Evidence map›Paper›PMID 35759802›Full record

ArticleJournal of substance abuse treatment2022

Using machine learning to examine predictors of treatment goal change among individuals seeking treatment for alcohol use disorder.

Frank J Schwebel, Noah N Emery, Rory A Pfund, Matthew R Pearson, Katie Witkiewitz

Open access · greenAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 37% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Frank J SchwebelCenter on Alcohol, Substance Use, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: fschwebel@unm.edu.
Noah N EmeryDepartment of Psychology, Colorado State University, Fort Collins, CO 80523, USA.
Rory A PfundCenter on Alcohol, Substance Use, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA.
Matthew R PearsonCenter on Alcohol, Substance Use, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA.
Katie WitkiewitzCenter on Alcohol, Substance Use, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA.
Colorado State University · US

Funding

Alcohol Research Training: Methods & MechanismsT32AA018108 · NIAAA · UNIVERSITY OF NEW MEXICO · PI Katie A Witkiewitz · 2010 to 2026
$5.6M
Precision Medicine and Recovery in a Telehealth Treatment Program for Alcohol Use DisorderR01AA022328 · NIAAA · UNIVERSITY OF NEW MEXICO · PI Katie A Witkiewitz · 2013 to 2026
$3.1M
Efficacy and neurobiological mechanisms of mobile-delivered cognitive retrainingK01AA023233 · NIAAA · UNIVERSITY OF NEW MEXICO · PI PEARSON, MATTHEW RYAN · 2014 to 2018
$832k
NIAAA NIH HHS K01 AA023233NIAAA NIH HHS R01 AA022328NIAAA NIH HHS T32 AA018108
6 · The paper itself

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.

Indexed as

AlcoholismSubstance-Related DisordersAlcohol DrinkingBlood Alcohol ContentGoalsHumansMachine LearningBlood Alcohol ContentAlcohol use disorderMachine learningRandom forestRecursive partitioningTreatment goalTreatment goal change

Identifiers

PMID35759802
PMCPMC9708382
OpenAlexW4283015021

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.