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      New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices

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      Author(s): 1 , 2 , * , 3 , 1
      Publication date (Electronic):
      Journal: Translational Psychiatry
      Publisher: Nature Publishing Group

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          Abstract

          Mobile and connected devices like smartphones and wearable sensors can facilitate the collection of novel naturalistic and longitudinal data relevant to psychiatry at both the personal and population level. The National Institute of Mental Health's Research Domain Criteria framework offers a useful roadmap to organize, guide and lead new digital phenotyping data towards research discoveries and clinical advances.

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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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            Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health.

            Jukka-Pekka Onnela, Scott L Rauch (2016)
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              Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder

              John Torous, Patrick Staples, Meghan Shanahan … (2015)
              Background Accurate reporting of patient symptoms is critical for diagnosis and therapeutic monitoring in psychiatry. Smartphones offer an accessible, low-cost means to collect patient symptoms in real time and aid in care. Objective To investigate adherence among psychiatric outpatients diagnosed with major depressive disorder in utilizing their personal smartphones to run a custom app to monitor Patient Health Questionnaire-9 (PHQ-9) depression symptoms, as well as to examine the correlation of these scores to traditionally administered (paper-and-pencil) PHQ-9 scores. Methods A total of 13 patients with major depressive disorder, referred by their clinicians, received standard outpatient treatment and, in addition, utilized their personal smartphones to run the study app to monitor their symptoms. Subjects downloaded and used the Mindful Moods app on their personal smartphone to complete up to three survey sessions per day, during which a randomized subset of PHQ-9 symptoms of major depressive disorder were assessed on a Likert scale. The study lasted 29 or 30 days without additional follow-up. Outcome measures included adherence, measured by the percentage of completed survey sessions, and estimates of daily PHQ-9 scores collected from the smartphone app, as well as from the traditionally administered PHQ-9. Results Overall adherence was 77.78% (903/1161) and varied with time of day. PHQ-9 estimates collected from the app strongly correlated (r=.84) with traditionally administered PHQ-9 scores, but app-collected scores were 3.02 (SD 2.25) points higher on average. More subjects reported suicidal ideation using the app than they did on the traditionally administered PHQ-9. Conclusions Patients with major depressive disorder are able to utilize an app on their personal smartphones to self-assess their symptoms of major depressive disorder with high levels of adherence. These app-collected results correlate with the traditionally administered PHQ-9. Scores recorded from the app may potentially be more sensitive and better able to capture suicidality than the traditional PHQ-9.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Transl Psychiatry
                Journal ID (iso-abbrev): Transl Psychiatry
                Title: Translational Psychiatry
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2158-3188
                Publication date (Print): March 2017
                Publication date (Electronic): 07 March 2017
                Publication date PMC-release: 1 March 2017
                Volume: 7
                Issue: 3
                Page: e1053
                Affiliations
                [1 ]Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School , Boston, MA, USA
                [2 ]Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School , Boston, MA, USA
                [3 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, USA
                Author notes
                [* ]Department of Psychiatry, Beth Israel Deaconess Medical Center , 330 Brookline Avenue, Boston, MA 02115, USA. E-mail: jtorous@ 123456bidmc.harvard.edu
                Article
                Publisher Item ID: tp201725
                DOI: 10.1038/tp.2017.25
                PMC ID: 5416670
                PubMed ID: 28267146
                SO-VID: 8b186a55-d755-45ab-a467-abf7700f7d8e
                Copyright © Copyright © 2017 The Author(s)
                License:

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Date received : 10 August 2016
                Date revision received : 30 December 2016
                Date accepted : 24 January 2017
                Categories
                Subject: Perspectives

                ScienceOpen disciplines: Clinical Psychology & Psychiatry
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                ScienceOpen disciplines: Clinical Psychology & Psychiatry

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