The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom.
Vibrent Health, Fairfax, VA, United States.
JMIR Mhealth Uhealth. 2019 Aug 1;7(8):e11734. doi: 10.2196/11734.
With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.
Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.
RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.
General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.
RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.
移动医疗(mHealth)在研究和临床领域有广泛的应用案例,从多个来源安全、高可扩展且可扩展地收集连续的移动健康数据流对开源 mHealth 社区非常感兴趣。欧盟创新药物倡议远程评估疾病和复发-中枢神经系统(RADAR-CNS)计划是一个典范项目,需要支持大规模地收集高分辨率数据;因此,远程评估疾病和复发(RADAR)-基础平台旨在满足这些需求,并为这个新兴领域的新一代 mHealth 项目提供便利。
宽带网络、智能手机普及和可穿戴传感器为从大量参与者收集近实时高分辨率数据集提供了新的可能性。本研究的目的是构建一个满足远程监测计划大规模数据收集需求的平台。关键标准是可扩展性、可扩展性、安全性和隐私性。
RADAR-base 被开发为一个模块化应用程序;后端建立在高度成功的 Confluent/Apache Kafka 框架的基础上,用于流式数据。为了便于扩展和轻松部署,我们使用 Docker 容器来打包平台的组件。RADAR-base 提供了 2 个主要的数据收集移动应用程序,即被动应用程序和主动应用程序。其他第三方应用程序和传感器也可以轻松集成到平台中。还提供了支持数据收集和注册的管理用户界面。
本文介绍了平台组件的一般原则和 RADAR-base 的设计,并展示了目前从 RADAR-CNS 项目中使用的设备中收集的数据类型示例:多发性硬化症、癫痫和抑郁症队列。
RADAR-base 是一个功能齐全的远程数据收集平台,它围绕 Confluent/Apache Kafka 构建,并为有兴趣大规模收集 mHealth 数据集的项目提供现成的组件。