Fitness Level and Not Aging per se, Determines the Oxygen Uptake Kinetics Response
Bibliographic record
Abstract
Although aging has been associated to slower V̇O2 kinetics, some evidence indicates that fitness status and not aging per se might modulate this response. The main goal of this study was to examine the V̇O2, deoxygenated hemoglobin+myoglobin (deoxy-[Hb+Mb]) kinetics, and the NIRS-derived vascular reperfusion responses in older compared to young men of different training levels (i.e., inactive, recreationally active, and endurance trained). Ten young inactive (YI; 26 ± 5; peak V̇O2 (V̇O2peak), 2.96 ± 0.55 L·min-1), 10 young recreationally active (YR; 26 ± 6; 3.92 ± 0.33 L·min-1), 10 young endurance trained (YT; 30 ± 4; 4.42 ± 0.32 L·min-1), 7 older inactive (OI; 69 ± 4; 2.50 ± 0.31 L·min-1), 10 older recreationally active (OR; 69 ± 5; 2.71 ± 0.42 L·min-1) and 10 older endurance trained (OT; 66 ± 3; 3.20 ± 0.35 L·min-1) men completed transitions of moderate intensity cycling exercise (MODS) to determine V̇O2 and deoxy-[Hb+Mb] kinetics, and the deoxy-[Hb+Mb]/V̇O2 ratio. The time constant of V̇O2 (τV̇O2) was greater in YI (38.8 ± 10.4 s) and OI (44.1 ± 10.8 s) compared with YR (26.8 ± 7.5 s) and OR (26.6 ± 6.5 s), as well as compared to YT (14.8 ± 3.4 s), and OT (17.7 ± 2.7 s) (p < 0.05). τV̇O2 was greater in YR and OR compared with YT and OT (p < 0.05). The deoxy-[Hb+Mb]/V̇O2 ratio was greater in YI (1.23 ± 0.05) and OI (1.29 ± 0.08) compared with YR (1.11 ± 0.03) and OR (1.13 ± 0.06), as well as compared to YT (1.01 ± 0.03), and OT (1.06 ± 0.03) (p < 0.05). Similarly, τV̇O2 was greater in YR and OR compared with YT and OT (p < 0.05). There was a main effect of training (p = 0.033), whereby inactive (p = 0.018) and recreationally active men (p = 0.031) had significantly poorer vascular reperfusion than endurance trained men regardless of age. This study demonstrated not only that age-related slowing of V̇O2 kinetics can be eliminated in endurance trained individuals, but also that inactive lifestyle negatively impacts the V̇O2 kinetics response of young healthy individuals.
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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".