The value of continuous cough monitoring: a narrative review

Abstract

Background and Objective: We live in the era of precision health in which parameters of clinical importance are quantified and used to tailor therapies. However, cough is a common and informative sign and symptom which is generally not quantified. Recently, advances in acoustic artificial intelligence (AI) have enabled accurate, passive and privacy preserving continuous cough monitoring (CCM). Analytically and clinically validated CCM provides a reproducible and potentially clinically useful measurement of cough as a physiological signal—quantifying the burden and day-to-day variability, supporting earlier detection of deterioration, offering a sensitive endpoint in clinical trials and even contributing to population syndromic surveillance. The objective of this review is to critically appraise the evidence for such use cases and identify future directions for research in this field and clinical adoption. Methods: In this article, we reviewed the literature and summarised insights gained from CCM across various diseases, and discussed future directions for this emerging field. Key Content and Findings: CCM has already provided novel and important insights into the biology and therapy of specific diseases such as refractory and unexplained chronic cough, chronic obstructive pulmonary disease (COPD), bronchiectasis, congestive heart failure and gastroesophageal reflux. In addition, it has demonstrated aspects of cough as individuals go about their daily lives, such as the inter and intra-subject variability in daily cough frequency, diurnal and episodic patterns of cough, and the correlation between its subjective and objective measurement. Predictions are presented about future research and uses of cough monitoring. Conclusions: AI-enabled CCM is a powerful new tool that is already improving cough research and patient care.