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

基本信息

批准号：
10708777
负责人：
Ipek Ensari
金额：
$ 38.07万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
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/10708777
关键词：
Abdominal Pain 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 Generations Health Health behavior Heterogeneity Individual Inflammatory Investigation Investments Length Location Measures Methods Mobile Health Application Modeling Monitor Nature Outcome Outcome Measure Output Pain Pain Disorder 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 Symptoms Techniques Technology Testing Time Translations Treatment outcome United States National Library of Medicine Variant Work analytical method 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 outcome prediction pain patient pain symptom patient engagement predictive modeling response supervised learning tool treatment response unsupervised learning

项目摘要

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.

项目概要该提案调查了使用患者开发移动医疗（mHealth）措施的统计模型具有高度复杂性和临时性的健康数据（PGHD）移动医疗技术的出现。和计算工具正在迅速扩大其在研究和临床环境中的使用，并吸引患者参与在自我管理方面，移动医疗技术进一步允许整合多种数据流以改进。然而，为了最大限度地提高其可操作性，结果测量和预测可以帮助临床决策。有必要研究基于移动医疗的设计、开发和评估的新方法我们的调查以慢性盆腔疼痛 (CPP) 作为疾病模型，这是一种普遍存在的疾病。具有较高社会负担和生活质量 (QoL) 影响的复杂疾病存在显着的异质性。因此，移动医疗方法尤其重要。对于捕获复杂的疾病场景很有价值没有针对 CPP 的自我报告措施。我们建议研究能够处理固有问题的模型。 PGHD 面临的挑战是为患者的治疗结果得出生态上有效且可操作的自我跟踪措施具体目标是：具体目标 1. 调查“关键跟踪窗口”。我们将招募 90 名参与者进行为期 12 周的基于移动健康的疾病结果测量。对他们的 CPP 进行物理治疗，使用移动医疗应用程序来跟踪他们的症状、日常功能和我们将把这些数据与临床医生的评估以及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) 来识别可用于结果监控的预测变量。具体目标 2. 研究基于 CPP 的功能数据分析框架，开发自我们将招募 180 名 CPP 患者来跟踪他们的疾病症状。 mHealth 应用程序和佩戴活动监测器持续 3 个月。利用功能数据分析方法的模型，我们将识别变量来为模型的设计提供信息目标 2a 设计和开发多维自我跟踪疼痛。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，目标 2b。我们将评估各个项目对 CPP 症状的相对预测能力。得出可用于日与周级别的 CPP 特定 QoL 衡量标准探索性目标 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。参数数据方法可以最大限度地发挥现有移动医疗技术的功能，这可以帮助建立强大的模型来为基于移动医疗的疾病措施的设计提供信息。拟议的工作解决了这一差距。移动医疗证据基础，以改进有效移动医疗评估的应用和翻译。