Prediction of the Maximal Metabolic Steady State From Heart Rate Variability Using a Submaximal Incremental Ramp Test.
Bibliographic record
Abstract
ABSTRACT: Rogers, B, Murias, JM, and Fleitas-Paniagua, PR. Prediction of the maximal metabolic steady state from heart rate variability using a submaximal incremental ramp test. J Strength Cond Res 39(10): e1256-e1264, 2025-Recent studies have demonstrated that the metabolic rate at the heavy-severe exercise intensity boundary can be determined by identifying the respiratory compensation point (RCP) and the second heart rate variability threshold (HRVT2) from incremental ramp testing. This study examined whether the HRVT2 could be extrapolated from submaximal portions of the incremental test. Fifteen subjects (5 men, 10 women, age 23 ± 4 years, V̇ o2 max 42.6 ± 8.0 ml·kg -1 ·min -1 ) underwent incremental cycling ramp testing measuring gas exchange variables along with an open-source application recording detrended fluctuation analysis (DFA a1) and RR intervals. RR data from ramp start to the point at which DFA a1 reached 0.75 were used for HRVT2 extrapolation. Comparisons were made between the V̇ o2 and HR at the RCP and HRVT2. Mean values for RCP vs. HRVT2 V̇ o2 and HR were not statistically different, 39.0 ± 9.7 vs. 38.8 ± 11.1 ml·kg -1 ·min -1 and 168 ± 9 vs. 168 ± 12 bpm, respectively, with equivalence verified. Pearson's r correlation coefficients were 0.92 and 0.60 for RCP vs. HRVT2 V̇ o2 and HR, respectively. Bland-Altman analysis showed negligible bias of 0.2 ml·kg -1 ·min -1 (LOA ±9.0) for V̇ o2 and +1 bpm (LOA ±20 bpm) for HR. DFA a1 at the RR interval testing limit was 0.72 ± 0.04 with an HR of 163 ± 12. In this group of healthy recreationally active subjects, the HRVT2 V̇ o2 and HR extrapolated from submaximal portions of the incremental test maintained similar agreement and equivalence to the V̇ o2 and HR at the RCP as seen in prior studies using testing to exhaustion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".