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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.
Graham et al. (Sat,) studied this question.
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