Low back joint loading and kinematics during standing and unsupported sitting
Bibliographic record
Abstract
The aim was to examine lumbar spine kinematics, spinal joint loads and trunk muscle activation patterns during a prolonged (2 h) period of sitting. This information is necessary to assist the ergonomist in designing work where posture variation is possible -- particularly between standing and various styles of sitting. Joint loads were predicted with a highly detailed anatomical biomechanical model (that incorporated 104 muscles, passive ligaments and intervertebral discs), which utilized biological signals of spine posture and muscle electromyograms (EMG) from each trial of each subject. Sitting resulted in significantly higher (p<0.001) low back compressive loads (mean +/- SD 1698 +/- 467 N) than those experienced by the lumbar spine during standing (1076 +/- 243 N). Subjects were equally divided into adopting one of two sitting strategies: a single 'static' or a 'dynamic' multiple posture approach. Within each individual, standing produced a distinctly different spine posture compared with sitting, and standing spine postures did not overlap with flexion postures adopted in sitting when spine postures were averaged across all eight subjects. A rest component (as noted in an amplitude probability distribution function from the EMG) was present for all muscles monitored in both sitting and standing tasks. The upper and lower erector spinae muscle groups exhibited a shifting to higher levels of activation during sitting. There were no clear muscle activation level differences in the individuals who adopted different sitting strategies. Standing appears to be a good rest from sitting given the reduction in passive tissue forces. However, the constant loading with little dynamic movement which characterizes both standing and sitting would provide little rest/change for muscular activation levels or low back loading.
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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) | 0.001 | 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".