ArticleJournal of substance abuse treatment2022
Using machine learning to examine predictors of treatment goal change among individuals seeking treatment for alcohol use disorder.
Article in Journal of substance abuse treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed, 4 citations in OpenAlex.
- Mapping and measuring addiction recovery: Recommended protocols through the PHENX toolkit.Drug and alcohol dependence · 2026Review
- The measurement of harm reduction goals and outcomes for substance use: Current state and future directions.Journal of prevention and health promotion · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Perspective on Using Artificial Intelligence in Alcohol Research and Treatment: Opportunities and Ethical Considerations.Alcohol research : current reviews · 2025Review
- Mode of mutual-help group attendance: Predictors and outcomes in a US national longitudinal survey of adults with lifetime alcohol use disorder.Journal of substance use and addiction treatment · 2024Article
- Analyzing Dropout in Alcohol Recovery Programs: A Machine Learning Approach.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
introductionThe goals of individuals seeking treatment for alcohol use disorder (AUD) are typically quantified as abstinent or nonabstinent (e.g., moderate drinking) goals. However, treatment goals can vary over time and be influenced by life circumstances. This study aims to identify predictors of treatment goal change and direction of change from baseline to six-month follow-up among individuals seeking treatment for AUD.
methodsThis study is a secondary analysis of data from the Relapse Replication and Extension Project. The study included participants who completed assessments at baseline and six-month follow-up in the analysis (n = 441). We used decision trees to examine 111 potential predictors of treatment goal change. The study cross-validated results using random forests. The team examined changes in goal between baseline and follow-up (Decision Tree 1) and quantified them as being toward or away from a complete abstinence goal (Decision Tree 2).
resultsNearly 50 % of the sample changed their treatment goal from baseline to 6 months, and 68.7 % changed from a nonabstinence goal toward a complete abstinence goal. The study identified seven unique predictors of goal change. The most common predictors of changing a treatment goal were number of recent treatment sessions prior to enrolling in the study, other substance use, negative affect, anxiety, social support, and baseline drinks per drinking day. Participants with a greater number of recent treatment sessions and who sought social support were most likely to change their goal. Additionally, individuals with more substance use tended to change away from complete abstinence goals. Cross-validation supported baseline drinks per drinking day, social support, baseline maximum blood alcohol concentration (BAC), lifetime tobacco use, baseline average BAC, lifetime cocaine use, Inventory of Drinking Situations total score, and Situational Confidence Questionnaire average score as important predictors.
conclusionsThis study identified seven unique predictors of treatment goal change while in AUD treatment. Prior treatment, drinking to cope, and social support were most associated with goal changes. This information can inform providers who seek to understand factors associated with treatment goal selection and changes in goals during treatment.
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