Constrained handgrip force decreases upper extremity muscle activation and arm strength
Bibliographic record
Abstract
Many industrial tasks require repetitive shoulder exertions to be performed with concurrent physical and mental demands. The highly mobile nature of the shoulder predisposes it to injury. The purpose of this study was to determine the effects of simultaneous gripping, at a specified magnitude, on muscle activity and maximal arm force in various directions. Ten female subjects performed maximal arm exertions at two different heights and five directions using both specified (30% maximum voluntary grip) and preferred (self-selected) grip forces. Electromyography was recorded from eight muscles of the right upper extremity. The preferred grip condition produced grip forces that were dependent on the combination of arm height and force direction and were significantly greater (arm force down), lower (to left, up and push forward), or similar to the specified grip condition. Regardless of the magnitude of the preferred grip force, specifying the grip resulted in decreased maximal arm strength (by 18-25%) and muscle activity (by 15-30%) in all conditions, indicating an interfering effect when the grip force was specified by visual target force-matching. Task constraints, such as specific gripping demands, may decrease peak force levels attainable and alter muscle activity. Depending on the nature of task, the amount of relative demand may differ, which should be considered when determining safety thresholds.
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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".