MiWaves Reinforcement Learning Algorithm

TL;DR

MiWaves is a reinforcement learning algorithm designed to personalize intervention prompts to reduce cannabis use among emerging adults, leveraging domain expertise and prior data, and tested in a clinical trial.

Contribution

The paper introduces MiWaves, a novel RL algorithm that personalizes intervention delivery for cannabis use reduction, integrating domain knowledge and prior data.

Findings

Successfully deployed in a clinical trial from March to May 2024

Demonstrated potential for personalized intervention optimization

Provides a framework for RL-based health interventions

Abstract

The escalating prevalence of cannabis use poses a significant public health challenge globally. In the U.S., cannabis use is more prevalent among emerging adults (EAs) (ages 18-25) than any other age group, with legalization in the multiple states contributing to a public perception that cannabis is less risky than in prior decades. To address this growing concern, we developed MiWaves, a reinforcement learning (RL) algorithm designed to optimize the delivery of personalized intervention prompts to reduce cannabis use among EAs. MiWaves leverages domain expertise and prior data to tailor the likelihood of delivery of intervention messages. This paper presents a comprehensive overview of the algorithm's design, including key decisions and experimental outcomes. The finalized MiWaves RL algorithm was deployed in a clinical trial from March to May 2024.