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Applying reinforcement learning to create a precision medicine weight loss intervention

Award Number: R34HL170205
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 03/15/2025
PERIOD OF PERFORMANCE END DATE: 02/28/2027

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Applying reinforcement learning to create a precision medicine weight loss intervention - ABSTRACT 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2025	UNIVERSITY OF UTAH	201 PRESIDENTS CIR	SALT LAKE CITY	UT	84112	SALT LAKE	USA	Cardiovascular Diseases Research	000	2	12/9/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $235,179 )
2025	2025	UNIVERSITY OF UTAH	201 PRESIDENTS CIR	SALT LAKE CITY	UT	84112	SALT LAKE	USA	Cardiovascular Diseases Research	002	2	7/18/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$235,179
2025	2024	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Cardiovascular Diseases Research	001	1	7/17/2025	NEW	$0
2025	2024	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Cardiovascular Diseases Research	000	1	12/4/2024	NEW	$0
														Subtotal = $235,179

Issue Date FY: 2024 ( Subtotal = $246,991 )
2024	2024	UNIVERSITY OF WISCONSIN SYSTEM	21 N PARK ST STE 6301	MADISON	WI	53715	DANE	USA	Cardiovascular Diseases Research	000	1	4/18/2024	NEW	$246,991
														Subtotal = $246,991

Grand Total All Awards = $482,170

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Applying reinforcement learning to create a precision medicine weight loss intervention

Award Number: R34HL170205

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 03/15/2025

PERIOD OF PERFORMANCE END DATE: 02/28/2027

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