Exercise performance is regulated during repeated sprints to limit the development of peripheral fatigue beyond a critical threshold
Bibliographic record
Abstract
New Findings What is the central question of this study? We asked whether exercise performance is regulated during all‐out repeated sprints (involving peak cycling‐specific muscle activation and limited pacing strategy) in order to restrain the total degree of peripheral fatigue development. What is the main finding and its importance? Our results showed that power output is adjusted during repeated sprints to limit the development of peripheral fatigue to a critical threshold, independently of the degree of pre‐existing fatigue. These findings emphasize the important role of peripheral fatigue in adjustment of power output during exercise. We hypothesized that exercise performance is adjusted during repeated sprints in order not to surpass a critical threshold of peripheral fatigue. Twelve men randomly performed three experimental sessions on different days, i.e. one single 10 s all‐out sprint and two trials of 10 × 10 s all‐out sprints with 30 s of passive recovery in between. One trial was performed in the unfatigued state (CTRL) and one following electrically induced quadriceps muscle fatigue (FTNMES). Peripheral fatigue was quantified by comparing pre‐ with postexercise changes in potentiated quadriceps twitch force (ΔQtw‐pot) evoked by supramaximal magnetic stimulation of the femoral nerve. Central fatigue was estimated by comparing pre‐ with postexercise voluntary activation of quadriceps motor units. The root mean square (RMS) of the vastus lateralis and vastus medialis EMG normalized to maximal M‐wave amplitude (RMS.Mmax−1) was also calculated during sprints. Compared with CTRL condition, pre‐existing quadriceps muscle fatigue in FTNMES (ΔQtw‐pot = −29 ± 4%) resulted in a significant (P < 0.05) reduction in power output (−4.0 ± 0.9%) associated with a reduction in RMS.Mmax−1. However, ΔQtw‐pot postsprints decreased by 51% in both conditions, indicating that the level of peripheral fatigue was identical and independent of the degree of pre‐existing fatigue. Our findings show that power output and cycling EMG are adjusted during exercise in order to limit the development of peripheral fatigue beyond a constant threshold. We hypothesize that the contribution of peripheral fatigue to exercise limitation involves a reduction in central motor drive in addition to the impairment in muscular function.
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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) | 0.001 | 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".