Fractal correlation properties of heart rate variability as a marker of exercise intensity during incremental and constant-speed treadmill running
Bibliographic record
Abstract
Abstract The short-term scaling exponent of detrended fluctuation analysis (DFAα1) applied to interbeat intervals may provide a method to identify ventilatory thresholds and indicate systemic perturbation during prolonged exercise. The purposes of this study were to i) confirm whether DFAα1 values of 0.75 and 0.5 coincide with the gas exchange threshold (GET) and respiratory compensation point (RCP), ii) quantify DFAα1 during constant-speed running near the maximal lactate steady state (MLSS), and iii) assess the repeatability of DFAα1 between MLSS trials. Seventeen runners performed an incremental running test, and eleven and ten runners also performed constant-speed running 5% below, at, and 5% above the MLSS, and a repeat trial at MLSS, respectively. GET (bias [LOA]: –3.6 [–9.1 to 1.9] mL·kg −1 ·min −1 ) and RCP (–3.5 [–14.1 to 7.2] mL·kg −1 ·min −1 ) were overestimated using DFAα1. DFAα1 responses during 30-min running trials near MLSS were variable (i.e., 0.27 to 1.24), and affected by intensity (p=0.019) and duration (p=0.001). No difference in DFAα1 was detected between MLSS trials (p=0.926). These results question whether DFAα1 values can accurately delineate exercise thresholds, but the dependency of DFAα1 on intensity and duration support its potential use to quantify systemic perturbations imposed by continuous exercise.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".