The Impact of Cognitive Fatigue and Sleep Quality on Reaction Time in Athletes
Bibliographic record
Abstract
This study aimed to investigate the relationship between cognitive fatigue, sleep quality, and reaction time in athletes and to determine the predictive value of cognitive fatigue and sleep quality on reaction time performance. The study employed a correlational descriptive design with a sample of 385 Canadian athletes, selected based on Morgan and Krejcie's sample size guidelines. Data were collected using the Deary-Liewald Reaction Time Task for reaction time, the Cognitive Fatigue Scale for cognitive fatigue, and the Pittsburgh Sleep Quality Index for sleep quality. Pearson correlation analyses were conducted to assess the relationships between reaction time and each independent variable, and multiple linear regression was used to examine the combined predictive power of cognitive fatigue and sleep quality on reaction time. All analyses were performed using SPSS version 27, with a significance threshold set at p < 0.05. The results demonstrated that cognitive fatigue was positively correlated with reaction time (r = .41, p < 0.01) and sleep quality was also positively correlated with reaction time (r = .37, p < 0.01). Multiple linear regression analysis showed that cognitive fatigue and sleep quality together significantly predicted reaction time (R = .46, R² = .21, F(2, 382) = 49.38, p < 0.001). Both cognitive fatigue (B = 2.38, p < 0.001) and sleep quality (B = 4.61, p < 0.001) were significant independent predictors of reaction time. The findings highlight that both increased cognitive fatigue and poorer sleep quality are associated with slower reaction times in athletes. These results underscore the importance of managing both mental fatigue and sleep health to optimize cognitive-motor performance in athletic settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".