Evidence map›Paper›PMID 40066105›Full record

ArticleHealth psychology and behavioral medicine2025

A N-of-1 social network approach to study the social dynamics of alcohol consumption.

Dominika Kwasnicka, Aileen O'Gorman, Martin Anderson, Louise Bowman, Mark McCann

Abstract read
In one paragraph

Article in Health psychology and behavioral medicine, 2025. 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
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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

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

5 authors.

Dominika KwasnickaMelbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Aileen O'GormanSchool of Education and Social Sciences, University of the West of Scotland, Paisley, Scotland.
Martin AndersonMRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland.
Louise BowmanScottish Drugs Forum, Glasgow, Scotland.
Mark McCannMRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The aim of this study was to investigate how the dynamics of the social environment impacted the alcohol consumption of individuals who self-identified as heavy drinkers. Methods: A mixed methods approach including N-of-1 study with daily Ecological Momentary Assessment (EMA) followed by a social network egonet interview. Qualitative data was analysed using deductive and inductive approaches. The main quantitative outcomes were a number of social contacts and the supportiveness of social networks. Results: Fifteen participants provided sufficient EMA data regarding social contact and six of these took part in the egonet interviews. EMA respondents reported 10.8 social contacts on average and rated approximately half of their networks as positive supports; approximately 10% of each respondents' networks were perceived as 'drinking a lot'. Interview data illustrated the influence of peer and family networks; stress; motivation levels; and coping strategies within the context of the social world. EMA and egonet methods proved feasible with this specific population demonstrating the utility of innovative approaches to study dynamic social contexts related to substance use. Discussion: Respondents either drew upon their social resources and implemented strategies to support behaviour change or experienced social strain and poor mental health in the absence of supportive social strategies. Future research should explore how social networks can impact maintaining non-drinking status and accessing supports. Mixed methods research combining N-of-1, EMA, and egonets can provide novel insights into social dynamics.

Indexed as

Alcoholecological momentary assessmentN-of-1social dynamicssocial network analysis‌

Identifiers

PMID40066105
PMCPMC11892041

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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.