Using Meta-level Smartphone Data to Promote Early Intervention inSchizophrenia

项目摘要 “利用元水平的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。它影响了超过240万人 每年都有成年美国人。美国国立卫生研究院院长托马斯·因塞尔博士宣布，“预防 精神分裂症患者的严重功能障碍可能是在精神分裂症的早期阶段进行干预 精神障碍，在精神病的第一次发作时，甚至在症状出现之前。然而，在症状出现之前采取行动 出现需要更好的预测能力“(NIMH 2011年预算)。创建工具来识别高风险、 “前驱”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 在初次住院后的头12个月内再次住院。即使在第一次之后 住院、预防复发和再次住院可能会减轻疾病的长期严重性。在……里面 这项SBIR第一阶段研究，我们建议确定筛查前驱体个体和 将解释算法应用于被动收集的Meta-Level的复发高危个体 智能手机数据(PGMSD)。我们推测PGMSD可以有效地辅助筛查前驱症状。 正在发展为精神病的个人以及远程评估有风险的个人 在他们第一次精神病发作(FEP)后的关键12个月期间复发。 在第一阶段，我们计划招募70名已经或正在接受前兆评估的人 哥伦比亚大学、加州大学圣迭戈分校和加州大学洛杉矶分校的诊所，估计有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，以便在面对面进行评估 社交连通性的变化；GPS、加速计数据，以评估身体活动、隔离和睡眠 模式。在过去，IRB批准的几项研究曾使用智能手机从 病人。算法将使用包括机器学习在内的多种技术来开发，以将 将元级别数据转化为社交功能、身体隔离、身体活动和睡眠/清醒的测量 反转。除了取得技术上的成功，我们在第一阶段的目标是提供我们的 能够使用PGMSD算法来区分对照组、前驱或 体验他们的FEP(SIP1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 产生巨大和持续的影响，因为我们的算法将帮助(1)识别处于风险中的个人 (2)作为治疗效果的客观衡量标准；(3)提高 到提交给EHR系统的临床报告，这些系统有望在关键的12个月期间防止复发 在初次诊断后几个月，可能会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.