Psychophysiology of slow breathing exercises using heart rate variability measurements for stress reduction
Bibliographic record
Abstract
Slow breathing exercises, associated with meditation and other eastern style modalities like tai chi and hatha yoga, are now increasingly employed in mainstream medicine to reduce stress, attenuate moderate hypertension, and alleviate symptoms of lifestyle-related illnesses. The clinical literature on slow breathing exercises includes studies employing various physiological measurements, including heart rate variability (HRV), galvanic skin response, and changes in skin temperature. HRV has been increasingly used to measure the activity of the autonomic nervous system in various human studies employing healthy and chronically ill subjects. 1. Objective: To understand the effects of slow breathing exercises on heart rate variability as a complementary intervention for stress reduction. 2. Method: Four subjects, through repetitive trials, were instructed to slow down their breathing following a metronome at 10 breaths per minute or 6 breaths per minute or spontaneously relax to slow down their respiratory rate. The ECG, heart rate, and respiratory rate were recorded using a Powerlab set-up (ADI). 3. Key Results: Results showed an increase in amplitude of heart rate variability during these slow breathing exercises, either through the metronome-guided or spontaneous slow-breathing exercises, especially around a breathing frequency of 6 breaths per minute. The increased amplitude of heart rate variability can be seen as a positive sign, a marker for sympathovagal balance. 4. Conclusion: HRV measurements have shown that slow breathing exercises can increase heart rate variability. Future protocols for clinical trials are being projected using the HRV technique and other physiological measurements for studying effects of yoga-based complementary interventions for stress reduction.
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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 | 0.001 |
| 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".