AI-Driven Lifestyle Interventions for Reducing Diabetes Risk: Systematic Review.

完整摘要 · 英文原文

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. RESULTS: Of 8032 records, 11 studies were included: 1 randomized, 1 nonrandomized comparative, 4 observational, and 5 uncontrolled pre-post studies. Seven evaluated AI lifestyle coaching systems, 2 predictive AI platforms, and 2 conversational AI chatbots. Ten of 11 studies used commercially developed platforms. Study quality was rated good for 5 studies, fair for 3, and poor for 3. The randomized controlled trial showed noninferiority of an AI-DPP versus human coach-led DPP for the combined 12-month outcome (23/183, 12.6% vs 23/185, 12.4%; risk difference 0.1%, 1-sided 95% CI -5.5%). Program initiation (171/183, 93.4% vs 153/185, 82.7%; risk difference 10.7%, 95% CI 4.2%-17.3%) and completion (117/183, 63.9% vs 93/185, 50.3%; risk difference 13.7%; 95% CI 3.6%-23.7%) were higher with AI, but this did not translate into superior clinical outcomes. The nonrandomized comparison found no advantage of AI-personalized messaging over static messaging in terms of BMI or physical activity. The remaining studies reported uncontrolled changes precluding causal inference. Evidence was insufficient to determine whether effects differed by population or delivery setting. CONCLUSIONS: One randomized trial supports noninferiority, not superiority, of a specific AI-driven DPP relative to human coaching; the remaining evidence was nonrandomized, predominantly commercially developed, and hypothesis-generating. To our knowledge, this is the first systematic review to stratify evidence on AI-driven diabetes prevention by AI mechanism-coaching, predictive, and conversational-and by outcome domain, extending prior scoping work that mapped apps without appraising study quality or comparative effectiveness. This structured mapping shows where evidence is the strongest (engagement and weight) and the weakest (glycemic causality and generalizability), giving health systems a concrete basis for deciding where AI-DPP tools may supplement, rather than replace, standard DPP delivery, pending independent, adequately powered randomized trials.