Heavy-Intensity Exercise Off-Transient Pulmonary O2 Uptake Kinetics and Muscle Deoxygenation in Young and Older Adults
Bibliographic record
Abstract
0100 Pulmonary O2 uptake (VO2p) kinetics at the onset of exercise are slowed with advancing age and age-associated changes in the cardiovascular system suggest that O2 delivery may be impaired in older adults. However, during the off-transition from exercise VO2p kinetics remain slower in older compared to young adults while muscle O2 delivery is in excess of O2 demand. PURPOSE: To examine the relationship between VO2p and muscle deoxygenation during the off-transient of heavy-intensity cycling exercise. METHODS: Young (Y; n = 6; 25 ± 3 yrs) and older (O; n = 6; 68 ± 3 yrs) adults performed step-transitions (6min) from heavy-intensity (Δ 50%) exercise to 20 W. VO2p was measured breath-by-breath. Deoxy-(HHb), oxy- (HbO2), and total Hb/Mb (Hbtot) of the vastus lateralis muscle were measured continuously by nearinfrared spectroscopy (NIRS; Hamamatsu NIRO-300). VO2p and HHb data were fit with a mono-exponential model. RESULTS: Off-transient VO2p kinetics were slower (p<0.01) in O (O: 40 ± 5 s; Y: 29 ± 4 s), whereas HHb kinetics were similar (O: 32 ± 9 s; Y: 22 ± 7 s). In O VO2p and HHb off-kinetics were similar, whereas in Y the offkinetics of HHb were faster (p<0.05) than VO2p off-kinetics. The ΔHHb/Δ VO2p (O: 15 ± 5 μM/L/min; Y: 11 ± 5 μM/L/min) ratio was similar in O and Y. CONCLUSIONS: These results suggest that relative to metabolic demand muscle perfusion is elevated in O and Y adults during the off-transient of heavy-intensity cycling exercise. These results also suggest that the slow off-transient VO2p kinetics in O were due to factors other than O2 delivery. Supported in part by NSERC, Canada
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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".