ABSTRACT
Background: The potential of continuous cough monitoring is in an early stage, while almost every other clinical symptom has a way to be objectively monitored. Objective(s): Study if continuous cough monitoring is useful for early notice of an onset or worsening of respiratory conditions. Method(s): A free mobile application was used to detect and record cough sounds. Only 0.5s snippets of explosive sounds are sent to the server for AI to analyse. The 3 cases presented were identified within a study in Navarra, Spain. Result(s): Case 1: a 56-year-old was using the app with an average of 600 coughs/d, unknown cause. A trial with gabapentin was started, which within a month resulted in 150 coughs/d. With omeprazole added, coughing reduced to ~50 coughs/d. Case 2: a 70-year-old smoker was using the app with an average 52 coughs/day in over 2 months. She quit smoking and noticed improvements in cough, app showing 12 coughs/d. The next month, smoking relapsed, reaching 34 coughs/d. Data dynamics renewed her motivation to quit. Case 3: a 35-year-old was using the app at night, with an average of 4 coughs/hr (not self-perceived). Suddently, the patient felt general malaise and the app detected 12 coughs/hr (not self-perceived). Next day, she received a diagnosis of uncomplicated COVID-19. Conclusion(s): Cough patterns correlate with clinical progress and perceived improvement, accurately indicate signs of smoking cessation and relapse.