Prolonged sitting in office environments: A scoping review of assessment methods
Bibliographic record
Abstract
BackgroundProlonged sitting in office environments is a major contributor to musculoskeletal disorders (MSDs), representing a growing concern for occupational health and ergonomics.ObjectiveThis scoping review aimed to examine the range of methods used to assess sitting postures among office workers, emphasizing their applications, strengths, and limitations.MethodsA comprehensive search identified 42 studies published between 2000 and December 2023 from an initial pool of 167 articles. Studies were categorized into self-assessment, observational, and instrument-based approaches following PRISMA-ScR guidelines.ResultsSelf-assessment methods were the most common (69.05%), capturing subjective reports of discomfort, followed by observational tools (38.09%) for postural risk evaluation and instrument-based approaches (45.24%) utilizing sensor- and vision-based technologies for objective analysis. Several studies combined two or more methods to improve data validity through cross-validation and to achieve a more comprehensive understanding of posture-related risks.ConclusionsThis review synthesizes current approaches for evaluating sitting postures in office settings and highlights methodological trends, gaps, and opportunities to guide future ergonomic research aimed at reducing MSD risks.
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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 | 0.044 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".