Contralateral muscle fatigue from slow, isokinetic contractions is not velocity-specific
Bibliographic record
Abstract
Non-local muscle fatigue (NLMF) describes exercise-induced fatigue of non-exercised muscles.An unexplored aspect of NLMF is whether the effects are velocity specific.In a randomized, crossover design, unilateral fatigue (4-sets of 15 maximal repetitions, separated by 15sec) was induced with low velocity (60 0 .s-1 ), reciprocating, isokinetic knee extensions (KE) and flexions (KF) or participants rested in the control conditions.Possible NLMF was tested with contralateral KE and KF maximal isokinetic discrete (single contraction) and repeated repetitions force and electromyography (EMG) when measured with low (12 repetitions at 60°.s -1 , slow) or high (48 repetitions at 240°.s -1 , fast) velocity conditions.Sixteen (10 males and 6 females) participants attended the laboratory on four occasions.Participants either rested (control) or were unilaterally fatigued prior to completing either the slow (60°.s-1 ) or fast (240°.s-1) testing conditions.The discrete KE and KF forces and EMG were not significantly different from control, with no significant relative force differences at 60°.s -1 or 240°.s-1 .A significant condition effect revealed that the intervention conditions fatigue index during the KE and KF repeated maximal test significantly decreased 11% (p = .02,Effect Size: ES = 0.34) and 10% (p = .005,ES = 0.41) more respectively than the two control conditions.This study highlights that prior slow maximal isokinetic, unilateral, dominant KE and KF fatigue did not demonstrate decreases or velocity specific testing effects with singular maximal force, with some evidence of NLMF with fatigue endurance in the contralateral muscles.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".