The Effect of a Mental Task Versus Unilateral Physical Fatigue on Non-Local Muscle Fatigue in Recreationally Active Young Adults
Bibliographic record
Abstract
Non-local muscle fatigue (NLMF) has been attributed to both physical and mental fatigue. The purpose of this study was to investigate the effects of mental exertion versus unilateral physical fatigue on NLMF. Sixteen recreationally active participants completed a physical task (2-sets of 100-s unilateral knee extension (KE) maximal voluntary isometric contractions (MVIC) with the dominant leg with 40-s recovery between sets, mental task (4-minute Stroop task), and control condition. Before and after each condition, blood lactate was collected, and contralateral 5-s KE, flexion (KF) and bilateral lateral trunk flexors MVIC (measure of trunk stability strength) was performed. Following the post-test 5-s MVICs, participants performed 12 non-dominant KE MVICs with a work-to-rest ratio of 5/10-s. Electromyography was monitored during the MVICs. Neither the 4-minute Stroop test or the unilateral KE physical fatigue intervention adversely affected the non-dominant KE forces or EMG activity with a single MVIC or 12 repetition MVICs. Although the non-dominant KF fatigue index forces and hamstrings EMG were not impaired by the interventions, there was a significant interaction (p = 0.001) small magnitude (d = 0.42) decrease in the non-dominant KF single MVIC force following the contralateral fatigue intervention, albeit with no significant change in hamstrings EMG. This MVIC deficit may be related to the significant decrease in dominant (p = 0.046, d = 2.6) and non-dominant external obliques (p = 0.048, d = 0.57) activation adversely affecting trunk stability. In conclusion, a 4-minute Stroop test or unilateral KE physical fatigue intervention did not impair non-dominant KE single or repeated 12 repetition MVIC forces or EMG activity. The small magnitude deficit in the non-dominant KF single MVIC force following the contralateral fatigue intervention are in accord with the heterogenous findings common in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".