Abstract
BackgroundWith 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.ObjectiveWide-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.MethodsRADAR-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.ResultsGeneral 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.ConclusionsRADAR-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.
Highlights
BackgroundThe opportunity in health care for continuous monitoring of patients has steadily grown in parallel with the widespread availability of smartphones, more capacious mobile networks, and development of new wearable sensors that are able to continuously measure a growing set of physiological and phenomenological parameters
General principles of the platform components and design of Remote Assessment of Disease and Relapse (RADAR)-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-Central Nervous System HDFS (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 mobile health (mHealth) datasets at scale
Summary
The opportunity in health care for continuous monitoring of patients has steadily grown in parallel with the widespread availability of smartphones, more capacious mobile networks, and development of new wearable sensors that are able to continuously measure a growing set of physiological and phenomenological parameters. Many of these devices are currently in the lifestyle or fitness domain; vendors are increasingly developing these devices for medical-grade applications. 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 facilitate a new generation of mHealth projects in this nascent field
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