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

使用来自智能手机和可穿戴设备的被动传感器数据建立重度抑郁症症状快速变化的个性化深度学习模型

• 批准号: 10629273
• 负责人: Nicholas Charles Jacobson
• 金额: $ 52.03万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-06 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629273
• 关键词: Address; Adult; Affect; Arousal; Artificial Intelligence; Behavior; Behavior Therapy; Behavioral; Big Data; Biological Markers; Cellular Phone; Cessation of life; Collection; Data; Depressed mood; Deterioration; Digital biomarker; Ecological momentary assessment; Enrollment; Exposure to; Fostering; Heterogeneity; Hour; Individual; Intervention; Life; Light; Location; Maintenance; Major Depressive Disorder; Measures; Modeling; Moods; Motor; National Institute of Mental Health; Negative Valence; Participant; Patient Self-Report; Patients; Pattern; Performance; Persons; Photoplethysmography; Physiological; Physiology; Population; Positive Valence; Process; Research; Research Domain Criteria; Sampling; Signal Transduction; Sleep; Sleep disturbances; Subgroup; Surveys; Symptoms; System; Techniques; Technology; Testing; Time; Variant; Wrist; actigraphy; analog; biomarker driven; burden of illness; cohort; deep learning; deep learning model; depressive symptoms; disability; heart rate variability; information gathering; innovation; learning strategy; meetings; microphone; multimodality; novel; personalized medicine; phenomenological models; premature; prevent; sensor; smartphone based device; social; social contact; treatment planning; treatment response; tv watching; wearable device; wearable sensor technology; wrist worn device

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.

抽象的 重度抑郁症（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，具有高度异质性。大多数MDD 症状变化会在几个小时内发生。因此，MDD 研究需要越来越多地关注 对这些快速症状波动的个性化评估。迄今为止，MDD 的个性化模型已经 表现出了希望，但仅依赖于自我报告措施。因此迫切需要开发个性化的 包含客观信号的 MDD 模型。从智能手机被动收集信息 可穿戴传感器可以连续、不引人注目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、社交接触、 行为激活、心率变异性和屏幕时间。初步数据表明，个性化 人工智能（即个人加权深度学习模型）非常适合创造新颖的 这些被动指标的个性化数字生物标记，并且这些生物标记可以快速预测 MDD 症状的变化。该提案将调查开发个性化深度学习的能力 在 120 种治疗的全国代表性样本中建立 MDD 症状快速变化模型 使用从智能手机和可穿戴设备被动收集的数据来寻找 90 天内患有 MDD 的成年人 传感器。该提案旨在测试个性化、子类型和基于队列的建模的准确性 技术并发现 MDD 症状即时变化的个性化数字生物标记。 该项目提出了以下创新： (1) 在一个实验室中进行首次 MDD 被动传感研究 具有全国代表性的群体； (2)利用深度学习模型来帮助发现新的维护方式 MDD症状变化的因素； (3) 使用 MDD 的个性化多模式评估来解决 MDD 中的异质性。根据 NIMH 研究领域标准 (RDoC) 的目标，该项目将 研究跨多个分析单元的 MDD 症状变化并整合多个系统。这项研究将 为发现 MDD 症状变化的新颖的个性化维护模式提供了关键的一步 在日常生活中。此外，它将允许使用技术开发可扩展的个性化治疗 在 MDD 症状快速变化之前的瞬间提供行为干预。

项目成果

Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.

• DOI: 10.2196/29412
• 发表时间: 2021-09-28
• 期刊: Journal of medical Internet research
• 影响因子: 7.4
• 作者: Teepe GW;Da Fonseca A;Kleim B;Jacobson NC;Salamanca Sanabria A;Tudor Car L;Fleisch E;Kowatsch T
• 通讯作者: Kowatsch T

Impact of online mental health screening tools on help-seeking, care receipt, and suicidal ideation and suicidal intent: Evidence from internet search behavior in a large U.S. cohort.

• DOI: 10.1016/j.jpsychires.2020.11.010
• 发表时间: 2022-01
• 期刊: Journal of psychiatric research
• 影响因子: 4.8
• 作者: Jacobson NC;Yom-Tov E;Lekkas D;Heinz M;Liu L;Barr PJ
• 通讯作者: Barr PJ

Integration of discrete and global structures of affect across three large samples: Specific emotions within-persons and global affect between-persons.

• DOI: 10.1037/emo0001022
• 发表时间: 2023-06
• 期刊: Emotion (Washington, D.C.)
• 作者: Jacobson NC;Evey KJ;Wright AGC;Newman MG
• 通讯作者: Newman MG

The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study.

• DOI: 10.2196/28003
• 发表时间: 2022-01-19
• 期刊: JMIR formative research
• 影响因子: 2.2
• 作者: Chan WW;Fitzsimmons-Craft EE;Smith AC;Firebaugh ML;Fowler LA;DePietro B;Topooco N;Wilfley DE;Taylor CB;Jacobson NC
• 通讯作者: Jacobson NC

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.

• DOI: 10.1038/s41398-023-02669-y
• 发表时间: 2023-12-09
• 期刊: TRANSLATIONAL PSYCHIATRY
• 影响因子: 6.8
• 作者: Price, George D.;Heinz, Michael V.;Song, Seo Ho;Nemesure, Matthew D.;Jacobson, Nicholas C.
• 通讯作者: Jacobson, Nicholas C.