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

Callum Stewart; Yatharth Ranjan; Pauline Conde; Zulqarnain Rashid; Heet Sankesara; Xi Bai; Richard J B Dobson; Amos A Folarin

doi:10.2196/32587

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

JMIR Res Protoc. 2021 Dec 8;10(12):e32587. doi: 10.2196/32587.

Authors

Callum Stewart¹, Yatharth Ranjan¹, Pauline Conde¹, Zulqarnain Rashid¹, Heet Sankesara¹, Xi Bai², Richard J B Dobson^{1

2

3

4

5}, Amos A Folarin^{1

2}

Affiliations

¹ Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
² Institute of Health Informatics, University College London, London, United Kingdom.
³ Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust and King's College London, London, United Kingdom.
⁴ Health Data Research UK London, University College London, London, United Kingdom.
⁵ NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, United Kingdom.

PMID: 34784292
PMCID: PMC8658240
DOI: 10.2196/32587

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.

International registered report identifier (irrid): DERR1-10.2196/32587.

Keywords: COVID-19; crowdsourced; data; digital health; feasibility; infectious disease; mental health; mobile health; mobile phone; monitoring; observational; recovery; smartphone; surveillance; wearable; wearable devices.

©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.

Abstract

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