Abstract
Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Despite recent contributions, the trade-off between privacy, optimization, stability and research-grade data quality is not well met by existing platforms. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital-phenotyping. JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. JTrack is developed and maintained to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes by using JTrack Android-based smartphone application. JTrack also provides an online study management dashboard to monitor data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. Smartphone-based Digital Biomarker data may provide valuable insight into daily-life behaviour in health and disease. As illustrated using sample data, JTrack provides as an easy and reliable open-source solution for collection of such information.
Highlights
Neurological and psychiatric diseases typically present with symptoms that are complex, atypical, fluctuant in disease progression, and display high variability between patients [1]
The time spent with the phone in total, communication and social platform significantly correlated with the passively-recorded estimates of respective phone usage measures (r = 0.38–0.49; all p < 0.001) (Figure 6B)
We developed JTrack as an open-source, smartphone-based platform for digital phenotyping
Summary
Neurological and psychiatric diseases typically present with symptoms that are complex, atypical, fluctuant in disease progression, and display high variability between patients [1]. Current diagnostic and efficacy evaluation methods often rely on in-clinic visits and subjective evaluation by patients, caregivers or clinicians. In-clinic evaluation methods are often costly, time-consuming and limited in their quality and quantity of observations [2]. They are often prone to high inter- and intra-rater variability [3]. Psychiatric and neurological diseases are typically long-term illnesses that cause significant fluctuations in symptoms over time. Remote monitoring of patients in their everyday-life using sensor-based at smart technologies is rapidly evolving and may assist
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