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Open AccessJournal ArticleDOI

New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research.

TLDR
The Beiwe platform is reported on, 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.
Abstract
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. [JMIR Ment Health 2016;3(2):e16]

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Journal ArticleDOI

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

TL;DR: A layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health is provided, focused principally on smartphones, but also including studies of wearables, social media, and computers.
Journal ArticleDOI

Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity

TL;DR: Investigation of the detailed network organization of four individuals each scanned 24 times using MRI discovered that the distributed network known as the default network is comprised of two separate networks possessing adjacent regions in eight or more cortical zones.
Journal ArticleDOI

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

TL;DR: Given that digital fingerprints reflect the lived experiences of people in their natural environments, with granular temporal resolution, it might be possible to leverage them to develop precise and temporally dynamic disease phenotypes and markers to diagnose and treat psychiatric and other illnesses.
Journal ArticleDOI

Nonsocial and social cognition in schizophrenia: current evidence and future directions.

TL;DR: The paper reviews the considerable efforts that have been directed to improve cognitive impairments in schizophrenia through novel psychopharmacology, cognitive remediation, social cognitive training, and alternative approaches, and considers areas that are emerging and have the potential to provide future insights.
Journal ArticleDOI

Relapse prediction in schizophrenia through digital phenotyping: a pilot study.

TL;DR: These findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic, therefore reducing patient suffering and reducing the cost of care.
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Journal Article

A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7

TL;DR: The GAD-7 is a valid and efficient tool for screening for GAD and assessing its severity in clinical practice and research.
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