RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices
Overview
Medical Informatics
Authors
Affiliations
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.
Althobiani M, Russell A, Jacob J, Ranjan Y, Ahmad R, Folarin A Front Med (Lausanne). 2025; 12:1361667.
PMID: 40078397 PMC: 11896871. DOI: 10.3389/fmed.2025.1361667.
Sankesara H, Denyer H, Sun S, Deng Q, Ranjan Y, Conde P JMIR Form Res. 2025; 9:e54531.
PMID: 39885373 PMC: 11798566. DOI: 10.2196/54531.
Cross-Platform Ecological Momentary Assessment App (JTrack-EMA+): Development and Usability Study.
Sahandi Far M, Fischer J, Senge S, Rathmakers R, Meissner T, Schneble D J Med Internet Res. 2025; 27:e51689.
PMID: 39874571 PMC: 11815298. DOI: 10.2196/51689.
Ibanez D, Condominas E, Haro J, Vazquez I, Radar-Mdd-Spain , Bailon R Front Psychol. 2024; 15:1436611.
PMID: 39606213 PMC: 11599828. DOI: 10.3389/fpsyg.2024.1436611.
Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study.
Slade C, Benzo R, Washington P J Med Internet Res. 2024; 26:e55694.
PMID: 39556828 PMC: 11632896. DOI: 10.2196/55694.