A systematic review and meta-analysis of the effect of treadmill desks on energy expenditure, sitting time and cardiometabolic health in adults
Bibliographic record
Abstract
BACKGROUND: As the health risks of sedentary working environments become more clear, greater emphasis on the implementation of walking interventions to reduce sitting time is needed. In this systematic review and meta-analysis, we investigate the role of treadmill-desk interventions on energy expenditure, sitting time, and cardiometabolic health in adults with sedentary occupations. METHODS: Relevant studies published in English were identified using CINAHL, EMBASE, MEDLINE, Web of Science, Scopus, and PubMed databases up to December 2020. Random effects meta-analysis models were used to pool study results. RESULTS: Thirteen relevant studies (six workplaces and seven laboratories) were found with a total of 351 participants. Pooled analysis of laboratory studies showed a significant increase in energy expenditure (105.23 kcal per hour, 95% confidence interval [CI]: 90.41 to 120.4), as well as metabolic rate (5.0 mL/kg/min, 95% CI: 3.35 to 6.64), among treadmill desk users compared to sitting conditions. No evidence of significant differences in blood pressure were found. In workplace studies, we observed a significant reduction in sitting time over a 24-h period (- 1.73 min per hour, 95% CI: - 3.3 to - 0.17) among users of treadmill desks, compared to a conventional desk. However, there were no evidence of statistically significant changes in other metabolic outcomes. CONCLUSIONS: Treadmill desks offer a feasible and effective intervention to increase energy expenditure and metabolic rate and reduce sitting time while performing work-related tasks. Future studies are needed to increase generalizability to different workplace settings and further evaluate their impact on cardiometabolic health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".