An Innovative Ergometer to Measure Neuromuscular Fatigue Immediately after Cycling
Bibliographic record
Abstract
PURPOSE: When assessing neuromuscular fatigue (NMF) from dynamic exercise using large muscle mass (e.g., cycling), most studies have delayed measurement for 1 to 3 min after task failure. This study aimed to determine the reliability of an innovative cycling ergometer permitting the start of fatigue measurement within 1 s after cycling. METHODS: Twelve subjects participated in two experimental sessions. Knee-extensor NMF was assessed by electrical nerve and transcranial magnetic stimulation with both a traditional chair setup (PRE- and POST-Chair, 2 min postexercise) and the new cycling ergometer (PRE, every 3 min during incremental exercise and POST-Bike, at task failure). RESULTS: The reduction in maximal voluntary contraction force POST-Bike (63% ± 12% PRE; P < 0.001) was not different between sessions and there was excellent reliability at PRE-Bike (intraclass correlation coefficient [ICC], 0.97; coefficients of variation [CV], 3.2%) and POST-Bike. Twitch (Tw) and high-frequency paired-pulse (Db100) forces decreased to 53% ± 14% and 62% ± 9% PRE, respectively (P < 0.001). Both were reliable at PRE-Bike (Tw: ICC, 0.97; CV, 5.2%; Db100: ICC, 0.90; CV, 7.3%) and POST-Bike (Tw: ICC, 0.88; CV, 11.9; Db100: ICC, 0.62; CV, 9.0%). Voluntary activation did not change during the cycling protocol (P > 0.05). Vastus lateralis and rectus femoris M-wave and motor-evoked potential areas showed fair to excellent reliability (ICC, 0.45-0.88). The reduction in maximal voluntary contraction and Db100 was greater on the cycling ergometer than the isometric chair. CONCLUSIONS: The innovative cycling ergometer is a reliable tool to assess NMF during and immediately postexercise. This will allow fatigue etiology during dynamic exercise with large muscle mass to be revisited in various populations and environmental conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".