A comparison of trunk biomechanics, musculoskeletal discomfort and productivity during simulated sit-stand office work
Bibliographic record
Abstract
Sedentary office work has been shown to cause low back discomfort and potentially cause injury. Prolonged standing work has been shown to cause discomfort. The implementation of a sit-stand paradigm is hypothesised to mitigate discomfort and prevent injury induced by prolonged exposure to each posture in isolation. This study explored the potential of sit-stand to reduce discomfort and prevent injury, without adversely affecting productivity. Twenty-four participants performed simulated office work in three different conditions: sitting, standing and sit-stand. Variables measured included: perceived discomfort, L4-L5 joint loading and typing/mousing productivity. Working in a sit-stand paradigm was found to have the potential to reduce discomfort when compared to working in a sitting or standing only configuration. Sit-stand was found to be associated with reduced lumbar flexion during sitting compared to sitting only. Increasing lumbar flexion during prolonged sitting is a known injury mechanism. Therefore, sit-stand exhibited a potentially beneficial response of reduced lumbar flexion that could have the potential to prevent injury. Sit-stand had no significant effect on productivity. Practitioner Summary: This study has contributed foundational elements to guide usage recommendations for sit-stand workstations. The sit-stand paradigm can reduce discomfort; however, working in a sit-stand ratio of 15:5 min may not be the most effective ratio. More frequent posture switches may be necessary to realise the full benefit of sit-stand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".