Optimizing Smart Technology for Addiction Recovery

Purpose

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 system

Conditions

  • Alcohol Use Disorder
  • Alcohol Use Disorder (AUD)

Eligibility

Eligible Ages
Over 18 Years
Eligible Sex
All
Accepts Healthy Volunteers
No

Inclusion Criteria

  • meet criteria for alcohol use disorder with at least moderate severity (>= 4 DSM-5 criteria) - 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

Study Design

Phase
N/A
Study Type
Interventional
Allocation
N/A
Intervention Model
Single Group Assignment
Primary Purpose
Treatment
Masking
None (Open Label)

Arm Groups

ArmDescriptionAssigned Intervention
Experimental
STAR
Participants will use the STAR automated recover support system for 4 months
  • Device: STAR
    Automated recovery support messaging system for participants with alcohol use disorder (AUD), paired with a machine learning guided relapse risk prediction model.
    Other names:
    • Smart Technology for Addiction Recovery

Recruiting Locations

University of Wisconsin
Madison, Wisconsin 53706

More Details

Status
Recruiting
Sponsor
University of Wisconsin, Madison

Study Contact

Susan Wanta
608-262-0387
schneck2@wisc.edu

Detailed 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.