Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment

IF 3.7 2区医学 Q1 PSYCHOLOGY, CLINICAL

Addictive behaviors Pub Date : 2025-02-25 DOI:10.1016/j.addbeh.2025.108273

Frank J. Schwebel, Adam D. Wilson, Matthew R. Pearson, Matison W. McCool, Katie Witkiewitz

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引用次数: 0

Abstract

Background

Recent investigations of recovery from alcohol use disorder (AUD) have distinguished subgroups of high and low functioning recovery in data from randomized controlled trials of behavioral treatments for AUD. Analyses considered various indicators of alcohol use, life satisfaction, and psychosocial functioning, and identified four recovery profiles from AUD three years following treatment.

Objectives

The present study integrates these profiles into a two-part machine learning framework, using recursive partitioning and random forests to distinguish a) clinical cut-points across 28 end-of-treatment biopsychosocial measurements that are predictive of high or low functioning recovery three years after treatment; and b) a rank-ordered list of the most salient variables for predicting individual membership in the high-functioning recovery sub-groups. Methods: This secondary data analysis includes individuals (n = 809; 29.7% female) in the outpatient arm of Project MATCH who completed the end-of-treatment assessment and three-year follow-up batteries.

Results

Recursive partitioning found individuals with low depressive symptoms and less than 25% drinking days were more likely to be in a high functioning recovery profile (68%), whereas those with at least mild depressive symptoms and low purpose in life were more likely to be in a low functioning recovery profile (70%). Random forests identified purpose in life, social functioning, and depressive symptoms as the best predictors of recovery profiles.

Conclusions

Recovery profiles are best predicted by variables often considered of secondary interest. We demonstrate the utility of two machine learning approaches, highlighting how random forests can overcome recursive partitioning limitations.

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来源期刊

Addictive behaviors 医学-药物滥用

CiteScore

8.40

自引率

4.50%

发文量

283

审稿时长

46 days

期刊介绍： Addictive Behaviors is an international peer-reviewed journal publishing high quality human research on addictive behaviors and disorders since 1975. The journal accepts submissions of full-length papers and short communications on substance-related addictions such as the abuse of alcohol, drugs and nicotine, and behavioral addictions involving gambling and technology. We primarily publish behavioral and psychosocial research but our articles span the fields of psychology, sociology, psychiatry, epidemiology, social policy, medicine, pharmacology and neuroscience. While theoretical orientations are diverse, the emphasis of the journal is primarily empirical. That is, sound experimental design combined with valid, reliable assessment and evaluation procedures are a requisite for acceptance. However, innovative and empirically oriented case studies that might encourage new lines of inquiry are accepted as well. Studies that clearly contribute to current knowledge of etiology, prevention, social policy or treatment are given priority. Scholarly commentaries on topical issues, systematic reviews, and mini reviews are encouraged. We especially welcome multimedia papers that incorporate video or audio components to better display methodology or findings. Studies can also be submitted to Addictive Behaviors? companion title, the open access journal Addictive Behaviors Reports, which has a particular interest in ''non-traditional'', innovative and empirically-oriented research such as negative/null data papers, replication studies, case reports on novel treatments, and cross-cultural research.