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Grant Details

Grant Number: 1R21CA260092-01 Interpret this number
Primary Investigator: Gottfredson, Nisha
Organization: Univ Of North Carolina Chapel Hill
Project Title: Building a Reinforcement Learning Tool for Individually Tailoring Just-in-Time Adaptive Interventions: Extending the Reach of Mhealth Technology for Improved Weight Loss Outcomes
Fiscal Year: 2021


Abstract

Project Summary Excess weight is associated with 13 types of cancer. These cancers disproportionately affect Black and Latinx individuals, as well as those with lower socioeconomic status, because overweight and obesity incidence is higher in these groups. Behavioral weight loss interventions are effective, but in-person interventions tend to have low reach. As mobile phone ownership is increasing in the United States, mHealth technology holds promise for reaching a larger population than in-person behavioral interventions. Furthermore, because they travel with individuals and can collect digital information in real-time, mHealth tools make it possible to intervene with individuals at the precise point when the interventions are needed with just-in-time adaptive interventions (JITAIs). As currently implemented, mHealth JITAIs are adaptive in the sense that interventionists can specify decision rules a priori that result in intervention messages that are triggered or tailored by certain events. These experimenter-specified decision rules are generally based upon results of prior studies, specifically micro-randomized trials that provide sequential tests of mHealth intervention messages in order to determine causal effects of messages conditional on user context. However, JITAIs that are developed in this manner cannot be truly individually tailored because the same decision rules are equally applied to everybody without regard to information about how individuals actually respond to intervention messages. Rapidly evolving machine learning methods, specifically reinforcement learning (RL), makes it possible to improve upon the current approach to JITAIs by learning each person's unique response patterns and integrating this information into subsequent, person-specific, adaptive decision rules. However, the few behavioral interventionists who have created mHealth JITAIs for weight loss using RL have noted high practical barriers to doing so because implementation of RL requires specialized expertise and can be labor intensive. The field needs a user-friendly tool to reduce these barriers in order for RL methodology to become widely adopted. Aim 1 is to develop Adapt, a tool that iteratively integrates real-time data, applies RL algorithms, and performs micro-randomized trials to optimize JITAI decision rules for weight loss. Adapt will pull in digital health data in real-time and conduct micro-randomized trials using behavioral patterns and outcomes to arrive at the most efficacious intervention message, delivered at the right time, for promoting weight loss in each participant. Aim 2 is to conduct a 12-week pilot feasibility study testing usability of Adapt in a weight loss intervention (NudgeRL). NudgeRL will build upon the team's existing JITAI, Nudge, which did not incorporate RL. The sample will consist of 20 adults with overweight or obesity, at least 50% of whom are Black or Latinx. Although Adapt will be developed to improve weight loss interventions, its widespread use will result in more efficient and efficacious JITAIs across a broad range of health outcomes, resulting in a lower burden of cancer and other disease due to a wide spectrum of improved health behaviors.



Publications


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