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      For what it's worth. Unearthing the values embedded in digital phenotyping for mental health

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          Abstract

          Digital phenotyping for mental health is an emerging trend which uses digital data, derived from mobile applications, wearable technologies and digital sensors, to measure, track and predict the mental health of an individual. Digital phenotyping for mental health is a growing, but as yet underexamined, field. As we will show, the rapid growth of digital phenotyping for mental health raises crucial questions about the values that underpin and are reinforced by this technology, as well as regarding to whom it may become valuable. In this commentary, we explore these questions by focusing on the construction of value across two interrelated domains: user experience and epistemologies on the one hand, and issues of data and ownership on the other. In doing so, we demonstrate the need for a deeper ethical and epistemological engagement with the value assumptions that underpin the promise of digital phenotyping for mental health.

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          Containing the Atom: Sociotechnical Imaginaries and Nuclear Power in the United States and South Korea

          Sheila Jasanoff, Sang Kim (2009)
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            New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research

            John Torous, Mathew V. Kiang, Jeanette Lorme … (2016)
            Background A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health.
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              Google DeepMind and healthcare in an age of algorithms

              Julia E. Powles, Hal Hodson (2017)
              Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.
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                Author and article information

                Contributors
                Rasmus Birk: (View ORCID Profile)
                Anna Lavis: (View ORCID Profile)
                Federica Lucivero: (View ORCID Profile)
                Gabrielle Samuel: (View ORCID Profile)
                Journal
                Title: Big Data & Society
                Abbreviated Title: Big Data & Society
                Publisher: SAGE Publications
                ISSN (Print): 2053-9517
                ISSN (Electronic): 2053-9517
                Publication date Created: July 2021
                Publication date (Electronic): October 04 2021
                Publication date (Print): July 2021
                Volume: 8
                Issue: 2
                Page: 205395172110473
                Affiliations
                [1 ]Department of Communication & Psychology, Aalborg University, Aalborg, Denmark
                [2 ]Institute of Applied Health Research, University of Birmingham, Birmingham, UK
                [3 ]Nuffield Department of Public Health, University of Oxford, Oxford, UK
                [4 ]Department of Global Health & Social Medicine, King’s College London, London, UK
                Article
                DOI: 10.1177/20539517211047319
                SO-VID: 0d997f53-1483-4f19-a96c-2525871dfc76
                Copyright © © 2021
                License:

                https://creativecommons.org/licenses/by-nc/4.0/

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