An Innovative Ergometer to Measure Neuromuscular Fatigue Immediately after Cycling
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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