Gender Responses to Automobile and Office Sitting - Influence of Hip, Hamstring, and Low-Back Flexibility on Seated Postures
Bibliographic record
Abstract
Understanding factors that influence preferred sitting postures is considered important to prevent low-back pain (LBP) associated with seated exposures.The purpose of this study was to examine the influence of gender and flexibility (hip, hamstring, and low-back) on lumbo-pelvic postures adopted when performing laboratory-simulated computer work and automobile driving.Ten female and 9 male volunteers were exposed to 10 minutes each of the abovementioned sitting conditions.Sagittal lumbo-pelvic kinematics were recorded during each sitting condition.Correlation analyses were performed between lumbo-pelvic postures and various measures of hip, hamstring, and low-back flexibility.When driving, females exhibited 9.8 degrees more posterior pelvic tilt (p = 0.0329) and 10.5 degrees more lumbar flexion (p = 0.0116) than males with respect to their lumbo-pelvic alignments in upright standing.When performing seated computer work, it was males who experienced greater posterior pelvic tilt (p = 0.0048).Individuals with greater hip flexibility, typically females, adopted lumbar flexion postures closer to their voluntary end-range while driving (r = 0.5709; p = 0.0107).Individuals who exhibited greater posterior pelvic tilt in office chair sitting, typically males, were those with less hip (r = -0.5484;p = 0.0150) and hamstring (r = -0.4690;p = 0.0496) flexibility.Given that differences exist between males and females with respect to various indices of hip, hamstring, and low-back flexibility, it is possible that gender-based differences in seated postures are related to inherent differences in flexibility between the sexes.These findings suggest that strategies to prevent LBP associated with sitting may depend on both individual flexibility characteristics and the type of seated exposure.
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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) | 0.002 | 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".