A multifaceted adaptive mobile application to promote self-management and improve outcomes in heart failure

多方面的自适应移动应用程序，可促进自我管理并改善心力衰竭的结果

基本信息

批准号：
10606585
负责人：
Michael Dorsch
金额：
$ 18.68万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-15 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10606585
关键词：
Address Admission activity Affect Back Behavior Behavior Therapy Caring Chronic Clinical Clinical Trials Communities Data Diagnosis Dietary Sodium Dietary intake Disease Disease Management Disease model Double-Blind Method Effectiveness Elderly Failure Fostering Future Goals Health Health Personnel Health Promotion Health Status Healthcare Systems Heart failure Hospitalization Hospitals Intake Intervention Knowledge Link Machine Learning Measures Mediating Minnesota Modeling Monitor Morbidity - disease rate Multi-Institutional Clinical Trial Outcome Patient Readmission Patients Questionnaires Randomized Reproducibility Research Science Self Management Sodium System Techniques Technology Testing Time United States Visit Work comparison control cost design dietary dietary excess effectiveness evaluation experience follow-up health related quality of life hospital readmission improved improved outcome intervention effect machine learning model mobile application new technology novel patient oriented programs prospective remote monitoring tool

项目摘要

PROJECT SUMMARY Heart failure (HF) is the most common hospital discharge diagnosis among older adults in the United States. Strikingly, 2 in 5 patients are readmitted within 1-year following their first HF admission. This results in significant potentially avoidable costs to our already strained healthcare system, since hospitalizations result in 70% of yearly HF management costs. The most common causes of readmission are failure to recognize clinical worsening and dietary nonadherence. As a result, HF management is rapidly evolving from the traditional model of face-to-face follow-up visits toward a proactive real-time technological model of assisting patients with monitoring and self-management while in the community. While several HF studies have implemented technologically-driven disease management programs, results have been mixed. These systems effectively deliver monitoring data and alerts to healthcare providers. However, their effectiveness in delivering behavioral interventions to patients and modifying patient behavior, a crucial factor in HF self-management, is unknown. Therefore, it is imperative to develop and test patient- centered technologies that deliver behavioral interventions to promote self-management for the most common causes of readmissions with an overall goal of reducing HF readmissions. This proposed project will determine the effectiveness of two interventions within a mobile application and builds on our work from previous research. Our central hypothesis is that a patient-centered mobile application with contextual just-in-time interventions about self-management during a clinical worsening and dietary sodium will improve the health status of HF patients. The rationale for this project, which is supported by our preliminary data, is that a new model for disease management - placing patients in control of their condition - will have a substantial positive impact on HF outcomes. Our objectives are to: (1) determine the impact of two unique adaptive mobile application interventions on HF readmission and health-related quality of life (HRQOL) in HF patients, (2) establish the effect the interventions have on proximal outcomes and that the proximal outcomes mediate the intervention’s impact on HF readmission and HRQOL, and (3) develop data-driven machine learning models that can predict episodes of clinical worsening.

项目摘要心力衰竭（HF）是美国老年人中最常见的医院出院诊断。令人惊讶的是，在第一次HF入院后的1年内，有2例患者在1年内被重新入院。这导致我们已经紧张的医疗保健系统可能会避免巨大的避免成本，因为住院导致每年HF管理成本的70％。重新入院的最常见原因是无法识别临床担心和饮食不遵守。结果，HF管理正在迅速发展传统的面对面后续访问模型，以协助积极主动的实时技术模型在社区中进行监测和自我管理的患者。虽然几项HF研究已经实施了技术驱动的疾病管理计划，但结果混合在一起。这些系统有效地向医疗保健提供者提供了监视数据和警报。但是，它们在向患者提供行为干预和修改患者行为方面的有效性 HF自我管理的关键因素是未知的。因此，必须开发和测试患者 - 采用行为干预措施以促进最常见的自我管理的中心技术重新入院的原因，其总体目标是减少HF再入院。该提议的项目将确定移动应用程序中两种干预措施的有效性，基于我们以前的研究的工作。我们的中心假设是以患者为中心的移动应用在临床担忧和饮食中，有关于自我管理的上下文即时干预措施钠将改善HF患者的健康状况。该项目的理由，这是我们的支持初步数据是，一种新的疾病管理模型 - 使患者控制自己的病情 - 将对HF结果产生重大积极影响。我们的目标是：（1）确定两个的影响关于HF再入院和与健康相关的生活质量（HRQOL）的独特自适应移动申请干预措施（HRQOL）在HF患者中，（2）确定干预措施对近端结果的影响结果调解了干预对HF再入院和HRQOL的影响，（3）开发数据驱动可以预测临床担心发作的机器学习模型。