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

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.

Eligibility

Inclusion Criteria:

* Males/females, age ≥ 40, former/current smokers with ≥10 pack-year smoking history
* FEV1/FVC \< 0.7, with 80% \< FEV1 ≤50% (moderate, 'GOLD 2') 50% \< FEV1 ≤ 30% (severe, 'GOLD 3') or FEV1 \< 30% (very severe, 'GOLD 4') COPD
* History of 2 or more exacerbations in the preceding 12 months requiring corticosteroids, antibiotics, or both
* Ability to provide informed consent
* Ability to access internet at least once daily

Exclusion Criteria:

* No existing COPD diagnosis
* Any medical/cognitive/functional condition which renders inability to operate research equipment/devices, and/or to complete daily symptom response

Conditions2

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