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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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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 |
format | Online Article Text |
id | pubmed-8658240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
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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