High-frequency temperature monitoring at home using a wearable device: A case series of early fever detection and antibiotic administration for febrile neutropenia with bacteremia.

We present a case series of three febrile episodes in neutropenic pediatric cancer patients who wore a Food and Drug Administration approved high-frequency temperature monitoring (HFTM) wearable device (WD) at home. The WD detected fever events when temperature monitoring by thermometer did not detect fever or was not feasible to perform. Two of the episodes were associated with bloodstream infections and the WD detected fevers 5 and 12 h prior to fevers detected by thermometer, triggering earlier medical evaluation and more prompt administration of antibiotics.

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression.

Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods.

Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study.

Background: The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student's mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being.

Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study.

Background: Health care workers (HCWs) have been working on the front lines of the COVID-19 pandemic with high risks of viral exposure, infection, and transmission. Standard COVID-19 testing is insufficient to protect HCWs from these risks and prevent the spread of disease. Continuous monitoring of physiological data with wearable sensors, self-monitoring of symptoms, and asymptomatic COVID-19 testing may aid in the early detection of COVID-19 in HCWs and may help reduce further transmission among HCWs, patients, and families.

Machine learning-based cytokine microarray digital immunoassay analysis.

Serial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, the authors develop a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis.