The impact of office chair features on lumbar lordosis, intervertebral joint and sacral tilt angles: a radiographic assessment
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine which office chair feature is better at improving spine posture in sitting. METHOD: Participants (n = 28) were radiographed in standing, maximum flexion and seated in four chair conditions: control, lumbar support, seat pan tilt and backrest with scapular relief. Measures of lumbar lordosis, intervertebral joint angles and sacral tilt were compared between conditions and sex. RESULTS: Sitting consisted of approximately 70% of maximum range of spine flexion. No differences in lumbar flexion were found between the chair features or control. Significantly more anterior pelvic rotation was found with the lumbar support (p = 0.0028) and seat pan tilt (p < 0.0001). Males had significantly more anterior pelvic rotation and extended intervertebral joint angles through L1-L3 in all conditions (p < 0.0001). CONCLUSION: No one feature was statistically superior with respect to minimising spine flexion, however, seat pan tilt resulted in significantly improved pelvic posture. Practitioner Summary: Seat pan tilt, and to some extent lumbar supports, appear to improve seated postures. However, sitting, regardless of chair features used, still involves near end range flexion of the spine. This will increase stresses to the spine and could be a potential injury generator during prolonged seated exposures.
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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 | 0.002 |
| 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) | 0.004 | 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".