Loss of Weight Gained During the COVID-19 Pandemic: Content Analysis of YouTube Videos
Bibliographic record
Abstract
Background Many people experienced unintended weight gain during the COVID-19 pandemic, which has been discussed widely on social media. Objective This study aims to describe the content of weight loss videos on YouTube (Google LLC) during the COVID-19 pandemic. Methods By using the keywords weight loss during quarantine, the 100 most viewed English-language videos were identified and coded for content related to losing weight gained during the COVID-19 pandemic. Results In total, 9 videos were excluded due to having non-English content or posting data before the COVID-19 pandemic. The 91 videos included in the study sample acquired 407,326 views at the time of study and were roughly 14 minutes long. A total of 48% (44/91) of the sample videos included graphic comparisons to illustrate weight change. Videos that included a graphic comparison were more likely to have content related to trigger warnings (χ21=6.05; P=.01), weight loss (χ21=13.39; P<.001), negative feelings during quarantine (χ21=4.75; P=.03), instructions for losing weight (χ21=9.17; P=.002), self-love (χ21=6.01; P=.01), body shaming (χ21=4.36; P=.04), and special dietary practices (χ21=11.10; P<.001) but were less likely to include food recipes (χ21=5.05; P=.03). Our regression analysis results suggested that mentioning quarantine (P=.05), fat-gaining food (P=.04), self-care and self-love (P=.05), and body shaming (P=.008) and having presenters from both sexes (P<.001) are significant predictors for a higher number of views. Our adjusted regression model suggested that videos with content about routine change have significantly lower view counts (P=.03) than those of videos without such content. Conclusions The findings of this study indicate the ways in which YouTube is being used to showcase COVID-19–related weight loss in a pre-post fashion. The use of graphic comparisons garnered a great deal of attention. Additional studies are needed to understand the role of graphic comparisons in social media posts. Further studies that focus on people’s attitudes and behaviors toward weight change during the COVID-19 pandemic and the implications of social media on these attitudes and behaviors are warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".