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JMIR Mhealth Uhealth. 2019 Aug 1;7(8):e11734. doi: 10.2196/11734.

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices.

Author information

1
The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, London, United Kingdom.
2
Vibrent Health, Fairfax, VA, United States.
3
Janssen Pharmaceutica NV, Turnhoutseweg, Beerse, Belgium.
4
Epilepsy Center, Department of Neurosurgery, University of Hospital Freiburg, Freiburg, Germany.
5
The Hyve, MJ Utrecht, Netherlands.
6
Institute of Health Informatics, University College London, London, United Kingdom.
7
The RADAR-CNS Consortium, London, United Kingdom.

Abstract

BACKGROUND:

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.

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

mental health; mobile applications; remote sensing technology; telemedicine

PMID:
31373275
DOI:
10.2196/11734
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