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      Feasibility of Measuring Physiological Responses to Breakthrough Infections and COVID-19 Vaccine Using a Wearable Ring Sensor

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      Author(s): , , ,
      Publication date (Electronic):
      Journal: Digital Biomarkers
      Publisher: S. Karger AG
      Keywords: COVID-19, Vaccine, Wearable

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          Abstract

          Continuous monitoring using commercial-grade wearable technology was used to quantify the physiological response to reported COVID-19 infections and vaccinations in five biometric measurements. Larger responses were observed following confirmed COVID-19 infection reported by unvaccinated versus vaccinated individuals. Responses following reported vaccination were smaller in both magnitude and duration compared to infection and mediated by both dose number and age. Our results suggest commercial-grade wearable technology as a potential platform on which to build screening tools for early detection of illness, including COVID-19 breakthrough cases.

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          Wearable sensor data and self-reported symptoms for COVID-19 detection

          Giorgio Quer, Jennifer M Radin, Matteo Gadaleta … (2020)
          Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.
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            Pre-symptomatic detection of COVID-19 from smartwatch data

            Tejaswini Mishra, Meng Wang, Ahmed A. Metwally … (2020)
            Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.
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              Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG

              Hannu Kinnunen, Aleksi Rantanen, Tuomas Kenttä … (2020)
              Article 0 comments Cited 52 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Digit Biomark
                Journal ID (iso-abbrev): Digit Biomark
                Journal ID (hwp): DIB
                Journal ID (publisher-id): DIB
                Title: Digital Biomarkers
                Publisher: S. Karger AG (Basel, Switzerland )
                ISSN (Electronic): 2504-110X
                Publication date Collection: Jan-Dec 2023
                Publication date (Electronic): 29 March 2023
                Volume: 7
                Issue: 1
                Pages: 1-6
                Affiliations
                [1]Ōura Health Ltd, San Francisco, CA, USA
                Author notes
                Correspondence to: Gerald Pho, gerald.pho@ 123456ouraring.com
                Article
                Publisher ID: 528874
                DOI: 10.1159/000528874
                PMC ID: 10062187
                SO-VID: 17b597b9-29ff-4f44-91b7-70197a4f2f1e
                Copyright © © 2023 The Author(s). Published by S. Karger AG, Basel
                License:

                This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) ( http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission.

                History
                Date received : 6 September 2022
                Date accepted : 16 December 2022
                Date: 2023
                Page count
                Figures: 2, Tables: 1, References: 12, Pages: 6
                Funding
                The study was funded by Oura Health Ltd., San Francisco.
                Categories
                Subject: Research Reports – Brief Report

                Keywords: covid-19,vaccine,wearable
                Data availability:
                Keywords: covid-19, vaccine, wearable

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