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

项目摘要 心力衰竭（HF）是美国老年人中最常见的出院诊断。 引人注目的是，2/5的患者在首次HF入院后1年内再次入院。这导致 对我们已经紧张的医疗保健系统来说，这是一笔巨大的潜在可避免的费用，因为住院会导致 每年HF管理成本的70%。最常见的原因是未能认识到 临床恶化和饮食不依从。因此，HF管理正在从 从传统的面对面后续访问模式转向积极主动的实时技术援助模式 患者在社区进行监测和自我管理。 虽然一些HF研究已经实施了技术驱动的疾病管理计划，但结果 已经混合。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，他们在向患者提供行为干预和改变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- 以提供行为干预为中心的技术，以促进最常见的自我管理 再入院的原因，总体目标是减少HF再入院。 该拟议项目将确定移动的应用程序中两种干预措施的有效性， 建立在我们之前研究的基础上。我们的中心假设是，以患者为中心的移动的应用程序 在临床恶化和饮食失调期间， 钠将改善HF患者的健康状况。该项目的基本原理，这是由我们的支持， 初步数据显示，一种新的疾病管理模式--让患者控制自己的病情-- 将对HF结局产生实质性的积极影响。我们的目标是：（1）确定两个 对HF再入院和健康相关生活质量（HRQOL）的独特自适应移动的应用干预 在HF患者中，（2）确定干预对近端结局的影响， 结果介导干预对HF再入院和HRQOL的影响，以及（3）开发数据驱动的 可以预测临床恶化事件的机器学习模型。

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.