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. Attached Files Name Description MIMEType Size Downloads 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, Scott orcid.org/0000-0002-3187-4937 Huckvale, Kit Christensen, Helen Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Mouzakis, Kon 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 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Open Access Collection A2I2 (Applied Artificial Intelligence Institute) Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Thu, 21 Nov 2019, 14:20:33 EST Fri, 22 Nov 2019, 04:04:05 EST Sat, 28 Dec 2019, 01:31:45 EST Sat, 03 Oct 2020, 06:02:07 EST Mon, 05 Oct 2020, 10:34:17 EST Mon, 05 Oct 2020, 10:38:08 EST Mon, 05 Oct 2020, 10:38:53 EST Mon, 05 Oct 2020, 11:20:53 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 55 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 21 Nov 2019, 14:20:33 EST

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.