Using ramp-incremental <i>V</i>O<sub>2</sub> responses for constant-intensity exercise selection
Bibliographic record
Abstract
Despite compelling evidence to the contrary, the view that oxygen uptake (V̇O2) increases linearly with exercise intensity (e.g., power output, speed) until reaching its maximum persists within the exercise physiology literature. This viewpoint implies that the V̇O2 response at any constant intensity is predictable from a ramp-incremental exercise test. However, the V̇O2 versus task-specific exercise intensity relationship constructed from ramp-incremental versus constant-intensity exercise are not equivalent preventing the use of V̇O2 responses from 1 domain to predict those of the other. Still, this “linear” translational framework continues to be adopted as the guiding principle for aerobic exercise prescription and there remains in the sport science literature a lack of understanding of how to interpret V̇O2 responses to ramp-incremental exercise and how to use those data to assign task-specific constant-intensity exercise. The objectives of this paper are to (i) review the factors that disassociate the V̇O2 versus exercise intensity relationship between ramp-incremental and constant-intensity exercise paradigms; (ii) identify when it is appropriate (or not) to use ramp V̇O2 responses to accurately assign constant-intensity exercise; and (iii) illustrate the technical and theoretical challenges with prescribing constant-intensity exercise solely on information acquired from ramp-incremental tests. Actual V̇O2 data collected during cycling exercise and V̇O2 kinetics modelling are presented to exemplify these concepts. Possible solutions to overcome these challenges are also presented to inform on appropriate intensity selection for individual-specific aerobic exercise prescription in both research and practical settings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".