||1R21CA260092-01 Interpret this number
||Univ Of North Carolina Chapel Hill
||Building a Reinforcement Learning Tool for Individually Tailoring Just-in-Time Adaptive Interventions: Extending the Reach of Mhealth Technology for Improved Weight Loss Outcomes
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.
If you are accessing this page during weekend or evening hours, the database may currently be offline for maintenance and should operational within a few hours. Otherwise, we have been notified of this error and will be addressing it immediately.
Please contact us
if this error persists.
We apologize for the inconvenience.
- The DCCPS Team.