Temporal trends in physical fitness among preschoolers from Macao Special Administrative Region between 2002 and 2020
Bibliographic record
Abstract
Objective: This study examined temporal trends in physical fitness among preschool children aged 3-5 years from the Macao Special Administrative Region, China between 2002 and 2020. Methods: = 4514). Body size (height, weight, and chest, waist and hip circumferences) and physical fitness (2x10-m shuttle run, standing long jump, walking balance, two-leg continuous jump, overhead throw, and sit-and-reach) were objectively measured. Trends in means were calculated using general linear models. Models were adjusted for gender, age, height, and weight. Trends in distributional characteristics were calculated as the ratio of the coefficients of variation and described visually. Results: We found significant but small increases in height, weight, and chest circumference (standardised effect size [ES] = 0.27-0.42), a significant moderate increase in hip circumference (ES = 0.59), and a negligible trend in waist circumference. Physical fitness trends were conflicting, with negligible to small declines in throwing (ES = -0.14) and balance (ES = -0.32) performance, and negligible to small improvements in other measures (ES = 0.19-0.34). We found negligible trends in distributional variability and differing trends in distributional asymmetry. Conclusion: Overall, these findings suggest modest growth and development among Macao preschoolers over the past two decades. Our findings also highlight the importance of ongoing monitoring to support physical fitness and overall health in early childhood. Continuous screening and monitoring are crucial for identifying trends and informing future health initiatives.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".