This paper is one in a series developed through a process of expert consensus to provide an overview of questions of current importance in research into engagement with digital behavior change interventions, identifying guidance based on research to date and priority topics for future research. The first part of this paper critically reflects on current approaches to conceptualizing and measuring engagement. Next, issues relevant to promoting effective engagement are discussed, including how best to tailor to individual needs and combine digital and human support. A key conclusion with regard to conceptualizing engagement is that it is important to understand the relationship between engagement with the digital intervention and the desired behavior change. This paper argues that it may be more valuable to establish and promote "effective engagement," rather than simply more engagement, with "effective engagement" defined empirically as sufficient engagement with the intervention to achieve intended outcomes. Appraisal of the value and limitations of methods of assessing different aspects of engagement highlights the need to identify valid and efficient combinations of measures to develop and test multidimensional models of engagement. The final section of the paper reflects on how interventions can be designed to fit the user and their specific needs and context. Despite many unresolved questions posed by novel and rapidly changing technologies, there is widespread consensus that successful intervention design demands a user-centered and iterative approach to development, using mixed methods and in-depth qualitative research to progressively refine the intervention to meet user requirements.

There is moderate-quality evidence that digital interventions may lower alcohol consumption, with an average reduction of up to three (UK) standard drinks per week compared to control participants. Substantial heterogeneity and risk of performance and publication bias may mean the reduction was lower. Low-quality evidence from fewer studies suggested there may be little or no difference in impact on alcohol consumption between digital and face-to-face interventions.The BCTs of behaviour substitution, problem solving and credible source were associated with the effectiveness of digital interventions to reduce alcohol consumption and warrant further investigation in an experimental context.Reporting of theory use was very limited and often unclear when present. Over half of the interventions made no reference to any theories. Limited reporting of theory use was unrelated to heterogeneity in intervention effectiveness.

BackgroundMobile phone apps have the potential to reduce excessive alcohol consumption cost-effectively. Although hundreds of alcohol-related apps are available, there is little information about the behavior change techniques (BCTs) they contain, or the extent to which they are based on evidence or theory and how this relates to their popularity and user ratings.ObjectiveOur aim was to assess the proportion of popular alcohol-related apps available in the United Kingdom that focus on alcohol reduction, identify the BCTs they contain, and explore whether BCTs or the mention of theory or evidence is associated with app popularity and user ratings.MethodsWe searched the iTunes and Google Play stores with the terms “alcohol” and “drink”, and the first 800 results were classified into alcohol reduction, entertainment, or blood alcohol content measurement. Of those classified as alcohol reduction, all free apps and the top 10 paid apps were coded for BCTs and for reference to evidence or theory. Measures of popularity and user ratings were extracted.ResultsOf the 800 apps identified, 662 were unique. Of these, 13.7% (91/662) were classified as alcohol reduction (95% CI 11.3-16.6), 53.9% (357/662) entertainment (95% CI 50.1-57.7), 18.9% (125/662) blood alcohol content measurement (95% CI 16.1-22.0) and 13.4% (89/662) other (95% CI 11.1-16.3). The 51 free alcohol reduction apps and the top 10 paid apps contained a mean of 3.6 BCTs (SD 3.4), with approximately 12% (7/61) not including any BCTs. The BCTs used most often were “facilitate self-recording” (54%, 33/61), “provide information on consequences of excessive alcohol use and drinking cessation” (43%, 26/61), “provide feedback on performance” (41%, 25/61), “give options for additional and later support” (25%, 15/61) and “offer/direct towards appropriate written materials” (23%, 14/61). These apps also rarely included any of the 22 BCTs frequently used in other health behavior change interventions (mean 2.46, SD 2.06). Evidence was mentioned by 16.4% of apps, and theory was not mentioned by any app. Multivariable regression showed that apps including advice on environmental restructuring were associated with lower user ratings (Β=-46.61, P=.04, 95% CI -91.77 to -1.45) and that both the techniques of “advise on/facilitate the use of social support” (Β=2549.21, P=.04, 95% CI 96.75-5001.67) and the mention of evidence (Β=1376.74, P=.02, 95%, CI 208.62-2544.86) were associated with the popularity of the app.ConclusionsOnly a minority of alcohol-related apps promoted health while the majority implicitly or explicitly promoted the use of alcohol. Alcohol-related apps that promoted health contained few BCTs and none referred to theory. The mention of evidence was associated with more popular apps, but popularity and user ratings were only weakly associated with the BCT content.

Our aim was to evaluate intervention components of an alcohol reduction app: Drink Less. Excessive drinkers (AUDIT> =8) were recruited to test enhanced versus minimal (reduced functionality) versions of five app modules in a 25 factorial trial. Modules were: Self-monitoring and Feedback, Action Planning, Identity Change, Normative Feedback, and Cognitive Bias Re-training. Outcome measures were: change in weekly alcohol consumption (primary); full AUDIT score, app usage, app usability (secondary). Main effects and two-way interactions were assessed by ANOVA using intention-to-treat. A total of 672 study participants were included. There were no significant main effects of the intervention modules on change in weekly alcohol consumption or AUDIT score. There were two-way interactions between enhanced Normative Feedback and Cognitive Bias Re-training on weekly alcohol consumption (F = 4.68, p = 0.03) and between enhanced Self-monitoring and Feedback and Action Planning on AUDIT score (F = 5.82, p = 0.02). Enhanced Self-monitoring and Feedback was used significantly more often and rated significantly more positively for helpfulness, satisfaction and recommendation to others than the minimal version. To conclude, in an evaluation of the Drink Less smartphone application, the combination of enhanced Normative Feedback and Cognitive Bias Re-training and enhanced Self-monitoring and Feedback and Action Planning yielded improvements in alcohol-related outcomes after 4-weeks.

BackgroundInterventions delivered by smartphone apps have the potential to help drinkers reduce their consumption of alcohol. To optimize engagement and reduce the high rates of attrition associated with the use of digital interventions, it is necessary to ensure that an app’s design and functionality is appropriate for its intended purposes and target population.AimsTo understand the usability of an app to help people reduce their alcohol consumption.MethodThe app, Drink Less, contains a core module focusing on goal setting, supplemented by five additional modules: self-monitoring and feedback, identity change, cognitive bias re-training, action planning, and social comparison. Two studies were conducted, a “think aloud” study performed with people using the app for the first time and a semistructured interview study performed after users had had access to the app for at least 2 weeks. A thematic analysis of the “think aloud” and interview transcripts was conducted by one coder and verified by a second.ResultsTwenty-four participants, half of whom were women and half from disadvantaged groups, took part in the two studies. Three main themes identified in the data were “Feeling lost and unsure of what to do next,” “Make the app easy to use,” and “Make the app beneficial and rewarding to use.” These themes reflected participants’ need for (i) guidance, particularly when first using the app or when entering data; (ii) the data entry process to be simple and the navigation intuitive; (iii) neither the amount of text nor range of options to be overwhelming; (iv) the app to reward them for effort and progress; and (v) it to be clear how the app could help alcohol reduction goals be reached.ConclusionFirst-time and experienced users want an alcohol reduction app to be easy, rewarding, and beneficial to use. An easy-to-use app would reduce user burden, offer ongoing help, and be esthetically pleasing. A rewarding and beneficial app would provide positive reinforcement, give feedback about progress, and demonstrate credibility. Users need help when first using the app, and they need a compelling reason to continue using it.

Objective: The objective of this study was to assess whether a version of the Smoke Free app with a supportive chatbot powered by artificial intelligence (versus a version without the chatbot) led to increased engagement and short-term quit success. Methods: Daily or non-daily smokers aged 18 years who purchased the 'pro' version of the app and set a quit date were randomly assigned (unequal allocation) to receive the app with or without the chatbot. The outcomes were engagement (i.e. total number of logins over the study period) and self-reported abstinence at a one-month follow-up. Unadjusted and adjusted negative binomial and logistic regression models were fitted to estimate incidence rate ratios (IRRs) and odds ratios (ORs) for the associations of interest. Results: A total of 57,214 smokers were included (intervention: 9.3% (5339); control: 90.7% (51,875). The app with the chatbot compared with the standard version led to a 101% increase in engagement (IRR adj ¼ 2.01, 95% confidence interval (CI) ¼ 1.92-2.11, p < .001). The one-month follow-up rate was 10.6% (intervention: 19.9% (1,061/5,339); control: 9.7% (5,050/51,875). Smokers allocated to the intervention had greater odds of quit success (missing equals smoking:

BackgroundDigital interventions to reduce excessive alcohol consumption have the potential to have a broader reach and be more cost-effective than traditional brief interventions. However, there is not yet strong evidence for their ability to engage users or their effectiveness.ObjectiveThis study aimed to identify the behavior change techniques (BCTs) and engagement strategies most worthy of further study by inclusion in a smartphone app to reduce alcohol consumption, using formal expert consensus methods.MethodsThe first phase of the study consisted of a Delphi exercise with three rounds. It was conducted with 7 international experts in the field of alcohol and/or behavior change. In the first round, experts identified BCTs most likely to be effective at reducing alcohol consumption and strategies most likely to engage users with an app; these were rated in the second round; and those rated as effective by at least four out of seven participants were ranked in the third round. The rankings were analyzed using Kendall’s W coefficient of concordance, which indicates consensus between participants. The second phase consisted of a new, independent group of experts (n=43) ranking the BCTs that were identified in the first phase. The correlation between the rankings of the two groups was assessed using Spearman’s rank correlation coefficient.ResultsTwelve BCTs were identified as likely to be effective. There was moderate agreement among the experts over their ranking (W=.465, χ2 11=35.8, P<.001) and the BCTs receiving the highest mean rankings were self-monitoring, goal-setting, action planning, and feedback in relation to goals. There was a significant correlation between the ranking of the BCTs by the group of experts who identified them and a second independent group of experts (Spearman’s rho=.690, P=.01). Seventeen responses were generated for strategies likely to engage users. There was moderate agreement among experts on the ranking of these engagement strategies (W=.563, χ2 15=59.2, P<.001) and those with the highest mean rankings were ease of use, design – aesthetic, feedback, function, design – ability to change design to suit own preferences, tailored information, and unique smartphone features.ConclusionsThe BCTs with greatest potential to include in a smartphone app to reduce alcohol consumption were judged by experts to be self-monitoring, goal-setting, action planning, and feedback in relation to goals. The strategies most likely to engage users were ease of use, design, tailoring of design and information, and unique smartphone features.

Excessive alcohol consumption poses a serious problem for public health. Digital behavior change interventions have the potential to help users reduce their drinking. In accordance with Open Science principles, this paper describes the development of a smartphone app to help individuals who drink excessively to reduce their alcohol consumption. Following the UK Medical Research Council's guidance and the Multiphase Optimization Strategy, development consisted of two phases: (i) selection of intervention components and (ii) design and development work to implement the chosen components into modules to be evaluated further for inclusion in the app. Phase 1 involved a scoping literature review, expert consensus study and content analysis of existing alcohol apps. Findings were integrated within a broad model of behavior change (Capability, Opportunity, Motivation-Behavior). Phase 2 involved a highly iterative process and used the "Person-Based" approach to promote engagement. From Phase 1, five intervention components were selected: (i) Normative Feedback, (ii) Cognitive Bias Re-training, (iii) Self-monitoring and Feedback, (iv) Action Planning, and (v) Identity Change. Phase 2 indicated that each of these components presented different challenges for implementation as app modules; all required multiple iterations and design changes to arrive at versions that would be suitable for inclusion in a subsequent evaluation study. The development of the Drink Less app involved a thorough process of component identification with a scoping literature review, expert consensus, and review of other apps. Translation of the components into app modules required a highly iterative process involving user testing and design modification.