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; 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.

项目摘要 该提案调查了使用患者制定移动健康（MHealth）措施的统计模型 具有高复杂性和暂时性的生成健康数据（PGHD）。 MHealth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，并吸引患者 在自我管理中。 MHealth技术进一步允许集成多种数据流以改进 结果测量和预测，以帮助临床决策。但是，为了最大化其可行性， 有必要调查基于MHealth的设计，开发和评估的新颖方法 措施。我们将我们在慢性骨盆疼痛（CPP）中的研究基础为疾病模型，一种普遍的疾病模型， 具有高社会伯恩的复杂疾病和生活质量（QOL）影响。有实质性的异质性 患者与CPP的日常变化之间。因此，MHealth方法特别是 对于捕获复杂疾病情景的有价值。没有CPP特定的自我报告措施 评估疾病状况或治疗反应。我们建议调查可以处理继承的模型 PGHD在生态上有效且可行的自我追踪措施的挑战 健康环境。具体目的是：特定目标1。调查“跟踪的关键窗口” 基于MHealth的疾病结果测量。我们将注册90名参加12周的参与者 CPP的物理疗法治疗使用MHealth应用程序来跟踪其症状，日常功能和 药物。我们将通过临床评估和有关睡眠和活动的被动数据进行三角测量 构建分布式滞后模型（DLM）以识别可用于结果监视的预测变量。具体的 具体目标2。研究基于CPP以发展自我的功能数据分析框架 跟踪疼痛和质量测量。我们将注册180名CPP患者，以通过A来追踪其疾病症状 MHealth应用程序并穿着活动监视3个月。通过一系列监督和无监督的 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测量。目标2a。设计和发展多维自我跟踪疼痛 措施。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）随着时间的流逝，利用功能数据分析方法。目标2B。设计并发展灵活 自追踪的QOL测量。我们将评估单个项目在CPP符号上的相对预测能力 得出可以在Day-vs Week级别使用的CPP特异性QOL测量。探索目标2：我们将 通过比较非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化可用MHealth技术的功能，这可以帮助 在强大的模型中，可以为基于MHealth的疾病措施设计提供信息。拟议的工作解决了差距 MHealth证据库，以改善有效的MHealth评估的应用和翻译。