Exploring biomechanical and metabolic determinants of lifting movement strategy
Bibliographic record
Abstract
We explored if lifters would adopt a spine sparing strategy when lifting heavier loads infrequently, and a metabolically cost-efficient strategy when lifting lighter loads more frequently. Twenty-six participants performed four 7-min bouts of lifting in high-load low-frequency and low-load high-frequency conditions. Participants chose self-selected lifting strategies for bouts 1 and 4 in each condition but were required to complete lifts using a stoop and squat for bouts 2 and 3 to sample a biomechanical and metabolic costs landscape. Whole-body kinematics, ground reaction forces, and oxygen consumption were collected to quantify peak sagittal low back moments, V ˙ O 2 consumption, and lifting strategy. No significant differences were found in biomechanical and metabolic cost exposure variables between the first and last self-selected lifting bouts after an exploration period of a cost landscape. However, participants did adopt different movement strategies between conditions, biased toward a more metabolically cost-efficient stoop-like lift in the low-load high-frequency condition. This evidence indicates that metabolic cost likely plays a task specific role in shaping lifting strategy, an important finding for researchers developing digital human models aiming to predict how people might lift at work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".