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Machine Learning Enabled Time Series Analysis in Medicine

NCT05802563 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2023-04-07

No results posted yet for this study

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. 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

Interventions

DEVICE

fitness tracker

Study subjects will wear a Fitbit fitness tracker

Sponsors & Collaborators

  • Delft University of Technology

    collaborator OTHER

  • HagaZiekenhuis

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-05-24
Primary Completion
2023-09-01
Completion
2023-09-01

Countries

  • Netherlands

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05802563 on ClinicalTrials.gov