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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

Barnett, Scott, Huckvale, Kit, Christensen, Helen, Venkatesh, Svetha, Mouzakis, Kon and Vasa, Rajesh 2019, Intelligent sensing to inform and learn (instil): A scalable and governance-aware platform for universal, smartphone-based digital phenotyping for research and clinical applications, Journal of Medical Internet Research, vol. 21, no. 11, Theme issue 23019: 20th Anniversary Issue, pp. 1-17, doi: 10.2196/16399.

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Title Intelligent sensing to inform and learn (instil): A scalable and governance-aware platform for universal, smartphone-based digital phenotyping for research and clinical applications
Author(s) Barnett, ScottORCID iD for Barnett, Scott orcid.org/0000-0002-3187-4937
Huckvale, Kit
Christensen, Helen
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Mouzakis, Kon
Vasa, RajeshORCID iD for Vasa, Rajesh orcid.org/0000-0003-4805-1467
Journal name Journal of Medical Internet Research
Volume number 21
Issue number 11
Season Theme issue 23019: 20th Anniversary Issue
Article ID e16399
Start page 1
End page 17
Total pages 17
Publisher JMIR Publications
Place of publication Toronto, Canada
Publication date 2019-11
ISSN 1438-8871
1438-8871
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
eHealth
e-Mental health
mHealth
digital phenotyping
personal sensing
smartphone
iPhone
software development
software framework
technology platform
MENTAL-HEALTH INTERVENTIONS
EFFICACY
METAANALYSIS
PRIVACY
CHALLENGES
DEPRESSION
SEVERITY
SYMPTOMS
APPS
Summary 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.
Language eng
DOI 10.2196/16399
Indigenous content off
Field of Research 08 Information and Computing Sciences
11 Medical and Health Sciences
17 Psychology and Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30132297

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.