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: 10029386
• 负责人: Nicholas Charles Jacobson
• 金额: $ 57.04万
• 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/10029386
• 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; Devices; 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; base; biomarker-driven; burden of illness; cohort; deep learning; depressive symptoms; digital; disability; heart rate variability; innovation; learning strategy; meetings; microphone; multimodality; novel; personalized medicine; phenomenological models; premature; prevent; sensor; social; treatment planning; treatment response; tv watching; wearable device; wearable sensor technology

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.

摘要