This editorial provides a behavioral science view on gamification and health behavior change, describes its principles and mechanisms, and reviews some of the evidence for its efficacy. Furthermore, this editorial explores the relation between gamification and behavior change frameworks used in the health sciences and shows how gamification principles are closely related to principles that have been proven to work in health behavior change technology. Finally, this editorial provides criteria that can be used to assess when gamification provides a potentially promising framework for digital health interventions.

BackgroundResearchers and practitioners have developed numerous online interventions that encourage people to reduce their drinking, increase their exercise, and better manage their weight. Motivations to develop eHealth interventions may be driven by the Internet’s reach, interactivity, cost-effectiveness, and studies that show online interventions work. However, when designing online interventions suitable for public campaigns, there are few evidence-based guidelines, taxonomies are difficult to apply, many studies lack impact data, and prior meta-analyses are not applicable to large-scale public campaigns targeting voluntary behavioral change.ObjectivesThis meta-analysis assessed online intervention design features in order to inform the development of online campaigns, such as those employed by social marketers, that seek to encourage voluntary health behavior change. A further objective was to increase understanding of the relationships between intervention adherence, study adherence, and behavioral outcomes.MethodsDrawing on systematic review methods, a combination of 84 query terms were used in 5 bibliographic databases with additional gray literature searches. This resulted in 1271 abstracts and papers; 31 met the inclusion criteria. In total, 29 papers describing 30 interventions were included in the primary meta-analysis, with the 2 additional studies qualifying for the adherence analysis. Using a random effects model, the first analysis estimated the overall effect size, including groupings by control conditions and time factors. The second analysis assessed the impacts of psychological design features that were coded with taxonomies from evidence-based behavioral medicine, persuasive technology, and other behavioral influence fields. These separate systems were integrated into a coding framework model called the communication-based influence components model. Finally, the third analysis assessed the relationships between intervention adherence and behavioral outcomes.ResultsThe overall impact of online interventions across all studies was small but statistically significant (standardized mean difference effect size d = 0.19, 95% confidence interval [CI] = 0.11 - 0.28, P < .001, number of interventions k = 30). The largest impact with a moderate level of efficacy was exerted from online interventions when compared with waitlists and placebos (d = 0.28, 95% CI = 0.17 - 0.39, P < .001, k = 18), followed by comparison with lower-tech online interventions (d = 0.16, 95% CI = 0.00 - 0.32, P = .04, k = 8); no significant difference was found when compared with sophisticated print interventions (d = –0.11, 95% CI = –0.34 to 0.12, P = .35, k = 4), though online interventions offer a small effect with the advantage of lower costs and larger reach. Time proved to be a critical factor, with shorter interventions generally achieving larger impacts and greater adherence. For psychological design, most interventions drew from the transtheoretical approach and were goal orientated, deploying numerous influence compon...

Scholars and research teams focus their efforts on studying ways to improve the lives of individuals, which often brings tangible social benefits. However, there is scarce scientific knowledge available on negative outcomes of behavior change interventions, and possibly even fewer that report a special type of negative outcome, called a backfire. In this paper, we start a wider scientific discussion on intervention backfiring. We introduce a framework to help facilitate the debate of this topic. We provide tools to aid academics in the study of this realm and support practitioners to remain mindful of the potential risks when designing behavior change interventions. We describe taxonomy of persuasive backfiring and propose tools in the form of intention-outcome and likelihood-severity matrices to outline a roadmap for further research and application. We open transparent discussion on backfiring, with an attitude of looking out and coming up with strategies to reduce them whenever identified.

This paper discusses two trends that threaten to undermine the effectiveness of online social marketing interventions: growing mistrust and competition. As a solution, this paper examines the relationships between Web site credibility, target audiences' active trust and behaviour. Using structural equation modelling to evaluate two credibility models, this study concludes that Web site credibility is best considered a three-dimensional construct composed of expertise, trustworthiness and visual appeal, and that trust plays a partial mediating role between Web site credibility and behavioural impacts. The paper examines theoretical implications of conceptualizing Web sites according to a human credibility model, and factoring trust into Internet-based behavioural change interventions. Practical guidelines suggest ways to address these findings when planning online social marketing interventions.

This paper discusses problems faced by planners of real-world online behavioural change interventions who must select behavioural change frameworks from a variety of competing theories and taxonomies. As a solution, this paper examines approaches that isolate the components of behavioural influence and shows how these components can be placed within an adapted communication framework to aid the design and analysis of online behavioural change interventions. Finally, using this framework, a summary of behavioural change factors are presented from an analysis of 32 online interventions.