Repeated-sprint training in the fasted state during Ramadan: morning or evening training?
Bibliographic record
Abstract
BACKGROUND: The present study assessed the optimal moment of the day for repeated-sprint training in the fasted state during Ramadan. METHODS: Thirty amateur soccer players were randomly assigned to a morning training group (MTG, training at ~08:00 a.m., N.=10), an evening training group (ETG, training around 06:00 p.m., N.=10), and a control group (N.=10). Training sessions, conducted on alternate days, consisted of 3 sets of 6×40-m shuttle sprints (2×20 m with 180° direction changes). A 20-second passive recovery and a 4-minute passive recovery were allowed between repetitions and sets, respectively. Before and after Ramadan, performance was assessed at both 08:00 a.m. and 06:00 p.m. by Countermovement Jump (CMJ), Repeated-Sprint Test (RST), and Yo-Yo Intermittent Recovery Test Level 1 (YYIRT1). RESULTS: After Ramadan, YYIRT1 performances were enhanced for both groups in the morning (7.82% and 6.29% for MTG and ETG, respectively, P<0.05), and in the evening (6.61% and 11.20%, respectively, P<0.05). Relative changes in YYIRT1 (P=0.33) and RST (-2.13% and -3.44% for MTG and ETG, respectively, P=0.49) at the specific time of training were similar for both groups. No differences were observed in CMJ performances before and after Ramadan for any group (P>0.05). CONCLUSIONS: Morning or evening repeated-sprint training conducted in the fasted state during Ramadan enhanced soccer-specific endurance similarly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".