National trends and ecological factors of physical activity engagement among U.S youth before and during the COVID-19 pandemic: A cohort study from 2019 to 2021
Bibliographic record
Abstract
BACKGROUND: This study aims to investigate the trends and ecological determinants of physical activity among U.S. children and adolescents during the 2019-2021 period, encompassing the COVID-19 pandemic's onset and subsequent years. METHODS: Utilizing data from the National Survey of Children's Health over three years, this cohort study analyzed physical activity levels and ecological determinants among 82,068 participants aged 6-17. The sample included 36,133 children (44%) and 45,935 adolescents (56%), with variables assessed by caregiver reports. RESULTS: The analysis revealed a significant decline in physical activity among children from 2019 to 2020, followed by a recovery in 2021, whereas adolescents showed a continued decrease without recovery. Over the study period, children were consistently more active than adolescents. Better health status, normal weight, less screen time, stronger peer relationships, higher parental involvement, better family resilience and greater school participation were consistently correlated with increased physical activity in both age groups. Sleep duration was a predictor of activity only in children, while mental health status was solely a predictor in adolescents. Neighborhood environment consistently predicted children's activity levels but was a significant factor for adolescents only in 2020. CONCLUSIONS: These findings highlight the differing impacts of the pandemic on physical activity between children and adolescents, emphasizing the need for targeted public health interventions, particularly for adolescents whose activity levels have not recovered from the pandemic period. Age-specific physical activity interventions should consider sleep duration and neighborhood environmental factors when targeting children and mental health factors when focused on 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 | 0.001 |
| 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 | 0.001 |
| 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".