# Maximizing Engagement, Trust, and Clinical Benefit of AI-Generated Recovery Support Messages for Alcohol Use Disorder: Protocol for an Optimization Study

## TL;DR

This study aims to optimize AI-generated daily messages to help people with alcohol use disorder monitor and manage their recovery more effectively.

## Contribution

The novel contribution is optimizing message components to improve engagement and clinical outcomes in recovery support systems.

## Key findings

- Daily support messages with personalized information will be tested for their impact on engagement.
- The study will identify which message components improve clinical outcomes for alcohol use disorder recovery.
- Findings will guide future digital therapeutics and recovery monitoring systems.

## Abstract

Successful recovery from alcohol use disorder requires long-term lapse risk monitoring. Self-monitoring is difficult, given the dynamic, complex interplay of the many risk factors over time. An automated recovery monitoring support system embedded with a machine learning lapse prediction model could improve sustained, adaptive, and personalized self-monitoring by delivering daily support messages.

We propose to optimize the components included in daily support messages to increase engagement with a recovery monitoring support system.

The participants will include 304 US adults with moderate to severe alcohol use disorder. Participants will complete daily surveys and provide geolocation data for 17 weeks. Participants will receive daily support messages, starting in week 2, that convey a combination of individualized information from a lapse prediction model. Manipulated message components include (1) lapse probability and lapse probability change, (2) an important model feature, (3) a risk-relevant recommendation, and (4) message personalization on tone preference.

The National Institute on Alcohol Abuse and Alcoholism funded this project (R01AA031762) on August 9, 2024, with a funding period from August 20, 2024, to July 31, 2029. The institutional research board of the University of Wisconsin-Madison Health Sciences approved this project (IRB #2024-0869). Enrollment will begin in December 2025.

Message components that either increase engagement or improve clinical outcomes will be recommended for use in future recovery monitoring support systems and digital therapeutics.

PRR1-10.2196/81697