Optimizing Algorithmic Feedback About Lapse Risk for Trust, Engagement, and Clinical Outcomes for Alcohol Use Disorder
University of Wisconsin, Madison
Summary
The goal of this study is to develop a machine-learning guided recovery messaging system. The main question it aims to answer is can messages be used to: * help people to improve their health * make changes in people's lives to address alcohol and substance use Participants will: * complete surveys * use a recovery-support digital therapeutic app
Description
This study seeks to optimize messaging components which can be implemented in a recovery support messaging system such as may accompany a digital therapeutic app, in order to determine optimal messaging to increase interaction with recovery support resources, and whether messaging has any effect on clinical outcomes.
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: * meet criteria for alcohol use disorder with at least moderate severity (\>= 4 DSM-5 criteria assessed via module E of the Structured Clinical Interview for DSM-5182) * in initial remission with most recent use of alcohol between 1 week and 3 months in the past * able to read English * have a smartphone and cellular plan that supports STAR use (Apple iOS or Android) Exclusion Criteria: * medical or psychiatric co-morbidities that preclude use of a smartphone
Interventions
- DeviceSTAR
Automated recovery support messaging system for participants with alcohol use disorder (AUD), paired with a machine learning guided relapse risk prediction model.
Location
- University of WisconsinMadison, Wisconsin