Evaluation techniques for mHealth outcome measures using patient generated health data

使用患者生成的健康数据进行移动医疗结果测量的评估技术

• 批准号: 10412721
• 负责人: Ipek Ensari
• 金额: $ 37.17万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10412721
• 关键词: Abdomen; Address; Benchmarking; Biological Markers; Chronic; Clinical; Complex; Control Groups; Data; Data Analyses; Data Analytics; Development; Dimensions; Disease; Disease Management; Disease Outcome; Disease model; Enrollment; Evaluation; Fibroid Tumor; Health; Health Technology; Health behavior; Heterogeneity; Individual; Inflammatory; Investigation; Length; Location; Measurement; Measures; Methods; Mobile Health Application; Modeling; Monitor; Nature; Outcome; Outcome Measure; Output; Pain; Pain Disorder; Pain quality; Participant; Pathology; Patient Self-Report; Patient-Focused Outcomes; Patients; Pelvic Pain; Persons; Pharmaceutical Preparations; Physical therapy; Prediction of Response to Therapy; Prevalence; Productivity; Quality of life; Research; Sampling; Self Management; Series; Severities; Sleep; Specificity; Statistical Models; Strategic Planning; Structure; Supervision; Symptoms; Techniques; Testing; Time; Translations; Treatment outcome; United States National Library of Medicine; Variant; Work; analytical method; base; chronic pelvic pain; clinical decision-making; computerized tools; daily functioning; data modeling; data streams; design; endometriosis; evidence base; flexibility; health assessment; health data; improved; improved outcome; interest; mHealth; novel; novel strategies; pain patient; pain symptom; predictive modeling; response; supervised learning; tool; treatment response; unsupervised learning

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.

项目总结 该提案调查了使用患者开发移动健康(MHealth)措施的统计模型 生成的健康数据(PGHD)具有很高的复杂性和时间性。移动健康技术的出现 计算工具正在迅速扩大它们在研究和临床环境中的使用，并吸引患者 在自我管理方面。移动健康技术进一步允许集成各种数据流以改进 结果测量和预测，以帮助临床决策。然而，为了最大限度地提高其可操作性， 需要研究新的方法来设计、开发和评估基于mHealth的 措施。我们以慢性骨盆疼痛(CPP)作为疾病模型的研究基础，一种流行的， 复杂障碍，社会负担重，影响生活质量。有很大的异质性 患者之间的差异和CPP如何展开的日常差异。因此，mHealth方法特别 对于捕捉复杂的疾病场景很有价值。没有特定于CPP的自我报告措施来 评估疾病状态或治疗反应。我们建议研究能够处理固有的 PGHD在为患者结果得出生态上有效和可操作的自我跟踪措施方面面临的挑战 运行状况设置。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果测量。我们将招募90名参与者，接受为期12周的 对他们的CPP进行物理治疗，使用mHealth应用程序跟踪他们的症状、日常功能和 药物。我们将用临床医生的评估和睡眠和活动的被动数据来三角测量这些数据 建立分布式滞后模型(DLMS)以确定可用于结果监测的预测因素。特定的 具体目标2.研究以CPP为基础的功能数据分析框架，以开发自我 跟踪疼痛和QOL测量。我们将招募180名CPP患者通过 MHealth应用程序和佩戴活动监测器3个月。通过一系列有监督和无监督的 模型利用功能数据分析方法，我们将确定变量来为设计提供信息 综合疼痛和生活质量测量。目标2a。设计并开发了一种多维自跟踪疼痛系统 测量。我们将构建其中观察单位是一组曲线(即，疼痛位置， 严重性、类型)随时间推移，利用功能性数据分析方法。目标2b。设计和开发灵活的 自我跟踪的QOL测量。我们将评估单个项目对CPP症状的相对预测能力 以得出可在日与周级别使用的CPP特定的QOL测量。探索性目标2：我们将 通过比较来自非CPP对照组的结果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发挥现有mHealth技术的功能，这可以帮助 在稳健的模型中，为基于mHealth的疾病措施的设计提供信息。拟议的工作解决了 移动健康证据基础，以改进有效的移动健康评估的应用和翻译。