Personalized Deep Learning Models of Rapid Changes in Major Depressive Disorder Symptoms using Passive Sensor Data from Smartphones and Wearable Devices

摘要 严重抑郁障碍(MDD)非常普遍，是全球疾病负担的主要原因。 MDD与1,000多种不同的症状相关联，具有高度的异质性。大多数MDD患者 症状会在几个小时内发生变化。因此，有必要日益将MDD研究的重点放在 对这些快速症状波动的个性化评估。到目前为止，MDD的个性化模型已经 显示出了希望，但完全依赖于自我报告措施。因此，迫切需要开发个性化的 结合客观信号的MDD模型。被动地从智能手机和 可穿戴传感器可以连续且不引人注意地跟踪与核心相关的行为和生理信号 与MDD相关的障碍，包括精神运动迟缓、睡眠障碍、社交、 行为激活、心率变异性和屏幕时间。初步数据显示，个性化的 人工智能(即，个人加权的深度学习模型)非常适合于创造新的 这些被动指标的个性化数字生物标志物，以及这些生物标志物可以预测快速 MDD症状的变化。该提案将调查开发个性化深度学习的能力 具有全国代表性的120例治疗样本中MDD症状快速变化的模型 使用从智能手机和可穿戴设备被动收集的数据，在90天内寻找患有MDD的成年人 传感器。该建议旨在测试个性化、子类型和基于队列的建模的准确性 技术和发现MDD症状每时每刻变化的个性化数字生物标记。 该项目提出了以下创新之处：(1)在全球范围内首次开展MDD被动传感研究 具有全国代表性的队列；(2)利用深度学习模型帮助发现新的维护 MDD症状变化的因素；以及(3)使用MDD的个性化多模式评估来解决 MDD的异质性。根据NIMH研究领域标准(RDoC)的目标，该项目将 跨多个分析单元研究MDD症状变化，并集成多个系统。这项研究将 为发现MDD症状变化的新的个性化维护模式迈出关键一步 在日常生活中。此外，它将允许使用以下技术开发可扩展的个性化治疗 在MDD症状快速变化之前的时刻提供行为干预。

ABSTRACT Major depressive disorder (MDD) is highly prevalent and the leading cause of global disease burden. Associated with over 1,000 different symptom profiles, MDD is highly heterogeneous. The majority of MDD symptom change occurs across hours. Consequently, there is a need to increasingly focus MDD research on personalized assessment of these rapid symptom fluctuations. To date, personalized models of MDD have shown promise, but relied solely on self-report measures. There is thus a critical need to develop personalized models of MDD that incorporate objective signals. Passively collected information from smartphones and wearable sensors can continuously and unobtrusively track behavioral and physiological signals related to core disturbances associated with MDD, including psychomotor retardation, sleep disturbances, social contact, behavioral activation, heart rate variability, and screen time. Preliminary data suggest that personalized artificial intelligence (i.e., personally weighted deep learning models) are well suited for creating novel personalized digital biomarkers of these passive indicators, and that these biomarkers can predict rapid changes in MDD symptoms. This proposal will investigate the ability to develop personalized deep learning models of rapid changes in MDD symptoms among a nationally representative sample of 120 treatment seeking adults with MDD across 90 days using passively collected data from smartphones and wearable sensors. This proposal aims to test the accuracy of personalized, subtyped, and cohort-based modeling techniques and uncover personalized digital biomarkers of moment-to-moment changes in MDD symptoms. The project proposes the following innovations: it will (1) conduct the first passive-sensing study of MDD in a nationally-representative cohort; (2) utilize deep learning models to aid in the discovery of novel maintenance factors of MDD symptom changes; and (3) use personalized multimodal assessments of MDD to address the heterogeneity in MDD. In line with the aims of the NIMH Research Domain Criteria (RDoC), this project will study MDD symptom changes across multiple units of analysis and integrate multiple systems. This study will provide a critical step towards uncovering novel personalized maintenance patterns of MDD symptom changes in daily life. Further, it will allow for scalable personalized treatments to be developed using technology to deliver behavioral interventions in the moments immediately preceding rapid MDD symptom changes.