Wearable devices for patient monitoring in Long QT Syndrome
Research type
Research Study
Full title
Application of wearable devices for remote QT interval monitoring and symptom investigation for patients with Long QT Syndrome
IRAS ID
344578
Contact name
William J Young
Contact email
Sponsor organisation
Queen Mary University of London
Duration of Study in the UK
1 years, 5 months, 31 days
Research summary
Long QT syndrome is a condition caused by a change in the coding of genes that regulate the heart's electrical activity. These changes increase the risk of dangerous heart rhythm disturbances and sudden death. An electrocardiogram is a non-invasive method of recording cardiac electrical activity, that can inform dosing of medication or need for a defibrillator to protect against sudden death. It is important to monitor the electrocardiogram regularly, however this requires attendance at outpatient clinics which disrupts patients’ daily lives and work. Wearable devices like Fitbits can record electrocardiograms that can be emailed to a clinician for review. This could enable remote monitoring of the heart’s electrical activity. However, for patients with Long QT syndrome, we do not know whether they will be as accurate as a standard electrocardiogram.
We will provide 80 patients with Long QT syndrome, a Fitbit that they will carry for 3 months. QT measurements from Fitbit and standard electrocardiograms will be compared. They will also be recorded at weekly intervals and if the participant experiences symptoms. We will perform statistical analyses to determine whether Fitbit electrocardiograms are accurate, whether remote monitoring is feasible, and whether they may help identify the cause of symptoms (e.g., a sensation of a rapid heartbeat or dizziness).
This study will provide insight into the use of electrocardiograms from wearable devices for monitoring of the heart’s electrical activity. This has potential to improve optimisation of medical treatment, identify the cause of symptoms earlier and reduce the need for frequent hospital attendances. This study will supply data for the principal investigator to use in a larger study that will utilise advanced electrocardiogram analysis techniques including machine learning, integrated with genetic data and blood markers to improve prediction of dangerous heart rhythms in affected patients.REC name
London - Riverside Research Ethics Committee
REC reference
24/PR/1293
Date of REC Opinion
12 Dec 2024
REC opinion
Further Information Favourable Opinion