The Impact of a Motor Imagery-Based Training Program on Agility, Speed, and Reaction Time in a Sample of Young Tennis Athletes during Ramadan Fasting: Insights and Implications from a Randomized, Controlled Experimental Trial
Bibliographic record
Abstract
The objective of this study was to explore whether a training program incorporating motor imagery could have an effect on physical performance outcomes in terms of agility, speed, and reaction time in a sample of tennis athletes who fasted during the month of Ramadan. Recruited subjects were 27 young male tennis players, randomly allocated to two groups: the imaging training group (n = 13) and a control group (n = 14). The study was designed as a randomized, controlled experimental study. The control group was engaged in watching videos concerning the history of the Olympic Games, whereas the motor imagery group followed a motor imagery-based training program. Physical performance outcomes were assessed during four sessions (one before Ramadan and three during Ramadan) by means of field tests. Our results revealed a drop in all performance outcomes measured in the middle and at the end of Ramadan for both groups (p < 0.01). The effect of the group × time interaction (p < 0.01) was reported for all physical performance outcomes measured for the two groups. This drop in performance was greater for the control group compared to the motor imagery group in the middle and at the end of Ramadan. This study showed that fasting during Ramadan reduced agility, speed, and reaction time performance for tennis players. A motor imagery-based training program could be an effective approach to reduce the effect of fasting during Ramadan and stabilize physical performance outcomes in terms of agility, speed, and reaction time for male tennis players.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".