Impact Of Mental Fatigue On Force Control And Muscle Activation
Bibliographic record
Abstract
Mental fatigue (MF) leads to performance declines in tasks requiring force control. However, the neuromuscular mechanisms leading to these declines are not well understood. PURPOSE: To determine the effect of MF on the ability to match a target force and identify associated changes in muscle activation in males and females. METHODS: Nineteen participants (10 female) performed one 10-s isometric dorsiflexion contraction at 20 and 50% maximum voluntary contraction (MVC) before and after completing 20 min of the psychomotor vigilance task (PVT). The PVT is a sustained attention reaction time (RT) task known to induce MF. Force, indwelling and surface electromyography (sEMG) of the tibialis anterior were measured prior to and immediately following the PVT. RESULTS: Mean values for all variables can be found in Table 1. PVT RT and subjective fatigue increased similarly in males and females over time, indicating successful induction of MF. Mean absolute force produced at 20% and 50% MVC increased in males and females from pre- to post-PVT. However, there were no significant changes in the root mean square error of force at either contraction intensity. sEMG amplitude declined after the PVT in the 20% MVC condition with a trend towards declining at 50% MVC in both males and females. This was accompanied by a slowing of motor unit discharge rate after the PVT at 20% MVC in both sexes, but only in males at 50% MVC. CONCLUSION: Inducing MF led to changes in mean force of submaximal isometric contractions. This was accompanied by a decline in agonist muscle activity, suggesting alterations to motor control in the presence of MF.Table 1: Impact of PVT
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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) | 0.003 | 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".