The Effect of Fatigue on the Visual Reaction Time of Greco-Roman Wrestlers
Bibliographic record
Abstract
Wrestling is a sport that involves high intensity and requires quick decisions; hence, visual-motor reaction time is a significant part of performance. However, research on reaction time in resting and fatigued states among young wrestlers has been very limited. This study aimed to compare the visual–motor reaction times of young male wrestlers who regularly take part in training programs under resting and post-fatigue conditions. Twenty healthy male wrestlers with an average age of 14.80 ± 1.15 years, height of 170.05 ± 8.61 cm, and body weight of 66.00 ± 15.57 kg voluntarily participated in the study. A wrestling-specific warm-up protocol was performed after which a visual–motor reaction test was conducted using the FitLight™ device (Fitlight Sports Corp., Canada) for both dominant and nondominant hands with three trials for each hand—the best time (as sec) being included in the analysis. The wrestlers then completed a wrestling training session, after which post-fatigue performance was assessed by repeating the same testing procedure immediately following training. Mean reaction times for all measurements were recorded automatically by the device. Data were analyzed using SPSS and a 2×2 repeated-measures ANOVA was used to analyze the data. There were no significant differences between resting and post-fatigue conditions nor were there any significant differences in reaction times between dominant and nondominant hands. It may be suggested from these findings that fatigue does not markedly affect visual–motor reaction performance in young wrestlers.
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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 | 0.002 |
| 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) | 0.002 | 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".