The Effect Of #wieiad Videos On Intentions To Change Diet And Exercise In Young Men
Bibliographic record
Abstract
TikTok is one of the most widely used social media platforms by young adults. A popular trend on TikTok is what I eat in a day (#WIEIAD) videos, which showcase meal plans, recipes, and physical appearance. It is important to understand the effects that viewing this idealized content has on body image and emotional outcomes, including in young men, a population often marginalized in body image research. PURPOSE. The purpose of the present study was to examine direct and indirect effects of viewing WIEIAD videos on TikTok on young men’s intentions to change diet and exercise. METHODS. Young men (N = 221; aged 18-30 years) were recruited on Cloud Research Connect. Participants were randomly assigned to view 7-minutes of WIEIAD videos or travel videos (control). Participants completed pre and post manipulation measures of fitness-related envy, and post manipulation measures of appearance comparisons and intentions to change diet and exercise. Direct and indirect effects were tested using PROCESS model 6 in SPSS (serial mediation). RESULTS: Viewing WIEIAD videos did not directly predict intentions to change diet (β = -.07, p = .691) or exercise (β = -.23, p = .271 ); however it was associated with higher upward appearance comparisons (β = 1.23, p < .001). Upward appearance comparisons predicted higher fitness-related envy (β = 2.58, p = < .001). Fitness-related envy did not predict higher intentions to change diet (β = .01, p = .553) or exercise (β = .01, p = .487); however, appearance comparisons predicted higher intentions to change diet (β = .43, p = <.001), and intentions to change exercise (β = .35, p = <.001). CONCLUSIONS: Viewing WIEIAD videos on TikTok influences intentions to change diet and exercise in young men through upward appearance comparisons. Given that social media has negative effects on mental health, researchers should further investigate if these intentions lead to adaptive or maladaptive health behaviors in young men. Supported by: This work was supported through funding obtained by Kimberley Gammage from the Social Science and Humanities Research Council (SSHRC) of Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".