Feasibility of continuous fever monitoring using wearable devices

Benjamin L Smarr,Anoushka Chowdhary,Karena Puldon,Ashley E Mason,Frederick M Hecht,Sarah M Fisher,Kirstin Aschbacher,Adam Rao,Stephan Dilchert

doi:10.1038/s41598-020-78355-6

Benjamin L Smarr, Anoushka Chowdhary + Show 7 more

Open Access

https://doi.org/10.1038/s41598-020-78355-6

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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.

Highlights

Elevated core temperature constitutes an important biomarker for COVID-19 infection; no standards currently exist to monitor fever using wearable peripheral temperature sensors
It is likely that our current reliance on single-point temperature assessment has led to missed case identification, as the COVID-19 pandemic has not abated despite growing use of temperature checks, for example, upon entry to restaurants, stores, and air travel
An example T record (e) with fever-like days identified by exceeding these thresholds before onset of symptom report (e; black dots represent daily min and max above thresholds) has similar changes in heart rate (HR), HR variability (HRV), and respiration rate (RR), to the reported fever event (f by variable, and g,h with overlay, respectively). (f–h) All lines are smoothed by 360 min radius, displaying the same smoothing used to generate median minimum and maximum values for each day

Summary

Introduction

Elevated core temperature constitutes an important biomarker for COVID-19 infection; no standards currently exist to monitor fever using wearable peripheral temperature sensors. Some researchers have used the difficulties with single-point measures to argue that data from wearable devices cannot be used to detect fever[7] This is not surprising, as sensitivity to detect small, but meaningful, changes in body temperature may be limited without contextual information, such as baseline variability in circadian body temperature[8], phase in menstrual cycle[8,9,10], and other temperaturemodulating biological r hythms[8] at the time of measurement. TemPredict is still ongoing, here we present early results from the first 50 subjects with enough data to meet analysis inclusion criteria In these analyses, we demonstrate that fever detection and prediction via wearables is a promising avenue for research focused on improving fever tracking in the COVID-19 pandemic and future pandemics

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