Increased Ability to Perceive Relevant Sensory Information Minimizes Low Back Exposures in Lifting
Bibliographic record
Abstract
We have previously shown evidence that some individuals seem to consistently minimize low back loads when lifting, while others do not. However, it is unknown why. Individual differences in ability to perceive relevant sensory information may explain differences in minimization of low back loads during lifting, consistent with considering load reduction in one's movement objective in an optimal feedback control theory framework. The purpose of this study was to investigate whether individuals' ability to perceive proprioceptive information (both force- and posture-senses) at the low back was associated with peak low back loads when performing generic or occupation-specific lifts. Seventy-two participants were recruited to perform 10 barbell (generic) and backboard (occupation-specific) lifts, while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear forces normalized to body mass were calculated as dependent variables. Both posture matching ability and force matching ability at the heavier force targets were associated with lower means and variability of peak low-back loads in both lift types, albeit with small effect sizes (R2 ≤ .17). These findings support the utility of an optimal feedback control theory framework to explore factors explaining interindividual differences in low back loads during lifting. Further, this evidence suggests improving proprioceptive ability may be a useful strategy in lift training programs designed for workplace injury prevention.
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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 | 0.003 |
| 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 | 0.001 | 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".