JMIR Publications

ABSTRACT

In this viewpoint we describe the architecture and design rationale for a new software platform designed to support the conduct of digital phenotyping research studies that seek to collect passive and active sensor signals from participant’s smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and identify specific considerations relevant to the design of platforms for digital phenotyping. In particular, we describe trade-offs relating to data quality and completeness versus user experience. We summarize distinctive features of the resulting platform, which includes support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We present a case study reflecting a current, real world use of the platform and conclude with learning and recommendations for future development. The development of a universal platform is a key enabler of our research vision for a population-scale international digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.