Journal of medical Internet research
AI-Driven Lifestyle Interventions for Reducing Diabetes Risk: Systematic Review
By Ekwunife OI, Nauta T, Fraser RA and 4 others.
Abstract
Background
Artificial intelligence (AI)-driven lifestyle interventions are increasingly used for reducing diabetes risk. However, whether they produce clinical benefit beyond standard Diabetes Prevention Programs (DPPs) and whether effects vary by AI type, population, or delivery setting, remains uncertain.
Objective
This study aimed to evaluate the effects of AI-driven lifestyle interventions on clinical, behavioral, and engagement outcomes among people with prediabetes or elevated diabetes risk and to assess whether effects differed by AI type, population, or delivery setting.
Methods
PubMed, Embase, CINAHL, Web of Science, IEEE Xplore, ClinicalTrials.gov, and citation searches were completed on August 3, 2026, for English-language reports published from 2015. Eligible studies evaluated AI-driven digital lifestyle interventions among people with prediabetes or elevated diabetes risks, or healthy participants in feasibility studies. One reviewer (OIE) screened records and extracted data; a second reviewer (TN) independently verified exclusions and extraction. Study quality was assessed with National Heart, Lung, and Blood Institute tools appropriate to each design. Given substantial heterogeneity in designs, estimands, and outcome metrics, findings were synthesized narratively following the Synthesis Without Meta-Analysis guideline rather than pooled statistically.