New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research.
New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research.
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DOI：
10.2196/mental.5165
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发表时间：
2016-05-05
影响因子：
5.2
通讯作者：
Onnela JP
中科院分区：
文献类型：
--
作者：
Torous J;Kiang MV;Lorme J;Onnela JP
无论是在临床环境还是在研究试验中，精神病学进展的一个长期障碍是准确可靠地量化疾病表型的持续困难。手机技术与数据科学相结合，有可能为医学提供有关疾病表型的大量附加信息，但现有的大多数智能手机应用程序并不打算用作生物医学研究平台，因此不会生成研究质量的数据。我们的目标不是创建另一个应用程序本身，而是建立一个平台来收集研究质量的智能手机原始传感器和使用模式数据。我们的最终目标是开发统计、数学和计算方法，使我们和其他人能够从智能手机数据中提取生物医学和临床见解。我们报告了 Beiwe 的开发和早期测试，这是一个研究平台，具有研究门户、智能手机应用程序、数据库以及数据建模和分析工具，专为透明、可定制和可重复的生物医学研究用途而设计和开发，特别是用于精神和神经疾病的研究。我们还概述了一项使用该平台针对精神分裂症患者的拟议研究。我们展示了北为平台的被动数据能力及其分析能力的早期成果。智能手机传感器和手机使用模式与适当的统计学习工具相结合，能够在自然环境中捕捉患者生活和经历的疾病的各种社会和行为表现。智能手机的普及使得这种疾病表型的即时量化具有高度可扩展性，并且当集成到透明的研究平台中时，将为研究、发现和患者健康提供巨大的机会。
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.