Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform

Zulqarnain Rashid; Amos Folarin; Yuezhou Zhang; Yatharth Ranjan; Pauline Conde; Heet Sankesara; Shaoxiong Sun; Callum Stewart; Petroula Laiou; Richard Dobson

doi:10.2196/51259

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform

Zulqarnain Rashid, Amos Folarin, Yuezhou Zhang, Yatharth Ranjan, Pauline Conde, Heet Sankesara, Shaoxiong Sun, Callum Stewart, Petroula Laiou, Richard Dobson

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient’s condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. Methods: 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 setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. Results: The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. Conclusions: RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Original language	English
Article number	e51259
Journal	JMIR Mental Health
Volume	11
DOIs	https://doi.org/10.2196/51259
Publication status	Published - 23 Oct 2024

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2196/51259

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title = "Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform",

abstract = "Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient{\textquoteright}s condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. Methods: 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{\textquoteright}s Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. Results: The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. Conclusions: RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.",

author = "Zulqarnain Rashid and Amos Folarin and Yuezhou Zhang and Yatharth Ranjan and Pauline Conde and Heet Sankesara and Shaoxiong Sun and Callum Stewart and Petroula Laiou and Richard Dobson",

note = "Publisher Copyright: {\textcopyright} Zulqarnain Rashid, Amos A Folarin, Yuezhou Zhang, Yatharth Ranjan, Pauline Conde, Heet Sankesara, Shaoxiong Sun, Callum Stewart, Petroula Laiou, Richard J B Dobson. Originally published in JMIR Mental Health (https://mental.jmir.org), 23.10.2024.",

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month = oct,

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doi = "10.2196/51259",

language = "English",

volume = "11",

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T1 - Digital Phenotyping of Mental and Physical Conditions

T2 - Remote Monitoring of Patients Through RADAR-Base Platform

AU - Rashid, Zulqarnain

AU - Folarin, Amos

AU - Zhang, Yuezhou

AU - Ranjan, Yatharth

AU - Conde, Pauline

AU - Sankesara, Heet

AU - Sun, Shaoxiong

AU - Stewart, Callum

AU - Laiou, Petroula

AU - Dobson, Richard

N1 - Publisher Copyright: © Zulqarnain Rashid, Amos A Folarin, Yuezhou Zhang, Yatharth Ranjan, Pauline Conde, Heet Sankesara, Shaoxiong Sun, Callum Stewart, Petroula Laiou, Richard J B Dobson. Originally published in JMIR Mental Health (https://mental.jmir.org), 23.10.2024.

PY - 2024/10/23

Y1 - 2024/10/23

N2 - Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient’s condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. Methods: 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 setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. Results: The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. Conclusions: RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

AB - Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient’s condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. Methods: 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 setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. Results: The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. Conclusions: RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

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JO - JMIR Mental Health

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Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform

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