The Digital Phenotype of Bipolar Disorder: Harnessing Technology to Identify Bipolar Mood Symptoms

双相情感障碍的数字表型：利用技术识别双相情感障碍的症状

基本信息

批准号：
10471803
负责人：
Anna Robinson Van Meter
金额：
$ 19.55万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-04 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10471803
关键词：
Accelerometer Adolescence Adolescent Adult Affect Agitation Android Award Bees Behavior Biological Markers Bipolar Disorder Caregivers Cellular Phone Data Development Diagnostic Diurnal Rhythm Doctor of Philosophy Early Intervention Early identification Effectiveness Evaluation Exposure to Future Goals Human In Situ Individual Intervention Interview Learning Life Link Longitudinal Studies Measures Mental Health Mental Health Services Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Methods Moods Morbidity - disease rate National Institute of Mental Health Nature Occupations Outcome Participant Patients Periodicity Persons Phenotype Population Psyche structure Psychiatry Public Health Quality of life Recurrence Relapse Reporting Research Research Design Research Personnel Resources Risk Signal Transduction Sleep Sleep Disorders Specific qualifier value Speech Symptoms Techniques Technology Telephone Testing Text Messaging Time Training Translational Research Youth aged base career clinically significant common symptom computer science cost digital emerging adult follow-up high risk higher education improved indexing innovation meter mobile computing mood symptom mortality novel novel strategies patient oriented patient oriented research predictive modeling pressure prevent prognostication programs prospective screening sensor service utilization skills smartphone Application social statistical and machine learning statistical learning

项目摘要

Project Summary Bipolar disorder (BD) is associated with significant mortality and morbidity. It typically begins in adolescence or early adulthood, an important developmental period during which higher education, first jobs, and relationships are pursued. Recurrent mood episodes during this period can have a devastating impact on a young person's ability to achieve a high quality of life as an adult. A method by which to predict the onset of mood symptoms in adolescence would create an opportunity to intervene and reduce exposure to the harmful effects of recurrent episodes. A new approach – digital phenotyping – may make this possible. Digital phenotyping is defined as the “moment-by-moment quantification of the human phenotype in situ” using data collected from smartphone sensors (accelerometer, texts, calls, GPS). Digital phenotyping has been used to identify mood changes and potential signs of relapse in adults with BD, but has not yet been applied to adolescents. We will use Beiwe, a digital phenotyping application for iOS and Android phones, to collect digital phenotypes from participants (aged 14-19) over 18-months (N=120; n=70 with BD [I, II, Other Specified], n=50 typically-developing). Over the follow-up period, participants will complete biweekly mood assessments, and both participants and caregivers will be interviewed monthly to track changes in mood/behavior. This will allow the phone sensor data collected with Beiwe to be closely linked to symptom changes. The specific aims of this project are (1) to characterize the digital phenotype of BD symptoms in adolescents, (2) to describe differences in the digital phenotypes of the BD and typically developing groups, and (3) to develop a model for predicting mood symptoms prospectively. The proposed study is consistent with all four NIMH strategic objectives for the future of mental health research. This K23 Award will provide Anna Van Meter, PhD with the necessary training and mentorship to (1) gain proficiency in computational psychiatry by learning to analyze longitudinal data using statistical and machine learning techniques, (2) build expertise in patient-oriented translational research by designing and conducting a longitudinal study with youth participants; (3) learn to employ state-of-the-art mobile technology to personalize assessment and intervention using patient data. To accomplish these training goals, Dr. Van Meter has organized an outstanding mentorship team (Anil Malhotra, MD, Jukka-Pekka Onnela, DSc, John Kane, MD, Christoph Correll, MD, and Deborah Estrin, PhD), with expertise in patient-oriented research, technology-based mental health research, computational psychiatry, bipolar disorder in youth, and computer science. The proposed study will be the first to describe the digital phenotype of BD in adolescents, a population at great risk for the onset of BD as well as the damaging effects of repeated episodes. The completion of the proposed K23 Mentored Career Award will support an innovative program of patient-oriented research, and will provide Dr. Van Meter with the skills necessary to become an independent investigator pursuing novel technological solutions to improve patients' quality of life.

项目概要双相情感障碍 (BD) 与显着的死亡率和发病率相关，它通常始于青春期或青少年时期。成年早期，一个重要的发展时期，在此期间接受高等教育、第一份工作和人际关系在此期间反复出现的情绪发作可能会对年轻人产生毁灭性的影响。作为成年人实现高质量生活的能力。一种预测情绪症状发作的方法。青春期将创造一个机会来干预和减少反复出现的有害影响一种新方法——数字表型分析——可能使这成为可能。使用从智能手机收集的数据“对人类表型进行原位实时量化” 传感器（加速度计、短信、电话、GPS）已被用来识别情绪变化和成人双相情感障碍复发的潜在迹象，但尚未应用于青少年。我们将使用 Beiwe，一种。适用于 iOS 和 Android 手机的数字表型应用程序，用于收集参与者的数字表型（14-19 岁）超过 18 个月（N=120；n=70 患有 BD [I、II、其他指定]，n=50 典型发育）。在后续阶段，参与者将完成每两周一次的情绪评估，并且参与者和每月都会对护理人员进行访谈，以跟踪情绪/行为的变化，这将允许手机传感器。与 Beiwe 收集的数据与症状变化密切相关，该项目的具体目标是 (1) 描述青少年 BD 症状的数字表型特征，(2) 描述数字表型的差异 BD 和典型发展群体的表型，以及 (3) 开发预测情绪的模型拟议的研究与 NIMH 未来的所有四个战略目标一致。该 K23 奖将为 Anna Van Meter 博士提供必要的培训和指导 (1) 通过学习使用以下方法分析纵向数据来熟练掌握计算精神病学统计和机器学习技术，(2) 通过以下方式建立以患者为导向的转化研究方面的专业知识与青年参与者一起设计和开展纵向研究；（3）学习采用最先进的技术移动技术使用患者数据进行个性化评估和干预来完成这些培训。为了实现目标，Van Meter 博士组织了一支出色的导师团队（Anil Malhotra，医学博士、Jukka-Pekka Onnela、 DSc（约翰·凯恩医学博士、克里斯托夫·科雷尔医学博士和黛博拉·埃斯特林博士），具有以患者为中心的专业知识研究、基于技术的心理健康研究、计算精神病学、青年双相情感障碍，以及计算机科学提出的研究将是第一个描述青少年双相情感障碍的数字表型的研究。 BD 发病以及反复发作造成破坏性影响的高风险人群。完成拟议的 K23 指导职业奖将支持以患者为中心的创新计划研究，并将为 Van Meter 博士提供成为独立调查员所需的技能追求新颖的技术解决方案以改善患者的生活质量。