Fatigue Measured in Dynamic Versus Isometric Modes After Trail Running Races of Various Distances
Bibliographic record
Abstract
PURPOSE: Fatigue has previously been investigated in trail running by comparing maximal isometric force before and after the race. Isometric contractions may not entirely reflect fatigue-induced changes, and therefore dynamic evaluation is warranted. The aim of the present study was to compare the magnitude of the decrement of maximal isometric force versus maximal power, force, and velocity after trail running races ranging from 40 to 170 km. METHODS: Nineteen trail runners completed races shorter than 60 km, and 21 runners completed races longer than 100 km. Isometric maximal voluntary contractions (IMVCs) of knee extensors and plantar flexors and maximal 7-second sprints on a cycle ergometer were performed before and after the event. RESULTS: Maximal power output (Pmax; -14% [11%], P < .001), theoretical maximum force (F0; -11% [14%], P < .001), and theoretical maximum velocity (-3% [8%], P = .037) decreased significantly after both races. All dynamic parameters but theoretical maximum velocity decreased more after races longer than 100 km than races shorter than 60 km (P < .05). Although the changes in IMVCs were significantly correlated (P < .05) with the changes in F0 and Pmax, reductions in IMVCs for knee extensors (-29% [16%], P < .001) and plantar flexors (-26% [13%], P < .001) were larger (P < .001) than the reduction in Pmax and F0. CONCLUSIONS: After a trail running race, reductions in isometric versus dynamic forces were correlated, yet they are not interchangeable because the losses in isometric force were 2 to 3 times greater than the reductions in Pmax and F0. This study also shows that the effect of race distance on fatigue measured in isometric mode is true when measured in dynamic mode.
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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".