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Using Meta-level Smartphone Data to Promote Early Intervention inSchizophrenia

使用元级智能手机数据促进精神分裂症的早期干预

基本信息

批准号：
9201713
负责人：
BENJAMIN B BRODEY
金额：
$ 36.62万
依托单位：
TELESAGE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-18 至 2018-08-17
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9201713
关键词：
Accelerometer Adult Affect Age Algorithms American Aphasia Area Behavior Budgets Cellular Phone Client Clinic Clinical Collection Data Data Collection Databases Diagnosis Diagnostic and Statistical Manual of Mental Disorders Disease Early Intervention Electronic Health Record Electronic Mail Frequencies Goals Hospitalization Impairment Incipient Schizophrenia Individual Institutional Review Boards Interview Machine Learning Measurement Measures Morbidity - disease rate National Institute of Mental Health Occupational Participant Patient Self-Report Patients Pattern Persons Phase Physical activity Poverty Primary Prevention Privacy Process Protocols documentation Psychotic Disorders Recruitment Activity Relapse Reporting Research Resources Risk Role Schizophrenia Secondary Prevention Series Severity of illness Signs and Symptoms Sleep Small Business Innovation Research Grant Social Functioning Speech Staging Structure Symptoms Syndrome System Techniques Telephone Text Therapeutic Thinking Treatment Effectiveness Universities Work effective intervention experience first episode psychosis functional disability functional status high risk improved individualized medicine information gathering insight mortality outcome forecast personalized medicine phase 1 study phase 2 study prevent programs real world application relapse risk screening social success tool treatment strategy

项目摘要

Project Summary "Using Meta-level Smartphone Data to Promote Early Intervention in Schizophrenia” Schizophrenia is one of the most debilitating disorders in the world today. It affects over 2.4 million adult Americans each year. NIMH director Dr. Thomas Insel has declared “The best chance for preventing serious functional disability among people with schizophrenia may be to intervene at the earliest stages of the disorder, at the first episode of psychosis or even before symptoms appear. However, to act before symptoms appear requires improved predictive capacity” (NIMH 2011 Budget). Creating tools to identify high-risk, `prodromal' individuals may be the single most important step towards developing effective interventions to reduce the duration of untreated psychosis (DUP), and thereby also reduce the morbidity and mortality associated with schizophrenia. Recent studies have shown that over 54% of individuals with schizophrenia are re-hospitalized within the first 12 months following their initial hospitalization. Even after the first hospitalization, preventing relapse and re-hospitalization may lessen the long-term severity of the illness. In this SBIR Phase I study, we propose to determine the feasibility of screening for prodromal individuals and individuals at high risk of relapse by applying interpretive algorithms to Passively Gathered Meta-level Smartphone Data (PGMSD). We hypothesize that PGMSD can effectively assist in screening for prodromal individuals who are progressing toward psychosis as well as for remotely assessing individuals at risk for relapse during the critical 12-month period following their first episode of psychosis (FEP). In Phase I, we plan to recruit 70 individuals who have been or are being evaluated at the Prodromal clinics at Columbia, UCSD, and UCLA, where an estimated 70 to 90% of clients already own Smartphones. Data gathered may include: the frequency of telephone calls, emails, and texts, to assess within person changes in social connectedness; GPS, accelerometer data, to assess physical activity, isolation, and sleep patterns. In the past, several IRB-approved studies have used smartphones for gathering similar data from patients. Algorithms will be developed using several techniques including machine learning to convert the meta-level data into measures of social functioning, physical isolation, physical activity, and sleep/wake reversals. In addition to achieving technological success, our goal in Phase I is to provide evidence of our ability to use PGMSD algorithms to differentiating group means of participants who are controls, prodromal, or experiencing their FEP (SIPS 1 or 2; 3, 4, or 5; or 6). In Phase II, we will further develop and validate these algorithms. If successful, the Phase II project will have a large and sustained impact as our algorithms will help (1) identify at-risk individuals who `are' or `are not' progressing toward conversion, (2) serve as an objective measure of treatment effectiveness; (3) give rise to clinical reports delivered to EHR systems that hold promise for preventing relapse during the critical 12 months after initial diagnosis, potentially reducing hospitalization and re-hospitalization rates.

项目总结