The primary objective of this study is to detect HRV patterns which are related to cardiovasculair disease such as permanent atrial fibrillation or (congestive) heart failure using a wearable fitness tracker.
ID
Source
Brief title
Condition
- Other condition
- Cardiac arrhythmias
Synonym
Health condition
kunstmatige intelligentie
Research involving
Sponsors and support
Intervention
Outcome measures
Primary outcome
The primary objective of this study is to detect HRV patterns which are related
to cardiovas-cular disease such as permanent atrial fibrillation or
(congestive) heart failure using a weara-ble fitness tracker.
Secondary outcome
- Quantify study participant compliance rate of wearable fitness tracker
Background summary
Heart rate variability (HRV) is a non-invasive parameter which indicates the
variation in the heartrate within a timeframe. HRV provides a measure of how
study subjects react and adapt to stress, physical fatigue and
metabolic-request changes and disease. Several other studies have shown that
HRV is a prognostic indicator of arrhythmic events and mortality in study
participants experiencing following a myocardial infarction and in congestive
heart fail-ure study participants. In study participants with heart failure,
reduced or abnormal HRV are indicators of an increased risk of mortality.
Wearable fitness trackers, such as the FitBit, are non-invasive tools that can
easily monitor the HRV of study participants in an outstudy participant setting
using a photoplethysmo-graphic (PPG) sensor.
The recognition of a unique HRV pattern using AI in cardiovascular diseases
could be clinically relevant when realizing early detection of cardiovascular
events, without submitting study participant in the hospital to potential
invasive and burdensome tests.
With the help of machine learning, we hypothesize that for each respective
cardiovascular disease, such as permanent atrial fibrillation and systolic
heart failure, a unique HRV based pattern can be found.
Study objective
The primary objective of this study is to detect HRV patterns which are related
to cardiovasculair disease such as permanent atrial fibrillation or
(congestive) heart failure using a wearable fitness tracker.
Study design
Single-center observational feasibility cohort trial performed in the Haga
Teaching Hospital in The Hague, The Netherlands.
Study burden and risks
None
Els Borst-Eilersplein 275
Den Haag 2545AA
NL
Els Borst-Eilersplein 275
Den Haag 2545AA
NL
Listed location countries
Age
Inclusion criteria
In order to be eligible to participate in this study, a subject must meet all
of the following criteria:
- Study participants who are 18 years or older.
Group 1: Study participants who are diagnosed with permanent atrial
fibrillation.
Group 2: Study participants with systolic heart failure (LVEF < 35%) with an
implantable car-diac device without documented atrial fibrillation.
Group 3: Healthy individuals, with a normal electrocardiogram
Exclusion criteria
A potential study subject who meets any of the following criteria will be
excluded from participation in this study:
- paroxysmal or persistent atrial fibrillation
- Elderly study participants > 85 years old
- Recent pulmonary venous antrum isolation (PVAI) procedure (< 1 year)
- (end stage) Kidney failure
- (end stage) Liver failure
- Study participants with a history of cardiothoracic surgery
- Study participants with a history of multiple myocardial infarction
- Study participants with known systemic active inflammatory disease
- Study participants with impaired mental state
- Alternation in rhythm medication (Monthly)
- Inability to use a fitness tracker or mobile phone
- Impaired cognition and inability to understand the study protocol
- Study subject with known metastatic disease
Design
Recruitment
metc-ldd@lumc.nl
metc-ldd@lumc.nl
metc-ldd@lumc.nl
metc-ldd@lumc.nl
Followed up by the following (possibly more current) registration
No registrations found.
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
| Register | ID |
|---|---|
| CCMO | NL73708.058.20 |