Stewart Callum, Ranjan Yatharth, Conde Pauline, Rashid Zulqarnain, Sankesara Heet, Bai Xi, Dobson Richard J B, Folarin Amos A
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Institute of Health Informatics, University College London, London, United Kingdom.
JMIR Res Protoc. 2021 Dec 8;10(12):e32587. doi: 10.2196/32587.
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.
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.
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.
As of June 2021, there are over 17,000 participants-largely from the United Kingdom-and enrollment is ongoing.
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.
手机的普及以及可穿戴健身追踪器使用的增加为了解人们的健康和幸福状况提供了一个广泛的窗口。在新冠疫情的背景下，使用远程监测技术来深入了解健康状况具有明显优势。
“新冠合作研究”是一项众包研究，旨在调查通过远程监测技术识别、监测和理解严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染及康复分层的可行性。此外，我们将评估新冠疫情及相关社会措施对人们行为、身体健康和心理健康的影响。
参与者将通过“大众科学”应用程序远程报名参加该研究，以捐赠历史和未来的手机数据、健身追踪可穿戴设备数据以及定期的新冠相关和心理健康相关调查数据。数据收集期将涵盖一个连续时间段（即任何报告感染之前和之后），以便能够与参与者自身的基线进行比较。我们计划在几个领域进行分析，包括症状学；风险因素；基于机器学习的疾病分类；以及康复、心理健康和活动轨迹。
截至2021年6月，有超过17000名参与者——主要来自英国——且报名仍在进行中。
本文介绍了一项众包研究，该研究将包括远程报名的参与者，以在整个新冠疫情期间记录移动健康数据。收集到的数据可能有助于研究人员调查多个领域，包括新冠疫情的发展；疫情期间的心理健康；以及远程数字报名参与者的依从性。
国际注册报告识别号（IRRID）：DERR1-10.2196/32587
