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.
UNASSIGNED: DERR1-10.2196/32587.
目的:CovidCollab是一项众包研究,旨在调查确定,监测,并通过远程监测技术了解SARS-CoV-2感染和恢复的分层。此外,我们将评估COVID-19大流行和相关的社会措施对人们行为的影响,身体健康,和心理健康。
方法:参与者将通过MassScience应用程序远程参与研究,以捐赠历史和前瞻性手机数据,健身跟踪可穿戴数据,和定期COVID-19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的时期(即,在任何报告的感染之前和之后),以便与参与者自己的基线进行比较。我们计划在几个方面进行分析,这将涵盖症状学;风险因素;基于机器学习的疾病分类;和康复轨迹,心理健康,和活动。
结果:截至2021年6月,有超过17,000名参与者-主要来自英国-并且正在进行注册。
结论:本文介绍了一项众包研究,该研究将包括远程注册的参与者,以记录整个COVID-19大流行期间的移动健康数据。收集的数据可能有助于研究人员调查各个领域,包括COVID-19进展;大流行期间的心理健康;以及远程,数字登记的参与者。
■DERR1-10.2196/32587。