The Influence of Hydration Status on Cognitive Function and Athletic Performance
Bibliographic record
Abstract
Hydration is essential for maintaining cognitive function and athletic performance, particularly in athletes training under high temperatures. This study examines the impact of hydration status on cognitive abilities and athletic performance among football players at Lead City Football Academy, Ibadan, Nigeria. Using a cross-sectional design, hydration levels of 88 athletes aged 16–25 were assessed through urinary specific gravity (USG) and pre- and post-training body weight changes. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA), while athletic performance was rated by coaches based on key physical and mental attributes. Results showed that hydration status differed significantly by gender (p = 0.034), with male athletes exhibiting greater dehydration levels. USG analysis revealed that 94.6% of female athletes had minimal dehydration compared to 84.3% of males, while 15.7% of male athletes experienced significant dehydration (USG > 1.021), compared to 5.4% of females. Body weight analysis indicated an average reduction of 0.84% post-training, suggesting fluid loss. Cognitive assessments revealed impairments, with a mean MoCA score of 22.78 (below the normal threshold of ≥26). However, no statistically significant correlation was found between hydration status and cognitive function (p > 0.05). Athletic performance was significantly influenced by hydration status (p = 0.003), with endurance, speed, and mental toughness most affected. Although dehydration was associated with reduced endurance, the effect was relatively weak. These findings highlight the need for structured hydration protocols, personalized fluid intake plans, and athlete education on optimal hydration practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".