Screen time is independently associated with health‐related quality of life in overweight and obese adolescents
Bibliographic record
Abstract
AIM: Excessive screen time and diminished health-related quality of life (HRQoL) are greater problems for obese than nonobese adolescents, but no research has examined the relationship between these two variables. This study examined the association between screen time and HRQoL in overweight and obese adolescents. METHODS: A sample of 358 overweight and obese adolescents aged 14-18 years were assessed at baseline between 2005 and 2010 as part of the Canadian Healthy Eating, Aerobic and Resistance Training in Youth (HEARTY) trial. We used the Pediatric Quality of Life (PEDS-QL) and other self-report measures to assess HRQoL and screen time, defined as how long the 261 females and 97 males spent viewing TV, using the computer and playing video games. RESULTS: After adjusting for socio-demographic variables, adiposity, physical activity and diet, screen time duration was associated with reduced overall HRQoL (adjusted r = -0.16, ß = -0.16, p = 0.009) and psychosocial HRQoL (adjusted r = -0.16, ß = -0.18, p = 0.004), but not physical HRQoL. No differences were found between males and females. CONCLUSION: Screen time was associated with reduced overall and psychosocial HRQoL in overweight and obese adolescents. Future research should determine whether reducing screen time improves overall and psychosocial HRQoL in obese adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".