On Oct. 27, 2025, researchers from Johns Hopkins Medicine and the Johns Hopkins Bloomberg School of Public Health report that an AI-powered lifestyle intervention app for prediabetes reduced the risk of diabetes similarly to traditional, human-led programs in adults in a recent study.

Funded by the National Institutes of Health and published in JAMA on Oct. 27, the study is believed to be the first phase III randomized controlled clinical trial to demonstrate that an AI-powered diabetes prevention program app helps patients meet diabetes risk-reduction benchmarks established by the Centers for Disease Control and Prevention at rates comparable to those in human-led programs.

An estimated 97.6 million adults in the United States have prediabetes, a condition in which blood sugar levels are above normal but below the threshold for type 2 diabetes, putting them at increased risk of developing type 2 diabetes within the next five years. Previous research has shown that adults with prediabetes who complete a human-led diabetes prevention program, which help participants make lifestyle changes to diet and exercise, are 58% less likely to develop type 2 diabetes, as shown in the CDC’s original Diabetes Prevention Program clinical study. However, access barriers, such as scheduling conflicts and availability, have limited the reach of these programs.

Of the approximately 100 CDC-recognized digital diabetes prevention programs available, AI-powered programs represent only a minor subset, and data demonstrating their effectiveness compared with human-led programs is lacking. In the study, the researchers tested whether a fully AI-driven program could provide adults with prediabetes similar health benefits as yearlong, group-based programs led by human coaches.

During the COVID-19 pandemic, 368 middle-aged participants with a median age of 58 volunteered to be referred to either one of four remote, 12-month, human-led programs or a reinforcement learning algorithm app that delivered personalized push notifications guiding weight management behaviors, physical activity, and nutrition. All participants met race-specific overweight or obese body mass index cutoffs and had a diagnosis of prediabetes prior to starting the study. In both groups, a wrist activity monitor was used to track participant physical activity for seven consecutive days each month during the 12-month study.

While participating, study volunteers continued to receive medical care from their primary care providers but could not participate in other structured diabetes programs or use medications that would affect glucose levels or body weight, such as metformin or GLP-1 agonists. Once referred, the researchers did not promote engagement in the program and only followed up with both groups at the 6- and 12-month marks.

After 12 months, the study team found 31.7% of AI-based program participants and 31.9% of human-led program participants met the CDC-defined composite benchmark for diabetes risk reduction (at least 5% weight loss, 4% weight loss plus 150 minutes of physical activity per week, or an absolute A1C reduction of at least 0.2%).

Results demonstrated that similar outcomes can be achieved by a human coach-based program and an AI-based program. Moreover, the AI group had higher rates of program initiation (93.4% vs 82.7%) and completion (63.9% vs 50.3%) in comparison to the traditional programs.

Researchers believe ease of access increased participant engagement in the AI group, showing that AI interventions could be an effective alternative to existing human-coached programs. As such, primary care providers may consider AI-led diabetes prevention programs for patients in need of a lifestyle change program, especially those with considerable logistical constraints.