Effects of mental fatigue on muscle activation and resistance exercise performance
Bibliographic record
Abstract
Cognitive control exertion leads to mental fatigue and has been shown to impart negative effects on subsequent physical performance. Although the mechanisms underlying these effects are not well understood, previous research suggests mental fatigue causes neuromuscular perturbations which may impair physical performance. This study examined the effects of a mentally-fatiguing, cognitive control task on physical performance and muscle activation while performing a resistance exercise task. The study employed a randomized, cross-over design. On Visit 1, participants (N = 10) performed a barbell biceps curl one-rep maximum (1RM) test. On Visits 2/3, participants performed 20 biceps curls at 50% of their 1RM, followed by their respective 10-minute cognitive experimental manipulation (high vs. low cognitive control), and then performed a second set of biceps curls at 50% of their 1RM to failure. Total repetitions performed on the post-manipulation exercise task, and muscle activation (electromyography amplitude) of the biceps, triceps and lumbar erector spinae muscles were recorded. Mental fatigue was significantly greater following the high cognitive control manipulation (d = 1.09). Total repetitions performed following the high (M=24.9) and low cognitive control manipulations (M=25.2) did not differ (p > .05). However, there was a significant increase in muscle activation for the biceps, triceps and lumbar erector spinae during repetitions performed while mentally fatigued that indicate greater involvement of the low-back musculature and co-contraction of the triceps while lifting. Findings suggest mental fatigue may alter motor unit recruitment, reducing the efficiency of movement patterns in ways that may leave people susceptible to injury.Acknowledgments: SSHRC
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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 | 0.002 |
| 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) | 0.002 | 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".