Increased exhalation to inhalation ratio during breathing enhances high‐frequency heart rate variability in healthy adults
Bibliographic record
Abstract
Heart rate variability (HRV) is a well-established surrogate of cardiac and emotional health that reflects the balance between sympathetic and parasympathetic activity of the autonomic nervous system. We examined the impact of manipulating exhalation to inhalation ratio (E:I) on HRV, without altering the intrinsic breathing rate of healthy individuals. We hypothesized that a longer exhalation relative to inhalation (E:I > 1) would shift HRV metrics in a direction consistent with increased parasympathetic activity. Twenty-eight individuals (16 young [6M, age = 21-28];12 older adults [6M, age = 66-80]) completed a task during which they paced breathing according to their intrinsic respiratory rate, but altered onset of exhalation and inhalation according to 1:1 sound cue (equal exhalation and inhalation duration) or 2:1 cue (exhalation twice as long as inhalation). Paced 1:1 breathing followed these task conditions to examine residual effects. Estimates of actual E:I ratio based on thoracic movement were 1.08(0.16) for 1:1 task and 1.33(0.20) for 2:1 task, which were significantly different from one another. HRV metrics derived from electrocardiogram included root mean square of the successive differences between normal heartbeats (RMSSD) and high-frequency (HF) HRV. Analyses of HRV metrics by block showed that RMSSD and HF-HRV were higher in the 2:1 task condition compared to 1:1. Time series analysis showed that HF-HRV increased after the end of the 2:1 task block and remained elevated for four minutes. These findings suggest that longer duration of exhalation relative to inhalation, without altering breathing rate, acutely increased RMSSD and HF-HRV, consistent with enhancement of cardiac vagal tone.
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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".