COVID-19 Impact on Adolescent 24 h Movement Behaviors
Bibliographic record
Abstract
This study aimed to examine the impact of the COVID-19 pandemic on the 24 h movement behaviors of adolescents. This was conducted to capture their evolution from February to December 2020, as well as to explore the use of technology for physical activity purposes by adolescents as a strategy to increase their physical activity during the pandemic. Physical activity, recreational screen time, sleep duration, and sleep quality were self-reported by 2661 adolescents using an online questionnaire. Participants also indicated, in comparison with the previous winter (regular in-class learning), how their different movement behaviors changed during the following 2020 periods: (1) spring (school closures), (2) summer (school break), and (3) autumn (hybrid learning). Finally, information about the use of technology during physical activity was collected. Results show that the 24 h movement behaviors of the participants varied across the different periods, and these variations were consistent with the restrictive measures imposed by the government. It was also observed that the negative effects of the COVID-19 pandemic on sleep duration and quality peaked in autumn. Finally, participants' physical activity levels were associated with the use of physical activity-related tools and applications. In conclusion, the restrictive measures due to the COVID-19 pandemic worsened the situation of the 24 h movement behaviors in adolescents, which has become critical.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".