Are hybrid sit–stand postures a good compromise between sitting and standing?
Bibliographic record
Abstract
Potential alternatives for conventional sitting and standing postures are hybrid sit-stand postures (i.e. perching). The purposes of this study were (i) to identify where lumbopelvic and pelvic angles deviate from sitting and standing and (ii) to use these breakpoints to define three distinct postural phases: sitting, perching, and standing, in order to examine differences in muscle activations and ground reaction forces between phases. Twenty-four participants completed 19 1-min static trials, from sitting (90°) to standing (180°), sequentially in 5°trunk–thigh angle increments. The perching phase was determined to be 145–175° for males and 160–175° for females. For both sexes, knee extensor activity was lower in standing compared to perching or sitting (p < .01). Anterior–posterior forces were the highest in perching (p < .001), requiring ∼15% of body-weight. Chair designs aimed at reducing the lower limb demands within 115–170° trunk–thigh angle may improve the feasibility of sustaining the perched posture.Practitioner summary: Individuals who develop low back pain in sitting or standing may benefit from hybrid sit-stand postures (perching), yet kinematic and kinetic changes associated with these postures have not been investigated. Perching can improve lumbar posture at a cost of increased lower limb demands, suggesting potential avenues for chair design improvement.Abbreviations: A/P: anterior-posterior; M/L: medial-lateral; LBP: low back pain; EMG: electromyography; TES: thoracic erector spinae; LES: lumbar erector spinae; VMO: vastus medialis obliquus; MVC: maximum voluntary contraction; ASIS: anterior superior iliac spine; PSIS: posterior superior iliac spine; BW: body weight; RMSE: root mean square error; SD: standard deviation; ROM: range of motion
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".