ArticleDigital health
Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Effectiveness of Text Messaging Interventions on BMI Among Adults With Prediabetes: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2026Pooled it
- User Engagement and Feature Preferences in an AI-Powered mHealth Intervention for Diabetes Prevention: Secondary Analysis of a Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- Trial
- Digital Health in Obesity Care: Current Evidence, Challenges, and Future Directions.Current obesity reports · 2026Review
- Personalization of Training and Weight Reduction Using Artificial Intelligence: A Scoping Review of Current Evidence and Practical Limitations.Journal of functional morphology and kinesiology · 2026Review
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- Cardiac Self-Efficacy Improvement in a Digital Heart Health Program: Secondary Analysis From a Feasibility and Acceptability Pilot Study.JMIR formative research · 2025Article
- Exploring Psychosocial Burdens of Diabetes in Pregnancy and the Feasibility of Technology-Based Support: Qualitative Study.JMIR diabetes · 2025Article
- Area deprivation levels of members of a fully digital diabetes prevention program compared to the US population.Frontiers in endocrinology · 2025Observational
- Health Professionals' Views on the Use of Conversational Agents for Health Care: Qualitative Descriptive Study.Journal of medical Internet research · 2024Article
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review.Annals of medicine and surgery (2012) · 2024Review
- Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.JMIR formative research · 2024Article
- Article
- Business vs After-Hours Use of an Artificial Intelligence-Powered Digital Health Platform Among Insured Patients.JAMA network open · 2023Article
- Weight loss and modeled cost savings in a digital diabetes prevention program.Obesity science & practice · 2023Article
- Predictors of program interest in a digital health pilot study for heart health.PLOS digital health · 2023Article
- The Effects of Providing a Connected Scale in an App-Based Digital Health Program: Cross-sectional Examination.JMIR mHealth and uHealth · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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; 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.