The Influence of Hand Location and Force Direction on Shoulder Muscular Activity in Females During Nonsagittal Multidirectional Overhead Exertions
Bibliographic record
Abstract
OBJECTIVE: We examined interactions of overhead work location and direction of force application on shoulder muscular activity. BACKGROUND: Overhead work tasks are common occupational stressors. Previous research has quantified influences of overhead work spatial placement and different force application directions but typically separately or exclusively for tasks done in the median plane. METHOD: Twenty female participants exerted 40 N of force in six directions (forward/backward, upward/downward, left/right) 150 cm off the floor while seated. An asymmetric pattern of 14 work locations spaced 15 cm centered directly overhead were evaluated. RESULTS: Force direction and work location strongly influenced mean muscle activity (F = 559, p < .01). Interaction effects existed between force direction and hand location in the transverse plane (F = 21, p < .01), with increases as high as 49% in normalized mean muscle activity. CONCLUSION: Backward exertions produced the highest mean overall muscle activity across hand force directions, exceeding 30% maximum voluntary isometric exertion (MVE) across work locations, with higher activation of anterior deltoid, biceps, infraspinatus, supraspinatus, and upper and lower trapezius. Downward exertions had the lowest mean overall activity, with <10% MVE across work locations. Altered (up to 47%) muscular activity occurred as exertions moved laterally from the origin, and increasingly forward hand positions generally yielded decreased mean overall activity for most force directions. APPLICATION: This study provides previously unavailable submaximal shoulder muscular activity data for a wide range of overhead tasks. As such, it enables novel work design considerations that include modifying existing overhead elements to reduce or redistribute associated muscular demands.
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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".