WearME system to monitor COPD severity and lung function

Effectiveness of WearME System for FEV1/FVC, Respiration and Activity Biomarkers in COPD Patients

This project will test whether the WearME wearable can accurately track breathing and lung function in adults with COPD by comparing its readings to standard lung tests.

Quick facts

• Study type: Observational
• Enrollment: 128 (estimated)
• Ages: 21 Years to 100 Years
• Sex: All
• Sponsor: LASARRUS Clinic and Research Center (Industry-sponsored)
• Locations: 2 sites (Baltimore, Maryland and 1 other locations)
• Trial ID: NCT06751537 on ClinicalTrials.gov

What this trial studies

This observational project will enroll about 128 adults with COPD at Johns Hopkins and TidalHealth sites to compare measurements from the WearME wearable with standard spirometry, capnography, and other clinical assessments. Participants will be stratified by COPD severity using GOLD criteria and will undergo concurrent spirometry while wearing the device to validate metrics such as respiratory rate, FEV1, FVC, and I:E ratio. The same hardware supports both Basic and Pro app functionality, allowing investigators to analyze multiple algorithms from a single data collection. The collaboration with NHLBI and the Johns Hopkins Clinical Research Network aims to include underserved, rural, and ethnically diverse patients in the Mid‑Atlantic to evaluate real-world performance.

Who should consider this trial

Good fit: Adults aged 21–100 with a documented COPD diagnosis per GOLD criteria who can consent in English or Spanish and can attend visits at the participating Mid‑Atlantic sites are ideal candidates.

Not a fit: People without COPD, those needing immediate acute care, or individuals unable or unwilling to wear the device or travel to study sites are unlikely to benefit from this validation.

Why it matters

Potential benefit: If successful, WearME could enable continuous remote detection of worsening lung function and earlier treatment of COPD exacerbations, which may reduce hospital readmissions.

How similar studies have performed: Previous pilot work with wearable respiratory monitors has shown promising ability to track breathing patterns and flag deterioration, but results have been mixed and these devices have not yet supplanted standard lung function tests.

Eligibility criteria

Inclusion Criteria:

* Adult participants aged 21-100 years, of any sex, and of any race/ethnicity, who are able to understand and sign consent documents in English or Spanish.
* Diagnosed with COPD as per the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria \[5\] , with the following severity levels (confirmed via spirometry performed after consent):

  * Mild: FEV1/FVC \< 0.7 and FEV1 ≥ 80%
  * Moderate: FEV1/FVC \< 0.7 and 50% ≤ FEV1 \< 80%
  * Severe: FEV1/FVC \< 0.7 and 30% ≤ FEV1 \< 50%
  * Very Severe: FEV1/FVC \< 0.7 and FEV1 \< 30% or FEV1 \< 50%, plus chronic respiratory failure.

Note: Participants will be consented and enrolled based on prior diagnosis of COPD from medical records within the past year to confirm eligibility and stratify COPD severity levels. Spirometry will be performed during concurrent data collection with the WearME device for comparison. Screening failures are expected and will be recorded and managed appropriately.

* Current, former, or never smoker; any amount of time between quitting and study enrollment is allowed).
* Ability to understand and willingness to sign consent documents.
* Able to participate in study procedures as required.

Exclusion Criteria:

* Participants with known skin allergies or conditions that may interfere with device application or signal transmission.
* Currently receiving invasive mechanical ventilation.

Where this trial is running

Baltimore, Maryland and 1 other locations

• Johns Hopkins — Baltimore, Maryland, United States (Recruiting)
• TidalHealth — Salisbury, Maryland, United States (Recruiting)

Study contacts

• Study coordinator: Lloyd E Emokpae, PhD.

Conditions Chronic Obstructive Pulmonary Disease; lung function; respiratory rate; FEV1; FVC; I:E ratio; Pulmonary Devices; Machine Learning