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Background Evidence suggests that unhealthy lifestyle behaviours are modifiable risk factors for postoperative complications. Digital behaviour change interventions (DBCIs), for instance text messaging programs and smartphone apps, have shown promise in achieving lifestyle behaviour change in a wide range of clinical populations, and it may therefore be possible to reduce postoperative complications by supporting behaviour change perioperatively using digital interventions. This scoping review was conducted in order to identify existing research done in the area of perioperative DBCIs for reducing alcohol consumption, improving dietary intake, increasing physical activity and smoking cessation. Main text This scoping review included eleven studies covering a range of surgeries: bariatric, orthopaedic, cancer, transplantation and elective surgery. The studies were both randomised controlled trials and feasibility studies and investigated a diverse set of interventions: one game, three smartphone apps, one web-based program and five text message interventions. Feasibility studies reported user acceptability and satisfaction with the behaviour change support. Engagement data showed participation rates ranged from 40 to 90%, with more participants being actively engaged early in the intervention period. In conclusion, the only full-scale randomised controlled trial (RCT), text messaging ahead of bariatric surgery did not reveal any benefits with respect to adherence to preoperative exercise advice when compared to a control group. Two of the pilot studies, one text message intervention, one game, indicated change in a positive direction with respect to alcohol and tobacco outcomes, but between group comparisons were not done due to small sample sizes. The third pilot-study, a smartphone app, found between group changes for physical activity and alcohol, but not with respect to smoking cessation outcomes. Conclusion This review found high participant satisfaction, but shows recruitment and timing-delivery issues, as well as low retention to interventions post-surgery. Small sample sizes and the use of a variety of feasibility outcome measures prevent the synthesis of results and makes generalisation difficult. Future research should focus on defining standardised outcome measures, enhancing patient engagement and improving adherence to behaviour change prior to scheduled surgery.

Food production and consumption present major sustainability challenges, and finding ways to reduce the environmental impact of food, for example through behavioural changes by consumers, is becoming increasingly important. In recent years, digital interventions have become important tools to change behaviours in many areas. In this review, we evaluate the status of current scientific knowledge of digital behaviour change interventions for sustainable food consumption practices. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist for how to conduct systematic reviews, we searched multiple databases for papers containing terms related to food, sustainability and digital behaviour change interventions. Only studies where the digital interventions were actually implemented and evaluated from a behaviour change perspective were included, resulting in 15 primary studies in the final review. The quality of the studies was evaluated from a behaviour change perspective, and the approaches used were categorised using two intervention frameworks, the Behaviour Change Wheel and the Behaviour Change Technique Taxonomy v1. The results show that all of the included studies had major quality issues when evaluated from a behaviour change perspective. This means that we could not find any evidence regarding whether the digital behaviour change interventions examined worked or not. Most studies further lacked theoretical grounding or a clear approach to how or why they should be effective for behaviour change for more sustainable food consumption practices. Our main recommendation for future research in the field is to expand from the current exploratory phase to conducting scientifically rigorous studies of higher quality, more thoroughly grounded in behaviour change theory and methods. Furthermore, based on our study, we suggest changes to the Behaviour Change Technique Taxonomy v1.

The effectiveness of digital health interventions is commonly assumed to be related to the level of user engagement with the digital health intervention, including measures of both digital health intervention use and users' subjective experience. However, little is known about the relationships between the measures of digital health intervention engagement and physical activity or sedentary behavior. This study aims to describe the direction and strength of the association between engagement with digital health interventions and physical activity or sedentary behavior in adults and explore whether the direction of association of digital health intervention engagement with physical activity or sedentary behavior varies with the type of engagement with the digital health intervention (ie, subjective experience, activities completed, time, and logins). Four databases were searched from inception to December 2019. Grey literature and reference lists of key systematic reviews and journals were also searched. Studies were eligible for inclusion if they examined a quantitative association between a measure of engagement with a digital health intervention targeting physical activity and a measure of physical activity or sedentary behavior in adults (aged ≥18 years). Studies that purposely sampled or recruited individuals on the basis of pre-existing health-related conditions were excluded. In addition, studies were excluded if the individual engaging with the digital health intervention was not the target of the physical activity intervention, the study had a non-digital health intervention component, or the digital health interventions targeted multiple health behaviors. A random effects meta-analysis and direction of association vote counting (for studies not included in meta-analysis) were used to address objective 1. Objective 2 used vote counting on the direction of the association. Overall, 10,653 unique citations were identified and 375 full texts were reviewed. Of these, 19 studies (26 associations) were included in the review, with no studies reporting a measure of sedentary behavior. A meta-analysis of 11 studies indicated a small statistically significant positive association between digital health engagement (based on all usage measures) and physical activity (0.08, 95% CI 0.01-0.14, SD 0.11). Heterogeneity was high, with 77% of the variation in the point estimates explained by the between-study heterogeneity. Vote counting indicated that the relationship between physical activity and digital health intervention engagement was consistently positive for three measures: subjective experience measures (2 of 3 associations), activities completed (5 of 8 associations), and logins (6 of 10 associations). However, the direction of associations between physical activity and time-based measures of usage (time spent using the intervention) were mixed (2 of 5 associations supported the hypothesis, 2 were inconclusive, and 1 rejected the hypothesis). The findings indicate a weak but consistent positive association between engagement with a physical activity digital health intervention and physical activity outcomes. No studies have targeted sedentary behavior outcomes. The findings were consistent across most constructs of engagement; however, the associations were weak.

Background The ubiquity of Internet connectivity, and widespread unmet needs, requires investigations of digital interventions for people seeking help with their drinking. The objective of this study was to test the effectiveness of a digital alcohol intervention compared to existing online resources for help seekers. Methods This parallel randomised controlled trial included 2129 risky drinkers with access to a mobile phone and aged 18 years or older. Randomised sub-studies investigated consent procedures and control group design. Simple computerised randomisation was used. Participants were aware of allocation after randomisation; research personnel were not. The digital intervention was designed around weekly monitoring of alcohol consumption followed by feedback and tools for behaviour change. Primary outcomes were total weekly consumption (TWC) and frequency of heavy episodic drinking (HED), measured 2 and 4 months post-randomisation. Results Between 25/04/2019 and 26/11/2020, 2129 participants were randomised (intervention: 1063, control: 1066). Negative binomial regression was used to contrast groups, with both Bayesian and maximum likelihood inference. The posterior median incidence rate ratio (IRR) of TWC was 0.89 (95% CI = 0.81;0.99, 98.2% probability of effect, P -value = 0.033) at 2 months among 1557 participants and 0.77 (95% CI = 0.69;0.86, > 99.9% probability of effect, P -value < 0.001) at 4 months among 1429 participants. For HED, the IRR was 0.83 (95% CI = 0.75;0.93, > 99.9% probability of effect, P -value = 0.0009) at 2 months among 1548 participants and 0.71 (95% CI = 0.63;0.79, probability of effect > 99.9%, P -value < 0.0001) at 4 months among 1424 participants. Analyses with imputed data were not markedly different. Conclusions A digital alcohol intervention produced self-reported behaviour change among online help seekers in the general population. The internal and external validity of this trial is strong, subject to carefully considered study limitations arguably inherent to trials of this nature. Limitations include higher than anticipated attrition to follow-up and lack of blinding. Trial registration The trial was prospectively registered ( ISRCTN48317451 ).

Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term “engagement,” thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as “Big E,” and DBCI engagement, referred to as “Little e.” DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.

This paper presents a systematic review of the literature on digital behaviour change ecosystems (DBCE) in the context of sustainable innovation. The concept of digital behaviour change ecosystem is gaining increasing attention across multiple domains, including agriculture, information systems, health, and environmental management. This emerging paradigm requires research institutions and organizations to design their digital models around innovative digital behaviour change ecosystems that contribute to organisational value while mitigating risks associated with opportunistic practices. However, research examining how agricultural business models and digital ecosystems can be designed to foster behavioural change toward sustainability remains fragmented and underdeveloped. Building on this premise, we identify DBCE trends, the methodological landscape, theoretical underpinnings, four thematic areas and future research direction. . Through this analysis, we seek to advance understanding of how digital ecosystems can be strategically designed to promote sustainable behavioural change within agricultural contexts and beyond.

Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches. Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms. A systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs' websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs' publications, websites, and mobile apps were reviewed with regard to the intervention characteristics. The 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A (HbA ) outcomes between the intervention and control groups. However, all the studies reported HbA improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs. Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions.

INTRODUCTION: The increasing popularity of exergames to promote the adoption of physical activity and healthy nutrition among different population groups is well established. However, due to the use of various types of exergames, their effectiveness in addressing specific behaviours varies.OBJECTIVES: This systematic review aims to identify, classify exergaming elements, and examine their efficacy in enhancing physical activity levels, improve nutrition habits, or a combination of both, across various populations.METHODS: A systematic search was conducted to identify relevant publications. Data on study characteristics pertaining to types of exergames, purpose, focus, target population, technologies used, and the theoretical framework were extracted. A classification scheme of exergaming components and characteristics has been developed to facilitate this systematic review.RESULTS: A total of 34 studies were included and n=21 of them were experimental. Most studies (n=31) were focused on Physical Activity using exergames, whereas n=9 studies addressed both Physical Activity and Nutrition simultaneously.CONCLUSION: All of the studies reported positive behavioural outcomes, although, prolonged and sustained engagement with exergames were not consistently reported.

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. We aimed to assess the psychometric properties of the DBCI Engagement Scale in users of a smartphone app for reducing alcohol consumption. 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. 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 [OR ]=1.14; 95% CI 1.03-1.27; P=.01), which was driven by the experiential (OR =1.19; 95% CI 1.05-1.34; P=.006) but not the behavioral subscale. 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.

Background The National Institute for Health and Care Excellence recommends the use of digital and mobile health technologies to facilitate behaviour change interventions. Due to its high prevalence and dependence upon patient self-management strategies, osteoarthritis is one musculoskeletal condition which may benefit from such approaches. This is particularly pertinent due to the increasing use of remote monitoring technologies to collect patient data and facilitate self-management in individuals outside of hospital clinics. In practice however, application of digital behaviour change interventions is difficult due to insufficient reporting of behaviour change theories in the current literature. When digital technologies are employed to alter behaviour change in osteoarthritis, they often focus on physical activity. Currently, such interventions focus of self-efficacy but do not often explicitly report the behaviour change techniques they use to facilitate these changes. Methods This paper proposes a new model of integrating specific behaviour change principles (persuasive design) in an integrated model of remote monitoring and digital behaviour change interventions for osteoarthritis. Results There is potential to combine remote monitoring systems of patient data through digital and mobile technologies with behaviour change principles to improve physical activity behaviours in individuals with osteoarthritis. The use of persuasive design principles (e.g. prompts or nudges) through mobile notifications and strategic system design can be directed to enhance behaviour change. A validated measure of behaviour change, such as the patient activation measure, will allow effective evaluation of such systems. Conclusions Digital behaviour change interventions should be directed towards the underlying principles of behaviour change they employ, although this is not commonly reported in practice. Such interventions can be integrated within remote monitoring pathways using persuasive design techniques to enhance patient activation. This approach can enhance self-management in individuals with musculoskeletal conditions, such as osteoarthritis.