Effects of subtracting sitting versus adding exercise on glycemic control and variability in sedentary office workers
Bibliographic record
Abstract
Recent evidence suggests that, like adding exercise, reducing sitting time may improve cardiometabolic health. There has not been a direct comparison of the 2 strategies with energy expenditure held constant. The purpose of this study was to compare fasting and postmeal glucose and insulin concentrations in response to a day with frequent breaks from sitting but no exercise versus considerable sitting plus moderate exercise. Ten sedentary overweight/obese office workers were tested in 3 conditions: (i) walking per activity guidelines (AGW): sitting for majority of workday with a 30 min pre-lunch walk; (ii) frequent long breaks (FLB): no structured exercise but frequent breaks from sitting during workday with energy expenditure matched to AGW; and (iii) frequent short breaks (FSB): number of breaks matched to FLB, but duration of breaks were shorter. Plasma glucose and insulin areas under the curve were measured in response to a meal tolerance test (MTT) at the end of the workday and interstitial glucose was evaluated throughout the day and overnight using continuous glucose monitoring. Using repeated-measures linear mixed models, area under the curve of plasma glucose or insulin after the MTT was not different between conditions. Glycemic variability was lower in FLB compared with AGW (p < 0.05), and nocturnal duration of elevated glucose (>7.8 mmol/L) was shorter after FLB (2.5 ± 2.5 min) than AGW (32.7 ± 16.4 min) or FSB (45.6 ± 29.6 min, p = 0.05). When energy expenditure was matched, breaks from sitting approximated the effects of moderate-intensity exercise on postmeal glucose and insulin responses and more effectively constrained glycemic variability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".