Atrial FibrillationME-TIMENCT05802563
Machine Learning Enabled Time Series Analysis in Medicine
An observational study of Atrial Fibrillation and Heart Failure, Systolic, run by HagaZiekenhuis at 1 site in Netherlands.
Trial 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: 1. 50 with heart failure 2. 50 with atrial fibrillation 3. Read more about this study
- Who is it for?
- Ages 18 to 85 yearsHealthy volunteers can take part
- Conditions the study is for
- Atrial Fibrillation, Heart Failure, Systolic
- What is being tested?
- fitness tracker
- Type of treatment
- Medical device
- How many people
- 200 participants
- Where
- 1 siteNetherlands
- Study dates
- 24 May 2022to 1 Sep 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: All included participants will receive an electrocardiogram.
Who may be able to join
- systolic heart failure (LVEF < 35%)
- Atrial fibrillation without heart failure
- Individuals without cardiovascular disease
Who cannot join
- > 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Pattern Recognition in Heart Rate Variability Using Fitness Trackers in Cardiovascular Disease
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.
- Conditions
- Atrial Fibrillation, Heart Failure, Systolic
- Being tested
- fitness tracker
- Keywords
- cardiovascular disease, Artificial intelligence, machine learning
- Medical subject headings
- Atrial Fibrillation, Heart Failure, Systolic, Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
fitness trackerDevice
Also called Fitbit Charge 5, Fitbit Inspire 2
Study subjects will wear a Fitbit fitness tracker
| Study group | Role | What they receive |
|---|---|---|
| Heart Failure | Study participants with systolic heart failure (Left ventricular ejection fraction < 35%) without documented atrial fibrillation Receives: Device: fitness tracker | |
| Atrial Fibrillation | Study participants with documented atrial fibrillation without heart failure Receives: Device: fitness tracker | |
| Reference | Individuals without cardiovascular disease Receives: Device: fitness tracker |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cardiovascular disease detection with an AI algorithm
adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure
Measured: Three months
Secondary outcomes (1)
Detection of absence of cardiovascular disease
Measured: Three months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Apr 2023
- Study start24 May 2022
- Primary completion1 Sep 2023 (estimated)
- Study completion1 Sep 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial FibrillationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- HagaZiekenhuis
- Sponsor type
- University, hospital or other
- Collaborators
- Delft University of Technology
- Responsible party
- Principal investigator, Ivo van der Bilt, HagaZiekenhuis
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05802563
- Sponsor's protocol ID
- metime
- Acronym
- ME-TIME
- Status last verified
- Apr 2023
- First submitted
- 27 Mar 2023
- Last updated
- 7 Apr 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: We are planning to release the full data set in the future.
Study at a glance
From the registry record, last updated by the sponsor on 7 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pattern Recognition in Heart Rate Variability Using Fitness Trackers in Cardiovascular Disease
- ClinicalTrials.gov ID
- NCT05802563
- Acronym / sponsor ID
- ME-TIME / metime
- Conditions
- Atrial Fibrillation, Heart Failure, Systolic
- Keywords
- cardiovascular disease, Artificial intelligence, machine learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- HagaZiekenhuisUniversity, hospital or other
- Collaborators
- Delft University of Technology
- Study start
- 24 May 2022 (actual)
- Primary completion
- 1 Sep 2023 (estimated)
- Study completion
- 1 Sep 2023 (estimated)
- First posted
- 6 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Heart Failure | – | Device: fitness trackerStudy participants with systolic heart failure (Left ventricular ejection fraction < 35%) without documented atrial fibrillation |
| Atrial Fibrillation | – | Device: fitness trackerStudy participants with documented atrial fibrillation without heart failure |
| Reference | – | Device: fitness trackerIndividuals without cardiovascular disease |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- All included participants will receive an electrocardiogram.
Key inclusion criteria (3 listed)
- systolic heart failure (LVEF < 35%)
- Atrial fibrillation without heart failure
- Individuals without cardiovascular disease
Key exclusion criteria (8 listed)
- > 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| fitness trackerAlso: Fitbit Charge 5, Fitbit Inspire 2Study subjects will wear a Fitbit fitness tracker | Device | Atrial Fibrillation, Heart Failure, Reference |
Primary outcome
Cardiovascular disease detection with an AI algorithm
Time frame: Three months
adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure
1 secondary outcome
Detection of absence of cardiovascular disease
Time frame: Three months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Apr 2023.
- Individual participant dataSharing planned: Yes. We are planning to release the full data set in the future.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Atrial Fibrillation and Heart Failure, Systolic, run by HagaZiekenhuis at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cardiovascular disease detection with an AI algorithm (time frame: Three months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Apr 2023.