mHealth for Heart Failure: Predictive Models of Readmission Risk and Self-care Using Consumer Activity Trackers

项目摘要/摘要 心力衰竭(HF)是一种使人衰弱的疾病，在美国有500多万人受到影响。发生 心力衰竭的发病率、发病率和住院费用都有严重的经济影响。在2012年，HF有一个 每年直接费用超过307亿美元，其中大部分是直接医疗费用。到2030年，高频 预计总直接成本将达到697亿美元，增长127%。成本增加将受到以下因素的推动 老龄化人口的增加，使得预防心力衰竭和提高护理效率势在必行。50%的 由于心力衰竭再次入院是可以预防的，缺乏对规定的自我护理的坚持是驱动因素。 支持坚持自我护理和改善心力衰竭结局的远程医疗干预研究的结果如下 没有定论。过去对心力衰竭的远程医疗干预使用了一系列方法，包括：无线 传感器、电话服务、网站和护士的家访。结构化电话支持已显示 在某些情况下，可减少心力衰竭的住院时间、改善临床结果并降低全因死亡率 病人。然而，患者参与远程医疗干预的情况差别很大。这种变化在一定程度上是由于 由于这种家庭监测干预措施给患者带来了沉重的治疗负担，这需要他们 从事新奇的行为，包括使用新的不熟悉的硬件和花时间与家人会面 保健员。 本R01的目标是：1)证明患者在以下情况下遵守家庭监护方案 使用微创监测技术，包括戴在手腕上的消费者活动跟踪器；2)结合 用预测算法预测再入院的微创家庭监护方案；3) 使用电子健康记录(EHR)数据和基线调查开发模型以预测遵从性水平 家庭监控方案；以及4)探索使用移动应用程序 在前瞻性先导研究中与患者交流。为了实现这些目标，我们将招募500名心力衰竭患者 参与一种微创的家庭监测养生法。我们将衡量遵守 方案，并使用收集的传感器数据和已知的重新接纳事件来创建新的隐藏半马尔可夫 持续预测再入院风险的模型。预测患者的依从性水平将被执行 电子病历数据和基线调查。最后，我们将开发一款移动应用程序，使患者能够 在一项对50名患者进行的试点研究中，监测他们的进展情况，并收到遵守通知和简短调查。 这项提案中概述的工作将产生一套执行家庭高频监测的基础工具 病人。我们将发现预测再次入院的EHR表型和移动传感器生物标记物 和依从性，这将使未来的随机试验能够精确地针对计算的患者概况 以行为经济学为基础的量身定做的激励措施，以减少再次住院。

PROJECT SUMMARY/ABSTRACT Heart failure (HF) is a debilitating disease that affects over five million people in the United States. Occurrence of, morbidity related to, and hospitalization due to HF have serious financial implications. In 2012, HF had a direct cost of over $30.7 billion annually, the majority of which was due to direct medical costs. By 2030, HF total direct costs are predicted to reach $69.7 billion, an increase of 127%. Increases in costs will be driven by an increase in the aging population, making prevention of HF and care efficiency imperative. Fifty percent of readmissions due to HF are preventable, with lack of adherence to prescribed self-care as the driving factor. Results of telemedicine intervention studies to support adherence to self-care and improve HF outcomes are inconclusive. Past telemedicine interventions for HF have utilized an array of methods including: wireless sensors, telephone services, websites, and home visits from nurses. Structured telephone support has shown in some cases to reduce hospitalization, improve clinical outcomes, and reduce all-cause mortality in HF patients. However, patient participation in telemedicine interventions varies widely. This variation is due in part to the high treatment burden placed upon patients in such home monitoring interventions, which require them to engage in novel behaviors, including using new unfamiliar hardware and spending time meeting with home health nurses. The goals of this R01 are to: 1) demonstrate that patients are adherent to a home monitoring regimen when using minimally-invasive monitoring technologies, including wrist-worn consumer activity trackers; 2) combine the minimally-invasive home monitoring regimen with predictive algorithms to forecast hospital readmission; 3) develop models using electronic health record (EHR) data and a baseline survey to predict levels of adherence to the home monitoring regimen; and 4) explore the pragmatic feasibility of using a mobile app for communicating with patients in prospective pilot study. Towards these goals, we will recruit 500 HF patients to participate in a minimally-invasive home monitoring regimen. We will measure levels of adherence to the regimen, and use collected sensor data and known readmission events to create a novel hidden semi-Markov model that continuously predicts readmission risk. Predicting a patient’s level of adherence will be performed with EHR data and a baseline survey. Finally, we will develop a mobile application that will allow patients to monitor their progress and receive adherence notifications and short surveys in a pilot study of 50 patients. The work outlined in this proposal will produce a set of foundational tools for performing home monitoring of HF patients. We will discover EHR phenotypes and mobile sensor biomarkers that are predictive of readmission and adherence, which will enable a future randomized trial that precisely targets computational patient profiles with tailored incentives based on behavioral economics to reduce hospital readmission.