Digital therapeutic method and system to monitor and assist in self-treating chronic cough

Abstract

A digital therapeutic method for delivering personalized Behavioral Cough Suppression Therapy through an electronic device, such as a smartphone, operates by collecting both objective data (such as cough sounds detected by the device's microphone and analyzed by a processor) and subjective data (self-reported by the patient through the app). By combining these two data streams, the device automatically generates personalized recommendations for cough suppression strategies. The method includes detecting patterns in cough events, such as trends over time, bouts of coughing, or periods of reduced coughing, and adjusting recommendations dynamically. The system also tracks adherence to therapy, guides patients through suppression training sessions, and generates progress reports. It can elicit potential cough triggers and deliver specific advice for managing them. Importantly, the recommendation engine can use machine learning to refine its suggestions based on ongoing data, enabling increasingly tailored and effective support.

Dependencies are inferred from the claim text.