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 < .0001), and days with an AI-powered coaching exchange (β = .43; P < .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.
Keywords: Preventive healthcare; chronic disease management; lifestyle behavior change; mobile health (mHealth); obesity; prediabetes; type 2 diabetes.
© The Author(s) 2022.