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Record W4414525952 · doi:10.1080/15213269.2025.2564698

Examining the Relationship Between Social Media Use and Addiction and Body Dysmorphic Disorder Symptoms: A Meta-Analytic Review

2025· article· en· W4414525952 on OpenAlexaff
M Sheehy, Tanvi Vora, Brooke B. Hiscock, M. Anne George, Michelle Swab, Jonathan M. Fawcett, Emily Fawcett

Bibliographic record

VenueMedia Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBody dysmorphic disorderAddictionSocial mediaHuman factors and ergonomicsPoison controlInjury prevention

Abstract

fetched live from OpenAlex

Relative to body image, comparatively less research has examined the relationship between social media (SM) use and body dysmorphic disorder (BDD). The current meta-analysis examined the cross-sectional relationship between BDD symptoms and SM use/addiction. Of the 1619 studies initially identified, 16 met inclusion criteria. Results revealed a moderate aggregate correlation between SM addiction and BDD symptoms (r = .38, p < .001; n = 9), which was significantly stronger than the weak aggregate correlation revealed between frequency of SM use and BDD symptoms (r = .18, p < .001; n = 10). Of the moderators examined, BDD measure, sex/gender, and SM platform were found to significantly moderate the relationship between BDD and SM addiction. Specifically, studies that measured BDD via the Body Image Concern Inventory, that had a lower percentage of female participants, and that focused on general SM use rather than Instagram use, produced stronger correlations. However, the latter two correlations were only significant when an outlier was removed. Although our results suggest a relationship exists between BDD and SM use/addiction, more experimental and longitudinal research is needed before the direction or presence of causality between these two variables can be determined.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.303
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.216
GPT teacher head0.395
Teacher spread0.179 · 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 routes1
Has abstractyes

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