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The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review
Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020803/ https://www.ncbi.nlm.nih.gov/pubmed/35461692 http://dx.doi.org/10.1016/S2589-7500(22)00019-X |
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author | Mitratza, Marianna Goodale, Brianna Mae Shagadatova, Aizhan Kovacevic, Vladimir van de Wijgert, Janneke Brakenhoff, Timo B Dobson, Richard Franks, Billy Veen, Duco Folarin, Amos A Stolk, Pieter Grobbee, Diederick E Cronin, Maureen Downward, George S |
author_facet | Mitratza, Marianna Goodale, Brianna Mae Shagadatova, Aizhan Kovacevic, Vladimir van de Wijgert, Janneke Brakenhoff, Timo B Dobson, Richard Franks, Billy Veen, Duco Folarin, Amos A Stolk, Pieter Grobbee, Diederick E Cronin, Maureen Downward, George S |
author_sort | Mitratza, Marianna |
collection | PubMed |
description | Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52–0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence. |
format | Online Article Text |
id | pubmed-9020803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90208032022-04-21 The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review Mitratza, Marianna Goodale, Brianna Mae Shagadatova, Aizhan Kovacevic, Vladimir van de Wijgert, Janneke Brakenhoff, Timo B Dobson, Richard Franks, Billy Veen, Duco Folarin, Amos A Stolk, Pieter Grobbee, Diederick E Cronin, Maureen Downward, George S Lancet Digit Health Review Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52–0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence. The Author(s). Published by Elsevier Ltd. 2022-05 2022-04-20 /pmc/articles/PMC9020803/ /pubmed/35461692 http://dx.doi.org/10.1016/S2589-7500(22)00019-X Text en © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Review Mitratza, Marianna Goodale, Brianna Mae Shagadatova, Aizhan Kovacevic, Vladimir van de Wijgert, Janneke Brakenhoff, Timo B Dobson, Richard Franks, Billy Veen, Duco Folarin, Amos A Stolk, Pieter Grobbee, Diederick E Cronin, Maureen Downward, George S The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review |
title | The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review |
title_full | The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review |
title_fullStr | The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review |
title_full_unstemmed | The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review |
title_short | The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review |
title_sort | performance of wearable sensors in the detection of sars-cov-2 infection: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020803/ https://www.ncbi.nlm.nih.gov/pubmed/35461692 http://dx.doi.org/10.1016/S2589-7500(22)00019-X |
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