Evidence map›Paper›PMID 28511286›Full record

ArticleAddiction (Abingdon, England)2017

Who achieves low risk drinking during alcohol treatment? An analysis of patients in three alcohol clinical trials.

Katie Witkiewitz, Matthew R Pearson, Kevin A Hallgren, Stephen A Maisto, Corey R Roos, Megan Kirouac, Adam D Wilson, Kevin S Montes, Nick Heather

Open access · greenAbstract read
In one paragraph

Article in Addiction (Abingdon, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

29 citing papers in PubMed, 48 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Social network moderators of brief alcohol intervention impact.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2022
    Trial
  4. Trial
  5. Trial
  6. Abstinence and low risk drinking during treatment: Association with psychosocial functioning, alcohol use, and alcohol problems 3 years following treatment.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2018
    Trial
  7. Article
  8. Review
  9. Community characteristics and substance-free activity and service access predict membership in alcohol use disorder risk profiles.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025
    Article
  10. Article
  11. Modeling the dynamics of addiction relapse via the double-well potential system.Journal of psychopathology and clinical science · 2025
    Article
  12. Article
  13. Article
  14. Examining differential responses to theAddictive behaviors reports · 2022
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Sex and Gender Effects in Recovery From Alcohol Use Disorder.Alcohol research : current reviews · 2020
    Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Katie WitkiewitzUniversity of New Mexico, Albuquerque, NM, USA.
Matthew R PearsonUniversity of New Mexico, Albuquerque, NM, USA.
Kevin A HallgrenUniversity of Washington, Seattle, WA, USA.
Stephen A MaistoSyracuse University, Syracuse, NY, USA.
Corey R RoosUniversity of New Mexico, Albuquerque, NM, USA.
Megan KirouacUniversity of New Mexico, Albuquerque, NM, USA.
Adam D WilsonUniversity of New Mexico, Albuquerque, NM, USA.
Kevin S MontesUniversity of New Mexico, Albuquerque, NM, USA.
Nick HeatherNorthumbria University, Newcastle upon Tyne, UK.
University of New Mexico · USNorthumbria University · GBSyracuse University · USUniversity of Washington · US

Funding

Psychology Training in Alcohol Research (PTAR)T32AA007455 · NIAAA · UNIVERSITY OF WASHINGTON · PI MARY E. LARIMER · 1985 to 2026
$11.4M
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
Research and Mentoring on Alcohol Clinical and Human Laboratory ResearchK05AA016928 · NIAAA · SYRACUSE UNIVERSITY · PI MAISTO, STEPHEN A · 2008 to 2017
$2.1M
Developing a tool to assess, provide feedback, and facilitate discussion of mechanisms of change in frontline addiction treatmentK01AA024796 · NIAAA · UNIVERSITY OF WASHINGTON · PI HALLGREN, KEVIN A. · 2016 to 2020
$870k
Efficacy and neurobiological mechanisms of mobile-delivered cognitive retrainingK01AA023233 · NIAAA · UNIVERSITY OF NEW MEXICO · PI PEARSON, MATTHEW RYAN · 2014 to 2018
$832k
Toward Identifying Clinically Useful Definitions of Treatment SuccessF31AA024959 · NIAAA · UNIVERSITY OF NEW MEXICO · PI KIROUAC, MEGAN · 2016 to 2017
$50k
NIAAA NIH HHS F31 AA024959NIAAA NIH HHS K01 AA023233NIAAA NIH HHS K01 AA024796NIAAA NIH HHS K05 AA016928NIAAA NIH HHS R01 AA022328NIAAA NIH HHS T32 AA007455NIAAA NIH HHS T32 AA018108
6 · The paper itself

Abstract

BACKGROUND AND

aimsThere is evidence that low-risk drinking is possible during the course of alcohol treatment and can be maintained following treatment. Our aim was to identify characteristics associated with low-risk drinking during treatment in a large sample of individuals as they received treatment for alcohol dependence.

designIntegrated analysis of data from the Combined Pharmacotherapies and Behavioral Intervention (COMBINE) study, Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) and the United Kingdom Alcohol Treatment Trial (UKATT) using repeated-measures latent class analysis to identify patterns of drinking and predictors of low-risk drinking patterns during treatment.

settingUnited States and United Kingdom.

participantsPatients (n = 3589) with alcohol dependence receiving treatment in an alcohol clinical trial were primarily male (73.0%), white (82.0%) and non-married (41.7%), with an average age of 42.0 (standard deviation = 10.7). MEASUREMENTS: Self-reported weekly alcohol consumption during treatment was assessed using the Form-90 and validated with biological verification or collateral informants.

findingsSeven patterns of drinking during treatment were identified: persistent heavy drinking (18.7% of the sample), increasing heavy drinking (9.6%), heavy and low-risk drinking (6.7%), heavy drinking alternating with abstinence (7.9%), low-risk drinking (6.8%), increasing low-risk drinking (10.5%) and abstinence (39.8%). Lower alcohol dependence severity and fewer drinks per day at baseline significantly predicted low-risk drinking patterns [e.g. each additional drink prior to baseline predicted a 27% increase in the odds of expected classification in heavy drinking versus low-risk drinking patterns; odds ratio = 1.27 (95% confidence interval (CI) = 1.10, 1.47, P = 0.002]. Greater negative mood and more heavy drinkers in the social network were significant predictors of expected membership in heavier drinking patterns.

conclusionsLow-risk drinking is achievable for some individuals as they undergo treatment for alcohol dependence. Individuals with lower dependence severity, less baseline drinking, fewer negative mood symptoms and fewer heavy drinkers in their social networks have a higher probability of achieving low-risk drinking during treatment.

Indexed as

Drinking BehaviorAcamprosateAdultAlcohol DeterrentsAlcoholismBehavior TherapyCombined Modality TherapyFemaleHumansMaleNaltrexoneRiskTaurineTreatment OutcomeUnited KingdomUnited StatesAcamprosateAlcohol DeterrentsnaltrexazoneNaltrexoneTaurineAlcohol dependencealcohol treatmentcontrolled drinkingdependence severitylow-risk drinkingmoderationrepeated-measures latent class analysis

Identifiers

PMID28511286
PMCPMC5673549
OpenAlexW2616463940

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.