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

可扩展性 计算机科学 数据科学 电话 公民科学 应用商店 移动电话 大数据 相关性(法律) 人机交互 万维网 数据挖掘 数据库 电信 生物 哲学 法学 植物 语言学 政治学
作者
John Torous,Mathew V. Kiang,Jeanette M. Lorme,Jukka‐Pekka Onnela
出处
期刊:JMIR mental health [JMIR Publications Inc.]
卷期号:3 (2): e16-e16 被引量:445
标识
DOI:10.2196/mental.5165
摘要

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.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.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.We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities.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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