Cycling performed on an innovative ergometer at different intensities–durations in men: neuromuscular fatigue and recovery kinetics
Bibliographic record
Abstract
The majority of studies have routinely measured neuromuscular (NM) fatigue with a delay (∼1–3 min) after cycling exercises. This is problematic since NM fatigue can massively recover within the first 1–2 min after exercise. This study investigated the etiology of knee extensors (KE) NM fatigue and recovery kinetics in response to cycling exercises by assessing NM function as early as 10 s following cycling and up to 8 min of recovery. Ten young males performed different cycling exercises on different days: a Wingate (WING), a 10-min task at severe-intensity (SEV), and a 90-min task at moderate-intensity (MOD). Electrically evoked and isometric maximal voluntary contractions (IMVC) of KE were assessed before, after, and during recovery. SEV induced the highest decrease in IMVC. Peak twitch (Pt) was more reduced in WING and SEV than in MOD (p < 0.001), whereas voluntary activation decreased more after MOD than WING (p = 0.043). Regarding Pt and the ratio between low- and high-frequency doublet (i.e., low-frequency fatigue), recovery was faster for WING, whereas IMVC and high-frequency doublet recovered slower during MOD (p < 0.05). Our results confirm that peripheral fatigue is greater after WING and SEV, while central fatigue is greater following MOD. Peripheral fatigue can substantially recover within minutes after a supramaximal exercise while NM function recovered slower after prolonged, moderate-intensity exercise. This study provides an accurate estimation of NM fatigue and recovery kinetics because of dynamic exercise with large muscle mass by significantly shortening the delay for postexercise measurements.
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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 | 0.001 | 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".