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Predicting Acute Exacerbations of COPD Using Wearable Devices and Remote Monitoring Technology With AI/ML Models

NCT06802003 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2025-05-28

No results posted yet for this study

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.

Conditions

Interventions

DEVICE

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).

Sponsors & Collaborators

  • Restech Srl

    collaborator INDUSTRY

  • McGill University Health Centre/Research Institute of the McGill University Health Centre

    lead OTHER

Eligibility

Min Age
40 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-05-22
Primary Completion
2026-05-31
Completion
2026-08-31

Countries

  • Canada

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 NCT06802003 on ClinicalTrials.gov