Do lumbar spine kinematics contribute to individual low back pain development in habitual sitting?
Bibliographic record
Abstract
This study explored differences in activities and spine kinematics by transient low back pain status throughout a week of their usual office work. Using a 100-mm visual-analogue scale, twenty participants were classified as non-pain (NPD) and pain developers (PD; ≥10 mm). Tri-axial accelerometers measured sitting time and thorax, pelvis, and lumbar spine angles. Amplitude probability distribution functions were constructed for postures and movements. PD (n = 6) exhibited increases in pain daily, with partial or complete recovery overnight and full recovery over the weekend, hence pain did not accumulate. PD sat more than NPD (n = 14), exhibited decreased peak posterior pelvic tilt (10°) and thorax inclination (8°), and tended to demonstrate less frequent spine movements. To decrease the risk of pain with sitting, reducing seated time, reclining on the seatback, and promoting seated movements should be recommended. With habitual exposures, small differences between pain groups could suggest a pathway to sitting-related back pain over time.Practitioner Summary: The biomechanical link between habitual sitting and low back pain remains unclear. Activity, spine kinematics, and pain ratings were collected throughout a week of individuals’ seated work at their own workstation. Small differences between those with and without transient low back pain could suggest a pathway to sitting-related back pain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".