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Real-time alerting system for COVID-19 and other stress events using wearable data.
Alavi, Arash; Bogu, Gireesh K; Wang, Meng; Rangan, Ekanath Srihari; Brooks, Andrew W; Wang, Qiwen; Higgs, Emily; Celli, Alessandra; Mishra, Tejaswini; Metwally, Ahmed A; Cha, Kexin; Knowles, Peter; Alavi, Amir A; Bhasin, Rajat; Panchamukhi, Shrinivas; Celis, Diego; Aditya, Tagore; Honkala, Alexander; Rolnik, Benjamin; Hunting, Erika; Dagan-Rosenfeld, Orit; Chauhan, Arshdeep; Li, Jessi W; Bejikian, Caroline; Krishnan, Vandhana; McGuire, Lettie; Li, Xiao; Bahmani, Amir; Snyder, Michael P.
Afiliación
  • Alavi A; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Bogu GK; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Wang M; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Rangan ES; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Brooks AW; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Wang Q; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Higgs E; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Celli A; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Mishra T; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Metwally AA; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Cha K; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Knowles P; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Alavi AA; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Bhasin R; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Panchamukhi S; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Celis D; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Aditya T; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Honkala A; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Rolnik B; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Hunting E; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Dagan-Rosenfeld O; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Chauhan A; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Li JW; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Bejikian C; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Krishnan V; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • McGuire L; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Li X; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Bahmani A; Department of Biochemistry, Case Western University, Cleveland, OH, USA.
  • Snyder MP; Center for RNA Science and Therapeutics, Case Western University, Cleveland, OH, USA.
Nat Med ; 28(1): 175-184, 2022 01.
Article en En | MEDLINE | ID: mdl-34845389
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Ejercicio Físico / Portador Sano / Dispositivos Electrónicos Vestibles / COVID-19 / Frecuencia Cardíaca Tipo de estudio: Diagnostic_studies / Observational_studies / Risk_factors_studies / Screening_studies Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Nat Med Asunto de la revista: BIOLOGIA MOLECULAR / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Ejercicio Físico / Portador Sano / Dispositivos Electrónicos Vestibles / COVID-19 / Frecuencia Cardíaca Tipo de estudio: Diagnostic_studies / Observational_studies / Risk_factors_studies / Screening_studies Límite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Nat Med Asunto de la revista: BIOLOGIA MOLECULAR / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos