Background: Prediabetes and the Promise of Digital Therapeutics
Prediabetes is a metabolic state where blood glucose levels are elevated above normal but do not meet diabetes criteria. Lifestyle modification and weight management are key to reducing progression risk. Digital therapeutics (DTx)—software-driven interventions delivered via mobile apps, web platforms, or connected devices—offer scalable, personalized support to drive behavior change essential for risk reduction.
The Role of Behavioral Science in DTx
Behavioral science targets action, not just knowledge. In prediabetes, interventions that consider motivation, capability, and environmental opportunities are more likely to produce sustainable changes. When DTx embed behavioral theory, they tailor content, set attainable goals, and provide ongoing feedback, helping users translate intentions into everyday healthier choices.
Key Theoretical Frameworks Shaping DTx
Several frameworks guide DTx design. The COM-B model (Capability, Opportunity, Motivation drive Behavior) helps diagnose barriers and map intervention components. The Behavior Change Wheel (BCW) links COM-B to actionable functions, while the Theoretical Domains Framework (TDF) offers a comprehensive map of determinants. The Health Action Process Approach (HAPA) emphasizes planning, self-efficacy, and action control. Together, these models inform targeted education, prompts, and social support within digital programs.
Common Behavior Change Techniques (BCTs) in Prediabetes DTx
Effective DTx frequently incorporate BCTs such as self-monitoring of diet and activity, goal setting, personalized feedback, action planning, prompts/reminders, problem solving, and social or clinician support. When these techniques are delivered through user-friendly interfaces with adaptive feedback, adherence tends to improve, enhancing potential health benefits.
What Scoping Reviews Reveal About Effectiveness and Engagement
Scoping reviews indicate that DTx for individuals with prediabetes can improve weight, physical activity, and some metabolic risk markers in several populations. However, engagement and sustained use vary widely. Higher retention is often seen with personalization, ease of use, and integration into real-world care pathways. Barriers such as data privacy concerns, digital literacy gaps, and limited clinician involvement can hinder uptake, especially among underserved groups.
Design Considerations for Successful DTx in Prediabetes
For lasting impact, DTx should be user-centered, accessible across literacy levels and languages, and respectful of privacy. Personalization—through adaptive content, cultural relevance, and tailored progression—is critical. Interventions should complement clinical care by providing clinicians with clear, actionable dashboards and ensuring seamless data sharing (with consent). Simple onboarding, offline capabilities, and reminders aligned with daily routines support ongoing use. Addressing the digital divide and offering affordable options promotes equitable access.
Gaps, Challenges, and Future Directions
Evidence to date shows heterogeneity in intervention design and outcome reporting. There is a need for standardized measures of engagement, adherence, and long-term metabolic outcomes. Future research should explore integrating behavioral science with real-time analytics, multi-component and scalable personalization, and rigorous economic evaluations. Trials comparing theory-based DTx to non-theory-driven tools will help clarify added value and inform broader adoption in health systems.
Conclusion
Applying behavioral science to digital therapeutics holds promise for preventing progression from prediabetes. Grounding interventions in theory, applying validated behavior change techniques, and prioritizing user-centered design can enable meaningful, scalable lifestyle changes.
