Temporal trends in muscular fitness among French children and adolescents between 1999 and 2023
Bibliographic record
Abstract
The aim was to estimate national temporal trends in muscular fitness for French youth between 1999 and 2023. Data were obtained from five cross-sectional studies on 53,314 children and adolescents (age range: 6–16 years). Lower- and upper-body strength were assessed by standing broad jump (SBJ) and handgrip strength (HGS). BMI z-scores (BMIz) were calculated using WHO growth curves. We found a statistically significant negligible decline in SBJ performance (standardised effect size (ES) trend per decade [95%CI]: −0.08 [−0.10, −0.07] or −2.1 cm [95%CI: −2.4, −1.8]) and a statistically significant negligible improvement in HGS (ES trend per decade [95%CI]: 0.11 [0.08, 0.13]) or 0.6 kg [(95%CI: 0.4, 0.8]). We found evidence of significant increases in distributional variability and asymmetry, with poorer trends in the low performers (<25th percentile) and better trends in the high performers (>75th percentile) compared to the average performers. Our data suggest negligible trends in mean SBJ and HGS, coupled with increased distributional variability and asymmetry. Trends were not uniform across the population distribution with data suggesting an increase over time in the gap between low and high performers. These results reinforce the importance of interventions and programmes aiming at improving muscular fitness specifically in children and adolescents with low muscular strength.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".