Digital Phenotyping for Computational Models of Relapse Prediction in Early Course Psychosis

早期精神病复发预测计算模型的数字表型分析

• 批准号: 10133145
• 负责人: John Torous
• 金额: $ 19.1万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10133145
• 关键词: Accelerometer; Address; Anxiety; Award; Awareness; Back; Behavior; Behavioral; Biological Markers; Caregiver Burden; Caring; Cellular Phone; Chronic; Chronic Disease; Clinical; Clinical Informatics; Clinical Research; Cognition; Cognitive; Complex; Computer Models; Computing Methodologies; Data; Data Science; Detection; Development; Devices; Disease; Early Intervention; Effectiveness; Emergency department visit; Engineering; Ensure; Environment; Environmental Risk Factor; Event; Feedback; Goals; Health Sciences; Health Services Accessibility; Hospitalization; Human; Impaired cognition; In Situ; Individual; Interview; Israel; Learning; Logistic Regressions; Machine Learning; Measurement; Measures; Medical center; Medicine; Mental disorders; Mentors; Methods; Modeling; Moods; Nature; Outcome; Ownership; Participant; Patient Self-Report; Pharmaceutical Preparations; Phenotype; Physical activity; Physiological; Physiology; Pilot Projects; Population; Predictive Value; Process; Productivity; Prospective Studies; Psychiatry; Psychoses; Psychotic Disorders; Quality of life; Relapse; Reproducibility; Research; Research Domain Criteria; Research Methodology; Research Personnel; Risk; Risk Factors; Running; Schizophrenia; Science; Severity of illness; Sleep; Statistical Methods; Structure; Surveys; Symptoms; System; Technology; Time; Training; Training Programs; Treatment Cost; Variant; Work; base; biological research; biomarker development; care costs; care systems; clinical heterogeneity; clinical phenotype; clinically relevant; cognitive testing; computational basis; cost effective; data modeling; design; digital; disease classification; economic cost; evidence base; experience; fitbit; functional disability; functional outcomes; improved; in vivo; individual patient; longitudinal analysis; longitudinal course; mHealth; machine learning method; medical specialties; mobile computing; multidisciplinary; neural circuit; neuropsychiatry; neurotoxic; open source; personalized intervention; predictive modeling; prevent; primary outcome; relapse prediction; relapse risk; responsible research conduct; risk prediction; secondary outcome; sensor; severe mental illness; smart watch; social; tool; wearable device; wearable sensor technology

Project Summary The candidate requests support for a four-year program of training and research to better understand how smartphone based digital phenotyping and computational methods can predict relapse and create digital phenotypes of symptoms and clinical outcomes in early course psychosis. In the proposed training plan, the candidate will build upon his previous experiences in engineering, clinical informatics, and clinical psychiatry to perform a multidisciplinary project at Beth Israel Deaconess Medical Center. His training plan includes training in: 1) statistical methods for multivariate longitudinal analysis and predictive inference 2) the neuropsychiatric assessment of schizophrenia 3) longitudinal clinical research methodology with a focus on mobile technologies, and 4) the responsible conduct of research. Even with appropriate care, relapse is common in early course psychosis and each episode is associated higher costs of care, poorer lifetime outcomes, and chronicity of the disease. There is a need to learn more about the personal factors associated with relapse for individual patients in order to improve risk predictions, ensure appropriate early interventions, and support coordinated specialty care services for schizophrenia. This study proposes that smartphones sensors eg (GPS, accelerometer), wearable devices like smartwatches collecting physiology, and smartphone based surveys and cognitive tests, when combined with appropriate statistical methods, can capture digital biomarkers, refereed to here as digital phenotypes, of early course psychosis that can offer personalized relapse prediction and augment population level risk factors. This candidate's research plan seeks to: 1) propose digital phenotypes and relapse models of early course psychosis captured in an affordable and scalable manner from subject's personal smartphones as well as a wearable sensor in order to automatically collect self-report of symptoms, behaviors, cognition, and physiology 2) and evaluate the accuracy of digital phenotypes and the relapse prediction models. This study proposes to address this hypothesis by utilizing smartphone based digital phenotyping methods, primarily through running the Beiwe app on subjects' own smartphones, to capture longitudinal data on symptoms, behaviors, cognition, and physiology across subjects' natural environments. These studies will be performed across 3.5 years in subjects with early course psychosis and range between 6 to 12 months. The broader aim of this research is to understand the systems and processes, both personal and environmental, which contribute to relapse in early course psychosis. An understanding of the computational basis of relapse will inform better nosology, allow development of biomarkers of illness that may offer better targets for biological research, inform development of personalized interventions for psychotic illnesses, and help support early interventions for schizophrenia.

项目摘要 候选人要求支持一个为期四年的培训和研究计划，以更好地了解 基于智能手机的数字表型分析和计算方法如何预测复发并创建数字 早期病程精神病的症状表型和临床结果。 在拟议的培训计划中，候选人将建立在他以前的工程经验， 临床信息学和临床精神病学，在贝斯以色列女执事进行多学科项目 医学中心他的培训计划包括：1）多元纵向分析的统计方法 2）精神分裂症的神经精神评估3）纵向临床研究 以移动的技术为重点的方法; 4）负责任地进行研究。 即使有适当的护理，复发在早期精神病中也很常见，每次发作都是 相关的更高的护理费用，更差的终身结局和疾病的慢性化。有必要 了解更多与个别患者复发相关的个人因素，以提高风险 预测，确保适当的早期干预，并支持协调的专业护理服务， 精神分裂症这项研究提出，智能手机传感器，如（GPS，加速度计），可穿戴设备，如 智能手表收集生理数据，以及基于智能手机的调查和认知测试， 适当的统计方法，可以捕获数字生物标志物，这里称为数字表型， 可以提供个性化的复发预测和增加人口水平的风险因素。 该候选人的研究计划旨在：1）提出早期癌症的数字表型和复发模型 也可以从受试者的个人智能手机以负担得起的和可扩展的方式捕获课程精神病 作为可穿戴传感器，以自动收集症状、行为、认知的自我报告， 生理学2）并评估数字表型和复发预测模型的准确性。 本研究提出利用智能手机的数字表型来解决这一假设 方法，主要是通过在受试者自己的智能手机上运行Beiwe应用程序， 症状、行为、认知和生理学的研究。这些研究将 在早期精神病受试者中进行3.5年，范围为6至12个月。 这项研究的更广泛的目标是了解系统和过程，无论是个人的， 环境，这有助于早期精神病的复发。理解计算 复发的基础将为更好的疾病分类学提供信息，允许开发疾病的生物标志物， 生物学研究的目标，为精神病的个性化干预提供信息， 有助于支持精神分裂症的早期干预。