Mobile Health Interventions for Substance Use Disorders.

Substance use disorders (SUDs) have an enormous negative impact on individuals, families, and society as a whole. Most individuals with SUDs do not receive treatment because of the limited availability of treatment providers, costs, inflexible work schedules, required treatment-related time commitments, and other hurdles. A paradigm shift in the provision of SUD treatments is currently underway. Indeed, with rapid technological advances, novel mobile health (mHealth) interventions can now be downloaded and accessed by those that need them anytime and anywhere. Nevertheless, the development and evaluation process for mHealth interventions for SUDs is still in its infancy. This review provides a critical appraisal of the significant literature in the field of mHealth interventions for SUDs with a particular emphasis on interventions for understudied and underserved populations. We also discuss the mHealth intervention development process, intervention optimization, and important remaining questions.

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring.

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Evaluating the Acceptability of the Drink Less App and the National Health Service Alcohol Advice Web Page: Qualitative Interview Process Evaluation.

BACKGROUND: The extent to which interventions are perceived as acceptable to users impacts engagement and efficacy. OBJECTIVE: In this study, we evaluated the acceptability of (1) the smartphone app Drink Less (intervention) and (2) the National Health Service (NHS) alcohol advice web page (usual digital care and comparator) among adult drinkers in the United Kingdom participating in a randomized controlled trial evaluating the effectiveness of the Drink Less app. METHODS: A subsample of 26 increasing- and higher-risk drinkers (Alcohol Use Disorders Identification Test score≥8) assigned to the intervention group (Drink Less; n=14, 54%; female: n=10, 71%; age: 22-72 years; White: n=9, 64%) or usual digital care group (NHS alcohol advice web page; n=12, 46%; female: n=5, 42%; age: 23-68 years: White: n=9, 75%) took part in semistructured interviews. The interview questions were mapped on to the 7 facets of acceptability according to the Theoretical Framework of Acceptability: affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy. Alongside these constructs, we also included a question on perceived personal relevance, which previous research has linked to acceptability and engagement. Framework and thematic analysis of data was undertaken. RESULTS: The Drink Less app was perceived as being ethical, easy, user-friendly, and effective for the period the app was used. Participants reported particularly liking the tracking and feedback sections of the app, which they reported increased personal relevance and which resulted in positive affect when achieving their goals. They reported no opportunity cost. Factors such as negative affect when not meeting goals and boredom led to disengagement in the longer term for some participants. The NHS alcohol advice web page was rated as being easy and user-friendly with no opportunity costs. However, the information presented was not perceived as being personally relevant or effective in changing drinking behavior. Most participants reported neutral or negative affect, most participants thought the alcohol advice web page was accessible, and some participants reported ethical concerns around the availability of suggested resources. Some participants reported that it had acted as a starting point or a signpost to other resources. Participants in both groups discussed motivation to change and contextual factors such as COVID-19 lockdowns, which influenced their perceived self-efficacy regardless of their assigned intervention. CONCLUSIONS: Drink Less appears to be an acceptable digital intervention among the recruited sample. The NHS alcohol advice web page was generally considered unacceptable as a stand-alone intervention among the recruited sample, although it may signpost and help people access other resources and interventions.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

INTRODUCTION: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. AIMS AND METHODS: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. RESULTS: Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). CONCLUSIONS: Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. IMPLICATIONS: This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

User Engagement Clusters of an 8-Week Digital Mental Health Intervention Guided by a Relational Agent (Woebot): Exploratory Study.

BACKGROUND: With the proliferation of digital mental health interventions (DMHIs) guided by relational agents, little is known about the behavioral, cognitive, and affective engagement components associated with symptom improvement over time. Obtaining a better understanding could lend clues about recommended use for particular subgroups of the population, the potency of different intervention components, and the mechanisms underlying the intervention's success. OBJECTIVE: This exploratory study applied clustering techniques to a range of engagement indicators, which were mapped to the intervention's active components and the connect, attend, participate, and enact (CAPE) model, to examine the prevalence and characterization of each identified cluster among users of a relational agent-guided DMHI. METHODS: We invited adults aged 18 years or older who were interested in using digital support to help with mood management or stress reduction through social media to participate in an 8-week DMHI guided by a natural language processing-supported relational agent, Woebot. Users completed assessments of affective and cognitive engagement, working alliance as measured by goal and task working alliance subscale scores, and enactment (ie, application of therapeutic recommendations in real-world settings). The app passively collected data on behavioral engagement (ie, utilization). We applied agglomerative hierarchical clustering analysis to the engagement indicators to identify the number of clusters that provided the best fit to the data collected, characterized the clusters, and then examined associations with baseline demographic and clinical characteristics as well as mental health outcomes at week 8. RESULTS: Exploratory analyses (n=202) supported 3 clusters: (1) "typical utilizers" (n=81, 40%), who had intermediate levels of behavioral engagement; (2) "early utilizers" (n=58, 29%), who had the nominally highest levels of behavioral engagement in week 1; and (3) "efficient engagers" (n=63, 31%), who had significantly higher levels of affective and cognitive engagement but the lowest level of behavioral engagement. With respect to mental health baseline and outcome measures, efficient engagers had significantly higher levels of baseline resilience (P<.001) and greater declines in depressive symptoms (P=.01) and stress (P=.01) from baseline to week 8 compared to typical utilizers. Significant differences across clusters were found by age, gender identity, race and ethnicity, sexual orientation, education, and insurance coverage. The main analytic findings remained robust in sensitivity analyses. CONCLUSIONS: There were 3 distinct engagement clusters found, each with distinct baseline demographic and clinical traits and mental health outcomes. Additional research is needed to inform fine-grained recommendations regarding optimal engagement and to determine the best sequence of particular intervention components with known potency. The findings represent an important first step in disentangling the complex interplay between different affective, cognitive, and behavioral engagement indicators and outcomes associated with use of a DMHI incorporating a natural language processing-supported relational agent. TRIAL REGISTRATION: ClinicalTrials.gov NCT05672745; https://classic.clinicaltrials.gov/ct2/show/NCT05672745.

The Effect of the First UK COVID-19 Lockdown on Users of the Drink Less App: Interrupted Time Series Analysis of Sociodemographic Characteristics, Engagement, and Alcohol Reduction.

BACKGROUND: The first UK COVID-19 lockdown had a polarizing impact on drinking behavior and may have impacted engagement with digital interventions to reduce alcohol consumption. OBJECTIVE: We examined the effect of lockdown on engagement, alcohol reduction, and the sociodemographic characteristics of users of the popular and widely available alcohol reduction app Drink Less. METHODS: This was a natural experiment. The study period spanned 468 days between March 24, 2019, and July 3, 2020, with the introduction of UK lockdown measures beginning on March 24, 2020. Users were 18 years or older, based in the United Kingdom, and interested in drinking less. Interrupted time series analyses using generalized additive mixed models (GAMMs) were conducted for each outcome variable (ie, sociodemographic characteristics, app downloads and engagement levels, alcohol consumption, and extent of alcohol reduction) for existing (downloaded the app prelockdown) and new (downloaded the app during the lockdown) users of the app. RESULTS: Among existing users of the Drink Less app, there were increases in the time spent on the app per day (B=0.01, P=.01), mean units of alcohol recorded per day (B>0.00 P=.02), and mean heavy drinking (>6 units) days (B>0.00, P=.02) during the lockdown. Previous declines in new app downloads plateaued during the lockdown (incidence rate ratio [IRR]=1.00, P=.18). Among new app users, there was an increase in the proportion of female users (B>0.00, P=.04) and those at risk of alcohol dependence (B>0.00, P=.01) and a decrease in the proportion of nonmanual workers (B>-0.00, P=.04). Among new app users, there were step increases in the mean number of alcohol units per day (B=20.12, P=.03), heavy-drinking days (B=1.38, P=.01), and the number of days the app was used (B=2.05, P=.02), alongside a step decrease in the percentage of available screens viewed (B=-0.03, P=.04), indicating users were using less of the intervention components within the app. CONCLUSIONS: Following the first UK lockdown, there was evidence of increases in engagement and alcohol consumption among new and existing users of the Drink Less app.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial.

BACKGROUND: Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. OBJECTIVE: In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. METHODS: Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. RESULTS: For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. CONCLUSIONS: Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis.

INTRODUCTION: Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation. METHODS: Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression. RESULTS: Three qualitatively different sequence clusters were identified. "Disengagers" (576/1,313) almost exclusively used the goal-setting feature. "Tryers" (375/1,313) used goal setting and two of the values- and acceptance-based components ("Be Aware," "Be Willing"). "Committers" (362/1,313) primarily used two of the values- and acceptance-based components ("Be Willing," "Be Inspired"), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003). DISCUSSION: WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success. IMPLICATIONS: This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

The impact of celebrity influence and national media coverage on users of an alcohol reduction app: a natural experiment.

BACKGROUND: Smartphone apps are increasingly used for health-related behaviour change and people discover apps through different sources. However, it is unclear whether users differ by mode of app discovery. Drink Less is an alcohol reduction app that received national media coverage in the UK caused by celebrity influence (a male TV and radio national broadcaster, aged 51). Our aim was to compare users who discovered the app before and after this coverage. METHODS: A natural experiment assessing the impact of media coverage of Drink Less on users' socio-demographic and drinking characteristics, app engagement levels, and extent of alcohol reduction. The study period was from 17th May 2017 to 23rd January 2019, with media coverage starting on 21st August 2018. Users were 18 years or over, based in the UK and interested in drinking less. Interrupted time series analyses using Generalised Additive Mixed Models were conducted for each outcome variable aggregated at the weekly level. RESULTS: In 66 weeks prior to the media coverage, 8617 users downloaded the app and 18,959 in 23 weeks afterwards. There was a significant step-level increase in users' mean age (B = 8.17, p < .001) and a decrease in the percentage of female users (B = -27.71, p < .001), though these effects dissipated non-linearly over time. No effect of media coverage was detected on employment type or on the percentage of at-risk drinkers, though the mean Alcohol Use Disorders Identification Test score was lower after the media coverage (B = -1.43, p = .031). There was a step-level increase in app engagement - number of sessions (B = 3.45, p = .038) and number of days used (B = 2.30, p = .005) - which continued to increase over time following quadratic trends. CONCLUSIONS: Celebrity influence leading to national media coverage in the UK of the Drink Less app was associated with more people downloading the app who were male, older and engaged with the app; and did not appear to impact employment inequality.

Association between changes in harm perceptions and e-cigarette use among current tobacco smokers in England: a time series analysis.

BACKGROUND: There is a decreasing trend in the proportion of individuals who perceive e-cigarettes to be less harmful than conventional cigarettes across the UK, Europe and the US. It is important to assess whether this may influence the use of e-cigarettes. We aimed to estimate, using a time series approach, whether changes in harm perceptions among current tobacco smokers have been associated with changes in the prevalence of e-cigarette use in England, with and without stratification by age, sex and social grade. METHODS: Respondents were from the Smoking Toolkit Study, which involves monthly cross-sectional household surveys of individuals aged 16+ years in England. Data were aggregated monthly on ~ 300 current tobacco smokers between 2014 and 2019. The outcome variable was the prevalence of e-cigarette use. The explanatory variable was the proportion of smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes. Covariates were cigarette (vs. non-cigarette combustible) current smoking prevalence, past-year quit attempt prevalence and national smoking mass media expenditure. Unadjusted and adjusted autoregressive integrated moving average with exogeneous variables (ARIMAX) models were fitted. RESULTS: For every 1% decrease in the mean prevalence of current tobacco smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes, the mean prevalence of e-cigarette use decreased by 0.48% (ßadj = 0.48, 95% CI = 0.25-0.71, p < .001). Marginal age and sex differences were observed, whereby significant associations were observed in older (but not in young) adults and in men (but not in women). No differences by social grade were detected. CONCLUSIONS: Between 2014 and 2019 in England, at the population level, monthly changes in the prevalence of accurate harm perceptions among current tobacco smokers were strongly associated with changes in e-cigarette use.

Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.

BACKGROUND: Behavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) can help guide further incremental improvements. We aim to explore how simple visualizations can provide a good understanding of temporal patterns of engagement, as usage data are often longitudinal and rich. OBJECTIVE: This study aims to visualize behavioral engagement with Drink Less, a behavior change app to help reduce hazardous and harmful alcohol consumption in the general adult population of the United Kingdom. METHODS: We explored behavioral engagement among 19,233 existing users of Drink Less. Users were included in the sample if they were from the United Kingdom; were 18 years or older; were interested in reducing their alcohol consumption; had a baseline Alcohol Use Disorders Identification Test score of 8 or above, indicative of excessive drinking; and had downloaded the app between May 17, 2017, and January 22, 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualized with heat maps, timeline plots, k-modes clustering analyses, and Kaplan-Meier plots. RESULTS: The daily 11 AM notification is strongly associated with a change in engagement in the following hour; reduction in behavioral engagement over time, with 50.00% (9617/19,233) of users disengaging (defined as no use for 7 or more consecutive days) 22 days after download; identification of 3 distinct trajectories of use, namely engagers (4651/19,233, 24.18% of users), slow disengagers (3679/19,233, 19.13% of users), and fast disengagers (10,903/19,233, 56.68% of users); and limited depth of engagement with 85.076% (7,095,348/8,340,005) of screen views occurring within the Self-monitoring and Feedback module. In addition, a peak of both frequency and amount of time spent per session was observed in the evenings. CONCLUSIONS: Visualizations play an important role in understanding engagement with behavior change apps. Here, we discuss how simple visualizations helped identify important patterns of engagement with Drink Less. Our visualizations of behavioral engagement suggest that the daily notification substantially impacts engagement. Furthermore, the visualizations suggest that a fixed notification policy can be effective for maintaining engagement for some users but ineffective for others. We conclude that optimizing the notification policy to target both effectiveness and engagement is a worthwhile investment. Our future goal is to both understand the causal effect of the notification on engagement and further optimize the notification policy within Drink Less by tailoring to contextual circumstances of individuals over time. Such tailoring will be informed from the findings of our micro-randomized trial (MRT), and these visualizations were useful in both gaining a better understanding of engagement and designing the MRT.

Assessing the Psychometric Properties of the Digital Behavior Change Intervention Engagement Scale in Users of an App for Reducing Alcohol Consumption: Evaluation Study.

BACKGROUND: The level and type of engagement with digital behavior change interventions (DBCIs) are likely to influence their effectiveness, but validated self-report measures of engagement are lacking. The DBCI Engagement Scale was designed to assess behavioral (ie, amount, depth of use) and experiential (ie, attention, interest, enjoyment) dimensions of engagement. OBJECTIVE: We aimed to assess the psychometric properties of the DBCI Engagement Scale in users of a smartphone app for reducing alcohol consumption. METHODS: Participants (N=147) were UK-based, adult, excessive drinkers recruited via an online research platform. Participants downloaded the Drink Less app and completed the scale immediately after their first login in exchange for a financial reward. Criterion variables included the objectively recorded amount of use, depth of use, and subsequent login. Five types of validity (ie, construct, criterion, predictive, incremental, divergent) were examined in exploratory factor, correlational, and regression analyses. The Cronbach alpha was calculated to assess the scale's internal reliability. Covariates included motivation to reduce alcohol consumption. RESULTS: Responses on the DBCI Engagement Scale could be characterized in terms of two largely independent subscales related to experience and behavior. The experiential and behavioral subscales showed high (α=.78) and moderate (α=.45) internal reliability, respectively. Total scale scores predicted future behavioral engagement (ie, subsequent login) with and without adjusting for users' motivation to reduce alcohol consumption (adjusted odds ratio [ORadj]=1.14; 95% CI 1.03-1.27; P=.01), which was driven by the experiential (ORadj=1.19; 95% CI 1.05-1.34; P=.006) but not the behavioral subscale. CONCLUSIONS: The DBCI Engagement Scale assesses behavioral and experiential aspects of engagement. The behavioral subscale may not be a valid indicator of behavioral engagement. The experiential subscale can predict subsequent behavioral engagement with an app for reducing alcohol consumption. Further refinements and validation of the scale in larger samples and across different DBCIs are needed.

Smokers' and drinkers' choice of smartphone applications and expectations of engagement: a think aloud and interview study.

BACKGROUND: Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement. METHODS: Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis. RESULTS: Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions. CONCLUSIONS: Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.

A systematic review and meta-analysis of tertiary interventions in clinical burnout.

Clinical burnout is one of the leading causes of work absenteeism in high- and middle-income countries. There is hence a great need for the identification of effective intervention strategies to increase return-to-work (RTW) in this population. This review aimed to assess the effectiveness of tertiary interventions for individuals with clinically significant burnout on RTW and psychological symptoms of exhaustion, depression and anxiety. Four electronic databases (Ovid MEDLINE, PsychINFO, PubMed and CINAHL Plus) were searched in April 2016 for randomized and non-randomized controlled trials of tertiary interventions in clinical burnout. Article screening and data extraction were conducted independently by two reviewers. Pooled odds ratios (ORs) and hazard ratios (HRs) were estimated with random-effects meta-analyses. Eight articles met the inclusion criteria. There was some evidence of publication bias. Included trials were of variable methodological quality. A significant effect of tertiary interventions compared with treatment as usual or wait-list controls on time until RTW was found, HR = 4.5, 95% confidence interval (CI) = 2.15-9.45; however, considerable heterogeneity was detected. The effect of tertiary interventions on full RTW was not significant, OR = 1.33, 95% CI = 0.59-2.98. No significant effects on psychological symptoms of exhaustion, depression or anxiety were observed. In conclusion, tertiary interventions for individuals with clinically significant burnout may be effective in facilitating RTW. Successful interventions incorporated advice from labor experts and enabled patients to initiate a workplace dialogue with their employers.

Developing content for a virtual reality scenario that motivates quit attempts in adult smokers: A focus group study with art-based methods.

Virtual reality (VR) could be used to deliver messages to smokers that encourages them to attempt quitting. For a VR smoking cessation intervention to be effective, the target population must find the content engaging, relevant, inoffensive, and compelling. Informed by health behaviour theory and narrative transportation theory, this study used focus groups combined with art-based methods (participant sketches) to inform the development of VR content that will appropriately address smokers' beliefs about quitting smoking. Data were analysed using reflexive thematic analysis. Four in-person focus groups (N = 21) were held between July and August 2023. Just under half the sample were from an ethnic minority (42.8%) and women (42.9%), and the mean age was 33.6 years (standard deviation = 15.9). More than half the sample had a low motivation to quit (61.0%). We developed six themes concerning: the VR content suggested by participants, the rationale behind it, its technological execution and potential widescale implementation. Many participants downplayed the health consequences of smoking, prioritising the immediate rewards of smoking over quitting's long-term benefits. Therefore, participants suggested content set in the future, showing the benefits of cessation or the negative consequences of continued smoking. Family members were recommended as supporting VR characters to increase the contents' emotional salience. Participants also suggested graphic content that would trigger anxiety about smoking, suggesting that fear appeals were welcome. Participants wanted a truly novel intervention- not a leaflet about smoking statistics presented through VR. Participants suggested healthcare locations (e.g., doctors' offices) for implementation, as home ownership of VR headsets is low. Also, this would make the VR appear more legitimate as a health intervention (rather than casual entertainment) and could complement in-person advice. Future research will refine the participant-generated ideas with experts in VR design and smoking cessation.

Do engagement and behavioural mechanisms underpin the effectiveness of the Drink Less app?

This is a process evaluation of a large UK-based randomised controlled trial (RCT) (n = 5602) evaluating the effectiveness of recommending an alcohol reduction app, Drink Less, compared with usual digital care in reducing alcohol consumption in increasing and higher risk drinkers. The aim was to understand whether participants' engagement ('self-reported adherence') and behavioural characteristics were mechanisms of action underpinning the effectiveness of Drink Less. Self-reported adherence with both digital tools was over 70% (Drink Less: 78.0%, 95% CI = 77.6-78.4; usual digital care: 71.5%, 95% CI = 71.0-71.9). Self-reported adherence to the intervention (average causal mediation effect [ACME] = -0.250, 95% CI = -0.42, -0.11) and self-monitoring behaviour (ACME = -0.235, 95% CI = -0.44, -0.03) both partially mediated the effect of the intervention (versus comparator) on alcohol reduction. Following the recommendation (self-reported adherence) and the tracking (self-monitoring behaviour) feature of the Drink Less app appear to be important mechanisms of action for alcohol reduction among increasing and higher risk drinkers.

Barriers and Enablers to the Adoption of a Healthier Diet Using an App: Qualitative Interview Study With Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Adopting a healthy diet is one of the cornerstones of type 2 diabetes (T2D) management. Apps are increasingly used in diabetes self-management, but most studies to date have focused on assessing their impact in terms of weight loss or glycemic control, with limited evidence on the behavioral factors that influence app use to change dietary habits. OBJECTIVE: The main objectives of this study were to assess the enablers and barriers to adopting a healthier diet using the Gro Health app in 2 patient groups with T2D (patients with recently diagnosed and long-standing T2D) and to identify behavior change techniques (BCTs) to enhance enablers and overcome barriers. METHODS: Two semistructured qualitative interview studies were conducted; the first study took place between June and July 2021, with a sample of 8 patients with recently diagnosed (<12 mo) T2D, whereas the second study was conducted between May and June 2022 and included 15 patients with long-standing (>18 mo) T2D. In both studies, topic guides were informed by the Capability, Opportunity, Motivation, and Behavior model and the Theoretical Domains Framework. Transcripts were analyzed using a combined deductive framework and inductive thematic analysis approach. The Behavior Change Wheel framework was applied to identify appropriate BCTs that could be used in future iterations of apps for patients with diabetes. Themes were compared between the patient groups. RESULTS: This study identified similarities and differences between patient groups in terms of enablers and barriers to adopting a healthier diet using the app. The main enablers for recently diagnosed patients included the acquired knowledge about T2D diets and skills to implement these, whereas the main barriers were the difficulty in deciding which app features to use and limited cooking skills. By contrast, for patients with long-standing T2D, the main enablers included knowledge validation provided by the app, along with app elements to help self-regulate food intake; the main barriers were the limited interest paid to the content provided or limited skills engaging with apps in general. Both groups reported more enablers than barriers to performing the target behavior when using the app. Consequently, BCTs were selected to address key barriers in both groups, such as simplifying the information hierarchy in the app interface, including tutorials demonstrating how to use the app features, and redesigning the landing page of the app to guide users toward these tutorials. CONCLUSIONS: Patients with recently diagnosed and long-standing T2D encountered similar enablers but slightly different barriers when using an app to adopting a healthier diet. Consequently, the development of app-based approaches to adopt a healthier diet should account for these similarities and differences within patient segments to reduce barriers to performing the target behavior.

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.

BACKGROUND: Drink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to "Please complete your drinks and mood diary," yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users' reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement. OBJECTIVE: Our objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy. METHODS: We conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement. RESULTS: Receiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different. CONCLUSIONS: We found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase "in-the-moment" engagement. Further optimization is required to improve the long-term engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18690.

Within-person associations between psychological and contextual factors and lapse incidence in smokers attempting to quit: A systematic review and meta-analysis of ecological momentary assessment studies.

AIMS: When attempting to stop smoking, discrete smoking events ('lapses') are strongly associated with a return to regular smoking ('relapse'). No study has yet pooled the psychological and contextual antecedents of lapse incidence, captured in ecological momentary assessment (EMA) studies. This systematic review and meta-analysis aimed to synthesize within-person psychological and contextual predictor-lapse associations in smokers attempting to quit. METHODS: We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science. A narrative synthesis and multi-level, random-effects meta-analyses were conducted, focusing on studies of adult, non-clinical populations attempting to stop smoking, with no restrictions on setting. Outcomes were the association between a psychological (e.g. stress, cravings) or contextual (e.g. cigarette availability) antecedent and smoking lapse incidence; definitions of 'lapse' and 'relapse'; the theoretical underpinning of EMA study designs; and the proportion of studies with pre-registered study protocols/analysis plans and open data. RESULTS: We included 61 studies, with 19 studies contributing ≥ 1 effect size(s) to the meta-analyses. We found positive relationships between lapse incidence and 'environmental and social cues' [k = 12, odds ratio (OR) = 4.53, 95% confidence interval (CI) = 2.02, 10.16, P = 0.001] and 'cravings' (k = 10, OR = 1.71, 95% CI = 1.34, 2.18, P < 0.001). 'Negative feeling states' was not significantly associated with lapse incidence (k = 16, OR = 1.10, 95% CI = 0.98, 1.24, P = 0.12). In the narrative synthesis, negative relationships with lapse incidence were found for 'behavioural regulation', 'motivation not to smoke' and 'beliefs about capabilities'; positive relationships with lapse incidence were found for 'positive feeling states' and 'positive outcome expectancies'. Although lapse definitions were comparable, relapse definitions varied widely across studies. Few studies explicitly drew upon psychological theory to inform EMA study designs. One of the included studies drew upon Open Science principles. CONCLUSIONS: In smokers attempting to stop, environmental and social cues and cravings appear to be key within-person antecedents of smoking lapse incidence. Due to low study quality, the confidence in these estimates is reduced.