Use of Stand-Biased Desks to Reduce Sedentary Time in High School Students: A Pilot Study
Bibliographic record
Abstract
Background: The purpose of this pilot study was to identify differences between sitting and standing time in high school students’ pre and post stand-biased desk intervention. Methods: ActivPal3™ activity monitors were affixed to 25 Bryan Collegiate High School students’ to monitor their standing time and activity levels. Data were collected at the beginning of the school year (fall) in traditional seated desks and in the spring in stand-biased desks. After attrition, 18 of the original 25 students were included in the final analysis. The physical activity data (steps) as well as standing and sitting time data provided by the monitors was used for within subject intervention analyses. Results: Descriptive statistics and a two-sided t-test were used to analyse differences between pre and post intervention sitting and standing times. Analysis indicated a significant reduction of sitting time post stand-biased desk intervention (p<0.0001) and a significant increase in standing time, post stand-biased desk intervention (p<0.0001). Analysis also revealed a non-statistically significant (p < 0.0619) average increase of 2,286 steps per school day when comparing mean steps pre-intervention (6,612) and post-intervention (8,898). Conclusions: Standing desks have the potential to reduce sedentary behavior and increase light to moderate physical activity for high school students during the school 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".