Evidence map›Paper›PMID 39786836›Full record

ArticlePsychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors2025

Common and uncommon risky drinking patterns in young adulthood uncovered by person-specific computational modeling.

Whitney R Ringwald, Kasey G Creswell, Carissa A Low, Afsaneh Doryab, Tammy Chung, Junier B Oliva, Zachary F Fisher, Kathleen M Gates, Aidan G C Wright

Abstract read
In one paragraph

Article in Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Common and uncommon risky drinking patterns in young adulthood uncovered by person-specific computational modeling.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025
    Article
  3. Article
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

9 authors.

Whitney R RingwaldDepartment of Psychology, University of Minnesota, Twin Cities.
Kasey G CreswellDepartment of Psychology, Carnegie Mellon University.
Carissa A LowDepartment of Medicine, University of Pittsburgh.
Afsaneh DoryabDepartment of Computer Science, University of Virginia.
Tammy ChungDepartment of Psychiatry, Rutgers, The State University of New Jersey.
Junier B OlivaDepartment of Computer Science, University of North Carolina.
Zachary F FisherCollege of Health and Human Development, Penn State University.
Kathleen M GatesDepartment of Psychology and Neuroscience, University of North Carolina.
Aidan G C WrightDepartment of Psychology, University of Michigan.

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
Using Smartphone Assessments for Personalized Prediction of Problematic Alcohol UseR01AA026879 · NIAAA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WRIGHT, AIDAN GREGORY CRAVER · 2020 to 2024
$3.1M
Identifying Personality-Related Behavioral Phenotypes for Binge Drinking Using Smartphone Sensors and Machine LearningF31AA030500 · NIAAA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RINGWALD, WHITNEY · 2022 to 2022
$47k
National Institutes of Health; National Institute on Alcohol Abuse and AlcoholismNCATS NIH HHS UL1 TR001857NIAAA NIH HHS F31 AA030500NIAAA NIH HHS R01 AA026879NIH HHS
6 · The paper itself

Abstract

objectiveAlcohol use offers social benefits for young adults, but also carries risk of significant negative consequences. Better understanding of processes driving alcohol use for those who experience negative consequences can prevent these harms. These at-risk young adults likely have drinking patterns in common and patterns unique to each individual. Evidence for these processes have been limited by methods that fail to capture the complex, heterogeneous, multivariate nature of drinking. We overcome these limitations with idiographic computational models.

methodWe studied a sample of 97 young adults who regularly binge drink and experience negative drinking consequences. Participants completed daily surveys for 120 days. We estimated temporal networks of each person's drinking patterns by searching all possible dynamic relations among self-reported alcohol consumption and various cognitive, motivational, and emotional constructs. This method allowed us to identify common and uncommon drinking processes in a data-driven manner.

resultsWe found clear patterns of drinking characteristic of this population (i.e., shared by 60%-100% of the sample) in which young adults drink more per occasion, when they expect positive outcomes and are motivated to get drunk and enhance social experiences, which leads to positive and negative consequences. We also identified subsets of participants with uncommon (i.e., shared by < 51% of the sample) drinking patterns.

conclusionsMost young adults may continue to drink despite experiencing negative drinking consequences, because it also satisfies their desire for fun and social connection. Additionally, subsets of young adults have relatively uncommon drinking patterns that may reflect risk or resilience factors. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Indexed as

Alcohol DrinkingBinge DrinkingRisk-TakingAdolescentAdultComputer SimulationFemaleHumansMaleMotivationYoung Adult

Identifiers

PMID39786836
PMCPMC12238294

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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.