Obesity is a rapidly increasing public health problem, with more than 600 million people with obesity (body mass index [BMI] ≥30 kg/m²) worldwide [1,2]. One of the main challenges related to obesity is that although many people manage to lose weight, only 1 of 4 people manages to maintain the lost weight in the long term [3]. With several mechanisms interacting (eg, biological, environmental, behavioral, and cognitive) to make weight regain following weight loss common [4,5], novel ways to counterbalance these mechanisms are needed [6,7]. The significant weight loss maintenance challenge calls for the examination of new technologies and solutions in future studies of long-term weight control [6,8,9].

eHealth (electronic health) is a term often used to define the blending of digital technologies, health, and health services and can be delivered through information and communication technologies [10-12]. Although evidence is sparse regarding the impact of eHealth on health and health care, research indicates that eHealth can support health care delivery by providing greater efficiency, lead to better health outcomes, and lower health service costs [10,13-15]. eHealth technologies are also increasingly used to support a healthier life, improved well-being, and creation of new health behaviors [16-19] and have the potential to support the challenging behavior changes needed to sustain long-term weight loss maintenance [4,20].

Behavior change interventions are usually complex and may include many interacting components or techniques [21,22]. Behavior change techniques (BCTs) are observable and active intervention components aiming to regulate behavior (eg, goal setting, self-monitoring, and feedback) [21,23]. However, health behavior change requires motivation and persistence, and persuasive design [24] also appears to play an important role in this setting. Persuasive design of services or technologies focuses on influencing human behavior in a positive way. As such, persuasive system design (PSD) principles can be applied in eHealth design to match user profiles, motivate users to engage in self-management, and trigger health behavior change [16,24-26]. Several behavior change theories, BCTs, and PSD principles can be involved in an eHealth intervention [27], alone or in combination.

To date, there is limited knowledge about how behavior change interventions and design of technologies and services can impact behavior and motivation in support of sustainable health behavior change [25,28,29]. eHealth is often described as a black box, as knowledge is limited about its internal structure and how the use of various components of the technology can contribute to healthier lifestyles and improved health outcomes [11,22,30,31]. Finding the right mix of technological features to stimulate the motivation and adherence needed to support long-term weight loss maintenance is, therefore, still a conundrum [7]. Little is also known about how BCTs and PSD principles can be used in eHealth interventions to support long-term weight loss maintenance [8,32]. The application of the most effective BCTs and PSD principles, at the right time and in the best combination, could therefore be of essence to support motivation and adherence in the pursuit of sustainable weight loss maintenance [19,33-35].

The Goal of This Review

The overall goal of this review was to provide insight into the design of eHealth interventions aiming to support behavior change for long-term weight loss maintenance in adult people with obesity. This review identified BCTs and PSD principles to stimulate motivation and adherence in eHealth interventions built to support weight loss maintenance.

Research questions for this review are as follows: in eHealth interventions, (1) how are motivation and adherence defined and measured? (2) Are motivation and adherence linked to weight loss and weight loss maintenance? (3) What can be determined from behavior change theories, BCTs, and PSD principles used in weight loss and weight loss maintenance interventions? (4) Which behavior change theories, BCTs, and PSD principles have been used to stimulate motivation and adherence in eHealth weight loss maintenance interventions, and in what combination? (5) What are the reported effects (ie, weight outcomes) in eHealth weight loss maintenance interventions?

A scoping review methodology was considered suitable for mapping literature on BCTs and PSD principles, as this is an emerging topic where evidence is scarce and key concepts and gaps in existing research should be identified [36,37]. This scoping review applied the methodology by Arksey and O’Malley [36], with the following steps [37]: (1) identify the research questions; (2) identify relevant studies; (3) study selection; (4) chart the data; (5) collate, summarize, and report the results; and (6) consultation. To enhance the scoping study methodology, additional recommendations [37] were followed: (1) 2 independent researchers reviewed all full-text publications, and (2) the research group developed and continuously updated the data extraction form during the extraction process.

As research on eHealth interventions targeting weight loss maintenance is still in its infancy, eHealth interventions targeting weight loss were also examined to best identify weight loss maintenance–related factors. Research questions 1, 2, and 3 entailed broad scopes. The scope was then further narrowed in research questions 4 and 5, focusing on weight loss maintenance interventions and the BCTs and PSD principles applied to stimulate motivation and adherence, as well as any effects (ie, weight outcomes) related to weight loss maintenance.

This review applied Michie’s Behavior Change Taxonomy [21] developed to meet the need for standardized reporting on development and content of complex behavior interventions. Michie’s Cross-Domain Taxonomy consists of 93 distinct BCTs divided into 16 clusters, independent of any specific theory. Multimedia Appendix 1 provides detailed information about the BCT clusters. Similarly, the PSD model by Oinas-Kukkonen [16], building on previous research by Fogg [26], was used as a framework to identify persuasive principles applied in the included interventions. Multimedia Appendix 2 provides information about the 4 PSD categories: primary task support, dialog support, system credibility support, and social support, as well as operationalization of the individual principles [16]. For the purpose of this review, adherence to a technology was defined as use as intended or desired by the authors or developers of an intervention [19], whereas motivation was defined as a reason for doing something [38].

A systematic literature search to cover behavioral, technical, and clinical research aspects was conducted in the following databases: PsycINFO, Ovid MEDLINE (including PubMed), EMBASE, Scopus, Web of Science, and AMED. As digital technologies are advancing and developing fast, more recent evidence (ie, since 2007) was considered to be the most relevant and interesting. Publications during the period from January 1, 2007 to June 30, 2018, were therefore included. The terms weight loss and weight loss maintenance were used, in combination with a variety of the term eHealth interventions and the terms motivation, adherence, and/or behavior change. This search strategy was created and applied in close collaboration with librarians and domain experts (Multimedia Appendix 3).

Publications in English, clearly describing an eHealth intervention focusing on weight loss maintenance or weight loss, were included for assessment when containing persuasive design, behavior change theories, and techniques or when mentioning motivation and/or adherence. The target population for this review was people with overweight (ie, BMI 25-29.9 kg/m²) and/or obesity (ie, BMI ≥30 kg/m²). Multimedia Appendix 4 gives a complete overview of the inclusion and exclusion criteria.

A data charting form containing general as well as specific study characteristics was created in Microsoft Excel by the research team. The characteristics were extracted using elements from the CONSORT-eHealth checklist [39], focusing on characteristics about the interventions and technologies in the included publications. Michie’s Cross-Behavior Change Taxonomy [21] guided the extraction process to identify and group specific information about BCTs used. For the purpose of this review, representation of the 16 clusters as indicated in Multimedia Appendix 1 was applied, rather than presenting a detailed representation of up to 93 distinct techniques. Supplementary information about definitions, including specific examples, was reviewed [40], and behavior change theories mentioned or described were recorded. Persuasive principles were extracted from the included publications using the PSD model [16] presented in Multimedia Appendix 2. PSD principles were coded when executed by or through the technology. Due to lack of reporting on the system credibility category, this category was not part of the analysis. Goal setting, feedback, and social support were added to the model as separate persuasive principles, as they could not always easily be linked to specific design elements in the PSD model.

Two researchers (RA and MS) independently coded and categorized the identified BCTs and PSD principles, using the presented frameworks. A third researcher (CV) validated 11% (5/45) of the included interventions. The first and second author also recorded additional information on motivation and adherence when mentioned, including how motivation/adherence was defined, stimulated, and measured. Data on effect (ie, weight outcomes) were recorded when reported, including when weight was self-reported or measured by the researcher or coach/clinician. The research team also extracted relevant information (eg, intervention components, BCTs, and PSD principles) from the incorporated interventions, including illustrations, figures, tables, and additional websites when referred to in the publications. To enhance and support the relevance of the review, clinicians and researchers specialized in the fields of weight loss/maintenance, health psychology, and eHealth were consulted regarding methodological approach, relevance, and current state of the evidence.

Weight loss BCTs do not necessarily equal weight loss maintenance BCTs. Long-term maintenance of lost weight is challenging, and there is a call for interventions evaluating novel methods to improve the maintenance of lost weight [8,41]. To meet this call and the overall goal of this review (ie, supporting long-term weight loss maintenance), the first part of the Results section focuses on weight loss and weight loss maintenance, whereas the rest of the results section focuses solely on weight loss maintenance interventions.

The search revealed 463 publications. Following removal of 146 duplicates, the remaining 317 titles and abstracts were screened for eligibility by 2 researchers (RA and either MS, CV or FS) using the Covidence software program [42]. After removing 224 publications not meeting eligibility based on title/abstracts, the remaining 93 full-text publications were reviewed by the first (RA) and second (MS) authors. The authors RA and MS discussed differences and disagreements until consensus was reached. If consensus could not be reached, consultation was sought from a third researcher (FS). After the full-text screening process, 45 publications remained to be assessed for further analyses (Figure 1).

All 45 publications included in this review were published in 2008 or later (Figure 2). First authors were mainly affiliated in the United States (25/45, 56%), the United Kingdom (5/45, 11%), and Australia (3/45, 7%); see Table 1.

Of the 45 included interventions, 34 (76%) targeted weight loss, and 11 (24%) targeted weight loss maintenance. Of 11 weight loss maintenance interventions, 5 (45%) had an initial weight loss phase. The most frequently described study design was randomized controlled trials for the weight loss (15/45, 33%) and weight loss maintenance (4/45, 9%) interventions alike. Although several of the included interventions evaluated effects (ie, weight outcome; 26/45, 58%), others described only the design of the intervention (6/45, 13%) or a protocol (7/45, 16%). Most interventions targeted people with overweight (24/45, 53%) or included both overweight and obesity (15/45, 33%). People with obesity (ie, BMI ≥30 kg/m²) were the sole target population in only 4 weight loss interventions and 2 weight loss maintenance interventions. Average duration of interventions was 24 weeks (range 4-104; median 13 weeks) for the weight loss interventions, and 27 weeks (range 12-52; median 26 weeks) for the weight loss maintenance interventions. User involvement during the technology development process was only described by 8 weight loss and 4 weight loss maintenance interventions. The users were usually only mentioned when involved in part of the development process (eg, identifying needs, content development, and usability testing). Two publications were included despite describing the same intervention, as they focused on different aspects of the intervention [43,44]. Multimedia Appendix 5 provides an overview of the included interventions including title, authors, publication year, country of origin, design, objectives, participants, aim and type of technology, intervention duration, and whether blended care was part of the intervention or not.

In the 45 included publications, mobile phones were the most frequently used technology (28/45, 62%), followed by Web-based solutions through computers (15/45, 33%), or a combination of computer and mobile phone (eg, for feedback or reminders through text messages; 6/45, 13%). Monitoring technologies used were activity trackers or step counters (14/45, 31%), wireless or digital weight scales (8/45, 18%), and glucose (2/45, 4%) or blood pressure (1/45, 2%) monitors, often combined with manually recorded self-monitoring data. Some technologies included game-based elements (3/45, 7%) [45-47], a virtual world with avatars [48] or a virtual coach [49], and other tools to enhance self-monitoring (eg, automated calculations of energy intake, expenditure, and energy balance). Several eHealth weight loss interventions (20/45, 44%) and weight loss maintenance interventions (7/45, 16%) had a blended care approach, including the combination of various formats of human coaching and/or face-to-face services from experts. Some eHealth interventions also integrated peer forums or social media groups (7/45, 16%) or included social support through a buddy or helper (eg, family, friend, colleague; 3/45, 7%).

The typical eHealth weight loss maintenance intervention was supported by mobile phone technology (9/11, 82%), in combination with an activity tracker or step counter (3/11, 27%) and/or wireless scale (3/11, 27%) [47,50-52]. The technology usually supported 2-way communication with a peer, dietitian, or coach [50,53-56] and provided automated, tailored feedback based on progress data [50-53,55,57,58]. The technology in these weight loss maintenance interventions usually aimed to support creation of healthy habits [51-54]; educational resources and information [51,53,54,59]; daily or weekly monitoring tools for weight, diet, activity (eg, number of steps) [47,50-52,55]; well-being (eg, mood, stress, and good days/bad days) [47,51,53]; and/or plans or strategies for individual action and coping [47,54,57].

Only 2 of the 45 included publications provided a definition of motivation [43,44], and these 2 publications originated from the same intervention. By referring to self-determination theory, the publications distinguished between autonomous motivation (a measure of a person’s internal or personal reasons for change) or controlled motivation (a measure of extrinsic reasons or external pressure to change) [44].

Motivation was measured in various ways by self-reported measures and questionnaires as presented in Table 2.

Motivation was evaluated (ie, link to weight loss/maintenance) in 7 of 35 weight loss intervention studies [43,44,49,60,63-65], but in none of the included weight loss maintenance interventions (n=11) as indicated in Multimedia Appendix 6. Of the 7 weight loss studies that evaluated motivation, 1 found high levels of controlled motivation at baseline to produce significantly greater weight loss in the motivation-enhanced intervention (ie, specific components were used to enhance autonomous motivation) compared with the standard intervention [43]. The motivation-enhanced group used the website more often than the control group, and the number of visits was associated with weight loss [43]. To increase autonomous motivation, principles of motivational interviewing [66] together with goal setting and journaling (eg, writing about the future when weight loss goals are achieved) and blended formats, such as face-to-face sessions, were also added to the Web-based weight loss program to improve autonomous motivation [43].

Another study showed the level of autonomous motivation after 4 weeks to be predictive of self-monitoring of adherence and weight loss at 16-week postbaseline [44]. For participants who reached 5% weight loss, autonomous motivation increased and remained higher than for those not reaching this clinically meaningful weight loss [44]. A third study suggested that diet-focused constructs were particularly important when developing weight loss interventions for men [60] because changes in diet-related autonomous motivation were linked to weight loss. In that study, the intervention group achieved greater weight loss than the control group [60].

The fourth study evaluating motivation found motivational orientation (eg, promotion focused or prevention focused) to be a predictor of behavior change when trying to lose weight, and framing messages with people’s motivational orientation was considered preferable to, for example, informational and prescriptive messages in terms of behavior change [63]. As high as 72% of the participants in another study found text messages received biweekly to be motivational [64], and 79% reported text messages to be helpful in performing healthy eating and exercise behaviors [64]. The sixth study evaluating motivation found that delivery of remote daily real-time feedback messages tailored to diary entries could enhance motivation, producing greater reductions in energy and saturated fat consumptions [67]. In the final study, 58% of the participants agreed that a virtual coach motivated them to become more active [49], suggesting that meetings with a virtual coach could be beneficial in maintaining activity level. However, no significant changes in step count were found in the intervention versus the control groups [49].

Of the 45 included publications in this review, 6 weight loss interventions measuring adherence provided a definition of the adherence concept or related terms such as usage and compliance [64,68-72]. These publications defined or operationalized adherence based on either self-monitoring or electronic entry of food and exercise records [68]. Low usage was defined as having no food records and high usage as having 1 food record on a randomly selected day of the sampled week [68] or as recording of 50% or more of prescribed daily calorie intake goal [69]. Some publications also defined adherence as responsiveness to text messages or health challenges [64,71,72] or looked at various aspects of adherence such as behavioral adherence (ie, attendance to counseling sessions) [68] and dietary adherence (ie, self-monitoring related to dietary goals) [68,70]. Other publications defined adherence as program compliance to habits and workout [71] or consistency to self-monitoring [73].

Regarding measurement of adherence, as the main scope of this review was weight loss maintenance, the results related to short-term weight loss interventions (≤6 months) measuring adherence are not reported in this review [18,43,44,49,69,70,73-77]. Moreover, 4 of the included long-term weight loss interventions (>6 months) measured adherence [68,71,72,78]. For weight loss maintenance, adherence was measured in 3 of the interventions [50,52,59], and the duration for 2 of these interventions was more than 6 months (Multimedia Appendix 6). The 4 long-term weight loss interventions measured various aspects of adherence, including self-monitoring data related to diet and physical activity [68], compliance to the Web-based program, daily habits and exercise [71], website usage by the number of self-tracking entries [78], or the total percentage of text messages that a participant responded to [72]. The 3 weight loss maintenance interventions measured adherence or engagement in relation to the coaching program through evaluating frequency of submitting self-monitoring data to their coach [52], the number of delivered text messages replied to by the participants [59], and by participants’ self-monitoring adherence through frequency of weigh-ins and use of activity tracking [50].

Adherence to technology was defined as use as intended or desired in this review [19]. Intended usage was reported in the 4 weight loss interventions (>6 months) and in all 3 weight loss maintenance interventions. Intended use was most frequently described as 1 time per day or more (≥1 per day).

As seen in Tables 3 and 4, weight loss BCTs and PSD principles do not necessarily equal weight loss maintenance techniques. To meet the call for interventions evaluating novel methods to improve maintenance of lost weight [8] and meet the overall goal of this review, the next part of the Results section focuses solely on weight loss maintenance interventions.

Of the 11 included interventions targeting weight loss maintenance, only 3 evaluated effects of the intervention on body weight or BMI. All 3 interventions found significant effects for weight loss maintenance at 12 weeks postbaseline [59], 12 months postintervention [52], and up to 24 months postbaseline [50].

In addition, one of the interventions had a 6-month weight loss phase followed by a 6-month weight loss maintenance phase and presented characteristics of high-performing participants who had lost 10% or greater of their starting weight at the 1-year follow-up [50]. High performers compared with low performers had greater adherence to self-monitoring of weight, more days wearing activity trackers, and higher average number of steps per day [50]. In another study, rewards were applied to stimulate adherence, suggesting that an internet-delivered cost-benefit approach might be effective to support weight loss maintenance [52]. The third study, entailing a mobile health (ie, mobile or cellular phone technology) lifestyle program, implemented weekly text messages to prevent weight gain, using praise to stimulate motivation [59]. This intervention appeared successful in preventing unhealthy weight gain, resulting in modest weight loss and improved health behaviors [59].

The 3 weight loss maintenance interventions achieving effect in terms of weight outcomes were similar in that they all applied techniques and principles related to tailoring, self-monitoring, feedback, goals and planning, and shaping knowledge. A human social support component delivered through a blended format by e-coaching [52], telephone support [59], or expert coach by a Web-based electronic messaging feature [50] was also present in all 3 interventions.

This scoping review aimed to identify BCTs and PSD principles applied in eHealth interventions to support weight loss and weight loss maintenance, as well as techniques and principles applied to stimulate motivation and adherence for long-term weight loss maintenance. The most successful eHealth weight loss maintenance interventions entailed a combination of BCTs and PSD principles, and the analysis identified several techniques and principles applied to stimulate motivation and adherence.

First, research into design and application of new, personalized digital technologies that integrate sensors and long-term monitoring of data of behaviors and decisions can provide opportunities that may contribute to ultimately solve the conundrum of sustainable health behavior change and long-term weight loss maintenance. Second, the identification of central BCTs and PSD principles to support behavior change, motivation, and adherence in this review allow for user testing in predesign phases of behavioral eHealth interventions, which again can aid in the evaluation of what is needed to truly support individuals in their health. This review also identified self-regulation techniques to support creation and maintenance of healthy habits, but the ideal combination of such techniques should be further investigated through design and evaluation of novel technologies to support long-term weight maintenance after weight loss. Building healthy habits and behaviors takes time, and future research should explore how personalized eHealth technologies can support patients’ motivation, long-term adherence, and sustained engagement to improve healthy behaviors over time.

Future research can also better facilitate comparison of interventions through following standardized guidelines and frameworks more diligently when reporting findings, including following guidelines and frameworks such as the CONSORT Guidelines [39], the BCT Taxonomy of Michie [21], and/or the PSD model by Oinas-Kukkonen [16].

Finally, eHealth interventions developed in line with user values and needs may have the potential to motivate and empower sustainable health behavior change, which calls for more user involvement and multidisciplinary approaches in design, development, and evaluation of eHealth interventions. Such an interwoven development process, combining the input, needs, and requirements of researchers, engineers, and stakeholders (including users), is needed to unravel or disentangle the black box and create technologies that engage, motivate, and support health behaviors that can be sustained to maintain lost weight for a lifetime.

This review has a number of limitations. First, identification and scoring of the persuasive features and BCTs could have been prone to subjectivity by the researcher(s). However, to prevent subjectivity, consultation and a 10% validation of included publications were performed. Second, system credibility support was not included in the analyses, as limited data were reported and examinations of the actual technology were not included in this review. However, the analysis reveals knowledge about the other 3 PSD categories, in particular, the primary tasks and dialog support. Third, no quality appraisal of evidence, often done in systematic reviews, was performed in this scoping review. This limits the possibility of drawing conclusions regarding cause and (long-term) effectiveness of interventions. However, as the aim of this scoping review was to provide insight into the design of eHealth interventions, particularly the PSD principles and BCTs mentioned applied to support sustained behavior change and weight loss maintenance, a quality assessment of the studies included was not considered to be as relevant. In addition, insight into various study designs provides an overview over this emerging research area. Finally, the heterogeneity of included designs and lack of long-term results complicate a comparison of the interventions and the possible impact of techniques and principles on reported outcomes, which may introduce bias.

The focus on weight loss maintenance is, however, a major strength of this scoping review, as weight loss maintenance is an area in dire need of further research and future recommendations. In addition, the inclusion of a variety of study designs allows for a consideration of existing interventions that describe design choices and formative evaluations, contributing to eventually opening up the black box and giving direction for future design of eHealth interventions.

To the best of our knowledge, this review is the first to identify BCTs and PSD principles applied in eHealth weight loss and weight loss maintenance interventions. Results reveal very limited existing research in the area of eHealth interventions to support weight loss maintenance. Motivation and adherence are clearly of essence in terms of achieving long-term weight loss maintenance, yet there is still a lack of standardization in definitions and measurement of these concepts. Results show how self-regulation strategies are applied in weight loss and weight loss maintenance interventions, reflected in the design through core techniques and principles such as self-monitoring, feedback, goals and planning, tailoring, and shaping knowledge. Frequently mentioned BCTs and PSD principles applied to stimulate motivation in weight loss maintenance interventions were personalization, praise, and feedback, whereas associations were mentioned to stimulate adherence. Social support and rewards were mentioned as being applied to stimulate both motivation and adherence. The most effective combination of techniques or design features to stimulate motivation, adherence, and weight loss maintenance nevertheless remains somewhat obscure. Although few weight loss maintenance eHealth interventions indicated effect (ie, weight outcome), the interventions with significant results all applied the identified core BCTs and PSD principles, as well as a human social support component.

In conclusion, this scoping review aimed to contribute to open the black box of eHealth in the design of weight loss maintenance interventions. The findings are expected to contribute to a better understanding of existing research in this field, and in addition to contribute to development and evaluation of future eHealth interventions and novel solutions to support sustained behavior change and long-term weight loss maintenance. The results of this review support the notion that the research of eHealth interventions in weight loss maintenance is still in its infancy, and more research is needed.