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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".