Effects of sitting and standing on upper extremity physical exposures in materials handling tasks
Bibliographic record
Abstract
Sitting or standing work configurations modulate musculoskeletal risk. Most existing investigations of these configurations have either studied them separately or lacked focus on the upper extremity, particularly during manual materials handling (MMH) tasks. To address this gap, upper extremity loading in 20 male and 20 females were assessed in 4 MMH tasks in sitting and standing. Differences in electromyographic (EMG) activity, local joint moments and body discomfort between configurations were examined. Interactions between task and sit/stand configuration resulted in increases of up to 500% in joint moments, 94% in EMG activity and 880% in discomfort when tasks were completed while sitting (p < 0.01). Future MMH task designers should consider placing workers in standing postures when feasible to reduce upper extremity loading, but workers should not remain in either configuration for extended periods of time as the negative effects of both workspace geometries can instigate future musculoskeletal disorders. Practitioner Summary: Sitting and standing modify occupational musculoskeletal risk. We examined how performing identical tasks while sitting or standing altered upper extremity and low back loading. In general, sitting increased muscle activity and discomfort, while standing increased local joint moments. The benefits of standing outweighed those of sitting across the range of tasks.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".