Evidence map›Paper›PMID 39349908›Full record

ArticleAlcohol, clinical & experimental research2024

Exploratory analysis of blood alcohol concentration-related technology use and drinking outcomes among young adults.

Sayre E Wilson, Hannah A Lavoie, Benjamin L Berey, Tessa Frohe, Bonnie H P Rowland, Liana S E Hone, Robert F Leeman

Abstract read
In one paragraph

Article in Alcohol, clinical & experimental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Sayre E WilsonDepartment of Public Health and Health Sciences, Bouvé College, Northeastern University, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-1546-5439
Hannah A LavoieDepartment of Health Education and Behavior, College of Health and Human Performance, University of Florida, Gainesville, Florida, USA.
Benjamin L BereyProvidence VA Medical Center, Providence, Rhode Island, USA.ORCID https://orcid.org/0000-0002-7979-2156
Tessa FroheDepartment of Psychiatry & Behavioral Sciences, School of Medicine, University of Washington, Seattle, Washington, USA.ORCID https://orcid.org/0000-0002-7796-1200
Bonnie H P RowlandDepartment of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0001-7481-7812
Liana S E HoneDepartment of Health Education and Behavior, College of Health and Human Performance, University of Florida, Gainesville, Florida, USA.ORCID https://orcid.org/0000-0002-6777-978X
Robert F LeemanDepartment of Public Health and Health Sciences, Bouvé College, Northeastern University, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-7003-8907

Funding

eHaRT-A: Adapting an evidence-based, in-person harm reduction treatment into a virtual care telehealth intervention for people with lived experience of homelessness and alcohol use disorderK01AA030053 · NIAAA · UNIVERSITY OF WASHINGTON · PI Tessa Marie Frohe · 2022 to 2026
$861k
Development and Initial Testing of a Multi-Component Breath Alcohol-Focused Intervention for Young AdultsR34AA029224 · NIAAA · UNIVERSITY OF FLORIDA · PI LEEMAN, ROBERT F · 2022 to 2024
$576k
Testing a Smartphone Breathalyzer and BAC Estimator in Young Adult Heavy DrinkersR21AA023368 · NIAAA · UNIVERSITY OF FLORIDA · PI LEEMAN, ROBERT F · 2016 to 2017
$384k
Applying Mixed Methods to Identify Links between Cannabis Use and Sleep Behaviors with Ramifications for Veterans Clinical Health OutcomesIK2CX002645 · VA · PROVIDENCE VA MEDICAL CENTER · PI BEREY, BENJAMIN LEONARD · 2024 to 2025
–
CSRD VA IK2 CX002645NIAAA NIH HHS K01 AA030053NIAAA NIH HHS L30 AA027013NIAAA NIH HHS L30AA027013-03NIAAA NIH HHS R21 AA023368NIAAA NIH HHS R34 AA029224
6 · The paper itself

Abstract

backgroundMobile health (mHealth) technology use may reduce alcohol use and related negative consequences; however, little is known about its efficacy without prompting from researchers or pay-per-use. This exploratory analysis assessed relationships between mHealth technology use frequency and alcohol-use outcomes.

methodsYoung adults who drink heavily (N = 97, M

resultsParticipants used one or more mHealth technologies on approximately 68% of drinking days (33% of field days), with multiple technologies used on 34% of drinking days. Bivariate correlations revealed that a higher percentage of study days with any mHealth technology use was related to higher mean weekly drinks. However, a higher percentage of drinking days with any mHealth technology use was related to lower mean weekly drinks, percent of heavy and high-intensity drinking days, and negative consequences. There were several significant, inverse correlations between alcohol variables and using the mHealth technologies that provided personalized feedback. Multiple regression analyses (holding sex and baseline alcohol variables constant) indicated that a higher percentage of drinking days with any mHealth technology use was related to lower mean weekly drinks and lower percentage of heavy drinking days.

conclusionsUsing mHealth technologies to moderate drinking without direct prompting from the research team or per-use incentives was related to less overall alcohol use and heavy drinking. This indicates potential real-world engagement with mHealth apps to assist with in-the-moment drinking. Normalizing mHealth technology use during drinking could help curb the public health crisis around harmful alcohol use in young adult populations.

Indexed as

blood alcohol concentrationharm reductionmhealthnegative consequencesyoung adults

Identifiers

PMID39349908
PMCPMC12369596

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LicenceCC BY-NC-ND
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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.