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Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)

BACKGROUND: The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people’s health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19...

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Detalles Bibliográficos
Autores principales: Stewart, Callum, Ranjan, Yatharth, Conde, Pauline, Rashid, Zulqarnain, Sankesara, Heet, Bai, Xi, Dobson, Richard J B, Folarin, Amos A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8658240/
https://www.ncbi.nlm.nih.gov/pubmed/34784292
http://dx.doi.org/10.2196/32587
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author Stewart, Callum
Ranjan, Yatharth
Conde, Pauline
Rashid, Zulqarnain
Sankesara, Heet
Bai, Xi
Dobson, Richard J B
Folarin, Amos A
author_facet Stewart, Callum
Ranjan, Yatharth
Conde, Pauline
Rashid, Zulqarnain
Sankesara, Heet
Bai, Xi
Dobson, Richard J B
Folarin, Amos A
author_sort Stewart, Callum
collection PubMed
description BACKGROUND: The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people’s health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19 pandemic. OBJECTIVE: Covid Collab is a crowdsourced study that was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of SARS-CoV-2 infection and recovery through remote monitoring technologies. Additionally, we will assess the impacts of the COVID-19 pandemic and associated social measures on people’s behavior, physical health, and mental well-being. METHODS: Participants will remotely enroll in the study through the Mass Science app to donate historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19–related and mental health–related survey data. The data collection period will cover a continuous period (ie, both before and after any reported infections), so that comparisons to a participant’s own baseline can be made. We plan to carry out analyses in several areas, which will cover symptomatology; risk factors; the machine learning–based classification of illness; and trajectories of recovery, mental well-being, and activity. RESULTS: As of June 2021, there are over 17,000 participants—largely from the United Kingdom—and enrollment is ongoing. CONCLUSIONS: This paper introduces a crowdsourced study that will include remotely enrolled participants to record mobile health data throughout the COVID-19 pandemic. The data collected may help researchers investigate a variety of areas, including COVID-19 progression; mental well-being during the pandemic; and the adherence of remote, digitally enrolled participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32587
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spelling pubmed-86582402022-01-05 Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study) Stewart, Callum Ranjan, Yatharth Conde, Pauline Rashid, Zulqarnain Sankesara, Heet Bai, Xi Dobson, Richard J B Folarin, Amos A JMIR Res Protoc Protocol BACKGROUND: The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people’s health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19 pandemic. OBJECTIVE: Covid Collab is a crowdsourced study that was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of SARS-CoV-2 infection and recovery through remote monitoring technologies. Additionally, we will assess the impacts of the COVID-19 pandemic and associated social measures on people’s behavior, physical health, and mental well-being. METHODS: Participants will remotely enroll in the study through the Mass Science app to donate historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19–related and mental health–related survey data. The data collection period will cover a continuous period (ie, both before and after any reported infections), so that comparisons to a participant’s own baseline can be made. We plan to carry out analyses in several areas, which will cover symptomatology; risk factors; the machine learning–based classification of illness; and trajectories of recovery, mental well-being, and activity. RESULTS: As of June 2021, there are over 17,000 participants—largely from the United Kingdom—and enrollment is ongoing. CONCLUSIONS: This paper introduces a crowdsourced study that will include remotely enrolled participants to record mobile health data throughout the COVID-19 pandemic. The data collected may help researchers investigate a variety of areas, including COVID-19 progression; mental well-being during the pandemic; and the adherence of remote, digitally enrolled participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32587 JMIR Publications 2021-12-08 /pmc/articles/PMC8658240/ /pubmed/34784292 http://dx.doi.org/10.2196/32587 Text en ©Callum Stewart, Yatharth Ranjan, Pauline Conde, Zulqarnain Rashid, Heet Sankesara, Xi Bai, Richard J B Dobson, Amos A Folarin. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 08.12.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Protocol
Stewart, Callum
Ranjan, Yatharth
Conde, Pauline
Rashid, Zulqarnain
Sankesara, Heet
Bai, Xi
Dobson, Richard J B
Folarin, Amos A
Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)
title Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)
title_full Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)
title_fullStr Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)
title_full_unstemmed Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)
title_short Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)
title_sort investigating the use of digital health technology to monitor covid-19 and its effects: protocol for an observational study (covid collab study)
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8658240/
https://www.ncbi.nlm.nih.gov/pubmed/34784292
http://dx.doi.org/10.2196/32587
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