Real-time alerting system for COVID-19 and other stress events using wearable data

Abstract

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Deploying wearable sensors for pandemic mitigation

“…We first investigated the baseline scenario in which detection algorithms that currently exist are made publicly available for device users to download and use (Figure 2) 10 . Upon notification of potential presymptomatic or asymptomatic infection, users are prompted to seek a confirmatory lab-based test, self-isolate while awaiting the result (nominally, for 2 days), and quarantine until recovery if positive.…”

“…The social and economic harm caused by solely promoting uptake or adherence without improvements to detection specificity would likely undermine public confidence in and compliance with a wearable-based pandemic mitigation strategy 22 . Alavi et al found that many false positives were due to the detection algorithm identifying lifestyle- driven changes in resting heart rate (e.g., after intense exercise or alcohol consumption); accounting for these factors using more advanced algorithms may be one way to target improved detection specificity 10 .…”

“…First, we considered a baseline scenario in which wearable device users can download an application with currently available detection algorithms 10 . Then, we investigated the impact of technology and behavioural parameters: detection sensitivity and specificity; uptake, defined as the proportion of the population that has downloaded the application and uses their wearable device often enough; and adherence, defined as the proportion of users who comply with recommended next steps after a positive notification.…”

“…Wearable sensors have already been established as tools to detect deviations from users’ physiological baselines 7 . Recent findings suggest that wearable sensors may also be able to detect infections caused by respiratory pathogens, including SARS- CoV-2, before or absent symptoms 8–10 . Alavi et al , for example, developed an algorithm that analyzes patterns in smartwatch- captured overnight resting heart rate and provides real-time alerts of potential presymptomatic and asymptomatic SARS- CoV-2 infection 10 .…”

“…Recent findings suggest that wearable sensors may also be able to detect infections caused by respiratory pathogens, including SARS- CoV-2, before or absent symptoms 8–10 . Alavi et al , for example, developed an algorithm that analyzes patterns in smartwatch- captured overnight resting heart rate and provides real-time alerts of potential presymptomatic and asymptomatic SARS- CoV-2 infection 10 . If such algorithms were widely deployed, wearable sensors could be promising tools for outbreak mitigation, enabling FTTI systems to more rapidly identify (and subsequently isolate) infectious individuals, particularly those without symptoms.…”