A smartphone-based application for cough counting in patients with acute asthma exacerbation

Ji Su Shim, Byung Keun Kim, Sae Hoon Kim, Jae Woo Kwon, Kyung Min Ahn, Sung Yoon Kang, Han Ki Park, Heung Woo Park, Min Suk Yang, Min Hye Kim, Sang Min Lee

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone application. In this prospective study, this AI-based cough algorithm was applied among real-world patients with an acute exacerbation of asthma. Methods: Patients with an acute asthma exacerbation recorded their cough sounds for 7 days (2 consecutive hours during awake time and 5 consecutive hours during sleep) using CoughyTM smartphone application. During the study period, subjects received systemic corticosteroids and bronchodilator to control asthma. Coughs collected by application were counted by both the AI algorithm and two human experts. Subjects also provided self-measured peak expiratory flow rate (PEFR) and completed other outcome assessments [e.g., cough symptom visual analogue scale (CS-VAS), awake frequency, salbutamol use] to investigate the correlation between cough and other parameters. Results: A total of 1,417.6 h of cough recordings were obtained from 24 asthmatics (median age =39 years). Cough counts by AI were strongly correlated with manual cough counts during sleep time (rho =0.908, P<0.001) and awake time (rho =0.847, P<0.001). Sleep time cough counts were moderately to strongly correlated with CS-VAS (rho =0.339, P<0.001), the frequency of waking up (rho =0.462, P<0.001), and salbutamol use at night (rho =0.243, P<0.001). Weak-to-moderate correlations were found between awake time cough counts and CS-VAS (rho =0.313, P<0.001), the degree of activity limitation (rho =0.169, P=0.005), and salbutamol use at awake time (rho =0.276, P<0.001). Neither awake time nor sleep time cough counts were significantly correlated with PEFR. Conclusions: The strong correlation between cough counts using the AI-based algorithm and human experts, and other indicators of patient health status provides evidence of the validity of this AI algorithm for use in asthma patients experiencing an acute exacerbation. Study findings suggest that CoughyTM could be a novel solution for objectively monitoring cough in a clinical setting.

Original languageEnglish
Pages (from-to)4053-4065
Number of pages13
JournalJournal of Thoracic Disease
Issue number7
StatePublished - Jul 2023

Bibliographical note

Publisher Copyright:
© Journal of Thoracic Disease. All rights reserved.


  • Cough
  • artificial intelligence (AI)
  • asthma exacerbation
  • objective cough frequency


Dive into the research topics of 'A smartphone-based application for cough counting in patients with acute asthma exacerbation'. Together they form a unique fingerprint.

Cite this