Purpose: To examine the value of repeated blood pressure screening during childhood, and exercise testing in adolescence, for predicting resting blood pressure at eighteen years.
Method: Regular measures of blood pressure and other variables in a birth cohort from 7 to 18 years. Cardiovascular responses to exercise testing at 15 and 18 years.
Results: The best predictors of resting blood pressure at 18 years were body size at 18 years and resting blood pressure at 15 years. Neither exercise and recovery blood pressures at 15 years, nor the way blood pressure had tracked through childhood, added importantly to the prediction.
Conclusion: We question the value of screening for hypertension during childhood, either by regular resting measures or by exercise testing in adolescence.
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http://dx.doi.org/10.1016/1054-139X(94)00207-U | DOI Listing |
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 PDFEClinicalMedicine
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 PDFWorld 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 PDFPatient 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.
Front Pharmacol
December 2024
Department of Obstetrics and Gynecology, Second Hospital of Hebei Medical University, Shijiazhuang, China.
Background: Medroxyprogesterone acetate (MPA), a synthetic progestogen, is extensively used for the treatment of various conditions, including contraception, irregular menstruation, functional uterine bleeding, and endometriosis. However, like all pharmaceutical agents, MPA is associated with adverse drug reactions. This study aimed to evaluate the adverse events (AEs) associated with MPA in by analyzing real-world data from the U.
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