LifeSense: Transforming Behavioral Assessment of Depression Using Personal Sensing Technology

摘要 抑郁症是常见的，代价高昂的，也是导致残疾的主要原因。评估与以下方面有关的行为和经验 抑郁症倾向于依靠自我报告和基于访谈的方法。环境瞬时评估插页 评估人们的生活，但仍需要被评估者的积极参与。我们建议开发和开发 验证基于移动电话的个人感知系统，以检测依赖传感器的抑郁和相关行为 连续且不引人注意地收集的数据。因为人们倾向于随身携带他们的手机，手机 手机是一个理想的感知平台，因为它可以连续收集个人生活背景下的数据，而不需要 用户方面的持续努力。这样的系统已经被用来检测简单的行为，如活动 识别和睡眠量化，这更接近传感器数据。目标1将为广泛的 与主要抑郁发作症状(MDE；快感缺乏、消极情绪、睡眠)相关的行为指标范围 精神障碍、精神运动活动、疲劳和注意力分散)和相关领域(例如，社会功能， 压力、动力)对有代表性的参与者进行调查。目标2将使用机器组合所有行为目标 学习1)全面评估MDE和症状严重程度，2)确定从非抑郁到 抑郁状态，以及抑郁到非抑郁状态，以及3)预测4周和8周后的MDE和症状严重程度。 目标3将试图理解行为目标和抑郁之间的复杂关系。我们将完成 这是通过招募1200名有代表性的参与者，在6次为期4个月的数据收集中进行的。每个参与者都将 下载可收集各种传感器数据(GPS、加速度计、灯光、蓝牙、手机使用情况等)的软件。 以及一款收集生态瞬时评估(EMA)的应用程序。在每一波之后，我们将为 行为目标和特征的子集(包含含义的原始传感器数据的定义，如转换GPS 数据存入“家”)。然后，每个算法都将在后续的WAVE中得到验证。在5波(1000名参与者)之后， 行为目标和特征的所有标记集将使用机器学习进行组合，以进行检测和预测 抑郁症。这种分层方法从多个级别的数据中提取信息，这最终要多得多 比单纯依赖原始传感器数据更有可能成功。最后一波将用于复制和验证整个 抑郁预测模型。这一传感平台具有重要的科学意义，因为它将提供一种全新的工具 用于获得与抑郁症相关的连续的、客观的行为标记，以及许多其他精神疾病 和医疗疾病。该项目有可能对抑郁症的病因学有新的理解。它是 具有临床意义，因为它将允许连续、毫不费力地评估抑郁症风险人群和 正在进行治疗期间的评估。

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.