Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence

Abstract

Objective: The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC). Methods: We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n = 191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n = 223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members. Results: CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M = 5.3%, SE =.8 vs. M = 3.3%, SE =.8; p =.015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE =.1), with 35% achieving ≥5%. Male sex (β =.11; P =.009), weeks with ≥2 weigh-ins (β =.68; P