Evaluation techniques for mHealth outcome measures using patient generated health data

项目摘要 本提案调查了使用患者数据开发移动的健康（mHealth）措施的统计模型 生成的健康数据（PGHD）具有高度的复杂性和时间性。移动健康技术的出现 计算工具正在迅速扩大其在研究和临床环境中的使用， 自我管理。移动健康技术进一步允许整合各种数据流，以改善 结果测量和预测，以帮助临床决策。为了最大化他们的行动能力，然而， 有必要研究新的方法来设计，开发和评估基于移动健康的 措施我们以慢性盆腔疼痛（CPP）作为疾病模型进行研究， 具有高社会负担和生活质量（QoL）影响的复杂疾病。存在大量异质性 患者之间以及CPP如何展开的日常变化。因此，mHealth方法特别 对于捕捉复杂的疾病场景很有价值。没有专门针对CPP的自我报告措施， 评估疾病状态或治疗反应。我们建议调查模型，可以处理固有的 PGHD的挑战，以获得生态上有效的和可操作的自我跟踪措施，为患者的结果， 卫生环境。具体目标是：具体目标1。调查“跟踪的关键窗口”， 基于移动健康的疾病结局测量。我们将招募90名参与者， 物理治疗他们的CPP使用mHealth应用程序来跟踪他们的症状，日常功能， 药物治疗我们将这些数据与临床医生的评估和睡眠和活动的被动数据进行三角分析 建立分布式滞后模型（DLMs），以确定可用于结果监测的预测因子。具体 具体目标2。研究一个基于CPP的功能数据分析框架， 跟踪疼痛和生活质量指标。我们将入组180名CPP患者，通过 mHealth应用程序和佩戴活动监视器3个月。通过一系列有监督和无监督的 利用功能数据分析方法的模型，我们将确定变量，以告知设计 复合疼痛和QoL指标。目标2a。设计和开发一个多维的自我跟踪疼痛 measure.我们将建立估计模型，其中观察单位是一组曲线（即，疼痛位置， 严重性、类型），利用功能数据分析方法。目标2b。设计和开发灵活的 自我跟踪QoL测量。我们将评估单个项目对CPP症状的相对预测能力 推导出可用于日与周水平的CPP特异性QoL指标。探索目标2：我们将 通过比较来自非CPP对照组的输出来评估模型的疾病特异性。灵活，非 参数数据方法允许最大限度地利用现有的mHealth技术的功能，这可以帮助 在强大的模型中，为基于移动健康的疾病措施的设计提供信息。拟议工作解决以下方面的差距： 移动健康循证基础，以改善有效的移动健康评估的应用和翻译。

PROJECT SUMMARY This proposal investigates statistical models for developing mobile health (mHealth) measures using patient generated health data (PGHD) with high complexity and temporality. The emergence of mHealth technologies and computational tools are rapidly expanding their use in research and clinical settings, and engaging patients in self-management. mHealth technology further allows integration of multifarious data streams to improve outcome measurement and prediction to aid clinical decision making. To maximize their actionability, however, there is a need to investigate novel approaches for design, development and evaluation of mHealth-based measures. We ground our investigation in chronic pelvic pain (CPP) as the disease model, a prevalent, complex disorder with high societal burden and quality of life (QoL) impact. There is substantial heterogeneity between patients and day-to-day variations in how CPP unfolds. Therefore, mHealth methods are particularly valuable for capturing the complex disease scenarios. There are no CPP-specific self-reported measures to assess disease status or treatment response. We propose to investigate models that can handle the inherent challenges of PGHD to derive ecologically valid and actionable self-tracking measures for patient outcomes in health settings. The Specific Aims are: Specific Aim 1. Investigate “critical windows of tracking” for mHealth-based disease outcome measurement. We will enroll 90 participants undergoing 12 weeks of physical therapy treatment for their CPP to use a mHealth app for tracking their symptoms, daily function, and medications. We will triangulate these data with clinician assessments and passive data on sleep and activity to build distributed lag models (DLMs) to identify predictors that can be used for outcome monitoring. Specific Specific Aim 2. Investigate a functional data analytic framework grounded in CPP to develop self- tracking pain and QoL measures. We will enroll 180 CPP patients to track their disease symptoms through a mHealth app and wear activity monitors for 3 months. Through a series of supervised and unsupervised models leveraging functional data analytic methods, we will identify variables to inform the design of the composite pain and QoL measures. Aim 2a. Design and develop a multidimensional self-tracking pain measure. We will build estimation models where the unit of observation is a set of curves (i.e., pain location, severity, type) over time, leveraging functional data analytic methods. Aim 2b. Design and develop a flexible self-tracking QoL measure. We will assess the relative predictive ability of individual items on CPP symptoms to derive a CPP-specific QoL measure that can be used at the day- vs week-level. Exploratory Aim 2: We will assess disease specificity of the models by comparing output from a non-CPP control group. Flexible, non- parametric data approaches allow maximizing the features of the available mHealth technology, which can aid in robust models to inform design of mHealth-based disease measures. Proposed work addresses the gap in mHealth evidence-base to improve the application and translation of efficacious mHealth assessments.