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

NCT06802003 | Recruiting

• 1 other identifier: 2025-10606
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief 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

COPD; AE COPD

Keywords

acute exacerbation of COPD (AECOPD); chronic obstructive pulmonary disease (COPD); artificial intelligence/machine learning (AI/ML); remote patient monitoring (RPM); biometric wearable device; oscillometry

Outcome Measures

Primary Outcomes (25)

• Respiration

  Respiratory rate (RR) and respiratory rate variability (RRV) measured with wearable devices

  Daily/nightly for 12 months

• Cardiovascular

  Heart rate (HR) and HR variability (HRV) measured with wearable devices

  Daily/nightly for 12 months

• Oxygen level

  Blood oxygen saturation (SpO2) measured with wearable devices

  Daily/nightly for 12 months

• Step count

  Parameter measured with wearable devices, related to activity.

  Daily/nightly for 12 months

• Sleep duration

  Parameter measured with wearable devices.

  Nightly for 12 months

• Rapid eye movement (REM) sleep

  Parameter measured with wearable devices.

  Nightly for 12 months

• Deep sleep

  Parameter measured with wearable devices.

  Nightly for 12 months

• Body temperature

  Peripheral body temperature measured with wearable devices

  Nightly for 12 months

• Resistance at 5 Hz (R5)

  Parameter measured at 5 Hz frequency with handheld oscillometry, related to lung mechanics/function.

  Daily for 12 months

• Reactance at 5 Hz (X5)

  Parameter measured at 5 Hz frequency with handheld oscillometry, related to lung mechanics/function.

  Daily for 12 months

• Intra-breath difference between expiratory and inspiratory reactance (ΔXrs)

  Parameter measured with handheld oscillometry, related to lung mechanics/function.

  Daily for 12 months

• Tidal volume (Vt)

  Parameter measured with handheld oscillometry, related to lung mechanics/function.

  Daily for 12 months

• Respiratory flows

  Inspiratory and expiratory flows measured with handheld oscillometry, related to lung mechanics/function.

  Daily for 12 months

• Respiratory rate (RR)

  Parameter measured with handheld oscillometry.

  Daily for 12 months

• Minute ventilation (Ve)

  Parameter measured with handheld oscillometry, related to lung mechanics/function.

  Daily for 12 months

• Heart rate (HR) prior oscillometry test

  Parameter measured with handheld oscillometry before performing oscillometry test.

  Daily for 12 months

• Blood oxygen saturation (SpO2) prior oscillometry test

  Parameter measured with handheld oscillometry before performing oscillometry test.

  Daily for 12 months

• Daily symptom questionnaire

  • Visual analog scale (VAS) scores for dyspnea, sputum volume, sputum purulence, cough, wheeze, and fatigue, scaled 0-10. Higher scores indicate worse symptoms.

  Daily for 12 months

• Weekly exacerbation questionnaire

  • Self report on any exacerbation(s) which occurred in the preceding week and their date(s), whether/how the exacerbation was treated, and in what treatment setting.

  Weekly for 12 months

• Calories

  Parameter measured with wearable devices, related to activity.

  Daily/nightly for 12 months

• Metabolic equivalents

  Parameter measured with wearable devices, related to activity.

  Daily/nightly for 12 months

• Movement intensity

  Parameter measured with wearable devices, related to activity.

  Daily/nightly for 12 months

• Sleep efficiency

  Parameter measured with wearable devices.

  Nightly for 12 months

• Sleep oncet

  Parameter measured with wearable devices.

  Nightly for 12 months

• Sleep disturbance

  Parameter measured with wearable devices.

  Nightly for 12 months

Secondary Outcomes (3)

• COPD Assessment Test (CAT)

  Once (baseline visit)

• 6-Minute Walk Test (6MWT)

  Once (baseline visit)

• System Usability Scale (SUS)

  Through study completion, 1 year.

Interventions

Biometric wearable and handheld devices DEVICE

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).

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with COPD with a documented history of frequent exacerbations. While there is no specific method to estimate sample sizes for machine learning-based clinical research, based on two prior COPD studies measuring respiratory rate (RR) differences between stable-state and peak exacerbation phases, it was estimated that detecting a 4 breaths/min difference in RR (effect size 0.74) requires 17 prospectively collected exacerbation events, with alpha set to 0.05, power set to 0.8, and two-tailed analysis. Detecting a more subtle difference (i.e. 2 breaths/min; effect size 0.36) would require 63 events. Assuming each participant experiences two exacerbations annually, 32 participants would meet these requirements. To account for the long observation period, the potential for non-events, and a 25% attrition rate, up to 50 participants will be recruited for this one-year study.

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• McGill University Health Centre/Research Institute of the McGill University Health Centre: lead
• Restech Srl: collaborator

Study Sites (1)

McGill University Health Centre

Montreal, Quebec, H4A 3J1, Canada

RECRUITING

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Bryan A. Ross, MD, MSc (Physiol), MSc (Epi)

CONTACT

(514) 843-1465; bryan.ross@mcgill.ca

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinician-Scientist (RI-MUHC), Respirologist (MUHC), Assistant Professor (McGill University)

Study Record Dates

• First Submitted: January 25, 2025
• First Posted: January 30, 2025
• Study Start: May 22, 2025
• Primary Completion (Estimated): May 31, 2026
• Study Completion (Estimated): August 31, 2026
• Last Updated: May 28, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)