Chronic Obstructive Pulmonary Disease (COPD) COPD
Conditions
Interventions
The MOXBASE1 sensor, developed by IDEE-Research Engineering Maastricht University, the Netherlands, will be tested in the current study aiming to measure breathing frequency, pattern and variability i
Analog Devices, Norwood, MA, USA), bioimpedance and one-channel electrocardiogram (ECG) (ADS1292R, Texas Instruments, USA). It is attached on the participant's upper chest using two electrodes. In add
Sponsors
Universiteit Maastricht
Eligibility
Age
18 Years to 99 Years
Inclusion criteria
Inclusion criteria: To qualify for this study, all participants must meet the following criteria: Aged 18 years or older. Willing and able to wear the wearable sensor as required for the study protocol. Provided written informed consent. Additional inclusion criteria for healthy subjects: Normal pre-bronchodilator lung function test (defined as a FEV1/FVC > 0.7). Absence of respiratory symptoms (including but not limited to dyspnea and cough with or without sputum production). No current or former smoking history. No medical history of asthma. Additional inclusion criteria for patients with COPD, as defined by the international COPD GOLD guideline (2024): Spirometry confirmed diagnosis of airway obstruction defined by a post-bronchodilator FEV1/FVC
Exclusion criteria
Exclusion criteria: Individuals will be excluded from the study if they meet any of the following conditions: Experienced an exacerbation of COPD (ECOPD), as defined by GOLD, within 8 weeks prior to the study start. Have severe comorbidities that could interfere with the study measurements (i.e. significant heart failure, interstitial lung disease, neuromuscular disorders, CVA, asthma, cystic fibrosis, Parkinson's disease, bulbar palsy or vocal cord paralysis). Use mobility aids such as wheelchairs or walkers. Participation in any other study involving investigational products.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The main objective of this study is to evaluate the measurement properties of the in-house developed UM sensor patch for assessing breathing frequency, pattern and variability (Table 1). Signals from the wearable sensor will be extracted and pre-processed, after which they will be analysed to calculate the breathing frequency, pattern and variability . This will be compared with the breathing frequency, pattern and variability as determined by the gold standard, i.e. dynamic flow-based measurements using the Vyntus One CPX. Further details on the measurements and definitions are provided at section 8.3 below. Objective: To determine the reliability and measurement error of the UM wearable sensor for evaluation of breathing frequency, pattern and variability in healthy subjects and patients with COPD Endpoint: ICC and SEM based on breaths per minute obtained from the wearable sensor (index test) and 'the Vyntus One CPX spirometer (gold standard) | — |
Secondary
| Measure | Time frame |
|---|---|
| 1- Objective To explore potential differences between healthy subjects and patients with COPD in terms of reliability and measurement errors of the sensor. Endpoint ICC and SEM based on breaths per minute obtained from the specific sensor techniques in healthy subjects and patients with COPD 2- Objective To perform a sensitivity analysis on which sensor technique (accelerometer, bioimpedance, PPG and ECG embedded in the wearable is most accurate for measuring breathing frequency, pattern, and variability Endpoint ICC and SEM based on breaths per minute obtained from the specific sensor techniques 3- Objective To optimize the location and intensity of the PPG sensor to capture vital signs. Endpoint PPG signals 4- Objective To investigate if body activity (lying, sitting, exercise, speaking) can be determined from the signals from the wearable sensor Endpoint Accelerometer data (x, y, z axes), intensity output (vector magnitude of accelerometer axes), classification of activity (sedentary/dynamic/standing), bioimpedance and ECG signals 5- Objective To investigate whether other vital signs, such as SpO2 and HR can be extracted from the raw signals from the wearable sensor. Endpoint ICC and SEM based on SpO2 and HR obtained from the device and pulse oximeter (gold standard) 6- Objective To determine if dynamic lung volumes can be accurately determined with the wearable sensor. Endpoint ICC and SEM based on dynamic lung volume parameters signals from the device, compared to spirometry parameters (gold standard) as measured by the Vyntus SPIRO PC. 7- Objective To explore the association between breathing frequency, pattern and variability and speech features Endpoint Breaths per minute and SD of breathing frequency for breathing frequency, inspiratory and expiratory time, duty cycle and tidal volume for breathing pattern, coefficient of variation for breathing variability, recorded via the wearable. Speech features including b | — |
Countries
Netherlands
Contacts
Public ContactE.P.M. Smeets
Universiteit Maastricht
Outcome results
None listed