Does objectively measured prolonged standing for desk work result in lower ratings of perceived low back pain than sitting? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Prolonged sitting has been shown to induce transient low back pain (LBP). Height adjustable office desks now present the opportunity to replace sitting with standing in the workplace. Since standing has also been associated with LBP, this may not be an advisable alternative. OBJECTIVE: To determine if objectively measured prolonged exposures to desk work while standing, compared to sitting, results in lower perceived LBP in healthy adults. METHODS: A systematic search of several databases was conducted. Two independent reviewers screened titles/abstracts and conducted a quality assessment. The results of three studies were pooled using an inverse variance random-effects meta-analysis. Heterogeneity was tested using the Chi-squared test and I2 statistic. RESULTS: Objectively measured prolonged standing postures during desk work did not induce significantly less perceived LBP compared to seated postures (standardized mean difference 0.60, 95% CI -0.68 to 1.87, p = 0.36.) There was significant heterogeneity, I2 = 90%). CONCLUSIONS: It appears that replacing seated desk work postures with standing for prolonged periods of time would not be recommended. Larger studies, including a wider age range and health history, conducted in the field with objective measures is recommended to obtain more generalizable data on which to base ergonomic standards for work postures.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".