Unilateral isometric muscle fatigue decreases force production and activation of contralateral knee extensors but not elbow flexors
Bibliographic record
Abstract
Nonlocal muscle fatigue occurs when fatiguing 1 muscle alters performance of another rested muscle. The purpose of the study was to investigate if fatiguing 2 separate muscles would affect the same rested muscle, and if fatiguing the same muscle would affect 2 separate muscles. Twenty-one trained males participated in 2 studies (n = 11; n = 10). Subjects performed 2 pre-test maximum voluntary contractions (MVCs) with the nondominant knee extensors. Thereafter they performed two 100-s MVCs with their dominant knee extensors, elbow flexors, or rested. Between and after the sets, a single MVC with the nondominant rested knee extensors was performed. Subsequently, 12 nondominant knee extensors repeated MVCs were completed. Force, quadriceps voluntary activation (VA), and electromyography (EMG) were measured. The same protocol was employed in study 2 except the nondominant elbow-flexors were tested. Study 1: Compared with control conditions, a significant decrease in nondominant knee extensors force, EMG, and VA was found under both fatiguing conditions (P ≤ 0.05; effect size (ES) = 0.91-1.15; 2%-8%). Additionally, decrements in all variables were found from the first post-intervention MVC to the last (P ≤ 0.05; ES = 0.82-2.40; 9%-20%). Study 2: No differences were found between conditions for all variables (P ≥ 0.33; ES ≤ 0.2; ≤3.0%). However, all variables decreased from the first post-intervention MVC to the last (P ≤ 0.05; ES = 0.4-3.0; 7.2%-19.7%). Whereas the rested knee extensors demonstrated nonlocal effects regardless of the muscle being fatigued, the elbow-flexors remained unaffected. This suggests that nonlocal effects are muscle specific, which may hold functional implications for training and performance.
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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) | 0.002 | 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".