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

• 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
• 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

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.

项目概要 “利用元级智能手机数据促进精神分裂症的早期干预” 精神分裂症是当今世界上最使人衰弱的疾病之一。它影响了超过240万 每年成年美国人。 NIMH 主任 Thomas Insel 博士宣称“这是预防的最佳机会” 精神分裂症患者的严重功能障碍可能需要在早期阶段进行干预 精神病首次发作时甚至在症状出现之前。但是，要在出现症状之前采取行动 出现需要提高预测能力”（NIMH 2011 年预算）。创建工具来识别高风险、 “前驱期”个体可能是制定有效干预措施的最重要的一步 减少未经治疗的精神病（DUP）的持续时间，从而降低发病率和死亡率 与精神分裂症有关。最近的研究表明，超过 54% 的精神分裂症患者 首次住院后的前 12 个月内再次住院。即使在第一次之后 住院治疗、预防复发和再次住院可能会减轻疾病的长期严重程度。在 在这项 SBIR 第一阶段研究中，我们建议确定筛查前驱个体的可行性， 通过将解释算法应用于被动收集的元级别，具有高复发风险的个体 智能手机数据 (PGMSD)。我们假设 PGMSD 可以有效协助筛查前驱症状 正在发展为精神病的个人以及远程评估有精神病风险的个人 在首次精神病发作 (FEP) 后的关键 12 个月内复发。 在第一阶段，我们计划招募 70 名已经或正在 Prodromal 接受评估的人员 哥伦比亚大学、加州大学圣地亚哥分校和加州大学洛杉矶分校的诊所中，估计 70% 至 90% 的客户已经拥有智能手机。 收集的数据可能包括：电话、电子邮件和短信的频率，以进行个人评估 社会联系的变化； GPS、加速计数据，用于评估身体活动、隔离和睡眠 模式。过去，几项经 IRB 批准的研究曾使用智能手机从以下机构收集类似数据： 患者。将使用包括机器学习在内的多种技术来开发算法，以将 将元级数据转化为社会功能、身体隔离、身体活动和睡眠/觉醒的衡量标准 逆转。除了取得技术上的成功外，我们第一阶段的目标是提供我们的证据 能够使用 PGMSD 算法区分对照组、前驱期或前驱期参与者的群体平均值 正在经历他们的 FEP（SIPS 1 或 2；3、4 或 5；或 6）。 在第二阶段，我们将进一步开发和验证这些算法。如果成功的话，二期工程将 具有巨大且持续的影响，因为我们的算法将帮助 (1) 识别“正在”或“正在”的高危个体 没有“进展到转化”，(2) 作为治疗效果的客观衡量标准； (3) 产生 向 EHR 系统提交的临床报告有望在关键的 12 年期间预防复发 初步诊断后几个月，可能会降低住院率和再住院率。