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：
10669716
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/10669716
关键词：
Accelerometer Adolescence Adolescent Adult Affect Agitation Android Award Bees Behavior Biological Markers Bipolar Disorder Caregivers Cellular Phone Data Development Diagnostic Digital biomarker Diurnal Rhythm Doctor of Philosophy Early Intervention Early identification Effectiveness Evaluation Exposure to Future Goals Human In Situ Individual Intervention Interview Learning Life Life Expectancy Link Longitudinal Studies Measures Mental Health Mental Health Services Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Meta-Analysis Methods Moods Morbidity - disease rate National Institute of Mental Health Nature Occupations Outcome Participant Patients Periodicity Persons Phenotype Population Psyche structure Psychiatry Psychotic Disorders 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 Validity and Reliability Youth aged career clinically significant common symptom computer science cost digital emerging adult follow-up high risk higher education improved indexing innovation longitudinal analysis meter mobile computing mood symptom mortality new technology 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.

项目摘要双相情感障碍（BD）与显着的死亡率和发病率有关。它通常从青少年或成年早期，这是一个重要的发展时期被追捕。在此期间，经常性情绪发作可能会对年轻人的一种预测情绪症状发作的方法青少年将为干预和减少经常出现的有害影响而创造机会情节。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智能手机收集的数据的“原位对人类表型的瞬间量化” 传感器（加速度计，文本，呼叫，GPS）。数字表型用于识别情绪变化和 BD成年人中继电器的潜在迹象，但尚未应用于青少年。我们将使用beiwe，一个用于iOS和Android手机的数字表型应用，从参与者那里收集数字表型（14-19岁）超过18个月（n = 120; n = 70，用bd [i，ii，其他指定的]，n = 50通常开发）。随访期，参与者将完成双周的情绪评估，并护理人员将每月接受采访，以跟踪情绪/行为的变化。这将允许电话传感器用Beiwe收集的数据与症状变化密切相关。该项目的具体目的是（1）表征青少年BD症状的数字表型，（2）描述数字的差异 BD的表型和通常是发展组的表型，以及（3）开发一个模型来预测情绪前瞻性症状。拟议的研究与未来的所有四个NIMH战略目标一致心理健康研究。该K23奖将为Anna Van Meter，博士提供必要的培训和通过学习分析纵向数据的培训统计和机器学习技术，（2）在以患者为导向的翻译研究中建立专业知识与青年参与者设计和进行纵向研究；（3）学会采用最先进的移动技术使用患者数据个性化评估和干预。完成这些培训进球，范·米特（Van Meter）博士组织了一个杰出的心态团队（医学博士Anil Malhotra，Jukka-Pekka Onnela， DSC，医学博士John Kane，医学博士Christoph Correll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，青年躁郁症以及计算机科学。拟议的研究将是第一个描述青少年中BD的数字表型的研究， BD发作以及重复发作的破坏性影响的人口很大。拟议的K23指导职业奖的完成将支持以患者为导向的创新计划研究，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。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Clinician Perspectives on Using Computational Mental Health Insights From Patients' Social Media Activities: Design and Qualitative Evaluation of a Prototype.

DOI：
10.2196/25455
发表时间：
2021-11-16
期刊：
JMIR mental health
影响因子：
5.2
作者：
Yoo DW;Ernala SK;Saket B;Weir D;Arenare E;Ali AF;Van Meter AR;Birnbaum ML;Abowd GD;De Choudhury M
通讯作者：
De Choudhury M

The Impact of the COVID-19 Pandemic on Adolescents: An Opportunity to Build Resilient Systems.

COVID-19 大流行对青少年的影响：构建弹性系统的机会。

DOI：
10.1177/08901171221140641d
发表时间：
2023
期刊：
American journal of health promotion : AJHP
影响因子：
0
作者：
Nadeem,Erum;RVanMeter,Anna
通讯作者：
RVanMeter,Anna

Pramipexole to Improve Cognition in Bipolar Disorder: A Randomized Controlled Trial.

DOI：
10.1097/jcp.0000000000001407
发表时间：
2021-07-01
期刊：
Journal of clinical psychopharmacology
影响因子：
2.9
作者：
Van Meter AR;Perez-Rodriguez MM;Braga RJ;Shanahan M;Hanna L;Malhotra AK;Burdick KE
通讯作者：
Burdick KE

The stability and persistence of symptoms in childhood-onset ADHD.

儿童期多动症症状的稳定性和持续性。

DOI：
10.1007/s00787-023-02235-3
发表时间：
2024
期刊：
European child & adolescent psychiatry
影响因子：
6.4
作者：
VanMeter,AnnaR;Sibley,MargaretH;Vandana,Pankhuree;Birmaher,Boris;Fristad,MaryA;Horwitz,Sarah;Youngstrom,EricA;Findling,RobertL;Arnold,LEugene
通讯作者：
Arnold,LEugene

Associations of Social Capital with Mental Disorder Prevalence, Severity, and Comorbidity among U.S. Adolescents.