Evidence map›Paper›PMID 41144690›Full record

ArticleAlcohol, clinical & experimental research2025

How much is enough? Assessing 30- versus 60-day Timeline Followback stability in youth alcohol use patterns.

Anna E Kirkland, Pamela L Ferguson, Tirza J Alcala, Louise Mewton, Rachel L Tomko, Lindsay M Squeglia

Abstract read
In one paragraph

Article in Alcohol, clinical & experimental research, 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

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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.

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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.

Anna E KirklandDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.ORCID https://orcid.org/0000-0001-5308-2423
Pamela L FergusonDepartment of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
Tirza J AlcalaDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
Louise MewtonThe Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, New South Wales, Australia.
Rachel L TomkoDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
Lindsay M SquegliaDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.ORCID https://orcid.org/0000-0001-5994-6894

Funding

Mentoring Clinical Investigators in Patient-Oriented Adolescent Alcohol ResearchK24AA031052 · NIAAA · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Lindsay Squeglia · 2024 to 2026
$644k
The effects of adolescent alcohol use on oral microbiota and the brainK01AA031745 · NIAAA · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Anna E Kirkland · 2024 to 2026
$583k
NIAAA NIH HHS K01 AA031745NIAAA NIH HHS K01AA031745NIAAA NIH HHS K24 AA031052NIAAA NIH HHS K24AA031052
6 · The paper itself

Abstract

backgroundThe variability of youth alcohol use is thought to necessitate longer Timeline Followback (TLFB) reporting windows to accurately capture alcohol use patterns. However, longer TLFB windows can increase burden for participants and study staff. This study assessed youth alcohol use patterns across two different 30-day periods (TLFB days 1-30 and 31-60) to inform future research protocols.

methodsParticipants (N = 481 with 504 visits, ages: 15-25) completed a shared intake to determine eligibility for numerous substance-related studies. Alcohol consumption over the past 60 days was assessed using a modified TLFB that recorded total number of drinks, average drinks per day, average drinks per drinking day (DPDD), total number of drinking days, maximum number of drinks in a single day, and total number of binge drinking days (4+ drinks for females, 5+ drinks for males). TLFB data were divided into two 30-day periods: the most recent (Days 1-30) and the more distal (Days 31-60). Analyses were conducted overall and within subgroups by sex, age (under 21 or 21+), presence of alcohol use disorder (AUD), and other substance use. Alcohol use patterns were assessed using Spearman correlations, Wilcoxon signed rank tests, and intraclass correlations (ICC) for test-retest reliability.

resultsAll alcohol outcomes were significantly correlated between reporting windows (p's < 0.001). Alcohol use was significantly higher during the more recent reporting window (TLFB Days 1-30; p's < 0.05), except for DPDD (p = 0.58). Overall, the differences between reporting windows were small (on average, less than three total drinks and <1 drinking day or binge drinking day) and suggested clinically similar use patterns. The ICC values suggest moderate-to-good stability between reporting windows. Similar patterns were noted in each subgroup analysis.

conclusionsA 30-day TLFB window appears sufficient for youth alcohol use assessment, providing comparable and agreeable self-reported alcohol use patterns, with reduced burden.

Indexed as

Alcohol DrinkingUnderage DrinkingAdolescentAdultBinge DrinkingFemaleHumansMaleTime FactorsYoung Adultalcoholresearch methodssubstance useTimeline Followbackyouth

Identifiers

PMID41144690
PMCPMC12638277

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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.