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

• 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
• 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

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.

摘要： 基于言语中的行为和声学模式来对个人进行卫生保健优先级排序的能力将允许有效地使用卫生保健资源。使用源自移动的技术的声学的日常监测来预测情绪状态的能力为情绪和情感状态的实时代理测量提供了基础。识别和监测人类疾病的这些和其他方面的特征是预测结果的基础，为未来及时和减轻干预提供了可能性。技术和移动保健方法非常适合全球卫生界，因为这种方法具有灵活性和适应性;通过适度增加基础设施，可以很容易地扩大接触大量患者的能力。我们使用机器学习策略开发了一种准确的双相情感障碍（BP）患者情绪状态预测模型，并建立了一个过程，该过程包括预处理、特征提取以及对个人使用移动终端长达一年的临床和声学数据进行综合数据分析。结果显示，情绪状态的预测AUC统计值为0.74（躁狂）和0.77（抑郁）。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。BP是理想的研究，因为情绪状态和气质特征的范围很广。本研究旨在1）确定来自黎巴嫩和密歇根州东南部多语言社区的30名BP患者和10名健康对照，使用智能手机记录日常声学和行为数据，收集来自设备的所有传出语音，并从设备记录所有个人数字活动。我们建议研究参与者在黎巴嫩和东南密歇根州，以确定双相情感障碍患者之间的情绪变化的基本声学元素。2)使用静态应用集成计算分析（高斯混合模型和支持向量机）和动态（隐马尔可夫模型）对来自临床、声学和行为信号的分类、维度和衍生特征进行建模;我们将比较居住在美国的来自黎巴嫩的15个BP和来自密歇根东南部的15个BP的数据。2岁，但来自语言和文化与黎巴嫩相当的地理区域，15名美国出生的BP高加索人（来自我们当前的队列）。我们的假设是，有声学的基本要素，与情绪状态，无论文化。其影响是纵向使用移动的技术被动收集个人数据，以建立计算模型，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。这为预测建模提供了基础，可以将其整合到后续的临床干预研究中，以预测和测试特定干预措施对疾病机制的因果影响。临床、计算和技术学科的专业知识组成了实现这些目标的团队。