Supplement to Effectiveness of Digital Versus In-Person Diabetes Prevention Programs

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PROJECT SUMMARY We seek a budget supplement to bolster the completion of our ongoing randomized controlled trial (RCT), registered as NCT05056376 on ClinicalTrials.gov, funded by the National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK125780. The RCT studies the comparison between a fully automated digital diabetes prevention program (dDPP), Sweetch Health Ltd., which leverages AI technology for adaptive lifestyle coaching, and the standard of care human coach-based diabetes prevention programs (hDPPs). Despite prediabetes posing significant risks for developing type 2 diabetes, millions of U.S. adults lack adequate lifestyle counseling to mitigate this risk. Mobile health (mHealth) technologies, like Sweetch, provide a scalable solution to this widespread issue. This project’s objective is to determine whether the AI-driven dDPP is as effective as traditional hDPPs in improving health outcomes for prediabetic adults. This population is particularly susceptible and stands to benefit greatly from interventions. Our RCT addresses an evidence gap in chronic disease prevention and health behavior change, underpinned by promising short-term results from our preliminary trial. The study, set to complete randomization of final study participants by the end of 2023, will recruit 368 prediabetic adults aged 18-75 who are overweight/obese. Participants are split evenly into two arms: Arm 1 (N=184) receives the fully automated Sweetch digital health kit (dDPP arm) while Arm 2 (N=184) engages in a local CDC-recognized hDPP through in-person or distance learning. Both arms undergo rigorous physical activity measurements using actigraphy at baseline and 1-month intervals throughout the study. We hypothesize that the dDPP will be non-inferior to the hDPP for achieving the CDC’s type 2 diabetes risk reduction endpoint at 12 months. We also anticipate that the dDPP will garner higher engagement and acceptability due to its flexibility and convenience. The supplement is required for unexpected costs related to actigraphy, increased Medicare reimbursement for the hDPP, and travel expenses for home study visits, prompted by the COVID-19 pandemic. Completing this study will clarify the potential of fully automated digital interventions using AI for delivering effective, scalable, sustainable, and cost-effective health-promoting behavioral change interventions. Its success can profoundly impact scalability in diabetes prevention.