CovidStanfordV1, Main, Exploration, bibRecord, 000383

Pre-symptomatic detection of COVID-19 from smartwatch data.

Identifieur interne : 000383 ( Main/Exploration ); précédent : 000382; suivant : 000384

Pre-symptomatic detection of COVID-19 from smartwatch data.

Auteurs : Tejaswini Mishra [États-Unis] ; Meng Wang [États-Unis] ; Ahmed A. Metwally [États-Unis] ; Gireesh K. Bogu [États-Unis] ; Andrew W. Brooks [États-Unis] ; Amir Bahmani [États-Unis] ; Arash Alavi [États-Unis] ; Alessandra Celli [États-Unis] ; Emily Higgs [États-Unis] ; Orit Dagan-Rosenfeld [États-Unis] ; Bethany Fay [États-Unis] ; Susan Kirkpatrick [États-Unis] ; Ryan Kellogg [États-Unis] ; Michelle Gibson [États-Unis] ; Tao Wang [États-Unis] ; Erika M. Hunting [États-Unis] ; Petra Mamic [États-Unis] ; Ariel B. Ganz [États-Unis] ; Benjamin Rolnik [États-Unis] ; Xiao Li [États-Unis] ; Michael P. Snyder [États-Unis]

Source :

Nature biomedical engineering [ 2157-846X ] ; 2020.

RBID : pubmed:33208926

Descripteurs français

KwdFr :
- Adulte (MeSH), Dispositifs électroniques portables (MeSH), Femelle (MeSH), Humains (MeSH), Maladies asymptomatiques (MeSH), Monitorage physiologique (méthodes), Mâle (MeSH), Pandémies (prévention et contrôle), Études rétrospectives (MeSH).
MESH :
- méthodes : Monitorage physiologique.
- prévention et contrôle : Pandémies.
- Adulte, Dispositifs électroniques portables, Femelle, Humains, Maladies asymptomatiques, Mâle, Études rétrospectives.

English descriptors

KwdEn :
- Adult (MeSH), Asymptomatic Diseases (MeSH), COVID-19 (diagnosis), COVID-19 (prevention & control), Female (MeSH), Humans (MeSH), Male (MeSH), Monitoring, Physiologic (methods), Pandemics (prevention & control), Retrospective Studies (MeSH), SARS-CoV-2 (pathogenicity), Wearable Electronic Devices (MeSH).
MESH :
- diagnosis : COVID-19.
- methods : Monitoring, Physiologic.
- pathogenicity : SARS-CoV-2.
- prevention & control : COVID-19, Pandemics.
- Adult, Asymptomatic Diseases, Female, Humans, Male, Retrospective Studies, Wearable Electronic Devices.

Abstract

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.

DOI: 10.1038/s41551-020-00640-6
PubMed: 33208926

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000153
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Le document en format XML

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<author><name sortKey="Hunting, Erika M" sort="Hunting, Erika M" uniqKey="Hunting E" first="Erika M" last="Hunting">Erika M. Hunting</name>
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<author><name sortKey="Mamic, Petra" sort="Mamic, Petra" uniqKey="Mamic P" first="Petra" last="Mamic">Petra Mamic</name>
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<author><name sortKey="Li, Xiao" sort="Li, Xiao" uniqKey="Li X" first="Xiao" last="Li">Xiao Li</name>
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<author><name sortKey="Snyder, Michael P" sort="Snyder, Michael P" uniqKey="Snyder M" first="Michael P" last="Snyder">Michael P. Snyder</name>
<affiliation wicri:level="2"><nlm:affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.</nlm:affiliation>
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<term>Asymptomatic Diseases (MeSH)</term>
<term>COVID-19 (diagnosis)</term>
<term>COVID-19 (prevention & control)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Male (MeSH)</term>
<term>Monitoring, Physiologic (methods)</term>
<term>Pandemics (prevention & control)</term>
<term>Retrospective Studies (MeSH)</term>
<term>SARS-CoV-2 (pathogenicity)</term>
<term>Wearable Electronic Devices (MeSH)</term>
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<term>Dispositifs électroniques portables (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Maladies asymptomatiques (MeSH)</term>
<term>Monitorage physiologique (méthodes)</term>
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<term>Pandémies (prévention et contrôle)</term>
<term>Études rétrospectives (MeSH)</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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<DateCompleted><Year>2020</Year>
<Month>12</Month>
<Day>18</Day>
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<DateRevised><Year>2021</Year>
<Month>01</Month>
<Day>23</Day>
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<JournalIssue CitedMedium="Internet"><Volume>4</Volume>
<Issue>12</Issue>
<PubDate><Year>2020</Year>
<Month>12</Month>
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<Title>Nature biomedical engineering</Title>
<ISOAbbreviation>Nat Biomed Eng</ISOAbbreviation>
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<ArticleTitle>Pre-symptomatic detection of COVID-19 from smartwatch data.</ArticleTitle>
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<Abstract><AbstractText>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.</AbstractText>
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<ForeName>Tejaswini</ForeName>
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<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
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<Author ValidYN="Y" EqualContrib="Y"><LastName>Wang</LastName>
<ForeName>Meng</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
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<Author ValidYN="Y" EqualContrib="Y"><LastName>Metwally</LastName>
<ForeName>Ahmed A</ForeName>
<Initials>AA</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
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<Author ValidYN="Y" EqualContrib="Y"><LastName>Bogu</LastName>
<ForeName>Gireesh K</ForeName>
<Initials>GK</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
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<ForeName>Andrew W</ForeName>
<Initials>AW</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0002-9237-5349</Identifier>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
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<Author ValidYN="Y" EqualContrib="Y"><LastName>Bahmani</LastName>
<ForeName>Amir</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y" EqualContrib="Y"><LastName>Alavi</LastName>
<ForeName>Arash</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Celli</LastName>
<ForeName>Alessandra</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
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<Author ValidYN="Y"><LastName>Higgs</LastName>
<ForeName>Emily</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Dagan-Rosenfeld</LastName>
<ForeName>Orit</ForeName>
<Initials>O</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Fay</LastName>
<ForeName>Bethany</ForeName>
<Initials>B</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Kirkpatrick</LastName>
<ForeName>Susan</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Kellogg</LastName>
<ForeName>Ryan</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Gibson</LastName>
<ForeName>Michelle</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wang</LastName>
<ForeName>Tao</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hunting</LastName>
<ForeName>Erika M</ForeName>
<Initials>EM</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0002-9078-6582</Identifier>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mamic</LastName>
<ForeName>Petra</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ganz</LastName>
<ForeName>Ariel B</ForeName>
<Initials>AB</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0002-7173-2239</Identifier>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Rolnik</LastName>
<ForeName>Benjamin</ForeName>
<Initials>B</Initials>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Xiao</ForeName>
<Initials>X</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0003-2566-0796</Identifier>
<AffiliationInfo><Affiliation>The Center for RNA Science and Therapeutics, Case Western University, Cleveland, OH, USA. xiao.li9@case.edu.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Snyder</LastName>
<ForeName>Michael P</ForeName>
<Initials>MP</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0003-0784-7987</Identifier>
<AffiliationInfo><Affiliation>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.</Affiliation>
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<Month>11</Month>
<Day>18</Day>
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<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
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<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
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Pre-symptomatic detection of COVID-19 from smartwatch data.

Pre-symptomatic detection of COVID-19 from smartwatch data.

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