Slow breathing as a means to improve orthostatic tolerance: a randomized sham-controlled trial
Bibliographic record
Abstract
Endogenous oscillations in blood pressure (BP) and cerebral blood flow have been associated with improved orthostatic tolerance. Although slow breathing induces such responses, it has not been tested as a therapeutic strategy to improve orthostatic tolerance. With the use of a randomized, crossover sham-controlled design, we tested the hypothesis that breathing at six breaths/min (vs. spontaneous breathing) would improve orthostatic tolerance via inducing oscillations in mean arterial BP (MAP) and cerebral blood flow. Sixteen healthy participants (aged 25 ± 4 yr; mean ± SD) had continuous beat-to-beat measurements of middle cerebral artery blood velocity (MCAv), BP (finometer), heart rate (ECG), and end-tidal carbon dioxide partial pressure during an incremental orthostatic stress test to presyncope by combining head-up tilt with incremental lower-body negative pressure. Tolerance time to presyncope was improved (+15%) with slow breathing compared with spontaneous breathing (29.2 ± 5.4 vs. 33.7 ± 6.0 min; P < 0.01). The improved tolerance was reflected in elevations in low-frequency (LF; 0.07-0.2 Hz) oscillations of MAP and mean MCAv, improved metrics of dynamic cerebrovascular control (increased LF phase and reduced LF gain), and a reduced rate of decline for MCAv (-0.60 ± 0.27 vs. -0.99 ± 0.51 cm·s(-1)·min(-1); P < 0.01) and MAP (-0.50 ± 0.37 vs. -1.03 ± 0.80 mmHg/min; P = 0.01 vs. spontaneous breathing) across time from baseline to presyncope. Our findings show that orthostatic tolerance can be improved within healthy individuals with a simple, nonpharmacological breathing strategy. The mechanisms underlying this improvement are likely mediated via the generation of negative intrathoracic pressure during slow and deep breathing and the related beneficial impact on cerebrovascular and autonomic function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".