EasyTrack: A scalable and general purpose platform for reliable data collection in mHealth studies
Abstract
Mobile health (mHealth) studies increasingly rely on continuous, in-the-wild data from smartphones and wearable sensors, yet existing platforms often fall short in scalability, data quality assurance, and interoperability, limiting their ability to support reliable longitudinal research under real-world conditions. EasyTrack addresses these challenges by providing a cloud-based, general-purpose platform that supports heterogeneous sensing configurations, automated data quality monitoring, and integration with external data collection systems. The platform is implemented through a modular, layered architecture that enables efficient data ingestion, configurable study management, and real-time data quality inspection. EasyTrack has been deployed in multiple real-world mHealth studies involving diverse participant cohorts and sensor modalities, and cloud-based scalability experiments further demonstrate its capacity to support large-scale deployments. Together, these empirical evaluations show that EasyTrack provides a robust and extensible infrastructure for conducting high-quality longitudinal mHealth research.