We studied cardiovascular and respiratory changes during yogic breathing exercise kapalabhati (KB) in 17 advanced yoga practitioners. The exercise consisted in fast shallow abdominal respiratory movements at about 2 Hz frequency. Blood pressure, ECG and respiration were recorded continuously during three 5 min periods of KB and during pre- and post-KB resting periods. The beat-to-beat series of systolic blood pressure (SBP) and diastolic blood pressure (DBP), R-R intervals and respiration were analysed by spectral analysis of time series. The mean absolute power was calculated in three frequency bands--band of spontaneous respiration, band of 0.1 Hz rhythm and the low-frequency band greater than 15 s in all spectra. The mean modulus calculated between SBP and R-R intervals was used as a parameter of baroreceptor-cardiac reflex sensitivity (BRS). Heart rate increased by 9 beats per min during KB. SBP and DBP increased during KB by 15 and 6 mmHg respectively. All frequency bands of R-R interval variability were reduced in KB. Also the BRS parameter was reduced in KB. The amplitude of the high-frequency oscillations in SBP and DBP increased during KB. The low-frequency blood pressure oscillations were increased after KB. The results point to decreased cardiac vagal tone during KB which was due to changes in respiratory pattern and due to decreased sensitivity of arterial baroreflex. Decreased respiratory rate and increased SBP and low-frequency blood pressure oscillations after KB suggest a differentiated pattern of vegetative activation and inhibition associated with KB exercise.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Preeclampsia and eclampsia: Enhanced detection and treatment for morbidity reduction.
Best Pract Res Clin Anaesthesiol
September 2024
Division of Maternal-Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, L1, Boston, MA, 02115, USA. Electronic address:
Preeclampsia is a life-threatening complication that develops in 2-8% of pregnancies. It is characterized by elevated blood pressure after 20 weeks of gestation and may progress to multiorgan dysfunction, leading to severe maternal and fetal morbidity and mortality. The only definitive treatment is delivery, and efforts are focused on early risk prediction, surveillance, and severity mitigation.
View Article and Find Full Text PDFEstimation of Central Aortic Pressure Waveforms by Combination of a Meta-Learning Neural Network and a Physics-Driven Method.
Int J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
View Article and Find Full Text PDFEffect of digital health applications with or without gamification on physical activity and cardiometabolic risk factors: a systematic review and meta-analysis of randomized controlled trials.
EClinicalMedicine
October 2024
Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown.
View Article and Find Full Text PDFMachine learning applications in healthcare clinical practice and research.
World J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFFactors Influencing Self-Care Practices and Blood Pressure Among Individuals with Uncontrolled Hypertension: A Cross-Sectional Study.
Patient Prefer Adherence
January 2025
Division of Hypertension, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Introduction: Self-care practices are crucial for optimizing blood pressure control and are influenced by multilevel factors.
Objective: To examine the influences of multilevel factors on hypertension self-care practices among individuals with uncontrolled hypertension and to determine the relationship between hypertension self-care practices and blood pressure.
Methods: The study was conducted in primary, secondary, and tertiary care settings in Bangkok, selected for convenience, where individuals with uncontrolled hypertension were recruited using a convenience sampling method based on specific inclusion criteria.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!