Modifying Trunk Inclination and Low Back Curvature Inconsistently Affects Isometric Multijoint Lifting Strength Across Individuals
Bibliographic record
Abstract
Movement assessment and coaching within health and human performance settings has targeted trunk inclination and low back curvature to reduce low back injury risk. However, it remains unclear how modifying these postural characteristics may also impact the ability to exert force in multijoint tasks to influence physical performance. This study investigated the independent and interacting effects of trunk inclination and low back curvature on maximal isometric lifting strength. Forty participants (50% female) exerted maximal isometric lifting force in 4 experimentally controlled postural conditions consisting of 2 trunk inclinations (vertical and horizontal), each performed with 2 low back curvatures (neutral and flexed). A linear mixed-effects model revealed a significant group level interaction between trunk inclination and low back curvature (β = 0.13, P = .002), but heterogeneity was high and indicated that some individual responses opposed the estimated group level response (random effect SD = 0.12). Individual responses substantially varied in magnitude (up to 620 N) and direction (increase/decrease). Modifying trunk inclination and low back curvature each have a similar potential to influence strength in multijoint tasks, but the response varies across individuals. Strength in multijoint tasks cannot be inferred solely from posture, and a single postural profile cannot be generalized as the strongest for all individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".