Evidence map›Paper›PMID 39088014›Full record

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

Profiles of alcohol intoxication and their associated risks in young adults' natural settings: A multilevel latent profile analysis applied to daily transdermal alcohol concentration data.

Michael A Russell, Veronica L Richards, Robert J Turrisi, Cara L Exten, Ivan Jacob Agaloos Pesigan, Gabriel C Rodríguez

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  4. Common and unique latent transition analysis (CULTA) as a way to examine the trait-state dynamics of alcohol intoxication.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025
    Article
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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

6 authors.

Michael A RussellDepartment of Biobehavioral Health, Pennsylvania State University.ORCID 0000-0002-3956-604X
Veronica L RichardsEdna Bennett Pierce Prevention Research Center, Pennsylvania State University.
Robert J TurrisiDepartment of Biobehavioral Health, Pennsylvania State University.
Cara L ExtenEdna Bennett Pierce Prevention Research Center, Pennsylvania State University.
Ivan Jacob Agaloos PesiganEdna Bennett Pierce Prevention Research Center, Pennsylvania State University.ORCID 0000-0003-4818-8420
Gabriel C RodríguezDepartment of Biobehavioral Health, Pennsylvania State University.

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI COLLINS, LINDA M · 2015 to 2019
$13.9M
PREVENTION AND METHODOLOGY TRAINING (PAMT)T32DA017629 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI Eric D Claus, Rina D Eiden · 2005 to 2026
$9.5M
NIDA NIH HHS P50 DA039838NIDA NIH HHS T32 DA017629Social Science Research Institute at Penn State
6 · The paper itself

Abstract

objectiveTransdermal alcohol concentration (TAC) sensors capture aspects of drinking events that self-reports cannot. The multidimensional nature of TAC data allows novel classification of drinking days and identification of associated behavioral and contextual risks. We used multilevel latent profile analysis (MLPA) to create day-level profiles of TAC features and test their associations with (a) daily behaviors and contexts and (b) risk for alcohol use disorders at baseline.

methodTwo hundred twenty-two regularly heavy-drinking young adults (

resultsFour profiles emerged: (a) high-fast (8.5% of days), (b) moderate-fast (12.8%), (c) low-slow (20.4%), and (d) little-to-no drinking days (58.2%). Profiles differed in the odds of risky drinking behaviors and contexts. The highest risk occurred on high-fast days, followed by moderate-fast, low-slow, and little-to-no drinking days. Higher baseline AUDIT predicted higher odds of high-fast and moderate-fast days.

conclusionsDays with high and fast intoxication are reflective of high-risk drinking behaviors and were most frequent among those at risk for alcohol use disorders. TAC research using MLPA may offer novel and important insights to intervention efforts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Indexed as

Alcoholic IntoxicationEthanolAdolescentAdultAlcohol DrinkingFemaleHumansMaleMultilevel AnalysisYoung AdultEthanol

Identifiers

PMID39088014
PMCPMC12049083

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Registered trials

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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.