Wearable Devices for Patient Monitoring in Long QT Syndrome
Application of Wearable Devices for Remote QT Interval Monitoring and Symptom Investigation for Patients With Long QT Syndrome
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Locations
-
-
-
London, United Kingdom
- Recruiting
- Barts and London Hospital NHS Trust
-
Contact:
- William Young
- Phone Number: 07872533176
- Email: w.young@qmul.ac.uk
-
Contact:
- William Young
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Clinical diagnosis of Long QT Syndrome
- Aged 18 years or over
- Phone with iOS version 15 or Android OS 9.0 or higher
- Able and willing to provide informed consent
Exclusion Criteria:
- Unwilling or unable to give consent
- Ventricular pacing at recruitment
- Bundle branch block or pre-excitation at baseline
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
1. Determine the accuracy of repeated QT interval measurements using Fitbit-derived ECGs in patients with Long QT syndrome compared with the current standard (12-lead ECG and ambulatory monitors)
Time Frame: From enrollment to 3 months
|
From enrollment to 3 months
|
|
2. Establish intra-patient QT variability from weekly Fitbit-ECGs and frequency of measurements over 500ms.
Time Frame: From enrollment to 3 months
|
From enrollment to 3 months
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
1. Establish the utility of wearable devices for determining symptom aetiology in LQTS.
Time Frame: From enrollment to 3 months
|
From enrollment to 3 months
|
|
2. Develop pipelines for the analysis of ECG data collected remotely including use of in-house QT automated algorithms for future machine learning applications.
Time Frame: From enrollment to 3 months
|
From enrollment to 3 months
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: William J Young, MBBS, PhD, Queen Mary University of London and St Bartholomew's Hospital
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Cardiac Conduction System Disease
- Cardiovascular Diseases
- Pathologic Processes
- Heart Diseases
- Arrhythmias, Cardiac
- Congenital Abnormalities
- Cardiovascular Abnormalities
- Heart Defects, Congenital
- Congenital, Hereditary, and Neonatal Diseases and Abnormalities
- Pathological Conditions, Signs and Symptoms
- Long QT Syndrome
- Equipment and Supplies
- Electrical Equipment and Supplies
- Wearable Electronic Devices
Other Study ID Numbers
Other Study ID Numbers
- 173178
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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