Time Course of Recovery after Cycling Repeated Sprints
Bibliographic record
Abstract
PURPOSE: The present study investigated the recovery of performance and neuromuscular fatigue after cycling repeated sprints. METHODS: Ten participants performed two sessions of repeated sprints (one session: 10 × 10-s sprints, 30-s recovery) separated by 24 h (R24-S1 and R24-S2) and two sessions separated by 48 h (R48-S1 and R48-S2). The recovery condition (i.e., 24 or 48 h) was randomized and separated by 1 wk. All sessions were performed on a recumbent bike, allowing minimal delay between sprints termination and neuromuscular measurements. Neuromuscular function of knee extensors (neuromuscular assessment [NMA]) was assessed before sessions (presession), after the fifth sprint (midsession), and immediately after (postsession). Before sessions, baseline NMA was also carried out on an isometric chair. The NMA (bike and chair) was composed of maximal voluntary contraction (MVC) of knee extension and peripheral neuromuscular stimulation during the MVC and on relaxed muscle. RESULTS: The sprints performance was not significantly different between sessions and did not presented significant interaction between recovery conditions. MVC was significantly lower at R24-S2 compared with R24-S1 (-6.5% ± 8.8%, P = 0.038) and R48-S2 (-5.6% ± 8.2%, P = 0.048), whereas resting potentiated high-frequency doublet (Db100) was lower at R24-S2 compared with R24-S1 (-10.4 ± 8.3, P = 0.01) (NMA on chair). There were significant reductions in MVC (>30%, P < 0.001) and Db100 (>38%, P < 0.001) from pre- to postsession in all sessions, without significant interactions between recovery conditions (NMA on bike). CONCLUSION: Cycling repeated sprints induce significant fatigue, particularly at the peripheral level, which is fully restored after 48 h, but not 24 h, of recovery. One versus two days of recovery does not affect neuromuscular fatigue appearance during cycling repeated-sprint sessions.
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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 | 0.001 |
| 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".