Combining Near-Infrared Spectroscopy and Heart Rate Variability Derived Thresholds to Estimate the Critical Intensity of Exercise
Bibliographic record
Abstract
ABSTRACT: Fleitas-Paniagua, PR, de Almeida Azevedo, R, Trpcic, M, Murias, JM, and Rogers, B. Combining near-infrared spectroscopy and heart rate variability derived thresholds to estimate the critical intensity of exercise. J Strength Cond Res 38(1): e16-e24, 2024-Critical intensity determination often requires costly tools and several testing sessions. Alternative approaches display relatively large individual variation. Therefore, simpler estimations with improved precision are needed. This study evaluated whether averaging the heart rate (HR) and oxygen uptake (V̇O 2 ) responses associated with the muscle deoxyhemoglobin concentration breakpoint ([HHb] BP ) and the heart rate variability (HRV) given by the detrended fluctuation analysis second threshold (HRVT2) during ramp incremental (RI) test improved the accuracy of identifying the HR and V̇O 2 at the respiratory compensation point (RCP). Ten female and 11 male recreationally trained subjects performed a 15 W·minute -1 RI test. Gas exchange, near-infrared spectroscopy (NIRS), and RR interval were recorded to assess the RCP, [HHb] BP , and HRVT2. Heart rate (mean ± SD : 158 ± 14, 156 ± 13, 160 ± 14 and, 158 ± 12 bpm) and V̇O 2 (3.08 ± 0.69, 2.98 ± 0.58, 3.06 ± 0.65, and 3.02 ± 0.60 L·minute -1 ) at the RCP, [HHb] BP , HRVT2, and HRVT2&[HHb] BP average (H&H Av ), respectively, were not significantly different ( p > 0.05). The linear relationship between H&H Av and RCP was higher compared with the relationship between [HHb] BP vs RCP and HRVT2 vs RCP for both HR ( r = 0.85; r = 0.73; r = 0.79, p > 0.05) and V̇O 2 ( r = 0.94; r = 0.93; r = 0.91, p > 0.05). Intraclass correlation between RCP, [HHb] BP , HRVT2, and H&H AV was 0.93 for V̇O 2 and 0.79 for HR. The [HHb] BP and the HRVT2 independently provided V̇O 2 and HR responses that strongly agreed with those at the RCP. Combining [HHb] BP and the HRVT2 resulted in estimations of the V̇O 2 and HR at the RCP that displayed smaller variability compared with each modality alone.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".