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.
Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.
BACKGROUND The National Diabetes Prevention Program (DPP), governed by the Centers for Disease Control and Prevention (CDC), reduces the incidence of diabetes and diabetes-associated medical costs. Typically, providing this program is staffing-intensive, limiting the ability to scale the DPP and keep pace with the growing incidence of prediabetes. OBJECTIVE We investigated the average weight loss of users of a program called Lark DPP that has full CDC recognition and is powered by conversational artificial intelligence (AI). METHODS We analyzed weight loss of 674 users who met CDC qualifications (completed ≥3 lessons in months 1-6 with ≥9 months between first and last lessons). In addition to the weight loss expected from the CDC curriculum, we investigated whether user characteristics and engagement with AI coaching increased the likelihood of achieving the CDC’s benchmark of ≥5% weight loss at 12 months using logistic regression. RESULTS We observed that 279 users met CDC qualifications and achieved an average of 5.2% (SE=.4) weight loss at 12 months (46% achieved ≥5%). CDC qualifiers completed an average of 20.7 (SE=.4) of 26 available educational missions/lessons. The number of weeks with >2 weigh-ins (standardized coefficient β=.39; P<.001); days with an exchange with the AI coach (β=.24; P=.016); and days since last coaching exchange at final weigh-in (β=-.45; P<.001) were significantly associated with the likelihood of achieving ≥5% weight loss. CONCLUSIONS The Lark DPP resulted in weight loss and sustained engagement for 12 months that was equal to or greater than in-person or hybrid-digital DPPs. Beyond the association between educational mission completion and weight loss, the synchronous personalized feedback and exchanges with the AI coach increased the likelihood of achieving ≥5% weight loss. An AI-powered program is one method to deliver DPPs in a scalable and resource-effective manner to keep pace with the prediabetes epidemic.
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