BACKGROUND: Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design.
OBJECTIVE: This study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included.
METHODS: A literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes.
RESULTS: Digital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ21=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results.
CONCLUSIONS: There was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventions that were more strongly informed by theory demonstrated greater improvements in eating disorder outcomes compared to waitlist or treatment-as-usual controls. These results can be used to inform the development of future digital eating disorder interventions.
BACKGROUND: PROSPERO CRD42023410060; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=410060.

BACKGROUND: Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA.
OBJECTIVE: We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA.
METHODS: This study was a single-blind, pilot randomized controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 BCT conditions: self-monitoring (feedback on participants\' own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed.
RESULTS: Participants showed excellent adherence (mean valid-wear time of Fitbit=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). Fitbit-assessed step count during the intervention period was slightly higher in the goal setting and social comparison groups than in the self-monitoring group, although the effects did not reach statistical significance (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal setting group (P=.04).
CONCLUSIONS: Fitbit-implemented BCTs promote readiness and motivation for PA, although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA.
BACKGROUND: Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b.

BACKGROUND: Mobile health interventions delivered through mobile apps are increasingly used in physiotherapy care. This may be because of the potential of apps to facilitate changes in behavior, which is central to the aims of care delivered by physiotherapists. A benefit of using apps is their ability to incorporate behavior change techniques (BCTs) that can optimize the effectiveness of physiotherapeutic interventions. Research continues to suggest that despite their importance, behavior change strategies are often missing in patient management. Evaluating mobile apps that physiotherapists can use to drive behavior change may inform clinical practice and potentially improve patient outcomes. Examining the quality of apps and exploring their key features that can support behavior change and physiotherapy care are important aspects of such an evaluation.
OBJECTIVE: The primary aim of this study was to describe the range of mobile apps in app stores that are intended for use by patients to support physiotherapy care. The secondary aims were to assess app quality, BCTs, and their behavior change potential.
METHODS: A systematic review of mobile apps in app stores was undertaken. The Apple App Store and Google Play were searched using a 2-step search strategy, using terms relevant to the physiotherapy discipline. Strict inclusion and exclusion criteria were applied: apps had to be intended for use by patients and be self-contained (or stand-alone) without the requirement to be used in conjunction with a partner wearable device or another plugin. Included apps were coded for BCTs using the Behavior Change Technique Taxonomy version 1. App quality was assessed using the Mobile App Rating Scale, and the App Behavior Change Scale was used to assess the app\'s potential to change behavior.
RESULTS: In total, 1240 apps were screened, and 35 were included. Of these 35 apps, 22 (63%) were available on both the Apple App Store and Google Play platforms. In total, 24 (69%) were general in their focus (eg, not condition-specific), with the remaining 11 (31%) being more specific (eg, knee rehabilitation and pelvic floor training). The mean app quality score (Mobile App Rating Scale) was 3.7 (SD 0.4) of 5 (range 2.8-4.5). The mean number of BCTs identified per app was 8.5 (SD 3.6). BCTs most frequently included in the apps were instruction on how to perform a behavior (n=32), action planning (n=30), and self-monitoring of behavior (n=28). The mean behavior change potential score (App Behavior Change Scale) was 8.5 (SD 3.1) of 21 (range 3-15).
CONCLUSIONS: Mobile apps available to support patient care received from a physiotherapist are of variable quality. Although they contain some BCTs, the potential for behavior change varied widely across apps.
UNASSIGNED: RR2-10.2196/29047.

BACKGROUND: Although previous systematic reviews have studied medication adherence interventions among people with Type 2 diabetes (PwT2D), no intervention has been found to improve medication adherence consistently. Furthermore, inconsistent and poor reporting of intervention description has made understanding, replication, and evaluation of intervention challenging.
OBJECTIVE: We aimed to identify the behavior change techniques (BCTs) and characteristics of successful medication adherence interventions among PwT2D.
METHODS: A systematic search was conducted on Medline, Embase, CINAHL, PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus. Studies were included if they were randomized controlled trials with BCT-codable interventions designed to influence adherence to anti-diabetic medication for PwT2D aged 18 years old and above and have medication adherence measure as an outcome.
RESULTS: Fifty-five studies were included. Successful interventions tend to target medication adherence only, involve pharmacists as the interventionist, contain \"Credible source\" (BCT 9.1), \"Instruction on how to perform the behaviour\" (BCT 4.1), \"Social support (practical)\" (BCT 3.2), \"Action planning\" (BCT 1.4), and/ or \"Information about health consequences\" (BCT 5.1). Very few interventions described its context, used theory, examined adherence outcomes during the follow-up period after an intervention has ended, or were tailored to address specific barriers of medication adherence.
CONCLUSIONS: We identified specific BCTs and characteristics that are commonly reported in successful medication adherence interventions, which can facilitate the development of future interventions. Our review highlighted the need to consider and clearly describe different dimensions of context, theory, fidelity, and tailoring in an intervention.
Medication is the mainstay treatment for diabetes. However, the use of anti-diabetic oral medications and/or injections may be intrusive, inconvenient, and complicated, leading to poor medication adherence, which occurs in about 50% of patients. Medication adherence is the extent to which a person’s medication-taking behavior corresponds with agreed recommendations from a healthcare provider and is associated with suboptimal health outcomes and increased healthcare expenditure. Interventions to improve adherence have not been consistently effective or well described, which makes it difficult to ascertain what works best. In this study, we aimed to identify the behavior change techniques (BCTs) and characteristics of successful medication adherence interventions among adults with Type 2 diabetes. BCTs are active ingredients in an intervention that regulate medication adherence and have standardized definitions. After searching 7 databases systematically, we analyzed 55 studies published in January 2018—March 2022. We found that the more effective interventions were those which had a sole focus on medication adherence, involved pharmacists, and contained specific BCTs, namely “Credible source,” “Instruction on how to perform the behaviour,” “Social support (practical),” “Action planning,” and/or “information about health consequences.” These specific BCTs and characteristics can be considered in future interventions for improving medication adherence.

Oral health declines in older adults with cognitive impairment. We aimed to improve oral hygiene outcomes for individuals with mild cognitive impairment (MCI) or mild dementia (MD) by fostering behavior changes among carepartners assisting them. We used qualitative data of verbatim transcripts of coaching sessions with carepartners (n = 17 dyads:10 dyads for MCI, 7 dyads for MD). Directed and emergent coding were used to understand behavior change techniques (BCTs). BCTs were compared with carepartners of participants with MCI and MD. Most frequently used BCTs in both groups: prompts and cues, instruction on how to perform the behavior, review behavioral goal, and problem solving. Different BCTs emerged in study: social support-unspecified of the MCI group and credible source for MD group. Findings clarified active intervention components, common BCTs used by carepartners, and different BCT approaches for both participants. Findings help to elucidate the mechanisms of changes in individuals\' behaviors in these interventions.

Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of noncommunicable diseases (NCDs), it is unclear what specific components make a DHI effective.
This narrative umbrella review aimed to identify the most effective behavior change techniques (BCTs) in DHIs that address the prevention or management of NCDs.
Five electronic databases were searched for articles published in English between January 2007 and December 2022. Studies were included if they were systematic reviews or meta-analyses of DHIs targeting the modification of one or more NCD-related risk factors in adults. BCTs were coded using the Behavior Change Technique Taxonomy v1. Study quality was assessed using AMSTAR 2.
Eighty-five articles, spanning 12 health domains and comprising over 865,000 individual participants, were included in the review. We found evidence that DHIs are effective in improving health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest that credible source, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, human coaching and personalization components increase the effectiveness of DHIs targeting the prevention and management of NCDs.
This review identifies the most common and effective BCTs used in DHIs, which warrant prioritization for integration into future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines.
Digital health interventions (DHIs) that use technology to deliver lifestyle support for the prevention or treatment of noncommunicable diseases (NCDs) have grown in popularity and number in recent years. However, it is unclear what aspects make a DHI effective in changing lifestyle behaviors and improving health. The aim of this study was to review the existing scientific evidence to identify effective components in DHIs that address the prevention or management of NCDs and summarize the best available evidence to date. We conducted a comprehensive electronic search for peer-reviewed systematic reviews and meta-analyses published in English between January 1, 2007 and December 31, 2022. We systematically extracted details of the reviews and the intervention components and summarized the effectiveness of components for each health domain, prioritizing the best available evidence. Eighty-five articles, spanning 12 health domains and summarizing evidence from over 865,000 individual participants, were included in the review. We found good evidence that DHIs are effective in preventing and treating NCDs. Specific intervention components that are effective and should be prioritized for inclusion in future DHIs include: using a credible source; social support; prompts and cues; graded tasks; goals and planning, feedback and monitoring, human coaching and personalization.

Inclusion of evidence-based behavior change techniques (e.g., self-monitoring) in mobile health apps has the potential to promote adherence to inflammatory bowel disease treatment. While inflammatory bowel disease management apps exist, the extent to which they incorporate behavior change techniques remains unknown.
The present study systematically evaluated the content and quality of free, commercially available inflammatory bowel disease management apps.
Apps were identified using a systematic search of the Apple App and Google Play stores. Apps were evaluated using Abraham and Michie\'s taxonomy of 26 behavior change techniques. A literature search was conducted to identify behavior change techniques specific and relevant for people with inflammatory bowel disease. App quality was assessed using the Mobile App Rating Scale with scores ranging from 1 (Inadequate) to 5 (Excellent).
A total of 51 inflammatory bowel disease management apps were evaluated. Apps included 0-16 behavior change techniques (Mean = 4.55) and 0-10 inflammatory bowel disease management behavior change techniques (Mean = 3.43). App quality ranged from 2.03 to 4.62 (Mean = 3.39) out of 5.00. Two apps, My IBD Care: Crohn\'s & Colitis and MyGiHealth GI Symptom Tracker, included the highest number of overall and inflammatory bowel disease management behavior change techniques along with high-quality scores. Bezzy IBD was the only app with a high number of overall and inflammatory bowel disease management behavior change techniques with a primary focus on social support/change.
Most inflammatory bowel disease management apps reviewed included evidence-based inflammatory bowel disease management behavior change techniques.

Digital health interventions are efficacious in health-promoting behaviors (eg, healthy eating and regular physical activity) that mitigate health risks and menopausal symptoms in midlife. However, integrated evidence-based knowledge about the mechanisms of change in these interventions is unclear.
This systematic review aimed to evaluate studies on behavior change techniques (BCTs) and mechanisms of change in digital health interventions aimed at promoting health-enhancing behaviors in midlife women (aged 40-65 years).
A systematic literature search of the electronic databases PubMed, Web of Science, PsycINFO, and Cochrane Central Register of Controlled Trials in the Cochrane Library was conducted. In total, 2 independent reviewers selected the studies for inclusion, extracted data, and completed BCT mapping of eligible studies. The mechanism of action and intervention functions of eligible studies were evaluated using the behavior change wheel framework. Reporting of psychological theory use within these interventions was explored using the Theory Coding Scheme. Mode of delivery, psychological theory, and BCTs were presented as descriptive statistics.
In total, 13 interventions (including 1315 women) reviewed used 13 (SD 4.30, range 6-21) BCTs per intervention on average. The \"Shaping knowledge\" and \"Repetition and substitution\" behavior change categories were used most frequently, with 92% (12/13) of the interventions implementing at least one of the BCTs from these 2 categories. Only 13.98% (169/1209) of the 93 available BCTs were used, with \"Instructions on behaviour\" most frequently used (12/13, 92%). The behavior change wheel mapping suggests that half of the intervention content aimed to increase \"Capability\" (49/98, 50% of the intervention strategies), \"Motivation\" (41/98, 42%), and \"Opportunity\" (8/98, 8%). \"Behavioural Regulation\" was the most frequently used mechanism of action (15/98, 15%), followed by increasing \"Knowledge\" (13/98, 13%) and \"Cognitive and Interpersonal skills\" (10/98, 10%). A total of 78% (7/9) of the intervention functions were used in the studies to change behavior, primarily through \"Enablement\" (60/169, 35.5%), whereas no study used \"Restriction\" or \"Modelling\" functions. Although 69% (9/13) of the interventions mentioned a psychological theory or model, most (10/13, 77%) stated or suggested rather than demonstrated the use of a theoretical base, and none reported explicit links between all BCTs within the intervention and the targeted theoretical constructs. Technological components were primarily based on web-based (9/13, 69%) modes of delivery, followed by phone or SMS text message (8/13, 62%) and wearables (7/13, 54%).
The findings of this review indicate an overall weak use of theory, low levels of treatment fidelity, insignificant outcomes, and insufficient description of several interventions to support the assessment of how specific BCTs were activated. Thus, the identified limitations in the current literature provide an opportunity to improve the design of lifestyle health-enhancing interventions for women in midlife.
PROSPERO CRD42021259246; https://tinyurl.com/4ph74a9u.

BACKGROUND: A challenge facing researchers conducting mobile health (mHealth) research is the amount of resources required to develop mobile apps. This can be a barrier to generating relevant knowledge in a timely manner. The recent rise of \"no-code\" software development platforms may overcome this challenge and enable researchers to decrease the cost and time required to develop mHealth research apps.
OBJECTIVE: We aimed to describe the development process and the lessons learned to build Pathverse, a no-code mHealth app design platform.
METHODS: The study took place between November 2019 and December 2021. We used a participatory research framework to develop the mHealth app design platform. In phase 1, we worked with researchers to gather key platform feature requirements and conducted an exploratory literature search to determine needs related to this platform. In phase 2, we used an agile software framework (Scrum) to develop the platform. Each development sprint cycle was 4 weeks in length. We created a minimum viable product at the end of 7 sprint cycles. In phase 3, we used a convenience sample of adults (n=5) to gather user feedback through usability and acceptability testing. In phase 4, we further developed the platform based on user feedback, following the V-model software development process.
RESULTS: Our team consulted end users (ie, researchers) and utilized behavior change technique taxonomy and behavior change models (ie, the multi-process action control framework) to guide the development of features. The first version of the Pathverse platform included features that allowed researchers to (1) design customized multimedia app content (eg, interactive lessons), (2) set content delivery logic (eg, only show new lessons when completing the previous lesson), (3) implement customized participant surveys, (4) provide self-monitoring tools, (5) set personalized goals, and (6) customize app notifications. Usability and acceptability testing revealed that researchers found the platform easy to navigate and that the features were intuitive to use. Potential improvements include the ability to deliver adaptive interventions and add features such as community group chat.
CONCLUSIONS: To our knowledge, Pathverse is the first no-code mHealth app design platform for developing mHealth interventions for behavior. We successfully used behavior change models and the behavior change technique taxonomy to inform the feature requirements of Pathverse. Overall, the use of a participatory framework, combined with the agile and hybrid-agile software development process, enabled our team to successfully develop the Pathverse platform.

BACKGROUND: People with gambling problems frequently report repeated unsuccessful attempts to change their behavior. Although many behavior change techniques are available to individuals to reduce gambling harm, they can be challenging to implement or maintain. The provision of implementation support tailored for immediate, real-time, individualized circumstances may improve attempts at behavior change.
OBJECTIVE: We aimed to develop and evaluate a Just-In-Time Adaptive Intervention (JITAI) for individuals who require support to adhere to their gambling limits. JITAI development is based on the principles of the Health Action Process Approach with delivery, in alignment with the principles of self-determination theory. The primary objective was to determine the effect of action- and coping planning compared with no intervention on the goal of subsequently adhering to gambling expenditure limits.
METHODS: Gambling Habit Hacker is delivered as a JITAI providing in-the-moment support for adhering to gambling expenditure limits (primary proximal outcome). Delivered via a smartphone app, this JITAI delivers tailored behavior change techniques related to goal setting, action planning, coping planning, and self-monitoring. The Gambling Habit Hacker app will be evaluated using a 28-day microrandomized trial. Up to 200 individuals seeking support for their own gambling from Australia and New Zealand will set a gambling expenditure limit (ie, goal). They will then be asked to complete 3 time-based ecological momentary assessments (EMAs) per day over a 28-day period. EMAs will assess real-time adherence to gambling limits, strength of intention to adhere to goals, goal self-efficacy, urge self-efficacy, and being in high-risk situations. On the basis of the responses to each EMA, participants will be randomized to the control (a set of 25 self-enactable strategies containing names only and no implementation information) or intervention (self-enactable strategy implementation information with facilitated action- and coping planning) conditions. This microrandomized trial will be supplemented with a 6-month within-group follow-up that explores the long-term impact of the app on gambling expenditure (primary distal outcome) and a range of secondary outcomes, as well as an evaluation of the acceptability of the JITAI via postintervention surveys, app use and engagement indices, and semistructured interviews. This trial has been approved by the Deakin University Human Research Ethics Committee (2020-304).
RESULTS: The intervention has been subject to expert user testing, with high acceptability scores. The results will inform a more nuanced version of the Gambling Habit Hacker app for wider use.
CONCLUSIONS: Gambling Habit Hacker is part of a suite of interventions for addictive behaviors that deliver implementation support grounded in lived experience. This study may inform the usefulness of delivering implementation intentions in real time and in real-world settings. It potentially offers people with gambling problems new support to set their gambling intentions and adhere to their limits.
BACKGROUND: Australian New Zealand Clinical Trials Registry ACTRN12622000497707; www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383568.
UNASSIGNED: DERR1-10.2196/38919.