Objectives: The objective of this study was to assess the quality of ECG recordings and the concordance between the automatic detection of cardiac arrhythmia episodes by a patch ECG and an insertable cardiac monitor.
Design: Prospective cohort study.
Setting And Participants: Endurance athletes diagnosed with paroxysmal atrial fibrillation (AF) and no other relevant comorbidities participating in a randomised controlled trial on the effects of training adaption.
Methods: A patch ECG (ECG247 Smart Heart Sensor) was sent to 29 non-elite endurance athletes with clinical paroxysmal AF. ECG247 continuously monitors, detects and categorises cardiac arrhythmias. The participants were simultaneously monitored with an insertable cardiac monitor (Confirm Rx, Abbott). ECG recording quality was assessed by an experienced physician. Training sessions were monitored using sports watches.
Results: Out of 29 invited athletes, 14 athletes (mean age 60.4 years, 2 women) made use of the patch ECG and were monitored for a total of 2987 hours and a median of 14 (range 1-17) days. During this period, the athletes performed a total of 112 training sessions. ECG quality varied between athletes and by type of exercise, with poor quality in 16% and 40% of recordings during cross-country skiing and running, respectively. In two athletes, the patch ECG detected AF episodes that were confirmed with insertable cardiac monitor recordings. One technical artefact was falsely classified as ventricular tachycardia by the ECG247 Smart Heart Sensor system.
Conclusion: Monitoring with patch ECG was feasible in endurance athletes, but ECG recording quality varied between athletes and by type of exercise.
Trial Registration: NCT04991337 (for the related randomised controlled trial).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1136/bmjopen-2024-093250 | DOI Listing |
Commun Med (Lond)
January 2025
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.
BMJ Open
January 2025
Department of Medical Research Bærum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
Objectives: The objective of this study was to assess the quality of ECG recordings and the concordance between the automatic detection of cardiac arrhythmia episodes by a patch ECG and an insertable cardiac monitor.
Design: Prospective cohort study.
Setting And Participants: Endurance athletes diagnosed with paroxysmal atrial fibrillation (AF) and no other relevant comorbidities participating in a randomised controlled trial on the effects of training adaption.
J Mol Cell Cardiol
January 2025
Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QW, UK. Electronic address:
Introduction: Brugada Syndrome (BrS) is an inherited arrhythmia syndrome characterised by ST-segment elevation in the right precordial ECG leads and is associated with an increased risk of sudden cardiac death. We identify and characterise a novel SCN3B variant encoding the regulatory β3-subunit of the cardiac voltage-gated sodium channel, Na1.5.
View Article and Find Full Text PDFJ Electrocardiol
December 2024
VPG Medical, Inc., Rochester, NY, USA.
Passive cardiac monitoring has become synonymous with wearable technologies, necessitating patients to incorporate new devices into their daily routines. While this requirement may not be a burden for many, it is a constraint for individuals with chronic diseases who already have their daily routine. In this study, we introduce an innovative technology that harnesses the front-facing camera of smartphones to capture pulsatile signals discreetly when users engage in other activities on their device.
View Article and Find Full Text PDFAm J Crit Care
January 2025
Salah S. Al Zaiti is a professor, School of Nursing, University of Rochester, Rochester, New York.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!