Applying reinforcement learning to create a precision medicine weight loss intervention

Obesity affects 2 out of 5 American adults and leads to high individual and societal costs. Behavioral interventions focusing on diet and exercise can yield clinically significant weight loss of at least 5%. However, the proportion of people who achieve this benchmark in behavioral weight loss trials is limited due to variable and waning intervention adherence. One strategy to improve intervention adherence and weight loss is to provide small monetary incentives for behaviors such as calorie logging or outcomes such as interim weight loss. Current intervention structures are uniform, providing the same incentive timing and amount to all people. Thus, some receive incentives even though they do not need them, while others do not receive enough incentives to change their behavior. This uniform structure taxes limited budgets available for incentives. There is an urgent need for a precision medicine approach that distributes incentives to people who respond to them. To address this need, we apply reinforcement learning, a machine learning method, to create a precision medicine intervention whereby each participant receives an individualized sequence of incentives to increase the probability they achieve clinically significant weight loss. This novel approach takes data from cellular scales and a dietary logging application to inform the prediction of participant behavior in response to incentive amount. Using data from a previous incentives trial, we developed an algorithm with high predictive accuracy. We plan to conduct a future randomized trial comparing the efficacy of this precision approach to a uniform approach on weight loss. To prepare for the future trial, we will conduct a planning study with three aims: 1) Develop a digital health platform to incorporate an existing commercial mathematical optimization modeling software for prescriptive analytics to implement our algorithm. The platform will provide real-time analytics and deliver a weekly incentive based on past behavior. We will develop a manual of procedures for detecting and resolving errors for real-time data capture and processing. 2) Evaluate the feasibility of applying the algorithm prospectively in a clinical trial. We will enroll two successive cohorts of adults with obesity in a single-arm feasibility study and implement the personalized incentives intervention over six months. We will establish logistical feasibility of executing the protocol and provide estimates of screening-to-enrollment, retention, and incentives intervention response rates. 3) Characterize participant intervention acceptability as indicated by intervention adherence rates, safety criteria, and feedback from qualitative interviews. Our findings will be used to support a future, adequately powered randomized trial. If efficacious, our personalized intervention could maximize return on investment for healthcare payers by optimally distributing a preset budget across a population to maximize long-term, population-level weight loss in real-world settings. Project Number: 7R34HL170205-02 | Fiscal Year: 2025 | NIH Institute/Center: National Heart Lung and Blood Institute (NHLBI) | Principal Investigator: Corrine Voils (+1 co-PI) | Institution: UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH, SALT LAKE CITY, UT | Award Amount: $235,179 | Activity Code: R34 | Study Section: NHLBI Single-Site and Pilot Clinical Trials Study Section[SSPT(JA)] View on NIH RePORTER: https://reporter.nih.gov/project-details/7R34HL17020502