Habitual sedentary time and stationary time are inversely related to aerobic fitness
Bibliographic record
Abstract
A one metabolic-equivalent-of-task increase in peak aerobic fitness (peak MET) is associated with a clinically relevant improvement in survival risk and all-cause mortality. The co-dependent impact of free-living physical behaviours on aerobic fitness are poorly understood. The purpose of this study was to investigate the impact of theoretically re-allocating time spent in physical behaviours on aerobic fitness. We hypothesized that substituting sedentary time with any physical activity (at any intensity) would be associated with a predicted improvement in aerobic fitness. Peak volume rate of oxygen uptake (V˙O2peak) was assessed via indirect calorimetry during a progressive, maximal cycle ergometer protocol in 103 adults (52 females; [38 ± 21] years; [25.0 ± 3.8] kg/m2; V˙O2peak: [35.4 ± 11.5] ml·kg−1·min−1). Habitual sedentary time, standing time, light- (LPA), moderate- (MPA), and vigorous-physical activity (VPA) were assessed 24-h/day via thigh-worn inclinometry for up to one week (average: [6.3 ± 0.9] days). Isotemporal substitution modelling examined the impact of replacing one physical behaviour with another. Sedentary time (β = −0.8, 95% CI: [-1.3, −0.2]) and standing time (β = −0.9, 95%CI: [−1.6, −0.2]) were negatively associated with V˙O2peak, whereas VPA was positively associated with relative V˙O2peak (β = 9.2, 95%CI: [0.9, 17.6]). Substituting 30-min/day of VPA with any other behaviour was associated with a 2.4–3.4 higher peak MET. Higher standing time was associated with a lower aerobic fitness. As little as 10-min/day of VPA predicted a clinically relevant 0.8–1.1 peak MET increase. Theoretically, replacing any time with relatively small amounts of VPA is associated with improvements in aerobic fitness.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".