There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development.
View Article and Find Full Text PDFStudy Objectives: We assessed the real-world performance of the ANNE Sleep system against 2 Food and Drug Administration-cleared home sleep testing platforms and the intraindividual night-to-night variability of respiratory event index measured by ANNE Sleep.
Methods: We evaluated the home performance of the ANNE Sleep system compared with 2 Food and Drug Administration-cleared home sleep testing platforms (WatchPAT: n = 29 and Alice NightOne: n = 46) during a synchronous night with unsupervised patient application. Additionally, we evaluated night-to-night variability of respiratory event index and total sleep time using the ANNE Sleep system (n = 30).
Study Objectives: Evaluate per-patient diagnostic performance of a wireless dual-sensor system (ANNE sleep) compared with reference standard polysomnography (PSG) for the diagnosis of moderate and severe obstructive sleep apnea (OSA) with a minimum prespecified threshold of 80% for both sensitivity and specificity.
Methods: A multicenter clinical trial was conducted to evaluate ANNE sleep vs PSG to diagnose moderate and severe OSA in individuals 22 years or older. For each testing approach, apnea-hypopnea index (AHI) was manually scored and averaged by 3 registered sleep technologists blinded to the other system.