Cycle to cycle variability in a repetitive upper extremity task
Bibliographic record
Abstract
The purpose of this study was to examine the variability in muscle activity at rest and work during a repetitive task. A total of 20 participants performed a bimanual push task using three frequencies (4, 8, 16 pushes/min), three loads (1 kg, 2 kg, 4 kg) and two grip conditions (no grip, 30% maximum). The coefficient of variation (CoV) of muscle activity was determined for the anterior deltoid, biceps brachii, extensor digitorum and flexor digitorum superficialis. Faster push frequencies and heavier loads had lower work-rest ratio CoV and higher mean muscle activity (p < 0.01). Sixteen pushes per minute produced the lowest CoV for the anterior deltoid (p < 0.01), while the 1- kg load produced the lowest CoV for the extensor digitorum and flexor digitorum superficialis (p < 0.01). Changes were driven by the rest phase rather than by the work phase, except for grip decreasing forearm muscle CoV. These findings underscore the importance of variability at rest and indicate that low variability of muscle activity is associated with ergonomic risk factors. PRACTITIONER SUMMARY: Decreased motor variability has been associated with pain and injury. A cyclical push task, evaluated in terms of work and rest phases, found that greater workloads increased variability primarily due to changes in the rest phase. Muscle variability, especially for the rest phase, may provide insight into injury risk.
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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 | 0.005 |
| 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) | 0.001 | 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".