Application of Behavioral Science in Digital Therapeutics for Individuals With Prediabetes: Scoping Review

Background Digital therapeutics are increasingly used to manage prediabetes due to their accessibility and potential for personalization. Their success depends heavily on applying behavioral science and integrating theoretical models into digital platforms. However, there has not been a comprehensive account of how behavioral science has been used in digital therapeutics for individuals with prediabetes. Objective This scoping review aimed to examine the use of behavioral theories and techniques in digital therapeutic interventions for individuals with prediabetes, and to identify opportunities to optimize theory-driven and technology-supported strategies. Methods A scoping review was conducted following the Arksey and O’Malley framework and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, Scopus, CNKI, and VIP Database for Chinese Technical Periodicals for studies published up to March 10, 2025. Eligible studies included adults (≥18 years) with prediabetes, as defined by the American Diabetes Association, and examined digital therapeutic interventions informed by behavioral science. All study designs were eligible; included studies were screened, and key characteristics were charted. Results Of the 21 included studies, 17 were randomized controlled trials. The most frequently used behavioral theories were social cognitive theory, theory of planned behavior, and transtheoretical model; however, 11 studies applied behavior change techniques without explicitly stating a theoretical framework. In terms of delivery, digital modalities often comprised smartphone apps (14/21, 67%), human coaching (13/21, 62%), messaging tools (9/21, 43%), wearable devices (9/21, 43%), and web platforms (3/21, 14%). About behavior change techniques, the most frequently used were self-monitoring of behavior (19/21), instruction on performing the behavior (16/21), goal setting (15/21), information about health consequences (15/21), and unspecified social support (11/21). Across studies, outcomes were typically assessed for metabolic and body composition (19/21), glycemic control metrics (17/21), cardiovascular risk and physiological function metrics (16/21), behavioral and cognitive intervention indicators (11/21), and, less frequently, comprehensive health outcome measures (2/21). Conclusions Behavioral science plays a crucial role in developing effective digital therapeutics for individuals with prediabetes. However, greater clarity in theory selection, better integration between models and digital functions, and more culturally inclusive research are needed to improve the scalability and impact of these interventions.