PDF(Pubmed)
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.
UNASSIGNED: DERR1-10.2196/32587.
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.
摘要：
背景：手机的普及和可穿戴健身追踪器的日益普及为人们的健康和福祉提供了一个广泛的窗口。使用远程监测技术来深入了解健康有明显的优势，特别是在COVID-19大流行的阴影下。
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。
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。
