A Comparison of Methods to Identify the Mean Response Time of Ramp-Incremental Exercise for Exercise Prescription
Bibliographic record
Abstract
Introduction: The oxygen uptake (V˙O2) vs power output relationship from ramp incremental exercise is used to prescribe aerobic exercise. As power output increases, there is a delay in V˙O2 that contributes to a misalignment of V˙O2 from power output; the mean response time (MRT). If the MRT is not considered in exercise prescription, ramp incremental-identified power outputs will elicit V˙O2 values that are higher than intended. We compared three methods of determining MRT (exponential modeling (MRTEXP), linear modeling (MRTLIN), and the steady-state method (MRTSS)) and evaluated their accuracy at predicting the V˙O2 associated with power outputs approximating 75% and 85% of gas exchange threshold and 15% of the difference between gas exchange threshold and maximal V˙O2 (Δ15). Methods: Ten males performed a 30-W∙min−1 ramp incremental and three 30-min constant power output cycle ergometer trials with intensities at 75% gas exchange threshold, 85% gas exchange threshold, and ∆15. At each intensity, the measured steady-state V˙O2 during each 30-min test was compared to the V˙O2 predicted after adjustment by each of the three MRTs. Results: For all three MRT methods, predicted V˙O2 was not different (p = 1.000) from the measured V˙O2 at 75%GET (MRTEXP, 31 mL, MRTLIN, −35 mL, MRTSS 11 mL), 85%gas exchange threshold (MRTEXP −14 mL, MRTLIN −80 mL, MRTSS −32 mL). At Δ15, predicted V˙O2 based on MRTEXP was not different (p = .767) from the measured V˙O2, but was different for MRTLIN (p < .001) and MRTSS (p = .03). Conclusion: Given that the intensity is below gas exchange threshold, all model predictions implemented from the current study matched the exercise prescription.
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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 | 0.009 | 0.031 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".