The Duration of Non-Local Muscle Fatigue Effects
Bibliographic record
Abstract
Non-local muscle fatigue (NLMF) refers to a transient decline in the functioning of a non-exercised muscle following the fatigue of a different muscle group. Most studies examining NLMF conducted post-tests immediately after the fatiguing protocols, leaving the duration of these effects uncertain. The aim of this study was to investigate the duration of NLMF (1-, 3-, and 5-minutes). In this randomized crossover study, 17 recreationally trained participants (four females) were tested for the acute effects of unilateral knee extensor (KE) muscle fatigue on the contralateral homologous muscle strength, and activation. Each of the four sessions included testing at either 1-, 3-, or 5-minutes post-test, as well as a control condition for non-dominant KE peak force, instantaneous strength (force produced within the first 100-ms), and vastus lateralis and biceps femoris electromyography (EMG). The dominant KE fatigue intervention protocol involved two sets of 100-seconds maximal voluntary isometric contractions (MVIC) separated by 1-minute of rest. Non-dominant KE MVIC forces showed moderate and small magnitude reductions at 1-min (p < 0.0001, d = 0.72) and 3-min (p = 0.005, d = 0.30) post-test respectively. The KE MVIC instantaneous strength revealed large magnitude, significant reductions between 1-min (p = 0.021, d = 1.33), and 3-min (p = 0.041, d = 1.13) compared with the control. In addition, EMG data revealed large magnitude increases with the 1-minute versus control condition (p = 0.03, d = 1.10). In summary, impairments of the non-exercised leg were apparent up to 3-minutes post-exercise with no significant deficits at 5-minutes. Recovery duration plays a crucial role in the manifestation of NLMF.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".