavioral issues, including smoking, obesity, poor blood sugar
control, poor blood pressure control, inadequate exercise,
medication non-adherence, and neglect in attending follow-up
medical appointments [3]. As a result, patients must be
encouraged to become more involved with managing their own
care. Frequent, real-time communication and feedback areessential in supporting health behavior change and empowering
patient engagement in the health care process [4]. However, thetraditional model of care delivery, a face-to-face interaction
with an expert or trusted health care provider, can be
implemented only with a small number of patients and thus has
limited impact and limited reach [5]. In an effort to reach andengage larger numbers of patients, researchers and clinicians
have begun exploring the role of information technology (IT)platforms in patient engagement and health behavior changeinterventions [5,6]. It is assumed that face-to face interactionin the traditional model can be mimicked by peer-to-peer orpeer group support in social media.
IT platforms are being embraced as a way to enhance patientengagement in the health care process, improve quality of care,
support health care safety, and provide cost-effective healthservices for patients [6-9]. Numerous IT platforms are used tomotivate patient engagement in health behavior change including
short message service (SMS)-capable mobile devices,
Internet-based interventions, social media, and other online
communication tools [10-12]. Previous systematic reviews haveevaluated the potential benefit of IT platforms in managing
different health conditions and how these platforms have beenused to actively engage patients and change unhealthy patient
behavior. A systematic review conducted to assess theeffectiveness of IT platforms on physical activity and dietarybehavior change found that 51% of studies showed positive
results, although a significant proportion of the studies showedno significant effect [13]. The reviewed interventions tended tofocus on specific technology (eg, desktop applications), whilemobile devices, such as mobile phones and text messaging
devices were not included. Similarly, Webb et al reviewed 85
studies on the impact of Internet-based interventions on healthbehavior change and found small but significant effects on
health-related behavior, especially with regards to interventions
grounded in behavioral theory. Although the review mentionedthat the effectiveness of Internet-based interventions wasenhanced by using additional IT methods, such as text
messaging (SMS), it did not focus on the distinction between
these different interventions [8].
In addition, a meta-analysis performed to investigate theeffectiveness of Web-based interventions on health behavior
changes found that Web-based interventions improve patient
outcomes. This particular meta-analysis, however, referred only
to Web-based interventions in specific problem areas andhttp://medinform.jmir.org/2016/1/e1/

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Sawesi et alfocused on a relatively narrow range of technologies [14]. A
recent systematic review that investigated the effectiveness of
the IT platform on self-management among diabetic patients
showed positive effects in 74% of studies [15]. Another researchstudy showed that successful health behavior interventions may
contribute to understanding of health behavior theories and theirappropriate use [16]. Mobile-based interventions and Web-based
interventions developed based on health behavior theories are
more likely to effectively change patient health behavior andmaintain behavior change than non-theory-based interventions[8,17-19]. Basing IT interventions on behavior theories can help
test and detect why interventions succeed or fail [20]. Healthbehavioral theories can identify key determinants of the target
behaviors and identify behavior change strategies essential toobtain desired health outcomes; this knowledge can then betransformed into specific behavioral strategies that patients canadapt in their daily life [20].
Conclusions drawn from these reviews are important; they
provide insights but no clear answers about the effectivenessof IT platforms on patient engagement and behavior change.
They do not address which interventions are used most or are
most effective with which theory or model when it comes toimproving patients’ health behaviors and patient engagement.
IT platforms generally can have high potential benefits andsome proven effects; however, specific components in several
health conditions associated with success remain unclear. To
better understand how to build a successful intervention thatcan engage patients to change their behavior meaningfully, we
performed a systematic review.
Review aims were to systematically determine (1) the impactof IT platforms used to promote patient engagement and toeffect change in health behaviors and health outcomes, (2)behavioral theories or models applied as bases for developing
these interventions and their impact on health outcomes, (3)different ways of measuring health outcomes, (4) usability,
feasibility, and acceptability of these technologies among
patients, and (5) challenges and research directions for
implementing IT platforms to meaningfully impact patientengagement and health outcomes.

Methods
Search Strategy and Data Sources
Electronic literature searches were performed using four
databases: PubMed, Web of Science, PsycINFO, and Google
Scholar. Google Scholar was searched because it had sufficiently
wide coverage to be used instead of several databases [21-23].
The reference lists of retrieved articles from searches werescreened for additional articles. Searches used the following
medical subject headings (MeSH) terms in various
combinations: patient engagement, health, promotion, behavior,digital, technology, email, Internet, Web-based, cell phone,social media, computer, and intervention.

Inclusion and Excluding Criteria
The following criteria were used to select the articles: (1) all
types of study designs published in scientific journals between
2000 and December 2014 were included, excluding conference
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proceedings, book chapters, reviews, dissertations, andprotocols. (2) studies that evaluated and reported the impact ofhealth information technology platforms on patients’ healthoutcome, (3) studies that focused on disease management rather
than more general health promotion including but not limited
to patient education, symptom monitoring, medication
adherence, diet, and physical activity, (4) studies that addressed
patient engagement and health-related behavior change throughthe use of IT platforms such as social networking sites, mobile
telephony, video and teleconferencing, email, SMS, andelectronic monitoring, (5) studies that explored different factors
affecting patient engagement and health behavior change were
excluded, (6) studies that were published in languages otherthan English were excluded, (7) studies where the patient was
not the main actor (ie, studies that were clinician-focused), and(8) the methodological quality (see Multimedia Appendix 1) ofarticles was evaluated to establish their inclusion in the review
using 10 items adopted from Critical Appraisal Skills
Programme (CASP) [24,25]. The criteria that were used in thequality assessment included (1) study name, (2) aims clearlystated, (3) appropriate research design, (4) appropriate
recruitment strategy, (5) theories clearly stated, (6) usability
tested within the study, (7) patient engagement part of study,(8) appropriate data collection method, (9) data analysis
sufficiently rigorous, and (10) findings clearly stated. After thecompletion of the methodological quality assessment, the studies
that met the criteria for the categories of “good” were reviewed(ie, bad=0-33%, satisfactory=34-66%, and good=67-100%)[26].

Data Extraction
Two investigators independently reviewed the titles and thenabstracts. The same investigators read and screened for full text
eligibility. Data extraction was carried out by 1 reviewer andwas rechecked for accuracy by another reviewer. The reasonsfor exclusion were recorded. Discrepancies were resolved byjoint probability of agreement (0.98) [15].
A meta-analysis was not feasible due to the varying datacollection methods and outcome measures. Therefore, eligible

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Sawesi et al
studies were broken down and evaluated in a narrative format
using some statistical analysis when feasible and summarized
systematically according to the following key information
abstracted from them: study details (including author name,year, country, and study design); study characteristics (including
sample size and condition/disease); intervention details(including technology used and duration); and outcome details(including direct and indirect assessment methods); and impact
of intervention, usability assessment, patient engagement, andtheory used in interventions classified according to Leventhal(biomedical model, behavioral learning, communicative,
cognitive theory, and self-regulative) [27-29].
The outcomes variable was classified into (1) positive impact
in which health information technology platform was associatedwith improvement in one or more aspects of care and (2) no
impact or no noticeable improvement or change in healthoutcomes. This was assessed based on the overall conclusion
made by the authors of each study. Most studies used statisticalmethods to test hypotheses or describe quantitative findings.
Patient engagement was measured based on the overall
conclusion. This was usually measured by timed patient log-ins,
communication with the health care provider via secure message,or data download.

Results
Search and Selection Results
Figure 1 shows the flow chart that describes the process ofidentifying the relevant literature. A separate comprehensive
search using 4 databases yielded 2235 articles. Following
removal of duplicates, our search identified 786 potentiallyrelevant articles. These were scanned keeping 219 papers forfull reading at full text level, of which 59 were screened andrejected, leaving 160 studies to be included in the review. Ten
additional papers were included from the reference lists ofretrieved articles. A total of 170 articles matched the initial
search criteria.

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Figure 1. Flow diagram of included and excluded studies.

Article Characteristics
Table 1 provides a summary of the studies, and Multimedia
Appendix 2 summarizes the 170 articles included in the researchstudy and their characteristics. Multimedia Appendix 1
summarizes the quality assessment of the 170 included articles.
Multimedia Appendix 3 contains definitions of terms used in
the paper. Different categories of IT platforms were identified
including Internet-based interventions (50.6%, 86/170),
mobile-based interventions (25.9%, 44/170), social media (9.4%,
16/170), video game technology (3.5%, 6/170), andtelemonitoring (10.6%, 18/170). Publication years ranged from2000 to 2014, with an overall increase in articles published more
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recently (21.8%, 37/170 in 2014). The majority of studies were
implemented in the United States (54.7%, 93/170). With respect
to the different targeted disorders, hormonal disorders weremost frequently targeted (22.4%, 38/170 studies, eg, diabetes).
The literature was dominated by randomized controlled trials(65.9%, 112/170). The duration of these studies ranged from 1
week to 48 months, and sample sizes ranged from 1-22,337
subjects. Articles included in this review were categorized infive topics based on study aims: impact of IT platform on healthoutcomes, patient engagement in health behavior change, theoryof health behavior, ways to assess health outcomes, and usability
assessment (see Table 1).

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Table 1. Summary of the review results based on types of IT platforms.
Internet (N=86)

Phone (N=44)

Video game (N=6)

Social network(N=16)

Tele-monitoring(N=18)

Brain, spinal cord, 7 (8)and nerve disorders

1 (2)

2 (33)

1 (6)

1 (6)

Cancer

5 (8)

2 (5)

1 (17)

2 (13)

2 (11)

Disorders of nutrition andmetabolism

13 (15)4 (9)1 (17)

2 (13)1 (6)Health condition, n (%)
Bone, joint, andmuscle disorders

3 (3)Ears, nose, andthroat disorders

1 (2)Eye disorders

1 (6)Health hazard

5 (6)

6 (14)

Heart and bloodvessel disorders

5 (6)

3 (7)

6 (33)

Hormonal disorders 20 (23)

11 (25)4 (25)

3 (17)

Immune disorders

4 (5)5 (11)

1 (6)

1 (6)

Lung and airway
disorders

2 (2)

1 (2)

1 (17)

1 (6)

1 (6)

Mental health disor- 12 (14)
ders

4 (9)

1 (17)

2 (13)

2 (11)

Skin disorders

1 (2)Women’s health is- 3 (3)
sues

1 (2)

Not specified

7 (8)

4 (9)

Australia

7 (8)5 (42)

1 (6)

2 (13)Country, n (%)

Austria

1 (6)Bangladesh

1 (2)Canada

4 (5)

Chile

1 (1)

2 (11)

China

1 (2)

France1 (2)Germany

3 (3)Israel

1 (6)

Italy

1 (1)

Japan

1 (1)

Kenya
Korea

1 (1)

1 (6)

1 (2)
4 (5)

1 (17)
2 (5)Norway1 (2)http://medinform.jmir.org/2016/1/e1/

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1 (2)New Zealand

PolandXSL• FO

1 (6)
1 (2)

Malaysia
Netherlands

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Internet (N=86)

RussiaVideo game (N=6)

Social network(N=16)

Tele-monitoring(N=18)1 (2)Slovenia

1 (1)South Korea

2 (2)

Spain
SwedenPhone (N=44)

4 (5)
1 (2)

1 (6)

2 (2)

Switzerland

1 (6)Taiwan1 (1)United Kingdom

5 (6)7 (16)

1 (17)

United States

53 (62)14 (32)4 (67)

14 (88)8 (44)

14 (78)Victoria

1 (2)Vietnam

1 (2)

1 (6)

Study design, n (%)
Randomized controlled trial

55 (64)34 (30)2 (33)

7 (44)

Case study

2 (2)

1 (2)

2 (33)

2 (13)

Cohort study

10 (12)4 (5)1 (17)

1 (6)

3 (17)

Cross-sectional
analysis

8 (9)

1 (2)

5 (31)1 (6)Quasi-experimental 11 (13)
trial

4 (5)

1 (17)

1 (6)Ways to measure health outcomes, n (%)
Direct28 (33)20 (45)

3 (50)1 (6)

6 (33)

Indirect58 (67)24 (55)

3 (50)

15 (94)

12 (67)Yes

75 (87)41 (93)6 (100)13 (81)16 (89)No

11 (13)3 (7)3 (19)

2 (11)Yes

38 (44)8 (18)

1 (17)

8 (50)

3 (17)

No

48 (56)36 (82)5 (83)

8 (50)

15 (83)Yes

68 (79)38 (86)6 (100)13 (81)16 (89)No

18 (21)6 (14)

3 (19)

2 (11)

Impact of technology, n (%)

Usability assessment, n (%)

Patient engagement, n (%)

Theory of behavior change, n (%)
Biomedical theory(chronic model)

1 (1)

1 (6)

Behavioral learning 3 (3)theoryCommunication
5 (6)(social support theory)

5 (11)

Cognitive theorya 40 (47)
(TPB, SOC, TTM,
self-efficacy, information motivation,and behavioral
skill)

9 (20)

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2 (13)

2 (33)

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Internet (N=86)

Phone (N=44)

Video game (N=6)

Social network(N=16)

1 (17)

2 (13)

Tele-monitoring(N=18)

Self-regulatory

6 (7)Not specified

31 (36)30 (69)3 (33)

10 (63)16 (88)

Min.

1

2

6

51

10

Max.

13564

22337

375

1754

784

Min.

1 mo

1 mo

1 mo

1 wk

2 mo

Max.

48 mo

16 mo

3 mo

36 mo

39 mo

Not specified

3

1

1

3

1

Sample size, n

DurationaTPB= theory of planned behavior, SOC=stage of change, TTM= transtheoretical model.

Impact of IT Platforms on Health Outcomes
Overall, IT platforms have been shown to improve healthbehavior among different disease categories (88.8%, 151/170),
although the majority of the positive impact has been shown
among hormonal disorders (20.6%, 35/170) (see Table 2).
Among studies utilizing Internet-based platforms, 87% (75/86)
of studies showed a significant impact on health outcomes.
Studies also showed that the use of Internet-based tailoredweight control programs was correlated with significant
increases in weight loss [30,31] and walking distance (P<.05)[32]. Similarly, mobile-based platforms showed significant
effects on health outcomes (91%, 40/44). For example, a study
examined use of text messages among patients with diabetes
and found a significant decrease in HbA1C level, improved
medication adherence, and decreased in emergency service use[33]. Social media showed a positive impact on health outcomes(81%, 13/16). For example, one study indicated that Twitter
usage among cancer patients was a valuable medium for sharing
information, discussing treatments, and also acted as a
psychological support [34]. The use of Facebook has also beenfound to help improve asthma care [35]. As such, this review
found that 100% (6/6) of studies had a positive impact on patient
health behavior when implementing a video game as anintervention to change health behavior. In a specific example,
King et al concluded that video games can be implemented
successfully among hyperfunctional voice disorder as a “voice
therapeutic protocol”, a voice and speech therapy program
including a set of vocal tasks using syllable repetitions andchanting of songs and phrases [36]. Furthermore, the literature

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showed that telemonitoring improved health outcomes (89%,16/18). One telemonitoring-based study assessed the effects ofa glucose monitoring system on HbA1c levels in diabetic
patients and found that usage of this system was correlated witha significant decrease in HbA1c (P=.001) [37]. Another study
evaluated the impact of home-based telemonitoring on patientswith heart failure and showed a significant correlative
improvement in patients’ health outcomes [38,39].
In contrast, 11% of studies (19/170) showed no impact of using
IT platforms on health behavior. Among studies using
Internet-based platforms, 13% (11/86) did not find significant
results. One study using a Web-based behavior change program
found no differences in smoking abstinence rates at 3- and6-month follow-up assessment [40] and no maintenance of
weight loss in an Internet-based intervention group comparedto the study’s control group [41]. Also, 7% of (3/44) mobile
phone studies reported non-significant impact [33,42-44]. Two
mobile phone platform studies did not find a significant
reduction in HbA1c level among diabetic patients when SMS
text messaging was used to manage their health care (P<.10)[33,34]. Moreover, 18% (3/16) of studies showed undesirable
effects from using social media [35,45,46]. For instance, Kaplan
et al found that psychiatric patients who participated in Internet
peer support reported higher levels of distress compared to those
who did not participate [45]. The literature shows that 12%(2/18) of telemonitoring studies had no effect on healthoutcomes. One particular study found significant changes inneither readmission rate [47] nor medication adherence [48]among patients with heart failure.

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Table 2. Impact of IT platforms among different disorders (Yes=positive impact, No=no impact).
Disorders

Impact of IT platforms, n (%)
Internet
Yes

MobileNo

Yes

Bone, joint, and muscle

3 (3)Brain, spinal cord, andnerves

7 (8)

1 (2)

Cancer

5 (6)

2 (5)

Nutrition and metabolism

10 (12)

4 (9)

3 (3)

Ears, nose, and throat

No

Social media

Tele-monitoring

Video
game

Yes

Yes

Yes

No

1 (6)

Total no Total

3 (2)

3 (2)

2 (33)

11 (6)

2 (13)

2 (11)

1 (17)

12 (7)

2 (13)1 (6)

1 (17)

18 (11)

1 (2)
1 (6)

Health hazard

4 (5)

1 (1)

6 (14)

Heart and blood vessel

4 (5)

1 (1)

3 (7)

Hormonal

19 (22)1 (1)9 (20)

Immune system

2 (2)

2 (2)

5 (11)

Lung and airway

2 (2)Mental health

11 (13)

Not specified

5 (6)Women’s health

3 (3)

Total

75 (87)4 (25)
1 (6)

12 (7)3 (3)

21 (12)

1 (1)

1 (1)

1 (1)

1 (1)
1 (1)

11 (6)

11 (6)3 (2)

14 (8)

3 (17)

35 (21)3 (2)38 (22)

1 (6)

8 (5)

3 (2)

11 (6)

2 (11)

1 (6)

1 (6)

1 (17)

6 (4)

1 (1)

4 (9)

2 (13)

2 (11)

1 (17)

20 (12)1 (1)21 (12)

2 (2)

4 (9)

1 (6)

10 (6)3 (2)

13 (8)

1 (2)

1 (6)

1 (6)

1 (2)
11 (13)41 (93)3 (7)

13 (81)

Patient Engagement
In total, 82.9% (141/170) of studies reported improvement inpatient engagement after using IT platforms (see Table 3).
Among Internet-based interventions, 79% (68/86) of studies
reported a high level of patient engagement. For example, a
research study reported that human immunodeficiency virus
patients used the Internet-based intervention a majority of thetime to access information and manage their health [49,50].
Among studies using mobile-based interventions, 86% (38/44)
reported improvement in patient engagement. One mobile-based
intervention study found that text messaging enhanced
successful engagement of diabetic patients in their own healthcare. Patients were able to use this study’s text message systemfor clinical data queries and communicating with health careproviders [51]. Similarly, 81% (13/16) of studies reported thatsocial media was helpful in improving patient engagement. One
study found that Facebook provided a forum for reporting
personal experiences, asking questions, and receiving direct

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12 (7)

10 (6)
4 (22)
2 (5)

1 (1)

1 (2)

Skin

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Total
yes

1 (6)

Eye

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3 (19)

16 (89)2 (11)

6 (100)

6 (4)

2 (1)

2 (1)

4 (2)

4 (2)

151 (89) 19 (11)170 (100)

feedback for people living with diabetes [46]. Another study
showed that social media was helpful to individuals with lower
patient activation [52-54]. In addition, it was found that video
games could enhance patients’ active participation in the healthcare process (100%, 6/6). One video game-based study
demonstrated that a health-based video game could help build
an effective client-therapist relationship, help structure sessions,and improve patient engagement in the therapeutic process[55,56]. Likewise, the literature showed that telemonitoring has
been particularly useful for improving patient engagement
remotely (88.8%, 16/18) [57-63], as traditional
point-of-care-based ways to monitor patients are costly anddifficult to implement [64].
Overall, analysis showed significant correlations between patientengagement in health care and the impact of IT platforms(χ21=39.8836, P .001). Only Internet-based platforms had asignificant association between patient engagement and impact
of technology on outcomes (χ21=28.2558, P .001).

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Table 3. Impact of IT platforms on patient engagement (Yes=positive impact, No=no impact).
Engagement

Impact of IT platforms, n (%)
Internet

MobileSocial media

Tele-monitoring

Video game

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Yes

66
(88)

2 (18)

36 (88)2 (67)

12 (92)

1 (33)

15 (94)

1 (50)

6 (100)

No

9
(12)

9 (82)

5 (12)

1 (33)

1 (8)

2 (67)1 (6)

1 (50)Total

75
11 (100) 41 (100) 3
(100)
(100)

13 (100) 3(100)

Behavior Theory
Overall results showed that 47.0% (80/170) of the literature
explicitly referenced theory (see Table 4). Among Internet-based
interventions, 64% (55/86) of studies mentioned the use of
behavior theories. Cognitive theories dominated this category(47%, 40/86). Further, 32% (14/44) of mobile-based intervention
studies reported use of behavior theories. Cognitive theories
were also the most widely used among this category (30%,
13/44) [42,51,65-71]. Moreover, 38% (6/16) of social media
studies used behavior change theory. Social support, cognitive,
and self-regulatory theories were the only models used in thiscategory [35,45,52,72-74]. The analysis showed 50% (3/6) ofvideo-game platforms used behavior change theories, where
the cognitive and self-regulatory theories are the only used
[75,76]. Only 11% (2/18) of telemonitoring studies used

16 (100) 2(100)

6 (100)Total yes

Total
no

Total

135 (63)

6 (32)

141(83)

16 (37)

13
(68)

29
(17)

151 (100)

19
(100)

170
(100)

biomedical and cognitive theories [77,78]. Literature showedthat 89% (71/80) of studies with behavior theories had asignificant impact on health outcomes. Only 11% (9/80) of
telemonitoring studies explicitly referenced the use of behavior
theories and showed no impact of technology on healthoutcomes. The result failed to show any relationship between
using behavior theory and the impact of technology on healthoutcomes (χ21=0.008, P=.977).
The analysis also found no significant correlative relationshipbetween behavior theory and patient engagement in health care(χ21=0.3055, P=.580479). However, there was a significant
relationship between patient engagement and Internet-based
interventions using behavior theories (χ21=7.3144, P=.00684)(see Table 5).

Table 4. Impact of IT platforms and theories of health behavior (Yes=positive impact, No=no impact).
Behavior theory

Impact of IT platforms, n (%)
Internet
Yes

Biomedical theory

MobileNo

Yes

No

Social media

Tele-monitoring

Video
game

Yes

Yes

Yes

No

1 (1)

No

1 (6)

Total
yes

Total no Total

2 (1)

2 (1)

(chronic model)
Behavioral learning theory 2 (2)1 (1)Communication

4 (5)

1 (1)

1 (2)

36 (42)4 (5)12 (27)

Self-regulatory

5 (6)

1 (1)

Total of used theory

48 (56)7 (8)13 (29)1 (2)5 (31)

1 (6)

2 (2)

Theory not reported

27 (31)4 (5)28 (64)2 (5)8 (50)

2 (13)

14 (78)Total

75 (87)11 (13)41 (93)3 (7)

13 (81)3 (19)

16 (89)

1 (6)

2 (1)

1 (1)

3 (2)

6 (4)

2 (1)

8 (5)

2 (33)

53 (31)5 (3)58 (34)

1 (17)

8 (5)

1 (1)

9 (5)

3 (50)

71 (42)9 (5)80 (47)

2 (11)

3 (50)

80 (47)

10 (6)

90 (53)2 (11)

6 (100)151 (89) 19 (11)1 (6)(social support theory)
Cognitive theory

1 (2)

2 (13)1 (6)(TPB, SOC, TTM, self-efficacy, information motivation, and behavioral skill)

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2 (13)

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Table 5. Patient engagement and theories of health behavior (Yes=positive impact, No=no impact).
Behavior theory

Patient engagement, n (%)
Internet
Yes

Biomedical theory

MobileNo

Yes

No

Social media

Tele-monitoring

Video
game

Yes

Yes

Yes

No

1 (1)

No

1 (6)

Total
yes

Total no Total

2 (1)

2 (1)

(chronic model)
Behavioral learning theory 2 (2)Communication

1 (1)

5 (6)

2 (1)
1 (2)

2 (13)

1 (1)

8 (5)

3 (2)
8 (5)

(social support theory)
Cognitive theory

30 (35)10 (12)11 (25)2 (5)2 (13)

1 (6)Self-regulatory

4 (5)

2 (2)

Total of used theory

42 (49)13 (12)12 (27)2 (5)6 (38)

Theory not reported

26 (30)5 (2)26 (59)4 (9)7 (44)

3 (19)

Grand Total

68 (79)18 (15)38 (86)6 (14)

13 (81)3 (19)

2 (33)

46 (27)

12 (7)

58 (34)

1 (17)

7 (4)

2 (1)

9 (5)

3 (50)

65 (38)15 (9)

80 (47)

2 (11)

3 (50)

76 (45)14 (8)

90 (53)2 (11)

6 (100)141 (83) 29 (17)(TPB, SOC, TTM, self-efficacy, information motivation, and behavioral skill)
2 (13)

Methods to Measure Health Outcomes
Most studies used indirect ways (such as self-reports) to measure
health outcomes (65.9%, 112/170). The literature showed that
57.6% (98/170) of studies showed a positive impact of IT
platforms when the health outcomes were assessed using indirect
ways. For example, self-reporting was used to assess whether

2 (11)
16 (89)170 (100)a text message could increase smoking cessation [68], reduce
methamphetamine use among human immunodeficiency virus
patients [79], and to assess medication adherence among patientswith congestive heart failure [48]. The analysis showed nosignificant association between ways to measure healthoutcomes and technology impact (χ21=0.5793, P=.446603) (seeTable 6).

Table 6. Impact of IT platforms and methods to measure health outcomes (Yes=positive impact, No=no impact).
Methods to measure health Impact of information technology platforms, n (%)
outcomes
Internet
MobileSocial media

Video
game

Yes

Yes

Total
yes

Total no Total

3 (50)

53 (31)5 (3)58 (34)

Yes

No

Yes

No

Yes

Direct25 (29)3 (3)18 (41)

2 (5)

1 (6)

Indirect

50 (58)8 (9)23 (52)1 (2)12 (75)3 (19)

10 (56)

2 (11)

3 (50)

98 (58)14 (8)

112 (66)

Grand Total

75 (87)11 (13)41 (93)3 (7)

13 (81)3 (19)

16 (89)2 (11)

6 (100)151 (89) 19 (11)170 (100)

Usability Assessment
Only 33.5% (57/170) of studies assessed the usability of IT
platforms. Of those, the majority were considered by authors
to be usable (89%, 51/57). Specifically, 75% (28/37) ofInternet-based IT intervention studies showed positive healthoutcomes with usable interventions [41,80-106]. In one study
that gauged usability, Steele et al performed a 3-monthrandomized controlled trial among 192 participants and foundan Internet-based physical activity behavior change program tobe usable, feasible, and acceptable among inactive participants[41]. Mobile-based interventions also showed 75% (6/8) of
usable interventions had a positive impact on health outcomes[42,69,93,107-109]. In one study, SMS was found to be usefulin helping patients to remember to take their medications andbe engaged in treatment planning [107]. SMS-based intervention
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Tele-monitoringNo

6 (33)

was also found to be useful in promoting communication withhealth care providers by delivering, receiving health information,
generating questions, and seeking information related to healthconditions [42]. Moreover, 87% (7/8) of studies reported thatthe usability of social media-based interventions was positively
correlated with good impact on health outcomes[34,35,46,52,53,110,111].
One
particularsocialnetworking-related study found that online health-related social
networking was useful and acceptable in chronic disease
management [52]. In addition, one study reported the usability
assessment in the video-game category and found that it was
usable and had a positive impact among patients withhyperfunctional voice disorders [36]. Overall, the analysis also
found that telemonitoring also showed similar results (100%,
3/3). One telemonitoring-based study found that

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Sawesi et al

telecommunication-based reminder tools are useful for
improving medication adherence [112].
Although our results failed to report any relationship between
usability of IT platforms and the impact on health outcomes(P=.1065), they showed significant association between usability
and patient engagement in health care (P=.0216) (Fisher’s exacttest) (see Tables 7 and 8).

Table 7. Impact of IT platforms and usability (Yes=positive impact, No=no impact).
Usability

Impact of information technology platforms, n (%)
Internet

MobileSocial media

Tele-monitoring

Video
game
Yes

Total
yes

Total no Total

1 (17)

43 (25)8 (5)51 (30)

4 (2)

2 (1)

6 (4)

1 (17)

12 (7)

45 (26)57 (34)

Yes

No

Yes

No

Yes

No

Yes

Usable28 (33)4 (5)6 (14)2 (5)

5 (31)

2 (13)

3 (17)

Not usable

1 (1)

4 (5)

No

1 (6)

Total of assessed usability 29 (34)8 (9)6 (14)2 (5)

6 (38)

2 (13)

3 (17)

Not assessed usability

46 (53)3 (3)35 (80)1 (2)7 (44)1 (6)

13 (72)2 (11)

5 (83)7 (4)106 (62) 113 (66)Grand total

75 (87)11 (13)41 (93)3 (7)

13 (81)3 (19)

16 (89)2 (11)

6 (100)19 (11)151 (89) 170 (100)Social media

Tele-monitoring

Video
game
Yes

Total
yes

Total no Total

1 (17)

41 (24)9 (5)51 (30)

2 (1)

4 (2)

6 (4)

1 (17)

43 (25)13 (8)

57 (34)

Table 8. Patient engagement and usability (Yes=positive impact, No=no impact).
Usability

Impact of information technology platforms, n (%)
Internet
Yes

Mobile
No

Yes

No

Yes

No

Yes

Usability assessed (usable) 26 (30)6 (7)7 (16)1 (2)

5 (31)

2 (13)

3 (17)

Usability assessed (not us- 1 (1)
able)

4 (5)

Total usability assessed

27 (31)10 (12)7 (16)1 (2)

6 (38)

2 (13)

3 (17)

Not assessed

41 (48)8 (9)31 (70)5 (11)

7 (44)1 (6)

13 (72)2 (11)

5 (83)

97 (57)16 (9)

113 (66)

Grand total

68 (79)18 (21)38 (86)6 (14)

13 (81)3 (19)

16 (89)2 (11)

6 (100)141 (83) 29 (17)170 (100)1 (6)Discussion
Principal Findings
Impact of IT Platforms on Health Outcomes
Overall, this review indicated that IT platform-based healthinterventions had a great impact on patients’ health outcomesin the United States and in other nations. IT-based healthinterventions have been viewed as driving positive healthbehavior change through patient engagement with mosttechnology platforms. IT-based health interventions also provide
necessary information and advice and counseling related tocertain diseases and conditions, such as mental disorders[113-120], asthma [57,76,96,121-123], obesity [30,32,124-132],
smoking [40,68,69,88,133-136], diabetes [11,137-151], sleep
disorder [152], hypertension [127,153,154], cancer[34,58,60,74,75,82,92,97,98,155-157], thereby encouraging
healthy living [30,31,72,124,158,159]. Moreover, theseinterventions enable patients to be engaged in self-monitoring,
thereby directing patients toward healthy eating, enhancing
attendance rate [136,160-166], improving medication adherence[162,167-173], increasing knowledge about disease andtreatment [33,42,47,75,85,90,94,96,115,119,168,174,175], andhttp://medinform.jmir.org/2016/1/e1/

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e n h a n c i n ge x e r c i s e
u s e[32,56,80-82,122,125,126,128-132,153,156,176-183]. Online
coaching by specialists enables patients to recover quickly,
ensuring that the pain they experience is reduced [89,184], anddoctor-patient communications are made readily available[73,157,185-191].
Apart from Internet-based technologies, mobile phone
technologies have been used extensively to engage patients andensure there is patient health behavior change. Mobile phone
technologies engage patients by using SMS to contact them andprovide necessary health information. This technology can bevery effective and efficient, since it is less expensive andtherefore more people can afford it. Studies have shown thatpatients can receive health-related information, receive
reminders of their health care attendance, as well as be
encouraged to adhere to their treatment [35,77-67,107,192].
Social media outlets, such as Twitter and Facebook, can ensure
patients get and exchange necessary health information [34,46].
Video game and telemonitoring technologies served a similar
purpose; these technologies tried to engage patients in order toprovide necessary health information and provided a platform
for helping patients adhere to treatment and helped patients
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JMIR MEDICAL INFORMATICS
actively become involved in the treatment process. These
technologies are of great importance to patients as well as
helpful to health care providers, therefore ensuring effectivenessand efficiency.
Although several studies demonstrated the positive impact ofIT platform usage, others showed no impact[33,35,41,43,45-48,81,83,91,133,141,178,193-198]. This could
be due to the timing of the follow-up assessments ranging fromone extremely short follow-up timing (1 week) to a relatively
long-term follow-up timing (48 months). The lack of consistency
in follow-up timing made it unclear as to how long these effects
on patient health last. Moreover, the technology adoption rate
may decline after a certain time period, thus diminishing its
effectiveness after significant results at the beginning of thestudy. This occurred in a study by Williamson et al who foundthat after 2 years of an IT-based intervention, the decrease inbody weight did not differ between the intervention and control
group [28]. Similarly, another research study found a slow
decline in HbA1c at 3 months follow-up (1.22%) versus (1.09%)
6 months follow-up [199,137]. Therefore, designing andevaluating IT platforms may become a significant challenge
because researchers are dealing with a large volume ofinterventions that have different impacts on patient healthbehavior. Thus, several issues need to be addressed if suchinterventions are to be evaluated or assessed, such as length ofintervention, type of technology, usability of the technology,
application of behavior theory, and how health outcomes aremeasured.

Patient Engagement in Health Care Using IT Platforms
Our review showed that IT platforms are playing a significant
role in patient engagement. This review implies that higher
patient participation in condition self-management wascorrelated with greater improvement in health outcomes. Many
studies have shown that patients who actively participated inhealth care experience better health outcomes compared to lessinvolved patients. One specific study showed a significant
association between patient engagement using the Internet andweight loss at 6 months (P .001) [77]. Another study reported
that a text messaging-based intervention could enhance patientengagement [33]. Social networks can also be particularly
helpful to individuals with lower patient activation [52]. Despite
the evidence regarding the importance of patient engagement,
it is challenging to draw solid conclusions. Many of the studies
conducted qualitative surveys to measure patient engagement
or relied solely on the number of times patients logged in or
uploaded data to determine their engagement. However, system
log-ins and upload and download data are not engagement.
Patient engagement is basically about interaction andparticipation in managing one’s health to achieve desired goals.
Therefore, further research is needed to determine the best ways
to measure patient engagement.

Association Between Usability of IT Platforms and Their
ImpactOur review found limited levels of evidence supporting thecorrelation between usability and impact of technology on healthoutcomes (P=.1065). Several factors may hinder the positive
impact of technology on health outcomes other than usability
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Sawesi et alissues. Patients’ willingness to participate in managing theirhealth care could be one of the main reasons. The review founda significant relationship with patient engagement and impact
of technology. It found also a significant positive correlation
between patient engagement and usability of IT platforms. Even
though the aim of this study was not to discover determinantsof patient engagement, several issues were identified including
unequal access to technology, technical issues, poor interface
design, suboptimal message content, privacy and confidentiality
issues[46,108,110,121,165,193,200],
andpatients’
self-perceived health illiteracy. The latter issue was seen insocial media, where patients think such a discussion should be
restricted to health care professionals [46]. Also, the majorityof technologies rely on patient-provider engagement from bothsides to exchange information and manage health conditions,such as in two-way SMS, thus increasing burden on providers
as well as patients. Moreover, in some countries like Sweden,
information dissemination can be restricted by legal and ethicalregulations for online patient-provider communication [201].
Therefore, more research on the usability and acceptability ofthese technologies and discovering the different factors that
impact patient engagement and their meaningful use will be
required in the future.

Association Between Technology Impact andIntervention Grounded in Behavior Change Theories
This review found that only a limited number of specificbehavioral theories and models were referenced among multiple
articles inferring a theoretical design. This could imply thatseveral IT interventions are designed in an ad hoc way, without
using any theoretical frameworks. This finding supports theresults of a previous study showing the majority of mobile-based
interventions used for improving medication adherence anddisease management were developed without a theoretical basis[202]. The review failed to detect any relationship between (1)behavioral theories and impact of technology or (2) theories
and patient engagement. This could imply that existing
theories/model were not developed to be used with thesetechnologies. We found a significant association between patientengagement in Internet-based interventions and use of behavior
theories in these interventions (χ21=7.3144, P=.00684). This
could imply that existing theories or models may have limited
applicability. However, it was difficult to draw a clear
conclusion whether or not using theory influenced intervention
effectiveness. Possible reasons for the lack of theory may
include the investigator not citing the theory, researchers’ lackof knowledge of the theories, struggling to define appropriate
theories, poorly operationalized theories, an absence of goodevaluation methods and usability testing, and theories containing
overlapping constructs and inconsistent use of terminology. For
example, the construct of self-efficacy can be found in Social
Cognitive Theory, Protection Motivation Theory, the Theory
of Planned Behavior, the Health Belief Model, andSelf-Regulation Theory. In addition, the simplicity of theinterventions could be another reason for not including behavior
theories. For example, reminding patients to take their treatment
through text message appears simple and consistent with the“cue to action” constituent of many health behavior theories ormodels, but these theories were not always described. Our
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findings of the lack of association between use of theory andoutcomes was based on the theory description within eachpublished article and should be interpreted cautiously.

Association Between Methods to Measure Health
Outcomes and the Impact of These Technologies
Overall, slightly more than half of the reviewed articles had apositive impact when assessed with patient questionnaires,
patient self-reports, pill counts, rates of prescription refills,
assessment of patients’ clinical response, and electronic
medication monitors. Even though the way to measure healthoutcomes is an important factor in determining the impact oftechnology, the review failed to detect any relationship between
methods used to measure health outcomes and the impact oftechnology. Therefore, further study is needed to replicate ourresults, because for each approach, there are different
assumptions related to what data to collect, how to collect thatdata, and how to make decisions about success. Indirect methods
may overestimate patient adherence. For instance, metformin
treatment adherence can be monitored either by recording thenumber of times the medication bottle was opened, or
alternately, adherence could be gauged by metformin plasma

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Sawesi et allevels. Both health behaviors are part of the same behavioral
class to control blood sugar levels. However, measuring
metformin in blood is more effective at measuring adherence
than recording the time when the bottle is opened because
patients may open and close the bottle without taking any
medication.

Limitations
Our review included some limitations. First, due to theheterogeneity of the research studies and the fact that some data
were not available for certain types of interventions and theircharacteristics, some statistical tests could not be performed,
hindering optimal quantitative assessment. Second, we excluded
studies not written in English; this criterion might have omitted
certain relevant research. Third, the majority of studies were
performed in the United States, which limits generalizability offindings. Finally, because of possible publication bias toward
positive findings, our review may overestimate the actual impactof these technologies.

Implications
The results from this review reveal several practical applications
worthy of future study (summarized in Table 9).

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Sawesi et al

Table 9. Implications of study.
Suggestion

Implications

Information technology platforms

It would be valuable to further evaluate IT platform-based interventions
to form a more coherent picture of their effectiveness in encouraging patientengagement for the purpose of enhancing lasting health behavior change.
A study with a long time frame may be useful to draw a clear conclusionon the effectiveness of these technologies and to determine the best ways
to guarantee positive long-term effects in patients.
Also, due to low availability of studies meeting our criteria, we could notprovide or conclude relationships between factors. Therefore, we recommend doing another review when there are more studies available in future.
In future, we can increase the quality of the review by limiting sample
size and study time frame.
IT platform interventions reviewed in this study are mutually inclusive;
they use different labels and contexts to describe the same concepts andlack of formal definitions. Therefore, a common framework for analyzingthese concepts is needed. A framework with an ontological approach may
serve this purpose.

Patient engagements

The outreach and engagement period prior to the intervention enrollment
are critical to the success of any intervention. Therefore, studies should
consider that when implementing the interventions
A study assessing determinant of patient engagement is highly recommended.

Usability

Assessment of user satisfaction toward IT platforms and their usability ofthese platforms are needed, and could be done through qualitative evaluations of user opinions of the respective IT platform(s).

Theories of health behavior

The literature also needs to focus more on referencing, selecting, and implementing behavioral theory to achieve the best possible impact. Reporting
accurate information about interventions is essential to assessing the effectiveness of these interventions and facilitating their successful implementation.
Also, new theories are needed to better understand how patients can participate and facilitate health behavior change, theories building on past
conceptual and focus only on one aspect, a triangulation model would
provide internally logical and comprehensible perception to achieve thesegoals.

Methods measure health outcomes

Conclusions
Based on our review, there is moderately strong evidence that
IT platforms can engage patients in health care and improvehealth outcomes. The usefulness and acceptability of IT
platforms can have great power in engagement and outcomes.
Studies grounded in behavior theory appeared to show a positive
impact on patient health behavior. To exploit the full potential

It would be valuable to further examine how different types of measurement
could affect patient outcomes reported in the study. A comparison between
direct and indirect methods could be helpful to draw a clear conclusion.

of IT platforms in health care, new theories may be needed tobetter understand how patients can participate and facilitate
health behavior change. Selecting appropriate ways to measure
health behavior change and developing a common framework
to analyze and understand the different components of IT
platforms and their safety, effectiveness, efficiency, andacceptability will also be of great importance.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Quality Assessment of Included Articles.
[PDF File (Adobe PDF File), 137KB-Multimedia Appendix 1]

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Sawesi et alMultimedia Appendix 2
List of the included articles and their characteristics.
[PDF File (Adobe PDF File), 233KB-Multimedia Appendix 2]

Multimedia Appendix 3
A glossary of terms.
[PDF File (Adobe PDF File), 107KB-Multimedia Appendix 3]

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Abbreviations
IT: information technology
SMS: short message service
SOC: stage of change
TPB: theory of planned behavior
TTM: transtheoretical model

Edited by G Eysenbach; submitted 09.04.15; peer-reviewed by R Schnall, L Morrison, N Kaufman; comments to author 29.07.15;
revised version received 07.09.15; accepted 09.10.15; published 21.01.16
Please cite as:
Sawesi S, Rashrash M, Phalakornkule K, Carpenter JS, Jones JF
The Impact of Information Technology on Patient Engagement and Health Behavior Change: A Systematic Review of the Literature
JMIR Med Inform 2016;4(1):e1
URL: http://medinform.jmir.org/2016/1/e1/
doi: 10.2196/medinform.4514
PMID: 26795082©Suhila Sawesi, Mohamed Rashrash, Kanitha Phalakornkule, Janet S Carpenter, Josette F Jones. Originally published in JMIR
Medical Informatics (http://medinform.jmir.org), 21.01.2016. This is an open-access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright andlicense information must be included.

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