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

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 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;   =  .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE  =  .1), with 35% achieving ≥5%. Male sex ( = .11;   =  .009), weeks with ≥2 weigh-ins ( = .68;  < .0001), and days with an AI-powered coaching exchange ( = .43;  < .0001) were associated with a greater likelihood of achieving ≥5% weight loss.

Conclusions: An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.