MHealth Monitoring of Acoustic and Behavioral Patterns in Bipolar Disorder Across Cultures

MHealth 监测跨文化双相情感障碍的声学和行为模式

基本信息

批准号：
9340389
负责人：
MELVIN G MCINNIS
金额：
$ 17.26万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-20 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9340389
关键词：
Acoustics Address Adherence Affect Affective American Arabs Behavioral Bipolar Disorder Bipolar I Caring Categories Caucasians Cellular Phone Characteristics Clinical Clinical assessments Communities Community Health Computer Analysis Computer Simulation Computing Methodologies Country Data Data Analyses Databases Depressed mood Development Devices Diagnosis Dimensions Discipline Disease Early Diagnosis Elements Emotions Evaluation Foundations Future Gaussian model Geographic Locations Goals Health Health Technology Healthcare Immigrant Immigration Impairment Individual Intervention Intervention Studies Interview Language Lebanon Locales Machine Learning Manic Measures Medical Mental Depression Metadata Methods Michigan Middle East Modeling Monitor Mood Disorders Moods Multilingualism Outcome Participant Pathologic Patient Care Patient Triage Patients Pattern Phonation Population Probability Process Protocols documentation Proxy Psychiatric therapeutic procedure Recording of previous events Research Infrastructure Resources Safety Secure Severities Signal Transduction Social Functioning Speech Supervision Symptoms Technology Telephone Temperament Testing Time Universities Variant base bipolar patients cognitive function cohort data management design digital flexibility global health handheld mobile device health data human disease learning strategy lexical lexical processing low and middle-income countries mHealth markov model mobile computing predictive modeling programs tool trait

Acoustics Address Adherence Affect Affective American Arabs Behavioral Bipolar Disorder Bipolar I Caring Categories Caucasians Cellular Phone Characteristics Clinical Clinical assessments Communities Community Health Computer Analysis Computer Simulation Computing Methodologies Country Data Data Analyses Databases Depressed mood Development Devices Diagnosis Dimensions Discipline Disease Early Diagnosis Elements Emotions Evaluation Foundations Future Gaussian model Geographic Locations Goals Health Health Technology Healthcare Immigrant Immigration Impairment Individual Intervention Intervention Studies Interview Language Lebanon Locales Machine Learning Manic Measures Medical Mental Depression Metadata Methods Michigan Middle East Modeling Monitor Mood Disorders Moods Multilingualism Outcome Participant Pathologic Patient Care Patient Triage Patients Pattern Phonation Population Probability Process Protocols documentation Proxy Psychiatric therapeutic procedure Recording of previous events Research Infrastructure Resources Safety Secure Severities Signal Transduction Social Functioning Speech Supervision Symptoms Technology Telephone Temperament Testing Time Universities Variant base bipolar patients cognitive function cohort data management design digital flexibility global health handheld mobile device health data human disease learning strategy lexical lexical processing low and middle-income countries mHealth markov model mobile computing predictive modeling programs tool trait

展开

项目摘要

Abstract: The ability to prioritize individuals for health care based on behavioral and acoustic patterns in speech will allow for efficient use of health care resources. The ability to predict mood states using daily monitoring of acoustics derived from mobile technology provides the basis for a real-time proxy measure of moods and affective states. Identification and monitoring of these and other dimensional features of human disease is the base for anticipating outcomes, offering the future possibility of timely and mitigating interventions. Technological and mHealth methods are well suited for the global health community due to the flexibility and adaptability of the approach; the capacity to reach large numbers of patients can be easily amplified with modest increase in infrastructure. We have developed an accurate prediction model for mood states in bipolar (BP) individuals using machine-learning strategies and established a process that involves preprocessing, feature extraction, and an integrated data analysis of clinical and acoustic data gathered from personal use of a mobile device for up to one year. The results show mood states are predicted with an AUC statistic of 0.74 (mania) and 0.77 (depression). We hypothesize that analyses across cultures will identify common features of illness that can be identified using our methods. BP is ideal for study because of the wide range of mood states and temperamental traits. This study aims to 1) ascertain 30 individuals with BP and 10 healthy controls from Lebanon and a multilingual community in SE Michigan, recording daily acoustic and behavioral data using a smart-phone, all outgoing speech from the device is gathered and all personal digital activity is recorded from the device. We propose to study participants in Lebanon and SE Michigan in order to identify the fundamental acoustic elements of mood variation among bipolar patients. 2) apply integrated computational analyses using static (Gaussian Mixture Models and Support Vector Machines) and dynamic (Hidden Markov Models) modeling of categorical, dimensional and derived features from clinical, acoustic, and behavioral signals; we will compare data from the 15 BP from Lebanon and 15 BP from SE Michigan that have been resident in USA >2 years but originate from a geographical region comparable to Lebanon in language and culture, and 15 American born BP Caucasians (from our current cohort). Our hypothesis is that there are fundamental elements of acoustics that associate with mood states regardless of the culture. The impact is the longitudinal use of mobile technology to passively gather personal data to establish computational models that use extensive individual state and trait data to accurately predict mood and health states. This provides a foundation for predictive modeling that can be integrated into subsequent clinical interventional studies to predict and test causal effects of specific interventions on disease mechanisms. Expertise in clinical, computational, and technology disciplines form the team to realize these goals.

抽象的：根据行为和语音中的声学模式优先考虑个人进行卫生保健的能力将有效利用医疗资源。使用从移动技术中获得的声学的每日监控来预测情绪状态的能力为实时代理情绪和情感状态提供了基础。对这些人类疾病的这些和其他维度特征的识别和监测是预期结果的基础，提供了及时和缓解干预措施的未来可能性。由于该方法的灵活性和适应性，技术和MHealth方法非常适合全球卫生社区；通过适度的基础设施增加，可以轻松扩大达到大量患者的能力。我们已经使用机器学习策略为双极（BP）个体中的情绪状态开发了一个准确的预测模型，并建立了一个过程，涉及预处理，功能提取以及对从移动设备的个人使用中收集的临床和声学数据的集成数据分析，最多一年。结果表明，情绪状态的AUC统计量为0.74（躁狂）和0.77（抑郁症）。我们假设跨文化的分析将确定可以使用我们的方法识别的疾病的共同特征。由于情绪状态和气质特征广泛，BP是研究的理想选择。这项研究的目的是1）确定30名来自黎巴嫩的BP和10个健康对照的人，以及在密歇根州SE密歇根州的多语言社区，使用智能手机记录每日声学和行为数据，该设备中的所有传出语音都均已收集，并且所有个人数字活动都是从设备中记录的。我们建议研究黎巴嫩和密歇根州SE的参与者，以确定双极患者情绪变化的基本声学元素。 2）使用静态（高斯混合模型和支持向量机）以及动态（隐藏的马尔可夫模型）模型，对临床，声学和行为信号的分类特征进行集成计算分析；我们将比较来自黎巴嫩的15桶和15个BP的数据，来自SE密歇根州的15个BP居住在美国> 2年，但源自语言和文化中与黎巴嫩相当的地理区域，以及15位美国出生的BP高加索人（来自我们目前的同胞）。我们的假设是，无论文化如何，声学的基本要素都与情绪状态相关。影响是移动技术的纵向使用以被动地收集个人数据，以建立使用广泛的个人状态和特质数据来准确预测情绪和健康状态的计算模型。这为预测建模提供了基础，可以将其整合到随后的临床介入研究中，以预测和测试特定干预措施对疾病机制的因果影响。临床，计算和技术学科方面的专业知识构成了团队实现这些目标的团队。