Data base of Cognitive functions and their relation to balance and agility in athletes from differents sports branches
Bibliographic record
Abstract
Successful performance in each sport requires high ability in various features, including motor and perceptual-cognitive skills. This study aimed to compare the balance and agility in athletes from several sports branches to find out how cognitive functions relate to these parameters. Seventy-three individuals aged 18-30 were included in this prospective-descriptive study. In the assessment of cognition, Montreal Cognitive Assessment Scale, d2 Test of Attention, and a Bassin Anticipation Timer Device were used. While Prokin-TecnoBody was used to measure the balance skills, Illinois Agility Test (IAT) was used for agility. IAT times showed positive weak correlations with both the absolute error-score (AES) at 8mph (r=0.260, p=0.040) and mediolateral balance score (ML)(r=0.255, p=0.043). While there was a negative weak correlation between AES at 3mph and anteroposterior score of balance (r=-0.267, p=0.035), we found positive weak correlation between AES at 8mph and ML of balance (r=0.253, p=0.046). It was found that the IAT scores of the sedentary group were significantly lower than athletes (p=0.000). According to AES at 3mph, there were significant differences between tennis players and both sedentary and volleyball players (p=0.008, p=0.002, respectively). When the AES at 8mph compared, the only statistically significant difference was between tennis players and sedentary (p=0.008). In conclusion, this study shows how cognitive functions, particularly coincidence anticipation timing (CAT), correlate with essential physical performance factors like agility and balance across different sport branches, suggesting that improving cognitive skills could enhance overall athletic performance and inform mental training strategies in sports. It is recommended that future sports science research focus on enhancing CAT through targeted training programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".