An analysis of the duration of non-local muscle fatigue effects
Bibliographic record
Abstract
Introduction 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. Purpose The aim of this study was to investigate the duration of NLMF effects by examining post-test durations of 1-, 3-, and 5-minutes as well as a Control condition, Methods In this randomized crossover study, 17 recreationally trained participants (four females) were recruited. The study aimed to investigate the acute effects of unilateral knee extensor (KE) muscle fatigue on the contralateral homologous muscle strength, activation, and fatigue resistance (endurance). The participants underwent four sessions, with a minimum 48- hour interval between visits. Each session included testing at one-, three-, or five-minutes posttest, or for a Control condition. Measurements included non-dominant KE muscle force, endurance, and electromyography (EMG) from the vastus lateralis and biceps femoris muscles. The fatigue protocol involved two sets of continuous 100-seconds maximal voluntary isometric contractions (MVIC) performed by the dominant KE, separated by 1-minute of rest. Results Non-dominant KE MVIC forces showed reductions of 15.81% (p<0.0001, d=0.72) at 1-min and 8.54% (p=0.005, d=0.30) at 3-min post-test. The KE MVIC instantaneous strength revealed a significant reduction 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 revealed large magnitude increases with the 1-minute versus control condition (p=0.03, d=1.10). Conclusions Recovery duration (recovery time was 5-min) plays a crucial role in the manifestation of NLMF. Moreover, the influence of factors such as familiarity with high intensity resistance training loads and the specific muscle group targeted during fatigue protocols were also highlighted.
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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 | 0.003 |
| 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) | 0.004 | 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".