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

Abstract

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 Coughy^TM 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 Coughy^TM could be a novel solution for objectively monitoring cough in a clinical setting.

Keywords: Cough; artificial intelligence (AI); asthma exacerbation; objective cough frequency.

Figure 1

Changes in sleep/awake cough counts measured by human and AI. (A) Total cough counts; (B) cough count per hour. Box plots show median (line), 25 and 75 percentiles (box), and range (whiskers). *P<0.05, **P<0.01, ***P<0.001 compared to Day 1 using paired Wilcoxon singed rank test. AI, artificial intelligence.

Figure 2

Changes in the clinical parameters through the study period. (A) Changes in overall symptom score using CS-VAS; (B) changes in frequencies of wake-up, and salbutamol use at sleep time and awake time; (C) changes in degree of activity limitation. Changes in A.M. and P.M. PEFR (D). Box plots show median (line), 25 and 75 percentiles (box), and range (whiskers). *P<0.05, **P<0.01, ***P<0.001 compared to Day 1 using paired Wilcoxon singed rank test. ^†, the degree of activity limitation consists of 0 (not at all), 1 (a little), 2 (moderately), 3 (quite a bit), and 4 (extremely). ^‡, the percent of the best values measured during study period in each subject. CS-VAS, cough symptom visual analogue scale; PEFR, peak expiratory flow rate.

Figure 3

Correlation between cough counts measured by human and AI at sleep time (A) and awake time (B) correlation between awake and sleep cough counts per hour measured by human (C) and AI algorithm (D). AI, artificial intelligence.

Figure 4

Correlation of sleep cough counts measured by AI with overall symptom score using CS-VAS (A), frequencies of wake-up (B), salbutamol use (C) at sleep time, and A.M. PEFR (D), degrees of asthma-related symptoms including cough (E), wheezing (F), dyspnea (G), chest tightness (H) at sleep time, changes in FEV1 (I), and FVC (J). CS-VAS, cough symptom visual analogue scale; AI, artificial intelligence; PEFR, peak expiratory flow rate; FEV1, forced expiratory volume in 1 second; FVC, force vital capacity.

Figure 5

Correlation of awake cough counts measured by AI with overall symptom score using CS-VAS (A), degrees of activity limitation (B), frequency of salbutamol use (C) at awake time, and P.M. PEFR (D), degrees of asthma-related symptoms including cough (E), wheezing (F), dyspnea (G), chest tightness (H) at awake time, changes in FEV1 (I), and FVC (J). CS-VAS, cough symptom visual analogue scale; PEFR, peak expiratory flow rate; FEV1, forced expiratory volume in 1 second; FVC, force vital capacity.