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

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          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.

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          Most cited references8

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          AWARE: Mobile Context Instrumentation Framework

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            Apple's ResearchKit: smart data collection for the smartphone era?

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              Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype

              Background Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient’s recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms. Therefore, more studies where digital data is collected in larger scales are needed to collect scientific evidence on the capacities of these platforms. Most of the existing platforms for digital psychiatry studies are designed as monolithic systems for a certain type of study; publications from these studies focus on their results, rather than the design features of the data collection platform. Inevitably, more tools and platforms will emerge in the near future to fulfill the need for digital data collection for psychiatry. Currently little knowledge is available from existing digital platforms for future data collection platforms to build upon. Objective The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features. Methods We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data. Results The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies. Conclusions The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                August 2019
                01 August 2019
                : 7
                : 8
                : e11734
                Affiliations
                [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
                Author notes
                Corresponding Author: Amos Folarin amos.folarin@ 123456kcl.ac.uk
                Author information
                http://orcid.org/0000-0003-3079-3120
                http://orcid.org/0000-0002-6843-9920
                http://orcid.org/0000-0001-9947-8677
                http://orcid.org/0000-0003-0513-0915
                http://orcid.org/0000-0002-3598-4498
                http://orcid.org/0000-0002-3683-175X
                http://orcid.org/0000-0002-3407-8290
                http://orcid.org/0000-0003-4224-9245
                http://orcid.org/0000-0002-0333-1927
                Article
                v7i8e11734
                10.2196/11734
                6694732
                31373275
                5fc5e2c4-4bad-4cef-bcdb-344b9b587150
                ©Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Pauline Conde, Mark Begale, Denny Verbeeck, Sebastian Boettcher, The Hyve, Richard Dobson, Amos Folarin, The RADAR-CNS Consortium. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 01.08.2019.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/.as well as this copyright and license information must be included.

                History
                : 29 August 2018
                : 12 October 2018
                : 28 November 2018
                : 9 December 2018
                Categories
                Original Paper
                Original Paper

                remote sensing technology,mobile applications,telemedicine,mental health

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