An active sitting chair can increase energy expenditure while performing standardized data entry work
Bibliographic record
Abstract
Background Active sitting chairs have been proposed as an effective approach for reducing sedentary behaviour in the workplace. Objective This cross-sectional study evaluated how an active sitting chair altered energy expenditure compared to a traditional office chair during seated computer work. Methods Sixteen participants (8M/8F) completed two 20-min sessions of seated standardized computer work in an active sitting chair, with a multiaxial rotating seat pan, and traditional office chair. Metabolic and ventilatory variables were collected with a customized metabolic cart and cardiac variables were collected by a Hexoskin © shirt. Average ventilatory, metabolic, and cardiac variables from the last 15-min of each block were compared between chairs and sexes. Results Statistically significant increases in oxygen uptake (V˙O 2 ) emerged in active sitting (0.02 L/min; 7.6%), and ultimately led to a 1.5 kcal increase in energy expenditure compared to traditional sitting. Proportional and significant changes in minute ventilation (V˙ E ; + 0.9 L/min), heart rate (HR; + 5.8 bpm), and heart rate variability (HRV; −0.05 s) occurred, which further support the greater metabolic demand in active sitting. Conclusions A 1.5 kcal per 15-min increase in energy expenditure translates to 6 kcal/hour and 48 kcal/day. Compared to other literature, this change is similar to caloric expenditure when climbing three to six flights of stairs and when using alternative workstation designs (e.g., standing or sitting on a stability ball). An active sitting chair with a multiaxial rotating seat pan and no back support, appears to be a good alternative for increasing energy expenditure at a workstation.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".