Long-lasting exercise involvement protects against decline in <i>V</i>O<sub>2max</sub> and <i>V</i>O<sub>2</sub> kinetics in moderately active women
Bibliographic record
Abstract
We studied the effects of age on different physiological parameters, including those derived from (i) maximal cardiopulmonary exercise testing (CPET), (ii) moderate-intensity step transitions, and (iii) tensiomyography (TMG)-derived variables in moderately active women. Twenty-eight women (age, 19 to 53 years), completed 3 laboratory visits, including baseline data collection, TMG assessment, maximal oxygen uptake test via CPET, and a step-transition test from 20 W to a moderate-intensity cycling power output (PO), corresponding to oxygen uptake at 90% gas exchange threshold. During the step transitions, breath-by-breath pulmonary oxygen uptake, near infrared spectroscopy derived muscle deoxygenation (ΔHHb), and beat-by-beat cardiovascular response were continuously monitored. There were no differences observed between the young and middle-aged women in their maximal oxygen uptake and peak PO, while the maximal heart rate (HR) was 12 bpm lower in middle-aged compared with young (p = 0.016) women. Also, no differences were observed between the age groups in τ pulmonary oxygen uptake, ΔHHb, and τHR during on-transients. The first regression model showed that age did not attenuate the maximal CPET capacity in the studied population (p = 0.638), while in the second model a faster τ pulmonary oxygen uptake, combined with shorter TMG-derived contraction time (Tc) of the vastus lateralis (VL), were associated with a higher maximal oxygen uptake (∼30% of explained variance, p = 0.039). In conclusion, long lasting exercise involvement protects against a maximal oxygen uptake and τpulmonary oxygen uptake deterioration in moderately active women. Novelty: Faster τ pulmonary oxygen uptake and shorter Tc of the VL explain 33% of the variance in superior maximal oxygen uptake attainment. No differences between age groups were found in τ pulmonary oxygen uptake, τΔHHb, and τHR during on-transients.
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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".