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

基本信息

批准号：
9898476
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
项目类别：
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/9898476
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 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 secondary outcome sensor severe mental illness smart watch social tool wearable device wearable sensor technology

项目摘要

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.

项目概要候选人请求支持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和计算方法如何预测复发并创建数字早期精神病的症状表型和临床结果。在拟议的培训计划中，候选人将基于他以前的工程经验，临床信息学和临床精神病学在贝斯以色列女执事执行多学科项目医疗中心。他的培训计划包括以下方面的培训：1）多元纵向分析的统计方法和预测推理 2) 精神分裂症的神经精神评估 3) 纵向临床研究重点关注移动技术的方法论，以及 4) 负责任的研究行为。即使采取适当的护理，早期精神病复发也很常见，并且每次发作都与较高的护理成本、较差的终生结果和疾病的慢性化相关。有必要了解更多与个体患者复发相关的个人因素，以降低风险预测，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（GPS、加速计）、可穿戴设备等智能手表收集生理学、基于智能手机的调查和认知测试，并与适当的统计方法，可以捕获早期的数字生物标记，这里称为数字表型精神病过程可以提供个性化的复发预测并增加人群水平的危险因素。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通过受试者的个人智能手机以经济实惠且可扩展的方式捕获课程精神病作为可穿戴传感器，自动收集症状、行为、认知和行为的自我报告生理学2）并评估数字表型和复发预测模型的准确性。本研究建议通过利用基于智能手机的数字表型分析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 应用程序来捕获纵向数据关于受试者自然环境中的症状、行为、认知和生理学。这些研究将对患有早期精神病的受试者进行 3.5 年，范围在 6 至 12 个月之间。这项研究更广泛的目标是了解个人和个人的系统和流程环境因素，这会导致早期精神病的复发。对计算的理解复发的基础将为更好的疾病学提供信息，允许开发疾病的生物标志物，从而提供更好的治疗效果生物学研究的目标，为精神疾病的个性化干预措施的开发提供信息，以及帮助支持精神分裂症的早期干预。