Investigating the relationship between aerobic fitness and lower-limb resistance vessel function before and after a bout of uninterrupted sitting
Bibliographic record
Abstract
Prolonged sitting reduces lower-limb resistance vessel function (RVF), whereas increasing aerobic fitness levels enhance lower-limb RVF. However, it is unknown whether having higher aerobic fitness offers protection against prolonged sitting-induced declines in RVF. This study investigated the relationships between aerobic fitness versus reductions in lower-limb RVF following a 3 h bout of uninterrupted sitting. In 30 healthy young adults (19♀, 24 ± 6 years), aerobic fitness was assessed during a graded, maximal cycling test. Relative peak oxygen consumption (V̇O2peak) was determined via indirect calorimetry (37.5 ± 8.6 (24.7–60.7 mLO2/kg/min). Popliteal blood flow (PBF) was recorded via duplex ultrasonography. Lower-limb RVF was assessed in the seated posture and quantified as the peak PBF and area under the curve (PBFAUC, first minute of hyperemia) responses to 5 min of distal cuff-induced ischemia. The lower-limb RVF assessment was performed before and after a sitting. Peak PBF decreased following sitting (473 ± 254 to 387 ± 199 mL/min, P = 0.024), while PBFAUC remained unchanged (6145 ± 3063 versus 6446 ± 3826 mL, P = 0.758). Relative V̇O2peak was not associated with Pre-sitting peak PBF ( R = 0.236, P = 0.210) or PBFAUC ( R = −0.026, P = 0.889). Furthermore, relative V̇O2peak was also not associated with sitting-induced reductions in peak PBF ( R = −0.145, P = 0.444). The reductions in peak PBF following sitting support previous work demonstrating that prolonged uninterrupted sitting negatively impacts lower-limb RVF. In contrast, prolonged sitting did not alter the PBFAUC response, suggesting that peak PBF responses may provide a more sensitive index of sitting-induced declines in RVF. In young, healthy individuals, aerobic fitness did not impact baseline or sitting-induced reductions in lower-limb RVF.
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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 | 0.001 |
| 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".