An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial
Clinical Snapshot
PICO Framework
| P — Population | Adults ≥18 years with prediabetes and overweight or obesity (n=368, median age 58 years, 71% female, 27% Black, 6% Hispanic, 61% White, median BMI 32.3) |
| I — Intervention | Referral to AI-powered DPP lifestyle intervention delivered via mobile app and Bluetooth-enabled digital scale |
| C — Comparator | Referral to human coach-led DPP lifestyle intervention delivered remotely |
| O — Outcomes | Composite primary outcome: maintaining HbA1c <6.5% throughout study AND achieving ≥5% weight loss, OR ≥4% weight loss plus ≥150 minutes weekly physical activity, OR absolute HbA1c reduction ≥0.2 percentage points at 12 months |
Bottom Line
This well-designed noninferiority trial demonstrates that digital lifestyle interventions can achieve similar diabetes prevention outcomes to human coaching. Among 368 adults with prediabetes, approximately one-third achieved clinically meaningful improvements in weight, physical activity, or glycemic control regardless of intervention type. The higher initiation rate for the digital intervention (93% vs 83%) suggests potential advantages for program uptake. For Australian practice, this supports expanding digital health options for diabetes prevention, particularly valuable given workforce constraints and geographic barriers. However, clinicians should consider individual patient preferences and digital literacy when making referrals. The composite outcome approach, while pragmatic, may mask important differences in specific components like weight loss versus physical activity improvements.
Key Findings
P Value: Not reported in abstract
Effect Size: Risk difference -0.2%
Primary Outcome: Composite outcome achieved by 31.7% (AI) vs 31.9% (human coaching)
Nnt Or Sensitivity: Noninferiority margin met (lower CI boundary did not cross -15%)
Confidence Interval: 1-sided 95% CI: -8.2%
Clinical Application
High feasibility given smartphone penetration and digital health infrastructure in Australia Relevant to RACGP diabetes prevention guidelines and potential PBS digital health initiatives. Could address workforce shortages in diabetes education and rural/remote access issues Australian adults with prediabetes and overweight/obesity, particularly those with limited access to traditional DPP programs
Abstract
IMPORTANCE: Prediabetes is common, yet evidence-based lifestyle interventions are underutilized. OBJECTIVE: To determine whether referral to an exclusively artificial intelligence (AI)-led lifestyle intervention based on the Diabetes Prevention Program (DPP) is noninferior to referral to a human-led DPP in achieving recommended thresholds for weight loss, hemoglobin A1c (HbA1c) reduction, and weekly physical activity among adults with prediabetes and overweight or obesity. DESIGN, SETTING, AND PARTICIPANTS: This phase 3, parallel-group, pragmatic, noninferiority randomized clinical trial was conducted from October 11, 2021, to December 16, 2024 (last follow-up) at 2 US clinical sites in Baltimore, Maryland, and Reading, Pennsylvania. Adults 18 years or older with prediabetes and overweight or obesity were enrolled. INTERVENTIONS: Participants were randomized in a 1:1 ratio to receive either a referral to an AI-powered DPP lifestyle intervention delivered via a mobile app and Bluetooth-enabled digital scale or a referral to a human coach-led DPP lifestyle intervention delivered remotely. Both interventions were delivered independently of the study team over a 12-month period. MAIN OUTCOMES AND MEASURES: The primary outcome was a composite of maintaining an HbA1c less than 6.5% throughout the study and achievement of at least 5% weight loss, at least 4% weight loss plus at least 150 minutes of weekly physical activity (assessed with actigraphy), or an absolute reduction in HbA1c of at least 0.2 percentage points at 12 months. Noninferiority of referral to the AI-led DPP compared with referral to the human-led DPP was prespecified to be determined if the 1-sided 95% CI lower boundary of the risk difference did not cross -15%. RESULTS: A total of 368 participants were included (median [IQR] age, 58 [50-65] years; 71% were female, 27% were Black, 6% were Hispanic, and 61% were White; median [IQR] BMI, 32.3 [28.5-37.1]). After referral, 171 of 183 participants (93.4%) initiated the AI-led DPP and 153 of 185 (82.7%) initiated the human-led DPP. The primary outcome was achieved by 58 of 183 participants (31.7%) in the AI-led DPP group and 59 of 185 (31.9%) in the human-led DPP group (risk difference, -0.2% [1-sided 95% CI, -8.2%]), meeting the criterion for noninferiority. Findings were consistent across individual components of the composite end point and in sensitivity analyses. CONCLUSIONS AND RELEVANCE: Among adults with prediabetes and overweight or obesity, referral to a fully automated AI-led DPP was noninferior to referral to a human-led DPP in achieving a composite outcome based on weight reduction, physical activity, and HbA1c. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05056376.
References
- 1.Mathioudakis, N., Lalani, B., Abusamaan, M. S., Alderfer, M., Alver, D., Dobs, A., Kane, B., McGready, J., Riekert, K., Ringham, B., Shehadeh, A., Vandi, F., Wanigatunga, A. A., Zade, D., & Maruthur, N. M. (2025). An AI-powered lifestyle intervention vs human coaching in the diabetes prevention program: A randomized clinical trial. *JAMA*. https://doi.org/10.1111/dme.15028
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