Physiological and Psychophysical Comparison Between a Lifting Task With Identical Weight but Different Coupling Factors
Bibliographic record
Abstract
The objective of the revised NIOSH (National Institute for Occupational Safety and Health) lifting equation is to prevent or reduce lifting-related injuries. The coupling component of the equation relates to quality of the grip (i.e., hand-to-object interface) and can be rated good, fair, or poor. Good coupling is theorized to reduce lifting stress, whereas poor coupling is theorized to increase lifting stress. This study compared the physiological and psychophysical stress between a lifting task with identical weight but different coupling factors. Subjects (n = 21; 26 +/- 6 years; 177.8 +/- 7.8 cm; 73.9 +/- 10.7 kg) transferred a milk crate or bag of dog food each weighing 12.5 kg back and forth from the floor to a table for 2, paced, 5-minute work bouts. Steady-state metabolic data were used to compare the lifting tasks. Results showed significantly higher oxygen consumption, caloric cost, heart rate, and rating of perceived exertion during the lifting task using the milk crate vs. the bag of dog food (p < 0.05). No difference in respiratory exchange ratio was observed (p > 0.05). In conclusion, a significantly higher metabolic cost and perceived exertion was observed when subjects performed a paced two-handed lifting task with good coupling factors than when using an object with poor coupling factors. When lifting stress is measured by metabolic cost and perceived exertion, these results are in contrast to expectations that a poor quality grip (i.e., poor coupling) would increase stress of a lifting task. Results of this study may help the work-place practitioner make decisions related to the use of the revised NIOSH lifting equation in the design and pacing of lifting-related tasks. Improved decision making may benefit productivity and enhance injury prevention in the workplace.
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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) | 0.001 | 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".