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Intelligent sensing to inform and learn (instil): A scalable and governance-aware platform for universal, smartphone-based digital phenotyping for research and clinical applications

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journal contribution
posted on 01.11.2019, 00:00 authored by Scott BarnettScott Barnett, K Huckvale, H Christensen, Svetha VenkateshSvetha Venkatesh, Kon MouzakisKon Mouzakis, Rajesh VasaRajesh Vasa
In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also 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 we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of 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.

History

Journal

Journal of Medical Internet Research

Volume

21

Issue

11

Season

Theme issue 23019: 20th Anniversary Issue

Article number

e16399

Pagination

1 - 17

Publisher

JMIR Publications

Location

Toronto, Canada

ISSN

1438-8871

eISSN

1438-8871

Language

eng

Publication classification

C1 Refereed article in a scholarly journal