Assessment of Asthma Severity Using Remote Sensors: A Pilot Study.

Abstract

Asthma is a chronic respiratory disease affecting millions globally. Asthma symptoms and exacerbations are due to inflammation and bronchoconstriction and can substantially diminish quality of life, resulting in significant societal and personal economic burden. Trials investigating the potential of smart devices to predict and mitigate asthma exacerbations have previously been conducted. This study used the Google Nest Hub to monitor nighttime respiratory metrics and predict asthma exacerbations. In this prospective observational study, individuals with severe asthma were provided a remote device featuring a microphone and miniature radar sensor. The primary aim was to assess whether sleep data from the device could predict impending exacerbations before patients perceived treatment need. Thirty-eight participants had adequate data for analysis. Participants were predominantly female (80.5%) and 82.9% were classified as having poor asthma control. Algorithms predicting exacerbations 1 and 2 days in advance predicted 18 and 16 exacerbations, respectively. The sensitivity in predicting an exacerbation 1 and 2 days before it occurred was 0.67 (95% CI, 0.46-0.84) and 0.64 (95% CI, 0.38-0.84), respectively; specificity was 0.85 (95% CI, 0.77-0.91) and 0.81 (95% CI, 0.72-0.87), respectively. Correlations for predicted and actual Asthma Control Test scores and for FEV₁ changes were modest. This study demonstrated the feasibility of a common home technology device to predict asthma exacerbations in advance, which may one day allow enable the implementation of measures that might avoid systemic corticosteroids and emergency care. Further large-scale studies are warranted to explore this approach.