Do Agreements between HRV and Gas Exchange Thresholds Still Hold under Hypoxic Conditions?
Bibliographic record
Abstract
PURPOSE: Heart rate (HR) variability thresholds (HRVT) based on detrended fluctuation analysis alpha 1 (DFA a1) generally show reasonable alignment of thresholds estimations based on gas exchange responses under normoxic conditions. This study examined whether acute hypoxia would affect the agreement between HRVTs and the gas exchange equivalents during incremental cycling. METHODS: Twelve participants (five females) completed an incremental ramp test in normobaric hypoxia (FIO 2 ≈ 13.5%) and normoxia. Gas exchange and ventilatory responses alongside a high sampling rate electrocardiogram for DFA a1 computation were used to determine thresholds. Comparisons were made between the oxygen consumption (V̇O 2 ) and HR at the gas exchange threshold (GET) and respiratory compensation point (RCP) with the responses at the first and second HRVTs (HRVT 1 and HRVT 2 , respectively). RESULTS: Mean V̇O 2 and HR values were not statistically different for GET:HRVT1 (normoxia: 1.74 ± 0.41 vs 1.74 ± 0.48 L·min -1 , 133 ± 18 vs 133 ± 16 bpm; hypoxia: 1.47 ± 0.21 vs 1.45 ± 0.37 L·min -1 , 135 ± 14 vs 133 ± 15 bpm) and RCP:HRVT2 (normoxia: 2.38 ± 0.55 vs 2.37 ± 0.48 L·min -1 , 158 ± 13 vs 158 ± 14 bpm, hypoxia: 2.07 ± 0.32 vs 1.90 ± 0.43 L·min -1 and 156 ± 13 vs 152 ± 15 bpm) in any condition. All normoxic comparisons passed equivalence testing, but only GET:HRVT1 responses passed during hypoxia. Pearson's r correlation coefficients were 0.86 to 0.96 in normoxia and 0.58 to 0.79 in hypoxia. Bland-Altman analysis indicated higher degrees of bias and limit of agreements (LOA) during hypoxic testing. CONCLUSIONS: Although the V̇O 2 and HR at HRVTs retained alignment with GET/RCP in both normoxia and hypoxia, the degrees of correlation, and equivalence were weaker and the bias and LOA were larger in hypoxia. Therefore, although using HRVT alone for training boundary guidance in hypoxia is a potential option, further investigation including incorporating complementary surrogate markers is recommended.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".