The effect of slow breathing on cardiovascular and electromyographic responses during standing perturbations in older adults
Bibliographic record
Abstract
A bi-directional interaction between the cardiovascular and postural control systems has been previously reported in young adults; however, limited data exist in older populations where physiological alternations in these systems are well known. The purpose of this study was to determine: (1) the effect of slow breathing on heart rate (HR) and systolic blood pressure (SBP) responses following surface support postural perturbations in older adults and (2) the effect of slow breathing on lower limb muscle burst onset and burst amplitude during postural perturbations of the support surface in older adults. Twenty community-dwelling older adults experienced posteriorly directed accelerations of treadmill belts during quiet standing while breathing spontaneously (SPON) or breathing at 6 breaths per minute (SLOW). SBP, HR, and muscle burst onset and burst amplitude were analyzed for 7 s from each perturbation's onset. Post-perturbation comparison of SLOW and SPON showed that SBP was significantly higher during SPON over the entire analyzed time period (0-7 s) (p < 0.001), while there was no difference in HR throughout the same analysis window (0-7 s) (p > 0.05). The muscle burst onset was shortened in the SLOW compared to SPON task (p < 0.001), while muscle burst amplitude was not significantly different between SPON and SLOW (p = 0.353). Although slow breathing affected cardiovascular and muscle activation onset responses during postural perturbations in older adults, they differed from the responses in younger adults reported previously. The findings highlight the physiological adaptations that may occur to maintain postural stability in older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".