A New Paradigm for Illness Monitoring and Relapse Prevention in Schizophrenia

精神分裂症疾病监测和复发预防的新范式

基本信息

批准号：
9119612
负责人：
Dror Ben-Zeev
金额：
$ 38.83万
依托单位：
DARTMOUTH COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-26 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9119612
关键词：
Accelerometer Accounting Acute Address Behavior Behavioral Caring Cellular Phone Chronic Clinical Clinical Trials Collaborations Communities Computer software Computers Coupled Data Data Analyses Detection Development Disease remission Early Diagnosis Ecological momentary assessment Elements Ensure Goals Health Health Care Costs Healthcare Systems Homelessness Hospitalization Imprisonment Individual Interdisciplinary Study Intervention Laboratories Left Light Location Machine Learning Mechanics Mental Depression Mental disorders Methods Modeling Monitor Moods National Institute of Mental Health Nature Notification Outcome Outpatients Partial Remission Patient Self-Report Patients Pattern Phase Physical activity Population Prevention Provider Randomized Relapse Research Research Infrastructure Research Personnel Resources Risk Schizophrenia Science Scientist Secure Self Management Self-Injurious Behavior Sleep Social isolation Speech Stress Suicide Symptoms System Techniques Technology Testing Time Update Victimization base computer generated computerized cost disability disorder later incidence prevention experience help-seeking behavior high reward high risk innovation mHealth medical complication mood symptom novel prevent programs psychotic symptoms relapse patients sensor severe mental illness severe psychiatric disorder software development treatment as usual treatment effect trend usability web site

项目摘要

DESCRIPTION (provided by applicant): Schizophrenia is a severe psychiatric disorder that is associated with staggeringly high individual and societal costs. Although the illness is typically chronic, it is not static, and the majority of people with schizophrenia vacillate between full or partial remission and episodes of symptomatic relapse. Relapses increase one's risk for major problems including homelessness, incarceration, victimization, and suicide. Moreover, patients with schizophrenia who relapse are three to four times more costly than those who do not. The goal of the proposed project is to develop and evaluate a novel paradigm for illness monitoring, detection of early warning signs, and relapse prevention in schizophrenia. Our interdisciplinary team of clinical researchers and computer scientists proposes to develop a mobile system that uses smartphone-embedded sensors (i.e. microphone, accelerometer, GPS, light sensor) coupled with computerized self-reports, to track a range of behaviors (i.e. paralinguistic aspect of speech, physical activity, location, sleep, mood, psychotic symptoms) that are relevant to relapse in schizophrenia. Using machine learning techniques, the system will leverage behavioral data and patient self-reported clinical updates to generate personalized early warning models. The models will evolve with use of the system over time, focusing on variability from one's typical behavioral patterns to calibrate a unique patient relapse signature. Treatment teams will be informed about patients' clinical status via secure website. When the mobile system "flags" trends that are consistent with one's relapse signature, it will trigger patient functions and provider functions (i.e. real-time notification, prompts to initiate contact, time-sensitive treatments) to help prevent progression to full psychotic relapse. In Phase 1 of the project, we will integrate multi-modal sensor, ecological momentary assessment, and machine learning technologies into a unified smartphone system that will be tested and refined in laboratory settings. In Phase 2, we will conduct field trials with individuals with schizophrenia i real-world conditions to identify and resolve technical and mechanical problems, adapt the software, and maximize system usability. In Phase 3, we will conduct a randomized 12- month trial of the monitoring and prevention system compared to treatment as usual in 150 outpatients with schizophrenia that are at high-risk for relapse. If successful, our proposed system can be rapidly made available to a population that is in dire need of more effective resources, and can serve as a template for mobile monitoring and treatment systems for a range of clinical conditions with an episodic nature.

描述（由申请人提供）：精神分裂症是一种严重的精神疾病，与高昂的个人和社会成本有关。尽管这种疾病通常是慢性的，但它不是静态的，大多数精神分裂症患者在全部或部分缓解和有症状复发的发作之间进行了液态。复发增加了一个人面临重大问题的风险，包括无家可归，监禁，受害和自杀。此外，复发的精神分裂症患者的成本是那些没有的患者的成本三到四倍。拟议项目的目的是开发和评估用于疾病监测，预警信号的检测和精神分裂症预防复发的新型范式。 Our interdisciplinary team of clinical researchers and computer scientists proposes to develop a mobile system that uses smartphone-embedded sensors (i.e. microphone, accelerometer, GPS, light sensor) coupled with computerized self-reports, to track a range of behaviors (i.e. paralinguistic aspect of speech, physical activity, location, sleep, mood, psychotic symptoms) that are relevant to relapse in schizophrenia.使用机器学习技术，该系统将利用行为数据和患者自我报告的临床更新来生成个性化的预警模型。随着时间的推移，模型将随着系统的使用而发展，重点是从一个人的典型行为模式来校准独特的患者复发签名。治疗团队将通过安全网站告知患者的临床状况。当移动系统“标志”趋势与一个人的复发签名一致时，它将触发患者功能和提供者功能（即实时通知，提示启动接触，时间敏感治疗），以帮助防止进展到充分的精神病性复发。在该项目的第一阶段中，我们将将多模式传感器，生态瞬时评估和机器学习技术集成到统一的智能手机系统中，该系统将在实验室环境中进行测试和完善。在第2阶段，我们将与精神分裂症I现实世界中的个人进行现场试验，以识别和解决技术和机械问题，适应软件并最大程度地提高系统可用性。在第3阶段，我们将进行监测和预防系统的12个月随机试验，与往常一样，在150名患有高危的精神分裂症的门诊患者中，以进行复发。如果成功的话，我们提出的系统可以迅速提供给迫切需要更有效资源的人群，并且可以作为具有情节性质的一系列临床状况的移动监控和治疗系统的模板。