Using Meta-level Smartphone Data to Promote Early Intervention inSchizophrenia

项目摘要 “使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令人衰弱的疾病之一。影响了240多万人 美国成年人每年NIMH主任托马斯·英塞尔博士宣布“预防 精神分裂症患者中严重的功能障碍可能是在早期阶段进行干预， 在精神病的第一次发作或甚至在症状出现之前，然而，在症状出现之前采取行动 需要提高预测能力”（NIMH 2011年预算）。创建识别高风险、 “前驱”个体可能是制定有效干预措施的最重要一步， 减少未经治疗的精神病（DUP）的持续时间，从而也降低了发病率和死亡率 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12个月内再次住院。即使在第一次 住院治疗、预防复发和再次住院治疗可减轻疾病的长期严重程度。在 这项SBIR I期研究，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水平， 智能手机数据（PGMSD）。我们假设PGMSD可以有效地帮助筛查前驱症状， 正在向精神病发展的个体以及远程评估处于精神病风险的个体。 在精神病首次发作（FEP）后的关键12个月内复发。 在第一阶段，我们计划招募70名已经或正在前驱期接受评估的人。 哥伦比亚、加州大学圣地亚哥分校和加州大学洛杉矶分校的诊所，估计70%到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，以评估个人内部 社会联系的变化; GPS，加速计数据，以评估身体活动，隔离和睡眠 模式.在过去，一些IRB批准的研究使用智能手机收集类似的数据， 患者将使用包括机器学习在内的多种技术开发算法，以将 元水平的数据纳入社会功能，身体隔离，身体活动和睡眠/觉醒的措施 逆转除了实现技术上的成功，我们在第一阶段的目标是提供证据， 能够使用PGMSD算法区分对照组、前驱组或 他们正在经历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，第二阶段项目将 有一个大的和持续的影响，因为我们的算法将有助于（1）确定在风险的个人谁'是'或'是 （2）作为治疗有效性的客观衡量标准;（3）引起 向EHR系统提供临床报告，这些系统有望在关键的12个月内预防复发 初步诊断后数月，可能降低住院和再住院率。

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.