Impact of repetitive mouse aiming on muscle fatigue and fine motor performance of the distal upper limb
Bibliographic record
Abstract
Gaming is demanding, however, the impact of gaming on muscle fatigue and performance changes is unclear. The purpose of this study was to evaluate muscle fatigue and performance impairments during an extended mouse aiming fatigue protocol. Twenty participants were recruited (8F, 12 M), separated into gaming and non-gaming groups. Surface electromyography was measured from eight muscles of the right distal upper limb. Participants performed a 30-second aiming task using aim training software. The fatiguing protocol involved six, 5-minute bouts of hitting targets in AimLab. To assess muscle fatigue, reference contractions of radial and ulnar deviation (30% max) as well as ratings of perceived fatigue (RPF) were collected throughout the experiment. The wrist extensors produced the greatest levels of muscle activity while aiming a mouse, producing up to 9.3% MVC. No changes in performance measures were observed throughout the experiment. However, significant fatigue of extensors was observed through changes in RPF, mean power frequency, median frequency, and spike shape analysis. Performance metrics indicated no impairments caused by the fatiguing protocol. Changes in EMG characteristics indicate that the wrist extensors became significantly fatigued through prolonged mouse aiming, indicating the extensors may be prone to gaming related fatigue and injury.
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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) | 0.002 | 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".