Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions

Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

A Mobile App to Aid Smoking Cessation: Preliminary Evaluation of SmokeFree28

Background: Little is known about the effectiveness of mobile apps in aiding smoking cessation or their validity for automated collection of data on smoking cessation outcomes. Objective: We conducted a preliminary evaluation of SF28 (SF28 is the name of the app, short for SmokeFree28)—an app aimed at helping smokers to be smoke-free for 28 days. / Methods: Data on sociodemographic characteristics, smoking history, number of logins, and abstinence at each login were uploaded to a server from SF28 between August 2012 and August 2013. Users were included if they were aged 16 years or over, smoked cigarettes at the time of registration, had set a quit date, and used the app at least once on or after their quit date. Their characteristics were compared with data from a representative sample of smokers trying to stop smoking in England. The percentage of users recording 28 days of abstinence was compared with a value of 15% estimated for unaided quitting. Correlations were assessed between recorded abstinence for 28 days and well-established abstinence predictors. / Results: A total of 1170 users met the inclusion criteria. Compared with smokers trying to quit in England, they had higher consumption, and were younger, more likely to be female, and had a non-manual rather than manual occupation. In total, 18.9% (95% CI 16.7-21.1) were recorded as being abstinent from smoking for 28 days or longer. The mean number of logins was 8.5 (SD 9.0). The proportion recording abstinence for 28 days or longer was higher in users who were older, in a non-manual occupation, and in those using a smoking cessation medication. / Conclusions: The recorded 28-day abstinence rates from the mobile app, SF28, suggest that it may help some smokers to stop smoking. Further evaluation by means of a randomized trial appears to be warranted

Do automated digital health behaviour change interventions have a positive effect on self-efficacy? A systematic review and meta-analysis

© 2019 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Health Psychology Review on 20/01/2020, available online: https://doi.org/10.1080/17437199.2019.1705873.Self-efficacy is an important determinant of health behaviour. Digital interventions are a potentially acceptable and cost-effective way of delivering programmes of health behaviour change at scale. Whether behaviour change interventions work to increase self-efficacy in this context is unknown. This systematic review and meta-analysis sought to identify whether automated digital interventions are associated with positive changes in self-efficacy amongst non-clinical populations for five major health behaviours, and which BCTs are associated with that change. A systematic literature search identified 20 studies (n=5624) that assessed changes in self-efficacy and were included in a random effects meta-analysis. Interventions targeted: healthy eating (k=4), physical activity (k=9), sexual behaviour (k=3), and smoking (k=4). No interventions targeting alcohol use were identified. Overall, interventions had a small, positive effect on self-efficacy (푔 = 0.190, CI [0.078; 0.303]). The effect of interventions on self-efficacy did not differ as a function of health behaviour type (Qbetween = 7.3704 p = 0.061, df = 3). Inclusion of the BCT ‘information about social and environmental consequences’ had a small, negative effect on self-efficacy (Δ푔= - 0.297, Q=7.072, p=0.008). Whilst this review indicates that digital interventions can be used to change self-efficacy, which techniques work best in this context is not clear.Peer reviewedFinal Accepted Versio

Toward an mHealth Intervention for Smoking Cessation

The prevalence of tobacco dependence in the United States (US) remains alarming. Invariably, smoke-related health problems are the leading preventable causes of death in the US. Research has shown that a culturally tailored cessation counseling program can help reduce smoking and other tobacco usage. In this paper, we present a mobile health (mHealth) solution that leverages the Short Message Service (SMS) or text messaging feature of mobile devices to motivate behavior change among tobacco users. Our approach implements the Theory of Planned Behavior (TPB) and a phase-based framework. We make contributions to improving previous mHealth intervention approaches by delivering personalized and evidence-based motivational SMS messages to participants. Our proposed solution implements machine learning algorithms that take the participant\u27s demographic profile and previous smoking behavior into account. We discuss our preliminary evaluation of the system against a couple of pseudo-scenarios and our observation of the system\u27s performance

The development of Drink Less: an alcohol reduction smartphone app for excessive drinkers

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

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