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To review eHealth intervention studies for adults and children that targeted behavior change for physical activity, healthy eating, or both behaviors.
Systematic literature searches were performed using five databases: Medline, PsychInfo, CINAHL, ERIC, and the Cochrane Library to retrieve articles.
Articles published in scientific journals were included if they evaluated an intervention for physical activity and/or dietary behaviors, or focused on weight loss; used randomized or quasi-experimental designs; measured outcomes at baseline and a follow-up period; and included an intervention where participants interacted with some type of electronic technology either as the main intervention or an adjunct component. All studies were published between 2000 and 2005.
Eighty-six publications were initially identified, of which 49 met the inclusion criteria (13 physical activity publications, 16 dietary behaviors publications, and 20 weight loss or both physical activity and diet publications), and represented 47 different studies. Studies were described on multiple dimensions, including sample characteristics, design, intervention, measures, and results. eHealth interventions were superior to comparison groups for 21/41 (51%) studies (3 physical activity, 7 diet, 11 weight loss/physical activity and diet). Twenty-four studies had indeterminate results, and in four studies the comparison conditions outperformed eHealth interventions.
Published studies of eHealth interventions for physical activity and dietary behavior change are in their infancy. Results indicated mixed findings related to the effectiveness of eHealth interventions. Interventions that feature interactive technologies need to be refined and more rigorously evaluated to fully determine their potential as tools to facilitate health behavior change.
The numerous health benefits of physical activity and healthy eating are well known, yet large proportions of modern societies do not meet recommended guidelines for these behaviors. In turn, inactivity and poor diet are the primary explanations for increasing obesity levels in these populations.1,2 Intervention programs aimed at changing activity and eating behaviors range from individual-level approaches to community-wide campaigns in a variety of settings, with generally small to moderate effects on physical activity, diet, and weight loss.3 Reasons for the lack of substantial impact have been postulated as a lack in effectiveness, reach, and sustainability.3
A growing area of research has been the incorporation of eHealth technologies to allow for more individualized behavior change interventions.4-6 The term “eHealth” has become increasingly common with the proliferation of the Internet and the ability to provide access to a broad range of health information. A 2001 report titled, “The eHealth Landscape” provided a broad definition: “eHealth is the use of emerging information and communication technology, especially the Internet, to improve or enable health and health care.7“
Interactive, computerized technologies offer several potential advantages for designing behavioral interventions. Computer-based programs can tailor information and messages to participants to personalize their experience, and may enhance the cultural sensitivity of an intervention.4,8 Access to information is quicker and it is easier to keep information accurate and updated. Computer interventions also offer some level of anonymity to users that may encourage individuals to seek out sensitive health information. The technology allows for asynchronous communication through Internet electronic bulletin boards that are always available online so individuals can exchange information, provide mutual support, and search for services at their convenience.9,10 Computer-based programs can be designed as games to make a health intervention more appealing and entertaining to help engage participants.4
Kroeze, Werkman and Brug 6 recently conducted a systematic review on randomized trials of computer tailored interventions for physical activity and dietary behaviors. Kroeze et al. identified 30 studies that were delivered to adult participants without person-to-person contact (i.e., by mail, computer, or other media device). The authors concluded that the evidence in favor of computer tailored interventions for dietary behaviors was strong but there was little evidence for effective computer-based physical activity interventions.
A distinction can be drawn between interventions that use computer-tailored materials (such as pamphlets, newsletters, and reports) and interactive computerized interventions where participants actually use the technology (such as websites and handheld computers). These applications can be thought of as “first” and “second” generation computerized interventions, respectively.
The present systematic review differed substantially from the review by Kroeze and colleagues 6 both in purpose and included studies. The purpose of this review is to present a description of studies that feature second generation computerized interventions for physical activity and diet behaviors. Here, eHealth is defined as any form of interactive technology (e.g., e-mail, Internet, CD-ROM program, handheld computer, kiosk, etc.) used by program participants to facilitate behavior change. The intention was to include a broad survey of studies that evaluated interventions where the interactive technology was either the main component or subcomponent of the intervention. This review provides a descriptive evaluation of interactive eHealth interventions for physical activity, dietary behaviors and combined activity and dietary interventions for weight loss. For each study, we assessed the quality of the study design, types of intervention technologies, use of behavior change theory, and the nature of the findings.
Systematic literature searches were performed using five databases (Medline, PsychInfo, CINAHL, ERIC, Cochrane Library) to retrieve articles written in English relating to eHealth interventions for physical activity, dietary behaviors, or weight loss among adults, as well as children. No beginning time limit was employed for search criteria as studies involving eHealth technology were expected to be relatively current. Searches were performed through 2005. Literature searches were conducted separately by two researchers for each domain and results compiled. The reference lists of retrieved articles were scanned for additional articles. A number of search terms were used to represent eHealth (e.g.: web, computer, e-mail, multi-media, Internet, PDA, cell phone) and the target domains of nutrition (e.g.: diet, fruit, vegetable, fat, healthy eating), physical activity/exercise, and obesity (e.g.: weight loss, body mass index, obesity).
Several criteria were established for inclusion. Articles published in scientific journals were included. Book chapters, abstracts from conference proceedings, and dissertations were excluded. Studies that intervened on physical activity, dietary behaviors or a combination of both were included. Only studies utilizing randomized or quasi-experimental designs were included. Target outcomes had to be measured at baseline and at follow-up. Studies examining the effectiveness or feasibility of interventions were included, while those focusing on acceptability or descriptions of technology-based interventions were excluded. Interventions had to be delivered using some type of electronic technology either as the main intervention component or as an adjunct component in the intervention program. Participants were not required to input information into the technology application (either for assessment or tailoring purposes) but they did have to receive information (such as educational messages) and directly interface with the eHealth technology. Therefore, interventions utilizing computer-assisted tailored feedback with no participant interaction were excluded. For example, some studies were identified where participants received computer generated tailored print materials but the participants did not interact with the technology (e.g., 11-13).
Study quality was rated on nine methodological characteristics and each study was given a score calculated as the percent of the maximum obtainable score. Tabulated quality scores along with scoring criteria rules are presented in Appendix A (see online Appendix at www.ajpm-online.net). Two researchers independently ranked each study and then compared rankings for agreement. Ranking disagreements were discussed among the co-authors and an agreed upon score was assigned. Each study was also characterized by the level of support for the eHealth intervention enhancing behavior change compared to a control condition. This index was a three-level ranking based on statistically significant effects where “+” indicated favoring the eHealth intervention condition, “o” indicated indeterminate findings, and “−” indicated the eHealth intervention condition resulted in worse outcomes than the comparison condition.
Because of the heterogeneity of studies with respect to study designs, participants, measures and outcomes; a meta-analysis was not conducted to estimate a pooled effect size. For studies with designs that specifically isolated and tested the effect of the eHealth technology in comparison to a control group that did not receive the technology), effect sizes were estimated. Effect sizes (r) were interpreted as 0.10, small effect; 0.24, medium effect; 0.37 large effect.14
Eighty-six studies were initially identified for potential inclusion. Thirty-seven studies were excluded due to: lack of behavioral outcomes (17 studies), no participant interaction with the eHealth technology (12 studies), focus on descriptions of the eHealth intervention with no data provided (3 studies), articles that were sub-studies of included studies (2 studies), articles that were published abstracts (2 studies), and an indeterminate nature of the study intervention (1 study). A list of excluded studies is available from the first author.
Of the 49 included articles, 13 articles focused on physical activity, 16 on dietary behaviors, and 20 on both physical activity and dietary behavior change. All articles were published between 2000 and 2005. Table 1 presents the study design quality scores for each study. The average design scores were 56% for physical activity, 68% for dietary behaviors, and 72% for the combined activity and diet studies. While most studies included a control group (91%), used individual randomization to treatment groups (74%), and validated measures of behavior change (79%); many studies did not isolate the eHealth technology in the design (51%) or present a rationale for sample size (38%).
Of the 13 physical activity interventions (Table B1, see online Appendix at www.ajpm-online.net), study sample sizes ranged from 28 to 655 (median n=78). Eleven studies focused on adults and two on children. Most samples had primarily female participants with seven of the studies including 64% to 100% females. Studies with adults recruited from the community,15,20,26 worksites,16,17,19,21,23 primary care,25,27 and online.22 The two studies with children were conducted in schools. Diabetic patients were the focus of two studies 22,27; the remaining 11 studies aimed at improving general health.
The most commonly used eHealth components were: website and e-mail,21–23 websites,15,20,24,27 and e-mail only.16,17,19,26 One study delivered the intervention program using a CD-ROM 18 and another used a computer automated telephone system to engage participants in counseling for physical activity.25 Three studies used pedometers as a tool for monitoring walking activity.15,16,27
The majority of studies (11 out of 13) were based on Social Cognitive Theory (SCT), the Transtheoretical Model (TTM) or a combination of the two. Two studies did not state a theoretical basis and another was based on a social-ecological model of diabetes self-management.22 Two studies manipulated theoretical elements in their experimental design to test the effect of the interventions' theoretical fidelity on physical activity behavior. Hageman and colleagues 20 found no differences in physical activity levels after participants received either stage tailored or standardized newsletters through the Internet. Rovniak and colleagues26 found that women significantly decreased their 1-mile walk test time when given precise and specific self-monitoring and feedback compared to women who received more general instruction and feedback.
Intervention durations ranged from 1 to 6 months with 8 programs lasting 2 months or less and four programs lasting greater than 3 months. Length of the intervention could not be determined in one study.27 Most studies had a final post-test assessment immediately following the intervention with the exception of one study with a 10 week follow-up after an 8 week intervention21 and one study with a 1 year follow-up after a 12 week intervention.26 Study completion rates ranged from 59% to 100% with 10 studies reporting completion rates of at least 75%.
Tests of the eHealth interventions could be assessed in 10 of the 13 studies (Table 1). Three studies had findings favoring eHealth interventions influencing physical activity.23,25,26 In six studies, the findings were statistically indeterminate 15,19,20–22,24 and in one study there was a between group difference at post-test that favored the control group for moderate physical activity levels.18
Sixteen publications had interventions targeting dietary behavior (representing 14 different studies), (Table B2) with study sample sizes ranged from 72 to 1,578 (median n=288). Twelve studies focused on adults and two on children in school settings.29,37 Seven of the adult studies recruited 70% to 100% women. Studies with adults were in worksite settings,30,36,39,40 primary care,33,34,38,41,43 and community settings.28,31,32 Three interventions were designed for treating populations with specific diseases (i.e. diabetes,34,38 and cardiovascular disease 43).
The eHealth components included: websites, 32,37,38,40,43 computer programs,34,41 interactive multi-media (IMM),29,36 a CD-ROM program,31 e-mail,30 interactive phone technology,33 and kiosks in grocery stores.28 One study featured a psycho-education multimedia game called “Squire's Quest!” designed for children.29 Thirteen studies targeted specific dietary behaviors, most commonly a combination of fat reduction and increasing fruit and vegetable consumption, while one study focused on general diabetes management.38 There was a sizable range in interventions' duration. In one study, the intervention was a single interaction with the intervention technology.31 Study participants had access to the intervention for 2 to 15 weeks in seven studies, while six studies had longer-term programs lasting from 6 to 12 months.
Only one of the studies did not mention a theoretical basis for the intervention with most based on SCT, TTM, the Precaution Adoption Process, or a combination of theories. Study completion rates were generally high and ranged from 45% to 95%. All but one study evaluated the intervention with a study design that included at least one comparison group. Thirteen studies measured dietary behavior outcomes usually assessed through validated self-report FFQs or dietary recalls. Five studies measured anthropometric or physiologic outcomes (e.g., BMI, serum cholesterol, HbA1c).
Tests of the eHealth interventions could be assessed in 13 of the 14 studies (Table 1). Seven studies indicated some evidence in favor of eHealth interventions influencing dietary behavior changes, 28,29,31,33,36,40,42 five studies provided indeterminate evidence, 35,37–39,43, and one study had findings that favored weekly in-person meetings compared to an internet-based intervention.32
Twenty publications had interventions that targeted both physical activity and dietary behaviors (Table B3) and study sample sizes ranged from 35 to 2,121 (median n=111). Twelve studies focused on adults with 49% to 100% female participants; while one study included only men in the U.S. Air Force.60 Seven of the adult studies specifically recruited overweight participants and focused on weight loss and/or weight maintenance. Seven studies focused on children. Two studies recruited African American girls and enrolled child-parent dyads.45,61
The adult studies were based in the community,44,49–51,58,59,61,62 primary care,46,53,63 worksites,54,57 and the military.60 Three of the studies with children were conducted through schools; one through a day camp, one in primary care, and anther in a community setting. In addition to general health improvement and weight loss or weight maintenance, one study focused on diabetes prevention58 and one study on enhancing fitness.60
The eHealth components included: websites,45,47–52,57,58,60–62 computers or kiosks,46,53,55,59,63 or e-mail.44,54 All studies included some aspect of physical activity and dietary behavior change. Intervention programs ranged from 1 session to 1 year in length with 13/20 (65%) of the programs lasting 4 months to 1 year.
Ten studies were theory-based with nine referencing the TTM, often in combination with SCT or TPB. One study tested the commercially available ediets.com program.62 All studies assigned participants to one of at least 2 study arms. However, in two studies all participants received the eHealth component of the intervention but were randomized to different follow-up conditions.46,53 Most studies (15/20; 75%) had post-test assessments occurring immediately following the intervention without any subsequent follow-up assessment. Extended follow-up assessments ranged from 6 weeks 52 to 6 months 50,51,59 post-intervention in five of the studies. Study completion rates ranged from 66% to 98%. Physical activity and dietary behavior change was generally assessed with self-report instruments. Only two studies reported measuring physical activity with accelerometers and diet with 24-hour recalls.45,55 Two studies did not measure behavior change60,62 but were among the 10 studies that measured body weight as a study outcome.
Tests of the eHealth interventions targeting multiple behaviors could be evaluated on at least one of the outcomes of physical activity, a dietary behavior, or weight loss for all 20 studies (Table 1). Of the 17 studies that measured physical activity, six favored eHealth interventions for increasing physical activity.47,48,52,54,55,59 Of the 17 studies that measured dietary behaviors, six favored eHealth interventions for changing dietary behaviors.47,48,52,54,59,61 Of the 11 studies that measured weight change, four studies favored eHealth interventions,56–58,60 and two studies found eHealth interventions to be less effective for weight loss compared to an in-person therapist 50 and a standardized weight loss manual.62 Eleven of the 20 studies found evidence in favor of eHealth interventions on at least one of three outcomes of physical activity, dietary behavior, or weight loss.47,48,52,54–61
Table 2 presents estimated effect sizes for the 24 studies that specifically compared eHealth technology to a non-technology control group. Effect sizes were generally in the small to medium range. Notable exceptions were Prochaska and colleagues who reported results for the subsample of boys physical activity in the large effect size range,55 and Harvey-Berino50 and colleagues who found an estimated medium effect size for Internet support being less effective for weight loss maintenance compared to an in-person therapist.
This review provided a systematic description of 47 studies of eHealth interventions for behavior change related to physical activity and/or dietary behaviors. All studies were published between 2000 and 2005 and featured some type of interactive technology that was expected to facilitate the behavior change process and represent a “second generation” of eHealth technology that go beyond using computer-tailored print materials. As a result, only five studies 28,36,39,59,63 in this review overlap with the 30 studies reviewed by Kroeze and colleagues.6 However, both reviews indicate that more rigorous research is needed to evaluate eHealth intervention technologies and understand the program mechanisms that promote physical activity and dietary behavior changes.
This study found that support for the interventions' efficacy for improving physical activity, diet, or facilitating weight loss over and above other intervention components can not be definitively discerned from the current body of research. Twenty-one of 41 (51%) studies had outcomes favoring the eHealth technology group compared to a control group. While some studies had high study design scores 21,43,54 and used randomized designs with control groups, less than half isolated the technology component and compared the intervention to a non-technology control group. Several studies included the eHealth intervention for all participants making it difficult to determine whether behavior changes were due to the eHealth application or other intervention components.16,17,27,34,35,38,46,53
The effect sizes estimated from the subset of studies that did isolate the eHealth technology in their study design tended to have small to medium effect sizes. This suggests that while eHealth interventions do not seem to have higher efficacy than other types of interventions 3, the potential reach of eHealth programs combined with their efficacy can result in a significant public health impact.64 However, meta-analysis of specific types of eHealth intervention for particular population segments is warranted to more accurately estimate efficacy.
This review included many studies that scored high on the quality of the study design and resulted in positive findings supporting the eHealth technology for behavior change.23,26,29,52,54,57,58 These studies are exemplars that may be of particular interest for researchers to learn more about the nature of the interventions and how they were evaluated. Detailed descriptions of the eHealth interventions, which may be of special interest to researchers designing similar interventions are provided in many of the studies.15,16,19,23,26,27,29,30,36,43,45,48,50 Some of these were small pilot or feasibility studies, which although they did not contain strong evaluations, did include extensive description of the intervention content.
Overall, the studies mainly aimed at improving physical activity and dietary behaviors in the context of preventing chronic diseases, but this review also included eight studies that focused on these behaviors to target weight loss or weight maintenance. However, many of the studies that targeted only behavior change sampled participants with an average BMI or percent body fat in the overweight to obese range, which is consistent with the majority of the adult U.S. population being overweight or obese.65 All of the reviewed studies met our inclusion criteria because they applied common principles of behavior change through interactive technology, which is relevant to primary prevention and weight loss.
In an effort to understand how eHealth interventions can facilitate health behavior change several issues surfaced. The reviewed studies can serve as a guide for continued development of eHealth interventions with consideration of topics such as measuring program utilization and dose, mode of intervention delivery, use of theoretical components, and targeting single versus multiple behaviors for change.
An important issue for eHealth interventions is getting participants to use the interactive technologies at a high enough frequency over a specified duration to receive an optimal dose of the intervention. Utilization rates give an index of how often participants used the eHealth component. Common utilization measures for website usage are “hit” rates (the number of times a web page is opened) and log-on rates. Intervention dose can be measured as the amount of intervention materials (e.g., modules, sessions, worksheets, assessments, self-monitoring) a participant completes during the course of the program. A benefit of using eHealth interventions is that utilization and dose can be objectively measured, though only some studies tracked this data. Studies with higher utilization and dose tended to have better behavior change outcomes.22,33,57,58,61,63 Outcomes were improved for subsamples that completed a certain amount of sessions.47 Still, many studies suffered from low dose and poor utilization with a majority of participants failing to engage in more than half of the expected eHealth activities25,33,43,45 or had few website log-ons.21 For web-based interventions, log-on rates tended to decrease over time.22,57 Participants had higher utilization of behavior change websites compared to educational or control websites in several studies.22,57,58,61 Higher log-on rates were found when the Internet program included peer support compared to programs without peer support.38 Others have suggested that a user-centered perspective, in which the way users interact with technology is engineered into interventions, is also critical for greater uptake of eHealth programs.66
The majority of studies did not explicitly state how often participants were expected to use the website or eHealth intervention making it difficult to assess whether dose adequacy. Only participants in one study had log-on rates at the expected level.22 Several studies did include self-reported measures of e-mail recall and found that most participants read information received electronically.19,23,44 Another study found a more modest number of participants reading e-mails.21
In studies where dosing information was available, the data suggested that most participants received inadequate doses. Methods are needed to motivate participants to use and reuse eHealth programs, so that optimal intervention doses are received by participants. For example, incentives and telephone prompts may help increase utilization rates in the short term.29 Alternatively, more engaging, dynamic website programs may help keep participants engaged as evidenced with a multimedia game29 that had high completion rates and positive findings.
Few of the reviewed studies were designed to make comparisons between programs delivered through eHealth technology versus in–person, face-to-face sessions. Those that did suggest that participants may not be ready to rely solely on computerized programs. Wylie-Rosett et al. 63 found that participants preferred face-to-face meetings and phone calls, and these interventions outperformed web/workbook groups. Harvey-Berino et al.'s studies found mixed results. In one study,50 results indicated that in-person groups outperformed an internet-based program for weight loss. However, follow-up research51 indicated that internet support was superior to in-person groups for weight loss maintenance. It may be that eHealth programs are optimal for implementing certain intervention tasks (e.g., such as conducting assessments and providing an information resource), which can then give health professionals more time to help patients with problem-solving and information synthesis.
Two studies addressed whether the accountability that comes from in-person meetings can be facilitated or mimicked via interactive technologies.15,58 Tate58 examined the effects of individualized e-counseling in a weight loss program and found that therapist contact improved outcomes. This method still required therapists to create tailored messages for each participant, which can be time-intensive. Bickmore15 examined a method for computerizing this individualized therapist contact via “agents”. These agents were computer-generated and had the ability to closely mimic human communication strategies through emotional and relational interactions. While the study found that participants were able to establish bonds and rapport with their agent, this did not translate into behavior change in the short term.
The majority of reviewed studies explicitly cited a behavioral theory as a guide in intervention design. Most prevalent was the Transtheoretical Model (TTM) and Social Cognitive Theory (SCT). Many intervention strategies are common to multiple health behavior theories. Goal setting was a frequently used behavioral strategy. One possibility of eHealth technology is the improved ability to break down large goals into smaller ones. For example, Croteau adjusted step count goals biweekly by 5% or 10% to slowly increase daily physical activity to the desired levels, and found large and significant effects over time.16 By assessing smaller milestones more frequently, the technology has the ability to automatically create new, slightly more challenging goals to enhance the likelihood of reaching the overall health behavior recommendation.
Another common strategy for health programs involves behavioral self-monitoring with specific feedback. Technology can facilitate this process as participants can fill out electronic logs for physical activity or food consumption, and then send them via email to a health professional who can respond with personalized feedback in a timely manner. For example, Tate and colleagues used a website for daily to weekly online submissions of calorie and fat intake, and energy expenditure.57,58 Both studies found significant weight loss for the intervention participants who received timely feedback from counselors via email. These findings demonstrate eHealth's potential for tracking and reinforcing behavior.
Unfortunately, the design of many studies precluded tests to determine whether the interventions were working through hypothesized theoretical constructs. As a result, when an intervention program resulted in weak findings, conclusions could not be drawn as to whether the lack of findings was due to a lack of theoretical fidelity or to other threats to study validity.
Two studies tested hypotheses specifically about targeting multiple health behaviors. Prochaska and Sallis 55 found no benefit when concurrently targeting physical activity and diet compared to targeting physical activity alone among school children. Conversely, Vandelanotte and colleagues 59 found some evidence that simultaneous targeting of physical activity and dietary fat reduction was more effective than sequentially targeting these behaviors.
Multiple behavior interventions have more content and likely take more time than single behavior interventions. Many individuals may feel they do not have time to complete assessments, receive feedback, read educational materials, set goals and engage in other behavior change strategies for both physical activity and diet concurrently. This presents the challenge of trying to use eHealth technologies to facilitate the behavior change process as a multidimensional lifestyle approach. Experiments are needed to test programs with different combinations and sequences of behavior targets. It may be that a menu of simultaneous and sequential behavior change intervention options may be needed to meet the needs of different individuals.
The rapid developments in interactive technologies in terms of processing power, data transmission and data storage leads to a continuing evolution of eHealth interventions. Programs have evolved from first-generation programs that facilitated intervention tailoring with computers to generate printed materials.67,68 What we have termed “second generation” e-health interventions allow for direct interaction between the participant and the technology to increase capabilities beyond tailored feedback messages. This second generation of interventions has allowed participants to select relevant psychoeducational information,39,59 report on goals and track their progress,57,58 and provide and receive social support either via bulletin boards 30,51 or synchronous chat rooms.49
This review was broad in scope to meet the objective of featuring eHealth technology implemented in interventions for changing physical activity and dietary behaviors. Because of the heterogeneity of studies, meta-analysis to determine pooled effect size estimates of eHealth interventions was not conducted. However, the review was systematic in addressing a specific research question, having explicit selection criteria, evaluating study quality, and describing study findings. Our summary of the support for eHealth interventions indexed by statistical significance is a limitation of this review and more narrowly focused meta-analytic reviews are needed to better quantify the effect of eHealth interventions.
A third generation of eHealth technologies is already emerging. The interventions in the current review consisted of desktop applications. However, mobile devices such as handheld computers, cellular telephones, and text messaging devices are emerging as new platforms for delivering health information. These platforms are also incorporating new functions such as sensing, monitoring, geospatial tracking, location-based knowledge presentation, and a host of other information processes 69 that will potentially enhance the ability for accurate assessment and tailored feedback. Research has already been conducted using PDAs for ecological momentary assessment (EMA), where real-time self-report data is collected throughout a person's day.70,71 The EMA concept can be expanded to “ecological momentary intervention,”69 such as “just in time” prompting for a behavior change based upon some set of predefined conditions.
eHealth behavior change interventions are still in the preliminary stages of development and the potential of novel technologies to impact health behaviors is just beginning to be evaluated.72 While eHealth is progressing, it is clear that more research is needed to better determine how technology can be incorporated into programs to enhance behavior change outcomes. We presented a description of the “state of the science” of recently published studies in this area that can serve as a guide to what has been accomplished and what future development and evaluation of eHealth interventions is needed.
No financial disclosures were reported by the authors of this paper.
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