Impact of tapering and proactive recovery on young elite rugbyunion players’ repeated high intensity effort ability
Why this work is in the frame
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Bibliographic record
Abstract
To assess the effects of a taper combined with proactive recovery on the repeated high intensity effort (RHIE) of elite rugby union players, and the possible interaction of pre-taper fatigue and sleep. Eighteen players performed a 3-week intensive training block followed by a 7-day exponential taper combined with a multicomponent recovery strategy. Following the intervention, players were divided into 3 groups (Normal Training: NT, Acute Fatigue: AF or Functional Overreaching: F-OR) based on their readiness to perform prior to the taper. Total sprint time [TST], percentage decrement [%D] and the number of sprints ≥90% of the best [N90] were analyzed to assess performance during a RHIE test. Subjective sleep quality was assessed through the Pittsburg Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS). No improvement in TST was reported in either NT or F-OR after the taper, whereas AF tended to improve (-1.58 ± 1.95%; p > 0.05; g = -0.20). F-OR players reported baseline PSQI and ESS indicative of sleep disturbance (6.2 ± 2.2 and 10.6 ± 5.4, respectively). AF displayed a small impairment in PSQI during intensive training (11.5 ± 80.6%; p > 0.05; g = 0.20), which was reversed following the taper (-34.6 ± 62.1%; p > 0.05; g = -0.73). Pre-taper fatigue precluded the expected performance benefits of the combined taper and recovery intervention, likely associated with a lack of strictly controlled intensive training block. Poor sleep quality before the intensive training period appeared to predispose the players to developing functional overreaching.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it