A Simple Method to Quantify the V˙O2 Mean Response Time of Ramp-Incremental Exercise
Bibliographic record
Abstract
During ramp-incremental exercise, the mean response time (MRT) of oxygen uptake (V˙O2) represents the time delay for changes in muscle V˙O2 to be reflected at the level of the mouth and is generally calculated by linear (MRTLIN) and monoexponential (τ') fitting of V˙O2 data. However, these methods yield MRT values that are highly variable from test-to-test. PURPOSE: Therefore, we examined the validity and the reproducibility of a novel method to calculate the MRT. METHODS: On two occasions, 12 healthy men (age, 30 ± 10 yr; V˙O2max: 4.14 ± 0.47 L·min, 53.5 ± 7.3 mL·kg·min) performed a ramp-incremental cycling test (30 W·min) that was preceded by a step transition to 100 W. The ramp power output corresponding to the steady-state V˙O2 at 100 W was determined and the difference between that power output and 100 W was converted to time to quantify the MRT (MRTSS). RESULTS: The values of MRTLIN, τ', and MRTSS were 28 ± 16 s, 27 ± 12 s, and 26 ± 11 s, respectively, which were not different (P > 0.05) from each other. However, compared to the MRT parameters derived from the fitting-based methods, MRTSS had a higher correlation coefficient (R = 0.87) and a smaller coefficient of variation (15% ± 9%) from test-to-test. CONCLUSIONS: In conclusion, the novel method proposed in the current study was found to be valid and highly reproducible in a test-retest design. Therefore, we advocate the use of this approach when a precise and accurate determination of the MRT is needed to properly align the V˙O2 data with power output during ramp-incremental exercise.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".