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Predicting Acute Exacerbations of COPD Using Wearable Devices and Remote Monitoring Technology With AI/ML Models
Sponsor: McGill University Health Centre/Research Institute of the McGill University Health Centre
Summary
This study is aimed to collect real-time physiological data using two wearable devices (a biometric ring and a biometric wristband), daily lung mechanical measurements by a handheld oscillometer, and participant-reported symptoms in patients with COPD remotely from their home environment. The data will be used to train and validate artificial intelligence and machine learning (AI/ML) models to predict COPD exacerbations in advance of their actual occurrence. The data will also be used to test the new severity classification system for exacerbations of COPD, as well as to determine important relationships between physiological measurements from the wearable devices, the handheld oscillometer, the self-reported symptoms, and the tests performed at the baseline visit.
Official title: Early Prediction of Acute Exacerbations of COPD Using Wearable and Portable Remote Monitoring Technology With AI/ML Empowered Platforms: A Prospective Clinical Study
Key Details
Gender
All
Age Range
40 Years - Any
Study Type
OBSERVATIONAL
Enrollment
50
Start Date
2025-05-22
Completion Date
2026-08-31
Last Updated
2025-05-28
Healthy Volunteers
No
Interventions
Biometric wearable and handheld devices
In this study, participants will be equipped with biometric wearable devices, i.e. ring and wristband, as well as with a handheld oscillometer, to measure their physiological parameters and lung mechanical changes (lung function).
Locations (1)
McGill University Health Centre
Montreal, Quebec, Canada