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

项目总结 心力衰竭(HF)是美国老年人最常见的出院诊断。 引人注目的是，5名患者中有2名在首次入院后1年内再次入院。这将导致 对我们本已紧张的医疗体系来说，由于住院导致 每年HF管理成本的70%。重新入院的最常见原因是未能认识到 临床恶化和饮食不坚持。因此，高频管理正在迅速从 传统的面对面跟踪访问模式转向主动的实时技术援助模式 在社区中对患者进行监测和自我管理。 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 好坏参半。这些系统有效地向医疗保健提供者提供监测数据和警报。 然而，它们在向患者提供行为干预和改变患者行为方面的有效性，a HF自我管理中的关键因素，目前尚不清楚。因此，开发和测试患者--是当务之急 以行为干预为中心的技术，以促进最常见的自我管理 重新入院的原因，总体目标是减少高频再入院。 这个拟议的项目将确定移动应用程序中两种干预措施的有效性 建立在我们之前研究工作的基础上。我们的中心假设是以患者为中心的移动应用程序 在临床恶化和饮食期间进行关于自我管理的上下文及时干预 钠能改善心力衰竭患者的健康状况。这个项目的基本原理是由我们的 初步数据是，一种新的疾病管理模式--让患者控制自己的病情-- 将对心力衰竭的结果产生重大的积极影响。我们的目标是：(1)确定两个方面的影响 关于心力衰竭再入院和健康相关生活质量(HRQOL)的独特适应性移动应用干预 在心力衰竭患者中，(2)确定干预措施对近期结局的影响，以及 结果中介干预对心力衰竭再入院和HRQOL的影响，(3)发展数据驱动 可以预测临床恶化发作的机器学习模型。

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.