At-home validation of remote breathing monitoring: A proof-of-concept for long-term care of respiratory patients using a non-contact, radar-based biomotion sensor

Purpose Long-term monitoring of respiratory rate (RR) is promising for the management of chronic conditions. Research interest is particularly high in chronic respiratory diseases (CRDs), especially for predicting acute exacerbations of COPD (AECOPD). The aim of the present study was to evaluate the long-term validity of a recent non-contact biomotion sensor in the home environment of CRD patients with domiciliary ventilator support, focusing on patient acceptance and usability of this device, as well as RR fluctuations related to AECOPD. Patients and methods In this prospective proof-of-concept study, 19 patients requiring non-invasive ventilation (NIV) and seven patients requiring invasive mechanical ventilation (IMV) were provided with the non-contact device for six and one month, respectively. Main indication for NIV therapy was COPD. Real-world validation of the device was performed by comparing nocturnal RR values between the non-contact system and both types of ventilators. The acceptance and operability of the biomotion sensor were evaluated using a questionnaire. COPD exacerbations that occurred during the study period were assessed for possible RR fluctuations preceding these events. Results Mean absolute error (MAE) of median RR between the NIV device and the non-contact system, based on 2326 nights, was 0.78 (SD: 1.96) breaths per minute (brpm). MAE between the IMV device and the non-contact system was 0.12 brpm (SD: 0.52) for 215 nights. The non-contact device was accepted by the patients and proved to be easy to use. In some of the overall 13 cases of AECOPD, RR time courses showed variations of increased nocturnal respiratory activity a few days before the occurrence of such events. Conclusion The present non-contact system is suitable and well accepted for valid long-term monitoring of nocturnal RR in the patient's home environment. This finding may serve as a starting point for larger studies, e.g., to develop robust AECOPD prediction rules.