Trends in physical fitness among Lithuanian adolescents aged 11–17 years between 1992 and 2022
Bibliographic record
Abstract
BACKGROUND: Physical fitness is an excellent marker of general health and performance. We aimed to calculate trends in physical fitness among Lithuanian adolescents between 1992 and 2022. METHODS: Using a repeated cross-sectional design, body size and physical fitness data for 17 918 Lithuanian adolescents (50.3% female) aged 11-17 years were collected in 1992, 2002, 2012 and 2022. Body mass index (BMI) was calculated from measured height and body mass, with BMI z-scores (zBMI) calculated using WHO growth curves. Physical fitness was measured using the Eurofit test battery, with results converted to z-scores using European norms. With adjustment for zBMI, trends in mean fitness levels were calculated using general linear models. Trends in distributional characteristics were visually described and calculated as the ratio of SDs. RESULTS: We found significant large declines (standardised effect size (ES) ≥ 0.80) in 20-m shuttle run and bent arm hang performance, and significant small declines (ES=0.20-0.49) in standing broad jump, plate tapping, sit-and-reach and sit-ups performance. In contrast, we found a significant moderate improvement (ES=0.50-0.79) in flamingo balance performance and a significant negligible improvement (ES<0.20) in 10×5-m shuttle run performance. Poorer trends were observed in low performers (below the 20th percentile) compared with high performers (above the 80th percentile). CONCLUSION: Health-related fitness (ie, cardiorespiratory and musculoskeletal fitness) levels have declined among Lithuanian adolescents since 1992, particularly among those with low fitness. National health promotion policies are required to improve current trends.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".