ArticleJMIR mHealth and uHealth2019
RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
106 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology.Advances in experimental medicine and biology · 2021Pooled it
- Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial.Nature medicine · 2024Trial
- Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial.JMIR mHealth and uHealth · 2024Trial
- Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.BMJ health & care informatics · 2026Observational
- A Clinical AI-Based System (MoodMon) for Affective Disorders: Algorithm Development and Validation.JMIR AI · 2026Article
- Evaluating Wearable Devices for Remote Monitoring in Psychosis: Pilot Study Nested Within the CONNECT Cohort Study.JMIR formative research · 2026Article
- Machine Learning Across Heterogeneous Biomedical Data: Representation, Integration, and Deployable Systems.Bioengineering (Basel, Switzerland) · 2026Review
- A framework of digital biomarkers for neurodegenerative diseases.Nature reviews bioengineering · 2026Article
- Ethical guidelines for geoprivacy: a framework for researchers and ethics committees.International journal of health geographics · 2026Article
- Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.Frontiers in digital health · 2026Article
- Full-day sleep pattern analysis in common mental disorders: Leveraging highly discrepant recordings from two consumer tracking devices.PloS one · 2026Article
- Sleep, Steps, and Screens: Between- and within-person effects of digital markers of daily life behaviors on smartphone-based assessments of cognitive functioning in depression.Neuroscience applied · 2026Article
- ADHD remote technology and ADHD transition: predicting and preventing negative outcomes (ART-transition) - an adolescent prospective cohort study protocol.BMC psychiatry · 2025Article
- Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress: Methodological development and longitudinal multicenter evaluation.Behavior research methods · 2025Article
- Remote monitoring of wide-ranging real-world changes in adults following ADHD medication initiation.Translational psychiatry · 2025Article
- Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Challenges and standardisation strategies for sensor-based data collection for digital phenotyping.Communications medicine · 2025Review
- Collection and Analysis of Repeated Speech Samples: Methodological Framework and Example Protocol.JMIR research protocols · 2025Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.