Root mean square of successive differences is not a valid measure of parasympathetic reactivity during slow deep breathing
Bibliographic record
Abstract
Deep breathing exercises are the second most used complementary health approach in the United States. Two heart rate variability (HRV) parameters, the root mean square of successive differences (RMSSD) and the respiratory sinus arrhythmia (RSA), are used to assess parasympathetic reactivity to deep breathing, but they are often not in agreement. Our purpose was to determine the cause of the disagreement. We investigated HRV parameters in 38 subjects during baseline, deep breathing, and recovery. Here we show that RMSSD as a measure of parasympathetic reactivity is unreliable; it does not reflect the increase in HRV during deep breathing as determined by RSA. We observed a decrease in RMSSD values despite a marked increase in HRV as determined by RSA and the standard deviation of normal heartbeat interval (SDNN) in healthy subjects and patients with functional bowel disorders. We show that RSA captures all aspects of HRV, whereas successive differences in heart rate intervals are only a small part of HRV, with decreasing variability during deep breathing in most subjects. We present a new measure of calculating RSA during deep breathing that may become an essential tool for researchers and clinicians. We also provide a unique visualization of the increased heart rate variability during deep breathing. Hence, RMSSD cannot be used to assess parasympathetic reactivity during deep breathing; using RSA is recommended. The use of RMSSD in previous influential studies may have led to erroneous conclusions about parasympathetic reactivity during deep breathing. Its continued use may undervalue the effects of the autonomic nervous system in slow deep breathing.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".