Effect of Various Lengths of Respiration on Heart Rate Variability during Simple Bhramari (Humming)
Bibliographic record
Abstract
Background: Heart rate varies during breathing and the heart rate variability (HRV) facilitates the autonomic homeostatic capacity. The maximum HRV was observed at around 10 s of prolonged respiration as per HRV biofeedback literature. However, there is a gap in understanding the variations in HRV by different respiration lengths during simple Bhramari practice. Objective: To assess the effect of various respiration lengths (8, 10, 12, and 14 s) on HRV during the simple Bhramari (humming) practice. Methodology: A total of 118 individuals (67 males, 51 females) signed up for the study based on convenience sampling at a wellness center. A randomized (simple permutation) within-subject crossover design with repeated measures was used to measure their HRV during the simple Bhramari practice. The results were analyzed with one-way ANOVA and paired samples t -test. Results: The results indicate that, unlike the 10 s of respiration during HRV biofeedback breathing, maximum HRV during humming happens between 12 and 14 s of long breathing. Conclusions: The unique findings of the study demonstrate the maximization of HRV between the respiration lengths of 12 and 14 s. Future work should explore expanding the research to a broader group of participants, including individuals with chronic conditions and other demographic variables and mantra chanting.
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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 | 0.003 |
| 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) | 0.003 | 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".