Using mobile phones for social and behavioral sensing of mood disorder patients

描述（由申请人提供）：情绪障碍是全球发病率和死亡率的主要原因之一，并对数百万人的生活质量和生产力产生深远影响。在临床环境和药物试验的背景下，一个长期阻碍进展的根本困难是准确测量情绪障碍患者的情绪状态、行为和社会功能。手机已经成为现代生活中不可或缺的一部分，我们之前的工作已经表明，它们可以用来收集大规模的行为和社交网络数据。以现有的开源软件为基础，我们将开发一个智能手机应用程序，作为一个数字自我报告工具，同时，以一种不引人注目的方式，收集受试者的行为和交流模式的被动数据。两个队列，一组精神病门诊病人和一组非精神病对照组，将在他们自己的手机上安装应用程序，这使我们能够在两年的时间内收集四个纵向数据流。首先，我们将收集主动数据，包括受试者对智能手机应用程序提出的有关其情绪和健康状况的问题的回答。其次，我们将利用内置加速度计和GPS设备收集被动移动和位置数据。第三，我们将跟踪受试者如何使用智能手机上的其他应用程序。第四，我们将使用匿名的电话和短信日志来了解受试者的电话介导的社会网络的结构和动态。本提案的主要目标是开发新的分析工具和技术，能够整合这四种数据流，以创建一套新的行为指标。这些指标，验证了门诊队列使用季度临床评估，将提供低成本，高度可扩展的工具监测社会

DESCRIPTION (provided by applicant): Mood disorders are among the leading causes of morbidity and mortality worldwide, and have a profound impact on quality of life and productivity for millions of people. A longstanding barrier to progress, both in the context of clinical setting and drug trials has been the fundamental difficulty of accurately measuring mood states, behaviors, and social functioning of individuals affected by mood disorders. Mobile phones have become an integral part of modern life, and our prior work has shown that they can be used to collect behavioral and social network data at very large scales. Using existing open-source software as a foundation, we will develop a smartphone application that functions as a digital self-report instrument and simultaneously, in an unobtrusive manner, collects passive data on both the behavior and communication patterns of subjects. Two cohorts, a group of psychiatric outpatients and a non-psychiatric control group, will install the application on their own phones, which enables us to collect four longitudinal streams of data over a two-year period. First, we will collect active data consisting of subjects' responses to questions, presented by the smartphone application, about their mood and well-being. Second, we will utilize built-in accelerometer and GPS devices to collect passive mobility and location data. Third, we will track how subjects use other applications on their smartphones. Fourth, we will employ anonymized call and text messaging logs to learn about the structure and dynamics of the phone-mediated social network of subjects. The main objective of this proposal is to develop new analytical tools and technology capable of integrating these four streams of data in order to create a set of novel behavioral metrics. These metrics, validated for the outpatient cohort using quarterly clinical evaluations, will provide low- cost, highly scalable tools for monitoring social functioning and behavioral patterns of participants. Such tools could profoundly improve clinical management of psychiatric illnesses, and expedite the development of new efficacious treatments. The project is innovative in the way it combines longitudinal active and passive data streams, collected unobtrusively on the smartphone, to study behavioral changes caused by mood disorders. The resulting smartphone software platform and the requisite data analytic tools will be openly shared with the scientific community, and subsequent studies will be able to leverage this platform at no cost. The PI has a strong track record of innovative, high impact multidisciplinary work and has published scientific articles in journals such as Science, PNAS, and JAMA; his work has been covered in Nature, Science, BBC News, and the New York Times, among others.