Knee extension fatigue attenuates repeated force production of the elbow flexors
Bibliographic record
Abstract
Non-local muscle fatigue has been demonstrated with unilateral activities, where fatiguing one limb alters opposite limb forces. Fewer studies have examined if non-local fatigue occurs with unrelated muscles. The purpose of this study was to investigate if knee extensors fatigue alters elbow flexors force and electromyography (EMG) activity. Eighteen males completed a control and fatiguing session (randomised). Blood lactate was initially sampled followed by three maximal voluntary contractions (MVC) with the elbow flexors and two with the knee extensors. Thereafter, subjects either sat (control) or performed five sets of bilateral dynamic knee extensions to exhaustion using a load equal to the dominant limb MVC (1-min rest between sets). Immediately afterwards, subjects were assessed for blood lactate and unilateral knee extensors MVC, and after 1 min performed a single unilateral elbow flexor MVC. Two minutes later, subjects performed 12 unilateral elbow flexor MVCs (5 s contraction/10 s rest) followed by a third blood lactate test. Compared to control, knee extensor force dropped by 35% (p < 0.001; ES = 1.6) and blood lactate increased by 18% (p < 0.001; ES = 2.8). Elbow flexor forces were lower after the fatiguing protocol only during the last five MVCs (p < 0.05; ES = ∼ 0.58; ∼ 5%). No changes occurred between conditions in EMG. Elbow flexor forces significantly decreased after knee extensors fatigue. The effect was revealed during the later stages of the repeated MVCs protocol, demonstrating that non-local fatigue may have a stronger effect on repeated rather than on single attempts of maximal force production.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".