LifeSense: Transforming Behavioral Assessment of Depression Using Personal Sensing Technology

摘要 抑郁症很常见，代价高昂，也是导致残疾的主要原因。评估相关行为和经验 抑郁症往往依赖于自我报告和访谈的方法。环境临时评估插入 评估融入人们的生活，但仍然需要被评估者的积极参与。我们建议开发和 验证基于手机的个人传感系统，以检测依赖于传感器的抑郁症和相关行为 持续且不引人注目地收集的数据。因为人们倾向于随身携带手机， 手机是一个理想的传感平台，因为它可以连续收集个人生活中的数据，而无需 用户的持续努力。此类系统已被用于检测简单的行为，例如活动 识别和睡眠量化，更接近传感器数据。目标 1 将为广泛的 与重度抑郁发作症状相关的一系列行为目标（MDE；快感缺乏、消极情绪、睡眠 干扰、精神运动活动、疲劳和注意力不集中）和相关领域（例如社会功能、 压力、动机）在参与者的代表性样本中。目标 2 将使用机器结合所有行为目标 学习 1) 横断面估计 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，以及抑郁到非抑郁状态，3) 预测 4 周和 8 周后的 MDE 和症状严重程度。 目标 3 将寻求理解行为目标与抑郁之间的复杂关系。我们将完成 这是通过在六轮为期 4 个月的数据收集中招募 1200 名代表性参与者来实现的。每位参与者将 下载收集各种传感器数据（GPS、加速度计、光、蓝牙、电话使用情况等）的软件 以及一个收集生态瞬时评估（EMA）的应用程序。在每一波浪潮之后，我们将开发算法 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，例如翻译 GPS 数据到“家”）。每个算法将在后续浪潮中得到验证。经过 5 波（1000 名参与者）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，最终获得更多信息 比仅仅依靠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 抑郁症预测模型。该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 用于获得与抑郁症以及许多其他精神病学相关的连续、客观的行为标记 和医疗疾病。该项目有可能对抑郁症的病因学产生新的认识。它是 具有临床意义，因为它将允许对有抑郁症风险的人群进行持续、轻松的评估 治疗期间持续评估。

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.