Aspects of Respiratory Muscle Fatigue in a Mountain Ultramarathon Race
Bibliographic record
Abstract
PURPOSE: Ultramarathon running offers a unique possibility to investigate the mechanisms contributing to the limitation of endurance performance. Investigations of locomotor muscle fatigue show that central fatigue is a major contributor to the loss of strength in the lower limbs after an ultramarathon. In addition, respiratory muscle fatigue is known to limit exercise performance, but only limited data are available on changes in respiratory muscle function after ultramarathon running and it is not known whether the observed impairment is caused by peripheral and/or central fatigue. METHODS: In 22 experienced ultra-trail runners, we assessed respiratory muscle strength, i.e., maximal voluntary inspiratory and expiratory pressures, mouth twitch pressure (n = 16), and voluntary activation (n = 16) using cervical magnetic stimulation, lung function, and maximal voluntary ventilation before and after a 110-km mountain ultramarathon with 5862 m of positive elevation gain. RESULTS: Both maximal voluntary inspiratory (-16% ± 13%) and expiratory pressures (-21% ± 14%) were significantly reduced after the race. Fatigue of inspiratory muscles likely resulted from substantial peripheral fatigue (reduction in mouth twitch pressure, -19% ± 15%; P < 0.01), as voluntary activation (-3% ± 6%, P = 0.09) only tended to be decreased, suggesting negligible or only mild levels of central fatigue. Forced vital capacity remained unchanged, whereas forced expiratory volume in 1 s, peak inspiratory and expiratory flow rates, and maximal voluntary ventilation were significantly reduced (P < 0.05). CONCLUSIONS: Ultraendurance running reduces respiratory muscle strength for inspiratory muscles shown to result from significant peripheral muscle fatigue with only little contribution of central fatigue. This is in contrast to findings in locomotor muscles. Whether this difference between muscle groups results from inherent neuromuscular differences, their specific pattern of loading or other reasons remain to be clarified.
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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 | 0.000 |
| 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".