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RADAR-base: The open source platform for remote assessment using wearable devices and mobile applications

RADAR-base (Remote assessment of Disease and Relapses) is an open source platform to leverage data from wearables and mobile technologies. The main focus of RADAR-base is seamless integration of data streams from various wearables to collect sensor data in real time and store, manage and share the collected data with researchers for retrospective analysis.

RADAR-base provides both passive and active data collection via two applications. Passive data collection using passive remote monitoring technologies (pRMT) applicatoin. It includes real time monitoring of movement, location, audio, calls and texts, and app usage. Passive collection can be done by directly sending data from phone sensors or device sensor data through (pRMT) app or device sensor data via cloud storage to RADAR-base or combination of these options. Active data collection using active Remote Monitoring Technologies (aRMT) includes the use of questionnaires that might ask patients about their mood, medication intake, or the severity of symptoms. All of the collected data can be extracted from the platform in organized and structured formats.

To conduct a remote monitoring study using RADAR-Base, you need to have a deployment of RADAR-base platform either in-house or on cloud, integration of selection of devices you would like to use for your study. Once these are available, you can start enrolling participants with the help of our study management system called ManagementPortal.

If you are interested in knowing more about RADAR-base, please visit our Getting Started page and/or try out a demo of RADAR-base.

Mission

Our mission is to improve people’s quality of life by leveraging clinical value from wearable sensor data and smartphones. To achieve this goal, we created the highly extensible RADAR-base platform that enables study design and set up, active and passive remote data collection, secure data transmission via Wifi and/or Bluetooth and scalable solutions for data storage, management and access. RADAR-base allows study participants to share their health data with clinicians and researchers in a secure way.  

The complete RADAR-base technology stack is available under an Apache 2 open source license. As an active community of developers, researchers and clinicians, we focus on continuously improving data quality, user experience, validation and extending the platform with new features and data sources. A key objective of RADAR-base is to stimulate the mHealth field by building a highly adaptable and scalable platform which can be freely reused.

Vision

A world in which we can make the most out of smartphone and wearables technology to preempt and prevent diseases in healthy people rather than diagnose and treat participants with long term goals of facilitating in-community care.

Story of RADAR-base platform and community

The RADAR-base community emerged from the IMI project RADAR-CNS, where a consortium of clinicians, developers, researchers, patient organizations and EFPIA partners joined forces to transform care by leveraging sensor data from wearable devices like fitness trackers and smartphones. The combination of passively collected physiological data with active self assessment via questionnaires and scheduled cognitive tests allows a comprehensive picture of the participant’s health state. RADAR-CNS is attempting to evaluate the clinical value of sensor data for relapse prediction with the focus on three disorders of the central nervous system (CNS), epilepsy, multiple sclerosis and major depression disorder.

 

Please cite this paper as a reference when using the RADAR-base platform:

Ranjan Y, Rashid Z, Stewart C, Kerz M, Begale M, Verbeeck D, Boettcher S, Dobson R, Folarin A, The Hyve , RADAR-CNS Consortium

RADAR-base: An Open Source mHealth Platform for Collecting, Monitoring and Analyzing data Using Sensors, Wearables, and Mobile Devices

JMIR Preprints. 29/08/2018:11734

DOI: 10.2196/preprints.11734

URL: http://preprints.jmir.org/preprint/11734