Pattern Recognition in Heart Rate Variability Using Fitness Trackers in Cardiovascular Disease
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Atrial Fibrillation
- Sponsor
- HagaZiekenhuis
- Enrollment
- 200
- Locations
- 1
- Primary Endpoint
- Cardiovascular disease detection with an AI algorithm
- Status
- Enrolling By Invitation
- Last Updated
- 3 years ago
Overview
Brief Summary
The goal of this observational cohort study is to investigate the potential of fitness trackers in combination with machine learning algorithms to identify cardiovascular disease specific patterns.
Two hundred participants will be enrolled:
- 50 with heart failure
- 50 with atrial fibrillation
- 100 (healthy) individuals without the former two conditions
All participants are given a Fitbit device and monitored for three months. Researchers will compare differences in heart rate variability patterns between the groups and devise a machine learning algorithm to detect these patterns automatically.
Investigators
Ivo van der Bilt
Principal Investigator
HagaZiekenhuis
Eligibility Criteria
Inclusion Criteria
- •systolic heart failure (LVEF \< 35%)
- •Atrial fibrillation without heart failure
- •Individuals without cardiovascular disease
Exclusion Criteria
- •\> 85 years old
- •Recent pulmonary venous antrum isolation procedure (\<1 year)
- •(end stage) kidney failure
- •(end stage) liver failure
- •Study participants with known systemic active inflammatory disease
- •Study participants with impaired mental state
- •Inability to use a fitness tracker or mobile phone
- •Impaired cognition and inability to understand the study protocol
Outcomes
Primary Outcomes
Cardiovascular disease detection with an AI algorithm
Time Frame: Three months
adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure
Secondary Outcomes
- Detection of absence of cardiovascular disease(Three months)