Predicting relapse in people with alcohol use disorder
Official title: Personalized relapse prediction in Alcohol Use Disorder
Funded at University of Minnesota · NIH-11043245 · Data updated 2026-11-12
This study is working on a special tool to help people with alcohol use disorder by predicting when they might relapse, using smart technology to look at different personal factors, so that support can be better tailored to each individual’s needs during their recovery journey.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Minnesota NIH-funded |
| Lab location | 1 site (Minneapolis, United States) |
| Project start | May 1, 2023 |
| Project end | February 29, 2028 |
| Funding year | 2025 |
| Award (this year) | $508,761 |
| NIH project number | 5R01AA029406-03 |
| Project ID | NIH-11043245 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults aged 21 and older who are experiencing alcohol use disorder and are seeking treatment.
✕Less likely to help
Patients who are not struggling with alcohol use disorder or who are under the age of 21 may not benefit from this research.
What this research studies
This research aims to develop a personalized tool to predict relapse in individuals with alcohol use disorder (AUD). By utilizing machine learning techniques, the study will analyze various neuro-behavioral factors that contribute to relapse risk, such as reward reactivity and executive control. Participants will be monitored to identify their specific subtype of alcohol use behavior, which will help tailor interventions and support. The goal is to create a continuous tracking system that can be used in treatment settings to improve recovery outcomes.
Why it matters
Potential benefit: If successful, this research could lead to more effective, personalized treatment strategies for individuals struggling with alcohol use disorder.
How similar studies have gone: Previous research using machine learning approaches to understand addiction behaviors has shown promise, indicating that this method could yield valuable insights.
Where this research is happening
-
University of Minnesota
Minneapolis, United States Active
Researchers
- Principal investigator: Zilverstand, Anna — University of Minnesota
- Study coordinator: Zilverstand, Anna
Related research
Other NIH-funded projects on the same topics.
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Mindfulness-Based Relapse Prevention via Video Calls for Long-Term Recovery from Alcohol Use Disorder
A funded program tests video-delivered mindfulness-based relapse prevention to support adults with alcohol use disorder in achieving long-term recovery.
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How a spouse's mental health and substance use may affect a partner's drinking problems
A study looks at how a spouse’s psychiatric disorders and drinking habits influence when a person starts, stops, or relapses into alcohol use problems within marriage.
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Cognitive Processing Therapy plus Relapse Prevention for people with alcohol problems and PTSD
This trial offers a combined Cognitive Processing Therapy and relapse-prevention program for adults with alcohol use disorder and PTSD to reduce drinking and PTSD symptoms.
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Personalized relapse risk tool for people with alcohol use disorder
This project aims to build a personalized tool using brain and behavior data plus machine learning to predict relapse risk for adults with alcohol use disorder.
-
Video mindfulness program to support long-term recovery from alcohol use
This offers live video mindfulness sessions as continuing care for adults with alcohol use disorder to help reduce heavy drinking and support recovery.
-
How a spouse's mental health affects alcohol problems during marriage
This project looks at whether a spouse's psychiatric problems or genetic risk change when married people develop, recover from, or relapse into alcohol use disorder.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.