Predicting Acute Exacerbations of COPD Using Wearable Devices and Remote Monitoring Technology With AI/ML Models
๐ฉบ Protocol 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.
Primary Research Facility: McGill University Health Centre (Montreal, Quebec)
๐ Basic Study Criteria & Logistics Checklist
0 of 3 CheckedReview these 3 basic logistical and protocol criteria extracted from the public NIH record. (This is a preliminary logistical checklist, not an official clinical eligibility determination).
๐ Primary Site Map
Site: McGill University Health Centre โ Montreal, Quebec
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