Evidence map›Paper›PMID 41537056›Full record

ArticleClinical psychological science : a journal of the Association for Psychological Science2026

Alcohol and cannabis use predicted by affect-urgency interactions in everyday life.

Jonas Dora, Connor J McCabe, Megan E Schultz, Christine M Lee, Yuichi Shoda, Megan E Patrick, Gregory T Smith, Kevin M King

Abstract read
In one paragraph

Article in Clinical psychological science : a journal of the Association for Psychological Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Jonas DoraUniversity of Washington.
Connor J McCabeUniversity of Washington.
Megan E SchultzUniversity of Washington.
Christine M LeeUniversity of Washington.
Yuichi ShodaUniversity of Washington.
Megan E PatrickUniversity of Michigan.
Gregory T SmithUniversity of Kentucky.
Kevin M KingUniversity of Washington.

Funding

Ecological Momentary Assessment of Negative Urgency's Effects on Alcohol and Marijuana MisuseR01DA047247 · NIDA · UNIVERSITY OF WASHINGTON · PI KING, KEVIN MICHAEL · 2019 to 2023
$2.9M
Passive mobile sensing and machine learning for the detection of drinking episodesK02AA028832 · NIAAA · UNIVERSITY OF WASHINGTON · PI KING, KEVIN MICHAEL · 2021 to 2025
$725k
NIAAA NIH HHS K02 AA028832NIDA NIH HHS R01 DA047247
6 · The paper itself

Abstract

The hypothesis that urgency, a trait quantifying individual differences in impulsive behaviors driven by intense emotions, moderates associations between affect and alcohol use has received inconsistent support in EMA research. This registered report tested whether trait- and state-level urgency moderate affect-substance use (alcohol and cannabis use) associations in young adults. 496 adults (aged 18-22) completed ecological momentary assessment surveys five times daily across 32 days over eight weekends. Positive affect was associated with increased alcohol use probability, while negative affect was associated with decreased alcohol use probability; cannabis use showed minimal associations with daily affect. Contrary to hypotheses, we found minimal evidence that urgency moderated daily affect-substance use associations. Interaction effects were consistently estimated around the null value with narrow credible intervals. Results challenge theoretical predictions about urgency's role in emotion-driven substance use and support simpler affect-substance use models.

Indexed as

affectalcoholcannabisecological momentary assessmenturgency

Identifiers

PMID41537056
PMCPMC12799203

What OpenQuestion holds

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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.