Active time at work following the introduction of a standing and a cycling workstation into worker’s office space
Bibliographic record
Abstract
Objectives. Active workstations have been proposed to counteract sedentary behavior at work. This study describes office workers’ use of and perceptions toward standing and cycling workstations, and assesses whether the two active workstations were sufficient to break sitting time and replace it with 2–4 h of light activity per workday. Methods. This mixed-method study utilized video recording, semi-structured interviews and a questionnaire. The quantitative data covered time spent sitting, standing and on a cycling workstation. The qualitative data were analyzed based on preferences, barriers and facilitators. Results. Participants (n = 15) used active workstations 125.3 (74.5) min/day and spent 79.0 (63.6) min/day using standing versus 46.3 (47.6) min/day using cycling workstations (p = 0.153, d = 0.58). Following the interviews, the standing workstation was preferred over cycling. The ergonomics of the cycling workstation were not optimal and caused discomfort in use. Seven participants broke their sitting time and accumulated 2+ h of light physical activity per workday. Those participants meeting recommendations were older, had a higher body fat percentage and engaged in less physical activity per week. Conclusion. With a preference for standing workstations, our results showed that 47% of workers used standing and cycling workstations to accumulate 2+ h of active time per day.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".