The integrated role of multiple healthy weight behaviours on overweight and obesity among adolescents: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: This study contributes to the limited number of studies that have explored the impact of not meeting the recommendations for moderate-to-vigorous physical activity, screen time, fruit and vegetable consumption and sleep on overweight and obesity among adolescents. METHODS: A cross-sectional study of data from the 2015 Ontario Student Drug Use and Health Survey (OSDUHS), a provincially representative survey of students in publically funded schools in Ontario, Canada, was conducted. This study included self-reported data from students aged 11-17 years (n = 9866). The main outcome variable was overweight or obesity, classified using WHO BMI cut-points. Four independent variables for healthy weight behaviours were examined: (1) moderate-to-vigorous physical activity (MVPA) (≥ 60 mins vs. < 60 mins everyday over the last seven days); (2) screen time (< 2 h daily vs. ≥ 2 h daily); (3) fruit and vegetable consumption (≥ 5 times/day vs. < 5 times/day); (4) sleep (adequate based on guidelines vs. inadequate). Covariates included sex, age, Subjective Social Status (SSS), parental education and ethnicity. Binomial and multinomial logistic regression models were fitted to determine whether not meeting the recommendations for healthy weight behaviours was associated with overweight or obesity status. RESULTS: Only 2% of students in Ontario met the recommendations for all four healthy weight behaviours and 33% of students did not meet any of the four recommendations. In both the binomial and multinomial models, not meeting the recommendations for MVPA was the only significant healthy weight behaviour associated with both overweight and obesity (AOR: 1.29, 95% CI: 1.03-1.62), and solely obesity (AOR: 1.45, 95% CI: 1.05-1.99). Males, students with lower SSS ratings, and students with parents with an education of 'High School' or less were also at significantly greater odds of being obese. CONCLUSION: Findings from this study show that inadequate levels of MVPA is a critical behavioural predictor of obesity status in adolescents between the ages of 11-17 years, after controlling for differences in screen time, fruit and vegetable consumption, sleep, and demographics. Findings from this study could have implications toward policies and programs targeted at reducing obesity, and increasing the physical activity rates of 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 | 0.002 | 0.002 |
| 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".