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

• 批准号: 10214506
• 负责人: Anna Robinson Van Meter
• 金额: $ 7.33万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214506
• 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; Healthcare; Human; In Situ; Individual; Intervention; Interview; Learning; Life; Link; Longitudinal Studies; Measures; Mental Health; 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; prognostic; programs; prospective; screening; sensor; service utilization; skills; smartphone Application; social; statistical and machine learning; statistical learning

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.

项目摘要 双相情感障碍（BD）与显著的死亡率和发病率相关。它通常开始于青春期或 成年早期，这是一个重要的发展时期，在此期间，高等教育，第一份工作和人际关系 被追捕。在此期间反复出现的情绪发作可能会对年轻人的生活产生毁灭性的影响。 有能力作为成年人获得高质量的生活。一种方法，通过它来预测情绪症状的发作， 青春期将创造一个机会，干预和减少接触的有害影响， 情节。一种新的方法--数字表型--可能使这成为可能。数字表型定义为 使用从智能手机收集的数据进行的“人类表型的实时定量” 传感器（加速度计、短信、通话、GPS）。数字表型分析已被用于识别情绪变化， 在成人BD患者中有潜在的复发迹象，但尚未应用于青少年。我们将使用Beiwe，a 用于iOS和Android手机的数字表型分析应用程序，用于收集参与者的数字表型 (aged 14-19）18个月（N=120; n=70例BD [I，II，其他指明]，n=50例典型发展）。超过 在随访期间，参与者将完成每两周一次的情绪评估， 护理人员将每月接受访谈，以跟踪情绪/行为的变化。这将允许手机传感器 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，（2）描述数字表型的差异， BD和典型发展组的表型，以及（3）开发用于预测情绪的模型 前瞻性的症状拟议的研究与NIMH未来的所有四个战略目标一致 心理健康研究的一部分该K23奖将为安娜货车仪表博士提供必要的培训， 导师（1）通过学习使用纵向数据分析来熟练掌握计算精神病学 统计和机器学习技术，（2）建立以患者为导向的翻译研究的专业知识， 设计和开展一项有青年参与者的纵向研究;（3）学会使用最先进的技术 移动的技术，使用患者数据进行个性化评估和干预。为了完成这些训练 目标，货车米博士组织了一个杰出的导师团队（阿尼尔马尔霍特拉，医学博士，Jukka-Pekka Onnela， DSc，John Kane，MD，Christoph Correll，MD和Deborah Estrin，PhD），具有以患者为导向的专业知识 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 计算机科学这项研究将首次描述青少年BD的数字表型， 人群中的发病风险很大，以及重复发作的破坏性影响。的 完成拟议的K23辅导职业奖将支持一个创新的计划，以病人为导向， 研究，并将提供博士货车米与必要的技能，成为一个独立的调查员 寻求新的技术解决方案，以改善患者的生活质量。