Use of Active Workstations in Individuals with Overweight or Obesity: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: This systematic review aims to collect existing literature and summarize the impact of active workstations on the health and work outcomes of participants with overweight or obesity. METHODS: Five databases were examined (until March 2018), and the keywords "desk," "workstation," "work station," and "work stations" were used with any one of the following terms: "active," "bik*," "cycling," "height adjustable," "stepping," "stand up," "standing," "treadmill*," "walk*," "elliptical," "bicycl*," "pedaling," "stability ball," "stability balls," "exercise ball," "exercise balls," "swiss ball," "swiss balls," "sit-to-stand," and "sit stand." RESULTS: Nineteen studies (two with school-aged children) were included in this review. Nine studies used treadmill, three used cycling, one used stepping, and twelve used standing desks. A decrease in sedentary time and an increase in physical activity level and energy expenditure were observed for most of the active workstations. Both the treadmill and the cycle desk improved glycemic control, but the treadmill desk was the only workstation for which improved work performance and help in body-weight management (body fat percentage, body weight, waist and hip circumference) were reported. CONCLUSIONS: Active workstations are a promising solution for decreasing occupational sedentariness. Overall, active workstations have a positive impact on energy expenditure and physical activity in individuals with overweight and obesity.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".