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Record W4415953718 · doi:10.1177/10901981251387139

Physical Activity Moderates the Relationship Between Screen Time and Body Dissatisfaction in Early Adulthood

2025· article· en· W4415953718 on OpenAlexafffundabout
Rachel Surprenant, David Bezeau, Isabelle Cabot, Jonathan Smith, Hyoun S. Kim, Caroline Fitzpatrick

Bibliographic record

VenueHealth Education & Behavior · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsToronto Metropolitan UniversityCegep Edouard MontpetitCegep de Saint HyacintheUniversité de Sherbrooke
FundersSocial Sciences and Humanities Research Council
KeywordsScreen timeRecreationPhysical activityYoung adultPsychological interventionEarly adulthoodPublic healthPhysical activity levelMultivariate analysisAssociation (psychology)

Abstract

fetched live from OpenAlex

The transition to adulthood is a vulnerable period for the development of body image issues, which can increase the risk of behavioral disorders such as body dysmorphic and eating disorders. This study explored whether adherence to physical activity guidelines moderates the association between recreational screen time and body dissatisfaction in early adulthood. A sample of 1,475 young adults (mean age 18.81 years, 60.9% female) from 17 French-speaking public colleges in Quebec, Canada, completed self-report questionnaires in Fall 2021 and Winter 2022. Participants reported their daily recreational screen time, engagement in physical activity over the past 3 months, and sociodemographic characteristics. The analysis, based on multivariate linear regression, showed that higher screen time was associated with greater body dissatisfaction, but this relationship was weaker among participants who met the World Health Organization's physical activity guidelines. These findings suggest that adherence to physical activity guidelines may buffer the negative effects of recreational screen time on body dissatisfaction in young adults, highlighting the value of promoting physical activity in interventions aimed at reducing body dissatisfaction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.421
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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