Comparison of heart and respiratory rate variability measures using an intermittent incremental submaximal exercise model
Bibliographic record
Abstract
To better understand the alterations in cardiorespiratory variability during exercise, the present study characterized the patterns of change in heart rate variability (HRV), respiratory rate variability (RRV), and combined cardiorespiratory variability (HRV-RRV) during an intermittent incremental submaximal exercise model. Six males and six females completed a submaximal exercise protocol consisting of an initial baseline resting period followed by three 10-min bouts of exercise at 20%, 40%, and 60% of maximal aerobic capacity (V̇O2max). The R-R interval and interbreath interval variability were measured at baseline rest and throughout the submaximal exercise. A group of 93 HRV, 83 RRV, and 28 HRV-RRV measures of variability were tracked over time through a windowed analysis using a 5-min window size and 30-s window step. A total of 91 HRV measures were able to detect the presence of exercise, whereas only 46 RRV and 3 HRV-RRV measures were able to detect the same stimulus. Moreover, there was a loss of overall HRV and RRV, loss of complexity of HRV and RRV, and loss of parasympathetic modulation of HRV (up to 40% V̇O2max) with exercise. Conflicting changes in scale-invariant structure of HRV and RRV with increases in exercise intensity were also observed. In summary, in this simultaneous evaluation of HRV and RRV, we found more consistent changes across HRV metrics compared with RRV and HRV-RRV.
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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 | 0.001 |
| 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 | 0.000 |
| 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".