Real-time wearable detection of respiratory infections in pediatric transplant households

Consumer-grade wearables can continuously and longitudinally measure health signals such as heart rate (HR), activity, and sleep. While changes in these signals have been implicated in the early detection of infection (e.g., influenza, SARS-CoV-2) and real-time alerting systems have been developed, evidence from prospective deployment in pediatric transplant households remains limited. Here, we study the application of a resting heart rate (RHR)-based real-time alerting algorithm to detect respiratory virus infection in a cohort of solid organ transplant recipients and their household members (n=146) in a Phase IV clinical trial (NCT06161454). Reliable and efficient alerting for infection events may be particularly relevant for this population, enabling isolation, clinical management, and/or treatment. Of 31 antigen-test outcomes, nearly all (29/31, 93.5%) correspond to either influenza, COVID, or other patient-reported symptoms. We describe RHR, HR, activity, and sleep/circadian rhythm features around clinical alarm events, identifying candidate features for further investigation and algorithm improvement.