Disease Insight through Digital Biomarkers Developed by Remotely Collected Wearables and Smartphone Data
Abstract
Digital Biomarkers and remote patient monitoring can provide valuable and timely insights into how a patient is coping with their condition (disease progression, treatment response, etc.), complementing treatment in traditional healthcare settings.Smartphones with embedded and connected sensors have immense potential for improving healthcare through various apps and mHealth (mobile health) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka, to support scalability, extensibility, security, privacy and quality of data. It provides support for study design and set-up, active (eg PROMs) and passive (eg. phone sensors, wearable devices and IoT) remote data collection capabilities with feature generation (eg. behavioural, environmental and physiological markers). The backend enables secure data transmission, and scalable solutions for data storage, management and data access. The platform has successfully collected longitudinal data for various cohorts in a number of disease areas including Multiple Sclerosis, Depression, Epilepsy, ADHD, Alzheimer, Autism and Lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base provides a modern open-source, community-driven solution for remote monitoring, data collection, and digital phenotyping of physical and mental health diseases. Clinicians can use digital biomarkers to augment their decision making for the prevention, personalisation and early intervention of disease.
Keywords
Cite
@article{arxiv.2308.02043,
title = {Disease Insight through Digital Biomarkers Developed by Remotely Collected Wearables and Smartphone Data},
author = {Zulqarnain Rashid and Amos A Folarin and Yatharth Ranjan and Pauline Conde and Heet Sankesara and Yuezhou Zhang and Shaoxiong Sun and Callum Stewart and Petroula Laiou and Richard JB Dobson},
journal= {arXiv preprint arXiv:2308.02043},
year = {2023}
}