Is muscle-strengthening exercise associated with better academic performance of Literacy, Mathematics and Language? Results from a cross-sectional study
Bibliographic record
Abstract
Objectives: The aim of this study was to assess the associations of muscle-strengthening exercise (MSE) with academic performance (AP) of different subjects in Chinese school-aged children and adolescents. Methods: Cross-sectional data on 67,281 students were included. Engagement in MSE was assessed using a specific question, and AP was assessed through self-reported grade in Literacy (Chinese), Mathematics, and Language (English). The analysis explored the associations between days of engagement in MSE and AP in Chinese, Mathematics, and English, separately. Results were presented as odds ratios (ORs). Results: There was a generally progressive increase in the likelihood of better AP of Chinese, Mathematics and English with increase in days of MSE. Specifically, one day of MSE per week was associated with an 11 % increase of better AP in Chinese, with a gradual increase up to 4 days per week (OR = 1.42), peaking at 6 days. The odds of enhanced AP related to Mathematics increased with days of MSE, starting with a 9 % increase for 1 day per week (OR = 1.09), and reaching a 39 % increase at 4 days per week. The odds of improved AP in English were 9 % higher for 1 day per week of MSE and continued to a 31 % increase for 4 days per week. Conclusion: This study suggests the potential positive role of MSE in enhancing AP in children and adolescents. Future studies are needed to confirm our findings based on longitudinal or intervention studies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".