Maximizing Engagement, Trust, and Clinical Benefit of AI-Generated Recovery Support Messages for Alcohol Use Disorder: Protocol for an Optimization Study
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View abstract on PubMed
Summary
This summary is machine-generated.This study optimizes daily messages for an automated alcohol use disorder recovery support system. Findings will inform future digital therapeutics for sustained self-monitoring and relapse prevention.
Area Of Science
- Digital therapeutics
- Machine learning applications in healthcare
- Behavioral science and addiction research
Background
- Long-term monitoring of lapse risk is crucial for alcohol use disorder (AUD) recovery.
- Self-monitoring in AUD recovery is challenging due to complex, dynamic risk factors.
- Automated support systems with machine learning can enhance sustained, personalized self-monitoring.
Purpose Of The Study
- To optimize daily support message components for increased user engagement.
- To enhance the effectiveness of an automated recovery monitoring support system.
- To improve sustained recovery from alcohol use disorder through personalized digital interventions.
Main Methods
- 304 US adults with moderate to severe AUD will participate over 17 weeks.
- Daily surveys and geolocation data collection will be employed.
- Participants will receive daily messages with individualized lapse prediction information, varying components like probability, model features, recommendations, and tone.
Main Results
- The project is funded by the National Institute on Alcohol Abuse and Alcoholism (R01AA031762).
- Institutional Review Board approval received from the University of Wisconsin-Madison Health Sciences (IRB #2024-0869).
- Participant enrollment is scheduled to commence in December 2025.
Conclusions
- Identifying message components that boost engagement or clinical outcomes is key.
- Recommendations will guide the development of future recovery monitoring systems.
- This research aims to improve digital therapeutics for AUD recovery.
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