Feasibility of continuous fever monitoring using wearable devices

Abstract

Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

The hopes and hazards of using personal health technologies in the diagnosis and prognosis of infections

“…21 Individual sensor data also has shown promise to identify pre-symptomatic infection, 39 including for individuals who tested positive for COVID-19, 23 which if confirmed would be especially valuable. Researchers have also looked at identifying the need for hos pitalisation looking at symp toms alone, 24 how elevations in peripheral temperature correlate with self-reported fever, 22 and how symptoms and physiological changes are more severe for COVID-19 positive individuals than for influenza positive individuals. 26 As new metrics are added to sensors, substantially greater research is needed to better understand wearable changes for different infections, asymptomatic infections, non-infectious insults, and tracking long-term consequences, such as with post-acute sequelae of SARS-CoV-2 infection.…”

Early detection of SARS‐CoV‐2 and other infections in solid organ transplant recipients and household members using wearable devices

“…with wearable sensor data (resting HR, sleep, and activity) resulted in greater ability to discriminate between COVID-19 and non-COVID-19 infection compared to symptoms alone (AUC 0.80 vs. 0.71, P < 0.01) [30]. The recent TemPredict study, using Oura wearable ring data from 65,000 subjects, examined 50 COVID-19 confirmed cases and showed the ability to detect early signs of fever in 93% of the cases on average 3 days before symptoms manifested [31]. Fitbit watch data on 2745 SARS-CoV-2 confirmed subjects showed that even with self-reported symptoms alone, an AUC of 0.82 AE 0.017 was observed for the prediction of the hospitalization requirement [32].…”

Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables

“…Fever is a hallmark symptom of infection that is characterized by marked elevation in basal body temperature. While some consumer wearables are capable of measuring temperature [55], many are primarily intended as fitness related devices equipped with optical heart rate sensors and accelerometers. Despite the lack of direct temperature monitoring, body temperature is mediated by and affects cardiac rhythm and function [24].…”