A longitudinal study of changes in physical activity during adolescence
Bibliographic record
Abstract
BACKGROUND: The majority of people worldwide, including 90% of Canadian adolescents, do not meet recommendations on physical activity. A better understanding of how, when, and why physical activity declines during adolescence is needed. OBJECTIVES: Among adolescents, 1) document how weather conditions affect physical activity; 2) determine whether participants enrolled in organised physical activities maintain healthier activity levels; 3) identify periods of marked decline in participation in specific physical activities; and 4) describe patterns of physical activity participation over five years. METHODS: A 7-day physical activity recall was completed by 1293 adolescents, initially aged 12-13 years, every 3 months over 5 years. The association between number of physical activity sessions daily, and each of season and weather conditions was assessed in Poisson regression models with random effects. Generalized estimating equation Poisson regressions were used to compare rates of decline in number of activity sessions per week between students who participated in organised physical activity in grade 7 and those who did not. We used survival analyses to ascertain when discontinuation of specific activities occurred among adolescents who reported the activity at baseline. Finally, latent class growth curve models were used to identify classes of participants with similar physical activity trajectories during adolescence. RESULTS: First analysis: Levels of physical activity were lowest during winter. Because warm-month increases did not compensate for winter decreases, activity declined by 7% yearly on average. Although statistically significant, the effect of weather on activity was modest. Second analysis: Adolescents who participated in organised physical activities in grade 7 reported 42% more activities on average than other active adolescents. However, organised activities did not protect against declines in physical activity. Third analysis: Within two
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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 | 0.000 |
| 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".