LifeSense: Transforming Behavioral Assessment of Depression Using Personal Sensing Technology

摘要 抑郁症是常见的，昂贵的，是残疾的主要原因。行为和经验的评估 抑郁症倾向于依赖自我报告和基于访谈的方法。环境瞬时评估插件 这是一个非常重要的问题，因为它有助于将评估纳入人们的生活，但仍然需要被评估者的积极参与。我们建议发展和 验证基于移动的电话的个人感测系统以检测依赖于传感器的抑郁和相关行为 这些数据是连续收集的，不引人注目。因为人们倾向于随身携带手机，所以移动的 手机是一个理想的传感平台，因为它可以在个人生活的背景下连续收集数据， 用户的持续努力。这样的系统已经被用来检测简单的行为，比如活动 识别和睡眠量化，更接近传感器数据。Aim 1将为广泛的 与重性抑郁发作（MDE;快感缺乏、消极情绪、睡眠）症状相关的一系列行为目标 干扰、精神活动、疲劳和注意力下降）和相关领域（例如，社会功能， 压力、动机）。目标2将使用机器联合收割机组合所有行为目标 学习1）横截面估计MDE和症状严重程度，2）识别从非抑郁到抑郁的转变 抑郁状态和抑郁到非抑郁状态，以及3）预测4周和8周后的MDE和症状严重程度。 目标3将试图了解行为目标和抑郁症之间的复杂关系。要全面完成 这是通过招募1200名有代表性的参与者，在6个4个月的数据收集波。每位参与者将 下载可收集各种传感器数据（GPS、加速度计、光线、蓝牙、手机使用情况等）的软件 以及一个收集生态瞬时评估（EMA）的应用程序。在每一波之后，我们将开发算法， 行为目标和特征的子集（包含意义的原始传感器数据的定义，如翻译GPS 数据进入“家”）。然后，每个算法将在随后的wave中进行验证。经过5波（1000名参与者）， 一组行为目标和特征的所有标记将使用机器学习来检测和预测 萧条这种分层的方法从多个级别的数据中提取信息， 而不是仅仅依靠原始传感器数据。最后一波将用于复制和验证整个 抑郁预测模型这个传感平台具有科学意义，因为它将提供一种全新的工具， 用于获得与抑郁症以及许多其他精神疾病相关的连续、客观的行为标记， 和医学疾病。本研究对抑郁症的病因学有潜在的新认识。是 具有临床意义，因为它将允许对处于抑郁症风险中的人群进行连续、轻松的评估， 治疗期间进行评估。

Abstract Depression is common, costly, and a leading cause of disability. Assessment of behavior and experience related to depression has tended to rely on self-report and interview-based methods. Environmental momentary assessment inserts assessment into people's lives, but still requires active engagement by those being evaluated. We propose to develop and validate a mobile phone-based personal sensing system to detect depression and related behaviors that relies on sensor data that are collected continuously and unobtrusively. Because people tend to keep their phones with them, the mobile phone is an ideal sensing platform, as it can continuously collect data in the context of the individual's life with no ongoing effort on the part of the user. Such systems are already being used to detect simple behaviors, such as activity recognition and sleep quantification, which are more proximal to the sensor data. Aim 1 will develop markers for a broad range of behavioral targets related to symptoms of major depressive episode (MDE; anhedonia, negative mood, sleep disruption, psychomotor activity, fatigue, and diminished concentration) and related domains (e.g. social functioning, stress, motivation) across a representative sample of participants. Aim 2 will combine all behavioral targets using machine learning to 1) estimate MDE and symptom severity cross-sectionally, 2) identify transition from non-depressed to depressed states, and depressed to non-depressed states, and 3) predict MDE and symptom severity 4 and 8 weeks out. Aim 3 will seek to understand the complex relationships among behavioral targets and depression. We will accomplish this by enrolling 1200 representative participants, in six 4-month waves of data collection. Each participant will download software that collects a wide variety of sensor data (GPS, accelerometry, light, Bluetooth, phone usage, etc.) and an app that collects ecological momentary assessments (EMA). Following each wave we will develop algorithms for a subset of behavioral targets and features (a definition of raw sensor data that incorporates meaning, like translating GPS data into “home”). Each algorithm will then be validated in the subsequent wave. After 5 waves (1000 participants), the set of all markers of behavioral targets and features will be combined using machine learning to detect and predict depression. This hierarchical approach extracts information from data at multiple levels, which ultimately is far more likely to succeed than relying solely on raw sensor data. The final wave will serve to replicate and validate the entire depression prediction model. This sensing platform is scientifically significant, as it will provide a fundamentally new tool for obtaining continuous, objective markers of behavior that are relevant to depression, as well as many other psychiatric and medical disorders. This project has the potential develop new understandings into the etiology of depression. It is clinically significant, as it will allow for continuous, effortless assessment of populations at risk for depression and ongoing evaluation during treatment.