Alcohol Use Disorder, Alcohol Use Disorder (AUD)
Conditions
Brief 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
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.
Interventions
Automated recovery support messaging system for participants with alcohol use disorder (AUD), paired with a machine learning guided relapse risk prediction model.
Sponsors
Study design
Eligibility
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in heavy drinking days in the previous 60 days | data collected at baseline, 2, 4 months | Data collected via Timeline Followback (TLFB) conducted by interview and 0, 2, and 4 months. Ecological Momentary Assessment (EMA) lapse reports will be used to supplement the TLFB. These are daily surveys where the participant answers the question "have you had a drink you have not yet reported?" (Yes/No). If yes they are asked to indicate the date of their drinking and how many drinks they consumed that day (less than 1, 1-2, 3-5, 5-6, 7-8, over 8). Greater than 3 drinks are considered heavy drinking days for women and greater than 4 drinks are considered heavy drinking days for men. |
| Change in drinking days in the previous 60 days | data collected at baseline, 2, 4 months | Data collected via Timeline Followback (TLFB) conducted by interview and 0, 2, and 4 months. EMA lapse reports will be used to supplement the TLFB. These are daily surveys where the participant answers the question "have you had a drink you have not yet reported?" (Yes/No). If yes they are asked to indicate the date of their drinking. |
| Change in lapse probability | Baseline to 4 months | Data measured via prediction model output which provides a daily lapse probability. This probability is a numeric value from 0-1, zero representing no probability of lapse and 1 representing 100% probability of lapse. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Flourishing Scale Score | up to 4 months | 10 items rated on 0-10 Likert scale where lower scores represent worse functioning and higher scores represent better functioning. Total possible range of scores is from 0-100. |
| Generalized Anxiety Disorder-7 (GAD-7) Score | up to 4 months | 7 items rated on 0-3 Likert scale where lower scores represent better functioning and higher scores represent worse functioning. Total possible range of scores is from 0-21. |
| Multidimensional Inventory of Recovery Capital (MIRC) Score | up to 4 months | 1-4 Likert scale. Some items are reverse coded but higher total scores represent better functioning and lower total scores represent worse functioning. Scored in 4 domains (social, physical, human, and cultural), each from 7-28. |
| Patient Health Questionnaire (PQH-9) Score | up to 4 months | 9 items rated on 1-4 Likert scale. Total possible range of scores from 0-27 where lower scores represent better functioning and higher scores represent worse functioning. |
Countries
United States
Contacts
University of Wisconsin, Madison