Teens and Screens: The Influence of Screen Time on Adiposity in Adolescents
Bibliographic record
Abstract
The effect of screen time during secondary school on percent body fat was examined in a cohort of 744 Canadian adolescents aged 12-13 years at baseline. Participants completed self-reported questionnaires on television viewing and computer use in 19 survey cycles over 57 months from 1999 to 2005. Triceps skinfold thickness and subscapular skinfold thickness were measured in survey cycles 1 and 19. Four screen-time trajectory groups identified in growth mixture modeling included steady-low screen time (73% of the sample), steady-high (10%), increasers (9%), and decreasers (8%). The effect of screen-time trajectory on percent body fat at survey cycle 19 was modeled in boys and girls separately by using linear regression, adjusting for baseline percent body fat and physical activity. Relative to that of steady-low screen-time trajectory group boys, percent body fat was 2.9 (95% confidence interval: 0.7, 5.0) and 2.4 (95% confidence interval: 0.5, 4.2) percentage units higher on average among "increasers" and "steady-high" trajectory group boys, respectively. There was no evidence that screen time has an effect on percent body fat in girls overall, although physical activity modified the association between screen time and percent body fat in both sexes. Efforts to prevent obesity in youth should emphasize reducing screen time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".