Changes in Leisure-Time Physical Activity and Active Commuting to School From Before to After the COVID-19 Pandemic Among Adolescents in Brazil: Repeated Cross-Sectional and Longitudinal Analyses
Bibliographic record
Abstract
PURPOSE: Compare leisure-time physical activity (LTPA) and active commuting to school among Brazilian adolescents between the periods before COVID-19 and after the reopening of schools. METHODS: This is a repeated cross-sectional study with a nested cohort targeting high school students from Southern Brazil. LTPA and active commuting to school were the self-reported outcomes. Zero-inflated Gamma and Logistic Mixed models were applied to compare, respectively, LTPA and active commuting between survey years. RESULTS: Cross-sectional analyses showed that the odds of engaging in any type of LTPA were similar between waves. However, active participants spent significantly more time in total LTPA (9.5 min/d), team sports (4.9 min/d), and fitness activities (7.7 min/d) in 2022 (n = 954; 52.0% female; 16.5 [1.2] y) than in 2019 (n = 824, 51.2% female; 16.4 [1.1] y). Prospective analyses of 286 adolescents (54.5% female) showed a reduction in the probabilities of engaging in total LTPA (-17%), team sports (-36%), and individual sports (-10%) from 2019 to 2022. No changes were found for active commuting. CONCLUSIONS: Cross-sectional and prospective differences were found for leisure but not for commuting-related physical activity between pre- and postpandemic periods. Efforts to promote physical activity should remain a public health priority.
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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 | 0.001 |
| 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".