Aerobic training status does not attenuate prolonged sitting-induced lower limb vascular dysfunction
Bibliographic record
Abstract
This study examined if the degree of aerobic training protects against the lower limb vascular dysfunction associated with a prolonged sitting bout. Ten young, aerobically trained (AT) and 10 young, untrained (UT) individuals completed a prolonged (3 h) sitting bout. Leg vascular function was measured prior to and at 1.5 and 3 h into the prolonged sitting bout using the passive leg movement (PLM) technique. PLM-induced hyperemia was significantly reduced from baseline at 1.5 and 3 h into the prolonged sitting bout in both groups when evaluated as peak change in leg blood flow from baseline (Δ LBF) (UT: 956 ± 140, 586 ± 80, and 599 ± 96 mL·min−1 at baseline, 1.5 h, and 3 h, respectively; AT: 955 ± 183, 789 ± 193, and 712 ± 131 mL·min−1 at baseline, 1.5 h, and 3 h, respectively) and LBF area under the curve (UT: 283 ± 73, 134 ± 31, and 164 ± 42 mL·min−1 at baseline, 1.5 h, and 3 h, respectively; AT: 336 ± 86, 242 ± 86, and 245 ± 73 mL·min−1 at baseline, 1.5 h, and 3 h, respectively), but no significant differences between groups were revealed. No significant correlations were observed when examining the relationship between maximal oxygen uptake (relative and absolute) and reductions in leg vascular function at 1.5 and 3 h into the prolonged sitting bout. This study revealed that aerobic training did not provide a protective effect against prolonged sitting-induced lower limb vascular dysfunction and further highlights the importance of reducing excessive sitting in all populations.
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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 | 0.000 |
| 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) | 0.001 | 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".