Description

Asthma and COPD respectively affect millions of people in the US. Chronic lower respiratory diseases represented the fourth leading cause of death in the country before the pandemic. For these and other pulmonary diseases like cystic fibrosis (CF), monitoring disease remotely but objectively could lead to marked improvements in disease control, quality of life, and overall prognosis. However, current disease monitoring and management often rely on subjective symptom report, and objective pulmonary function tests (PFTs) are often only done a handful of times a year at subspecialty referral centers. The primary hypothesis for this study is that smartphone-based sensing and machine learning (ML) approaches can advance pulmonary telemedicine by enabling comprehensive pulmonary disease evaluation with high accuracy, reliability, and adaptability. The investigators further hypothesize that AWARE can accurately help identify different lung diseases, estimate lung function, and detect changes associated with exacerbations. In Aim 1, investigators will develop and improve a smartphone sensing approach as an accurate and reliable aide in airway disease diagnosis. Investigators will recruit a sample of healthy individuals and individuals with asthma, COPD, CF, and other airway diseases, to assess whether AWARE can accurately classify subjects in their disease groups. In Aim 2, investigators will improve the ML approach to estimate lung function accurately and adaptively, including traditional PFT indices from spirometry and impulse oscillometry. And in Aim 3, investigators will develop deep learning techniques to identify changes in airway conditions associated disease exacerbations, by performing AWARE during stable disease and acute exacerbations. For these aims, investigators will recruit a cohort of pediatric and adult subjects with a wide range of demographic and anthropometric characteristics, to have adequate representation of various airway diseases, a broad range of lung function, and the ability to obtain measurements during acute disease exacerbations. The study protocol will include study questionnaires, anthropometry, body composition, and three sets of PFTs: spirometry, oscillometry, and AWARE. A subgroup of subjects will additionally perform AWARE at home for up to two weeks, allowing investigators to evaluate supervised vs unsupervised and in-clinic vs. at-home measurements. Similarly, a subgroup of subjects will perform AWARE dual testing (i.e., with both study smartphones and their own smartphone) to evaluate repeatability using diverse equipment and software platforms.