Masters or pawns? Examining injury and chronic disease in male Masters Athletes and chess players compared to population norms from the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Identifying the optimal type and amount of activity for the maintenance of function in older adults has proved challenging. On the one hand, Masters Athletes have been proposed as the ideal model of successful aging but most of this research has focused on physical functioning. On the other hand, the importance of cognitive engagement has been emphasized, which may be more strongly related to activities such as playing chess. The current study aimed to compare physical health outcomes (i.e., prevalence of physical injury and chronic disease) among older athletes and chess players. Masters Athletes and chess players were recruited from track and field and chess competitions within the province of Ontario. In addition to these primary groups, moderately active and inactive older adults from Canadian Community Health Survey were also included for comparison. RESULTS: Masters Athletes had significantly higher rates of injury with the lowest rates of chronic disease, compared to all other activity groups. In contrast, chess players reported lower rates of injury compared to Masters Athletes as well as lower prevalence of chronic diseases compared to the moderately active and inactive groups. The normative groups reported the lowest rate of injury, but increased prevalence of chronic diseases compared to Masters Athletes and chess players. CONCLUSIONS: Findings from this study indicate that both athletic and cognitive engagement may be positively related to the physical health of older adults, since Masters Athletes and chess players reported a lower prevalence of chronic disease. Importantly, the results expand our current understanding of health by providing evidence for physical health outcomes associated with an activity that is primarily associated with cognitive health.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".