@article{fdf546fd1a134b8dbe66eac91ee8a17c,
title = "Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)",
abstract = "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.",
keywords = "COVID-19, Crowdsourced, Data, Digital health, Feasibility, Infectious disease, Mental health, Mobile health, Mobile phone, Monitoring, Observational, Recovery, Smartphone, Surveillance, Wearable, Wearable devices",
author = "Callum Stewart and Yatharth Ranjan and Pauline Conde and Zulqarnain Rashid and Heet Sankesara and Xi Bai and Dobson, {Richard J.B.} and Folarin, {Amos A.}",
note = "Funding Information: The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), National Institute for Health Research (NIHR), or Department of Health and Social Care. This study was supported by (1) the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations (EFPIA); it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and the European Society of Cardiology (ESC); (4) the National Institute for Health Research University College London Hospitals Biomedical Research Centre; (5) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; (6) the NIHR Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust; and (7) the research computing facility at King's College London, Rosalind [38] Funding Information: The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), National Institute for Health Research (NIHR), or Department of Health and Social Care. This study was supported by (1) the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union{\textquoteright}s Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations (EFPIA); it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and the European Society of Cardiology (ESC); (4) the National Institute for Health Research University College London Hospitals Biomedical Research Centre; (5) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; (6) the NIHR Applied Research Collaboration South London (NIHR ARC South London) at King{\textquoteright}s College Hospital NHS Foundation Trust; and (7) the research computing facility at King{\textquoteright}s College London, Rosalind [38] Publisher Copyright: {\textcopyright} 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. 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.",
year = "2021",
month = dec,
doi = "10.2196/32587",
language = "English",
volume = "10",
journal = "JMIR research protocols",
issn = "1929-0748",
publisher = "JMIR Publications Inc.",
number = "12",
}