Statistical methods in mHealth to signal interventional needs for mental health patients

项目摘要 随着智能手机的普及，其作为可扩展的健康监测的潜力也在增长 治疗精神疾病的工具。有自杀意念的个体的行为警告信号， 双相情感障碍、饮食失调、抑郁症、精神分裂症和其他精神疾病， 在发生不良事件（如自杀企图或复发）之前，很难识别。 数字表型分析，对个体水平的人类表型进行实时定量分析 在 这使我们能够量化这些警告信号，并促使 适时的干预当前已发表的数字系统上的变点和异常检测的应用 到目前为止，表型分析数据是证明数字表型分析潜力的主要研究 用于行为和健康监测。这项建议旨在推动的更广泛的目标可以概括为： 分三个步骤，根据以下具体目标进行排序。目标1：开发新的统计 能够说明纵向特征的变化点和异常检测方法 数据缺失的普遍模式。目标2：开发降维技术， 统计功效和减少数字表型中的噪声。这将极大地提高 目标1中提出的方法。这两个目标的关键是开发计算效率高的 软件目标3：通过我们正在进行的和新的合作，在患者人群中实施该软件 以便在新的数字表型数据被上传时对其进行分析， 检测到行为警告信号。这最后一步是拟议工作的最终目标， 完成后将对患者健康产生直接影响，使干预措施能够预防复发， 各种各样的成瘾和失调利用我们在统计方法、数字表型分析和 软件开发，结合我们广泛的数字表型合作网络，我们很好地 定位于开发识别行为警告信号所需的统计方法和软件 从数字表型数据，以及通过合作研究实施这些方法。成功 这一项目的完成将对非洲的个性化医疗和移动的保健产生直接影响。 精神疾病的治疗。 使用来自个人数字设备的数据

Project Summary As smartphones have grown in prevalence, so too has their potential grown as a scalable health monitoring tool for the treatment of psychiatric disorders. Behavioral warnings signs in individuals with suicidal ideation, bipolar disorder, eating disorders, depression, schizophrenia, and other psychiatric disorders have, until this point, been difficult to identify prior to the occurrence of an adverse event, such as a suicide attempt or relapse. Digital phenotyping, the moment-by-moment quantification of the individual-level human phenotype in situ , has enabled us to quantify these warnings signs and prompt an appropriately-timed intervention. Current published uses of change point and anomaly detection on digital phenotyping data so far have been proof-of-principal studies demonstrating the potential of digital phenotyping for behavioral and health monitoring. The wider goal that this proposal aims to advance can be characterized in three steps, which are ordered according to the following specific aims. Aim 1: Develop novel statistical methods for change point and anomaly detection capable of accounting for longitudinal features with widespread and general patterns of missing data. Aim 2: Develop dimensional reduction techniques to improve statistical power and reduce noise in digital phenotypes. This will greatly improve the performance of the methods proposed in aim 1. Crucial to both of these aims is the development of computationally efficient software. Aim 3: Implement this software on patient populations through our ongoing and new collaborations so as to analyze new digital phenotyping data as it is uploaded and provide clinicians notifications when behavioral warning signs are detected. This final step is the ultimate goal of the proposed work, as successful completion will lead to an immediate impact on patient health, enabling interventions to prevent relapse in a wide variety of addictions and disorders. Using our expertise in statistical methods, digital phenotyping and software development, combined with our wide network of digital phenotyping collaboration, we are well positioned to both develop the statistical methods and software necessary to identify behavioral warnings signs from digital phenotyping data, as well as implement these methods through collaborative studies. Successful completion of this project will have an immediate impact on personalized medicine and mobile health in the treatment of psychiatric disorders. using data from personal digital devices