Does Lifelong Training Temper Age-Related Decline in Sport Performance? Interpreting Differences Between Cross-Sectional and Longitudinal Data
Why this work is in the frame
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Bibliographic record
Abstract
In the face of remarkable aging trends in North American society, organized sport/physical activity is an important vehicle for promoting physical health, and a domain in which long-term participation might mitigate pessimistic trends for age decline. This investigation examined patterns of age-related decline in performance for 45 Masters runners who rigorously trained continuously for at least a decade. Longitudinal data for age and performance were collected for 200 m, 1500 m, and 10 km events retrospectively across participants' careers. Cross-sectional (CS) data representing normal patterns of aging were derived from online archives. Longitudinal data reflected within-participant training effects whereas CS data did not. Second-order regression analyses were performed separately for each data type and quadratic beta coefficients, indicative of accelerated age decline, were compared for CS and longitudinal samples on a within-event basis. Results showed evidence of accelerated decline with advancing age for both samples, although rates for longitudinal samples were moderated for the 200 m and 1500 m events. Findings for the long-distance event were anomalous. Results provide evidence for moderated age-decline in physical performance measures for individuals who sustain engagement in organized sport for lengthy periods. Discussion focuses on methodological considerations for advancing future research that contrasts CS and longitudinal samples, and the importance of encouraging sport involvement opportunities to aging individuals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it