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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.
Our aim was to systematically review the (1) impact of IT platforms used to promote patients’ engagement and to effect change in health behaviors and health outcomes, (2) behavior 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 patient engagement and health outcomes.
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.
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).
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 those related to motivation for health behavior change, long-standing adherence, expenditure, satisfaction, and health outcomes.
Patient engagement is currently considered the cornerstone of the health care system revolution for its positive impact on health outcomes and health care costs [
IT platforms are being embraced as a way to enhance patient engagement in the health care process, improve quality of care, support health care safety, and provide cost-effective health services for patients [
In addition, a meta-analysis performed to investigate the effectiveness 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 and focused on a relatively narrow range of technologies [
Conclusions drawn from these reviews are important; they provide insights but no clear answers about the effectiveness of 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 to improving patients’ health behaviors and patient engagement. IT platforms generally can have high potential benefits and some proven effects; however, specific components in several health conditions associated with success remain unclear. To better understand how to build a successful intervention that can engage patients to change their behavior meaningfully, we performed a systematic review.
Review aims were to systematically determine (1) the impact of IT platforms used to promote patient engagement and to effect 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 patient engagement and health outcomes.
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 [
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 proceedings, book chapters, reviews, dissertations, and protocols. (2) studies that evaluated and reported the impact of health information technology platforms on patients’ health outcome, (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 through the use of IT platforms such as social networking sites, mobile telephony, video and teleconferencing, email, SMS, and electronic monitoring, (5) studies that explored different factors affecting patient engagement and health behavior change were excluded, (6) studies that were published in languages other than 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
Two investigators independently reviewed the titles and then abstracts. The same investigators read and screened for full text eligibility. Data extraction was carried out by 1 reviewer and was rechecked for accuracy by another reviewer. The reasons for exclusion were recorded. Discrepancies were resolved by joint probability of agreement (0.98) [
A meta-analysis was not feasible due to the varying data collection methods and outcome measures. Therefore, eligible 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, and theory used in interventions classified according to Leventhal (biomedical model, behavioral learning, communicative, cognitive theory, and self-regulative) [
The outcomes variable was classified into (1) positive impact in which health information technology platform was associated with improvement in one or more aspects of care and (2) no impact or no noticeable improvement or change in health outcomes. This was assessed based on the overall conclusion made by the authors of each study. Most studies used statistical methods 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.
Flow diagram of included and excluded studies.
Summary of the review results based on types of IT platforms.
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Internet (N=86) | Phone (N=44) | Video game (N=6) | Social network (N=16) | Tele-monitoring (N=18) | |
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Bone, joint, and muscle disorders | 3 (3) |
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Brain, spinal cord, and nerve disorders | 7 (8) | 1 (2) | 2 (33) | 1 (6) | 1 (6) |
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Cancer | 5 (8) | 2 (5) | 1 (17) | 2 (13) | 2 (11) |
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Disorders of nutrition and metabolism | 13 (15) | 4 (9) | 1 (17) | 2 (13) | 1 (6) |
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Ears, nose, and throat disorders |
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1 (2) |
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Eye disorders |
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1 (6) |
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Health hazard | 5 (6) | 6 (14) |
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Heart and blood vessel disorders | 5 (6) | 3 (7) |
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6 (33) |
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Hormonal disorders | 20 (23) | 11 (25) |
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4 (25) | 3 (17) |
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Immune disorders | 4 (5) | 5 (11) |
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1 (6) | 1 (6) |
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Lung and airway disorders | 2 (2) | 1 (2) | 1 (17) | 1 (6) | 1 (6) |
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Mental health disorders | 12 (14) | 4 (9) | 1 (17) | 2 (13) | 2 (11) |
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Skin disorders |
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1 (2) |
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1 (6) |
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Women’s health issues | 3 (3) | 1 (2) |
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Not specified | 7 (8) | 4 (9) |
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2 (13) |
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Australia | 7 (8) | 5 (42) |
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Austria |
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1 (6) |
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Bangladesh |
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1 (2) |
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Canada | 4 (5) |
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2 (11) |
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Chile | 1 (1) |
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China |
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1 (2) |
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France |
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1 (2) |
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Germany | 3 (3) |
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Israel |
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1 (6) |
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Italy | 1 (1) | 1 (2) |
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Japan | 1 (1) |
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1 (6) |
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Kenya |
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1 (2) |
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Korea | 1 (1) | 1 (2) |
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1 (6) |
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Malaysia |
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1 (2) |
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Netherlands | 4 (5) |
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1 (17) |
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2 (11) |
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New Zealand |
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2 (5) |
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Norway |
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1 (2) |
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Poland |
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1 (6) |
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Russia |
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1 (2) |
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Slovenia | 1 (1) |
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South Korea | 2 (2) | 4 (5) |
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Spain |
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1 (2) |
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1 (6) |
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Sweden | 2 (2) |
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Switzerland |
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1 (6) |
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Taiwan | 1 (1) |
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United Kingdom | 5 (6) | 7 (16) | 1 (17) |
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1 (6) |
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United States | 53 (62) | 14 (32) | 4 (67) | 14 (88) | 8 (44) |
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Victoria |
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1 (2) |
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Vietnam |
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1 (2) |
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Randomized controlled trial | 55 (64) | 34 (30) | 2 (33) | 7 (44) | 14 (78) |
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Case study | 2 (2) | 1 (2) | 2 (33) | 2 (13) |
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Cohort study | 10 (12) | 4 (5) | 1 (17) | 1 (6) | 3 (17) |
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Cross-sectional analysis | 8 (9) | 1 (2) |
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5 (31) | 1 (6) |
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Quasi-experimental trial | 11 (13) | 4 (5) | 1 (17) | 1 (6) |
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Direct | 28 (33) | 20 (45) | 3 (50) | 1 (6) | 6 (33) |
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Indirect | 58 (67) | 24 (55) | 3 (50) | 15 (94) | 12 (67) |
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Yes | 75 (87) | 41 (93) | 6 (100) | 13 (81) | 16 (89) |
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No | 11 (13) | 3 (7) |
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3 (19) | 2 (11) |
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Yes | 38 (44) | 8 (18) | 1 (17) | 8 (50) | 3 (17) |
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No | 48 (56) | 36 (82) | 5 (83) | 8 (50) | 15 (83) |
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Yes | 68 (79) | 38 (86) | 6 (100) | 13 (81) | 16 (89) |
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No | 18 (21) | 6 (14) |
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3 (19) | 2 (11) |
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Biomedical theory (chronic model) | 1 (1) |
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1 (6) |
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Behavioral learning theory | 3 (3) |
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Communication (social support theory) | 5 (6) | 5 (11) |
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2 (13) |
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Cognitive theorya (TPB, SOC, TTM, self-efficacy, information motivation, and behavioral skill) | 40 (47) | 9 (20) | 2 (33) | 2 (13) | 1 (6) |
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Self-regulatory | 6 (7) |
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1 (17) | 2 (13) |
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Not specified | 31 (36) | 30 (69) | 3 (33) | 10 (63) | 16 (88) |
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Min. | 1 | 2 | 6 | 51 | 10 |
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Max. | 13564 | 22337 | 375 | 1754 | 784 |
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Min. | 1 mo | 1 mo | 1 mo | 1 wk | 2 mo |
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Max. | 48 mo | 16 mo | 3 mo | 36 mo | 39 mo |
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Not specified | 3 | 1 | 1 | 3 | 1 |
aTPB= theory of planned behavior, SOC=stage of change, TTM= transtheoretical model.
Overall, IT platforms have been shown to improve health behavior 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
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- and 6-month follow-up assessment [
Impact of IT platforms among different disorders (Yes=positive impact, No=no impact).
Disorders | Impact of IT platforms, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game |
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Yes | No | Yes | No | Yes | No | Yes | No | Yes | Total yes | Total no | Total | |
Bone, joint, and muscle | 3 (3) |
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3 (2) |
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3 (2) |
Brain, spinal cord, and nerves | 7 (8) |
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1 (2) |
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1 (6) | 1 (6) |
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2 (33) | 11 (6) | 1 (1) | 12 (7) |
Cancer | 5 (6) |
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2 (5) |
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2 (13) |
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2 (11) |
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1 (17) | 12 (7) |
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12 (7) |
Nutrition and metabolism | 10 (12) | 3 (3) | 4 (9) |
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2 (13) |
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1 (6) |
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1 (17) | 18 (11) | 3 (3) | 21 (12) |
Ears, nose, and throat |
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1 (2) |
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1 (1) | 1 (1) |
Eye |
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1 (6) |
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1 (1) |
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1 (1) |
Health hazard | 4 (5) | 1 (1) | 6 (14) |
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10 (6) | 1 (1) | 11 (6) |
Heart and blood vessel | 4 (5) | 1 (1) | 3 (7) |
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4 (22) | 2 (11) |
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11 (6) | 3 (2) | 14 (8) |
Hormonal | 19 (22) | 1 (1) | 9 (20) | 2 (5) | 4 (25) |
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3 (17) |
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35 (21) | 3 (2) | 38 (22) |
Immune system | 2 (2) | 2 (2) | 5 (11) |
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1 (6) | 1 (6) |
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8 (5) | 3 (2) | 11 (6) |
Lung and airway | 2 (2) |
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1 (2) |
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1 (6) |
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1 (6) |
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1 (17) | 6 (4) |
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6 (4) |
Mental health | 11 (13) | 1 (1) | 4 (9) |
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2 (13) |
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2 (11) |
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1 (17) | 20 (12) | 1 (1) | 21 (12) |
Not specified | 5 (6) | 2 (2) | 4 (9) |
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1 (6) | 1 (6) |
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10 (6) | 3 (2) | 13 (8) |
Skin |
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1 (2) |
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1 (6) |
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2 (1) |
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2 (1) |
Women’s health | 3 (3) |
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1 (2) |
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4 (2) |
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4 (2) |
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) |
In total, 82.9% (141/170) of studies reported improvement in patient engagement after using IT platforms (see
Overall, analysis showed significant correlations between patient engagement in health care and the impact of IT platforms (χ2
1=39.8836,
Impact of IT platforms on patient engagement (Yes=positive impact, No=no impact).
Engagement | Impact of IT platforms, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game | Total yes | Total no | Total | |||||
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) | 135 (63) | 6 (32) | 141 (83) |
No | 9 (12) | 9 (82) | 5 (12) | 1 (33) | 1 (8) | 2 (67) | 1 (6) | 1 (50) |
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16 (37) | 13 (68) | 29 (17) |
Total | 75 (100) | 11 (100) | 41 (100) | 3 (100) | 13 (100) | 3 (100) | 16 (100) | 2 (100) | 6 (100) | 151 (100) | 19 (100) | 170 (100) |
Overall results showed that 47.0% (80/170) of the literature explicitly referenced theory (see
The analysis also found no significant correlative relationship between behavior theory and patient engagement in health care (χ2
1=0.3055,
Impact of IT platforms and theories of health behavior (Yes=positive impact, No=no impact).
Behavior theory | Impact of IT platforms, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game |
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Yes | No | Yes | No | Yes | No | Yes | No | Yes | Total yes | Total no | Total | |
Biomedical theory |
1 (1) |
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1 (6) |
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2 (1) |
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2 (1) |
Behavioral learning theory | 2 (2) | 1 (1) |
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2 (1) | 1 (1) | 3 (2) |
Communication |
4 (5) | 1 (1) | 1 (2) |
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1 (6) | 1 (6) |
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6 (4) | 2 (1) | 8 (5) |
Cognitive theory |
36 (42) | 4 (5) | 12 (27) | 1 (2) | 2 (13) |
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1 (6) |
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2 (33) | 53 (31) | 5 (3) | 58 (34) |
Self-regulatory | 5 (6) | 1 (1) |
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2 (13) |
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1 (17) | 8 (5) | 1 (1) | 9 (5) |
Total of used theory | 48 (56) | 7 (8) | 13 (29) | 1 (2) | 5 (31) | 1 (6) | 2 (2) |
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3 (50) | 71 (42) | 9 (5) | 80 (47) |
Theory not reported | 27 (31) | 4 (5) | 28 (64) | 2 (5) | 8 (50) | 2 (13) | 14 (78) | 2 (11) | 3 (50) | 80 (47) | 10 (6) | 90 (53) |
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) |
Patient engagement and theories of health behavior (Yes=positive impact, No=no impact).
Behavior theory | Patient engagement, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game |
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Yes | No | Yes | No | Yes | No | Yes | No | Yes | Total yes | Total no | Total | |
Biomedical theory |
1 (1) |
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1 (6) |
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2 (1) |
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2 (1) |
Behavioral learning theory | 2 (2) | 1 (1) |
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2 (1) | 1 (1) | 3 (2) |
Communication |
5 (6) |
|
1 (2) |
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2 (13) |
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|
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|
8 (5) |
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8 (5) |
Cognitive theory |
30 (35) | 10 (12) | 11 (25) | 2 (5) | 2 (13) |
|
1 (6) |
|
2 (33) | 46 (27) | 12 (7) | 58 (34) |
Self-regulatory | 4 (5) | 2 (2) |
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|
2 (13) |
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|
1 (17) | 7 (4) | 2 (1) | 9 (5) |
Total of used theory | 42 (49) | 13 (12) | 12 (27) | 2 (5) | 6 (38) |
|
2 (11) |
|
3 (50) | 65 (38) | 15 (9) | 80 (47) |
Theory not reported | 26 (30) | 5 (2) | 26 (59) | 4 (9) | 7 (44) | 3 (19) | 16 (89) | 2 (11) | 3 (50) | 76 (45) | 14 (8) | 90 (53) |
Grand Total | 68 (79) | 18 (15) | 38 (86) | 6 (14) | 13 (81) | 3 (19) |
|
2 (11) | 6 (100) | 141 (83) | 29 (17) | 170 (100) |
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 a text message could increase smoking cessation [
Impact of IT platforms and methods to measure health outcomes (Yes=positive impact, No=no impact).
Methods to measure health outcomes | Impact of information technology platforms, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game |
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Yes | No | Yes | No | Yes | No | Yes | No | Yes | Total yes | Total no | Total | |
Direct | 25 (29) | 3 (3) | 18 (41) | 2 (5) | 1 (6) |
|
6 (33) |
|
3 (50) | 53 (31) | 5 (3) | 58 (34) |
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) |
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) of Internet-based IT intervention studies showed positive health outcomes with usable interventions [
Although our results failed to report any relationship between usability of IT platforms and the impact on health outcomes (
Impact of IT platforms and usability (Yes=positive impact, No=no impact).
Usability | Impact of information technology platforms, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game |
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Yes | No | Yes | No | Yes | No | Yes | No | Yes | Total yes | Total no | Total | |
Usable | 28 (33) | 4 (5) | 6 (14) | 2 (5) | 5 (31) | 2 (13) | 3 (17) |
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1 (17) | 43 (25) | 8 (5) | 51 (30) |
Not usable | 1 (1) | 4 (5) |
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1 (6) |
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4 (2) | 2 (1) | 6 (4) |
Total of assessed usability | 29 (34) | 8 (9) | 6 (14) | 2 (5) | 6 (38) | 2 (13) | 3 (17) |
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1 (17) | 12 (7) | 45 (26) | 57 (34) |
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) |
Patient engagement and usability (Yes=positive impact, No=no impact).
Usability | Impact of information technology platforms, n (%) | |||||||||||
Internet | Mobile | Social media | Tele-monitoring | Video game |
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|
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Yes | No | Yes | No | Yes | No | Yes | No | Yes | Total yes | Total no | Total | |
Usability assessed (usable) | 26 (30) | 6 (7) | 7 (16) | 1 (2) | 5 (31) | 2 (13) | 3 (17) |
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1 (17) | 41 (24) | 9 (5) | 51 (30) |
Usability assessed (not usable) | 1 (1) | 4 (5) |
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|
1 (6) |
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2 (1) | 4 (2) | 6 (4) |
Total usability assessed | 27 (31) | 10 (12) | 7 (16) | 1 (2) | 6 (38) | 2 (13) | 3 (17) |
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1 (17) | 43 (25) | 13 (8) | 57 (34) |
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) |
Overall, this review indicated that IT platform-based health interventions had a great impact on patients’ health outcomes in the United States and in other nations. IT-based health interventions have been viewed as driving positive health behavior change through patient engagement with most technology platforms. IT-based health interventions also provide necessary information and advice and counseling related to certain diseases and conditions, such as mental disorders [
Apart from Internet-based technologies, mobile phone technologies have been used extensively to engage patients and ensure there is patient health behavior change. Mobile phone technologies engage patients by using SMS to contact them and provide necessary health information. This technology can be very effective and efficient, since it is less expensive and therefore more people can afford it. Studies have shown that patients can receive health-related information, receive reminders of their health care attendance, as well as be encouraged to adhere to their treatment [
Social media outlets, such as Twitter and Facebook, can ensure patients get and exchange necessary health information [
Although several studies demonstrated the positive impact of IT platform usage, others showed no impact [
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 was correlated with greater improvement in health outcomes. Many studies have shown that patients who actively participated in health care experience better health outcomes compared to less involved patients. One specific study showed a significant association between patient engagement using the Internet and weight loss at 6 months (
Our review found limited levels of evidence supporting the correlation between usability and impact of technology on health outcomes (
This review found that only a limited number of specific behavioral theories and models were referenced among multiple articles inferring a theoretical design. This could imply that several IT interventions are designed in an ad hoc way, without using any theoretical frameworks. This finding supports the results of a previous study showing the majority of mobile-based interventions used for improving medication adherence and disease management were developed without a theoretical basis [
Overall, slightly more than half of the reviewed articles had a positive 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 health outcomes is an important factor in determining the impact of technology, the review failed to detect any relationship between methods used to measure health outcomes and the impact of technology. Therefore, further study is needed to replicate our results, because for each approach, there are different assumptions related to what data to collect, how to collect that data, and how to make decisions about success. Indirect methods may overestimate patient adherence. For instance, metformin treatment adherence can be monitored either by recording the number of times the medication bottle was opened, or alternately, adherence could be gauged by metformin plasma levels. 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.
Our review included some limitations. First, due to the heterogeneity of the research studies and the fact that some data were not available for certain types of interventions and their characteristics, 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 of findings. Finally, because of possible publication bias toward positive findings, our review may overestimate the actual impact of these technologies.
The results from this review reveal several practical applications worthy of future study (summarized in
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 patient engagement for the purpose of enhancing lasting health behavior change. A study with a long time frame may be useful to draw a clear conclusion on 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 not provide 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 and lack of formal definitions. Therefore, a common framework for analyzing these 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 of these 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 these goals. | |
Methods measure health outcomes | 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. |
Based on our review, there is moderately strong evidence that IT platforms can engage patients in health care and improve health 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 of IT platforms in health care, new theories may be needed to better 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, and acceptability will also be of great importance.
Quality Assessment of Included Articles.
List of the included articles and their characteristics.
A glossary of terms.
information technology
short message service
stage of change
theory of planned behavior
transtheoretical model
None declared.