Estimating Trajectory of Recovery in Cardiac Rehabilitation using Mobile Health Technology and Personalized Machine Learning

使用移动医疗技术和个性化机器学习估计心脏康复的恢复轨迹

• 批准号: 10018016
• 负责人: Bobak Jack Mortazavi
• 金额: $ 17.7万
• 依托单位: TEXAS ENGINEERING EXPERIMENT STATION
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018016
• 关键词: Achievement; Adherence; Behavior; Biological Markers; Blood Pressure; Cardiac rehabilitation; Cardiovascular Diseases; Cardiovascular system; Caregivers; Caring; Cellular Phone; Characteristics; Chest; Clinic Visits; Clinical; Communities; Data; Data Collection; Development; Devices; EFRAC; Education; Event; Exercise; Exertion; Failure; Feedback; Feeling; Gender; Health Technology; Heart Rate; Heart failure; Home environment; Hour; Individual; Intervention; Investigation; Lead; Location; Machine Learning; Measurement; Measures; Metabolic; Modeling; Monitor; Outcome; Participant; Patient Care; Patient Monitoring; Patient Self-Report; Patient-Focused Outcomes; Patients; Pattern; Performance; Physical activity; Physiologic pulse; Positioning Attribute; Proxy; Pulse Pressure; Recovery; Recurrence; Rehabilitation therapy; Research; Rest; System; Techniques; Time; Training; Transportation; Update; Visit; Work; Wrist; advanced analytics; analytical method; base; compliance behavior; design; exercise capacity; heart function; heart rate monitor; hospital readmission; improved; longitudinal analysis; mHealth; mortality; mortality risk; novel; operation; prevent; programs; rate of change; sensor technology; smart watch; wearable device; wearable sensor technology

Project Summary/Abstract The objective of our work leverages mobile health technology to develop machine learning models for longitudinal trajectories of recovery like those needed in cardiac rehabilitation. The investigation uses mobile health technology to quantify trajectories of recovery measures, personalizing understanding of exercise capacity and cardiac function. Exercise-based cardiac rehabilitation programs reduce cardiovascular mortality risks and improve patient outcomes in such longitudinal fashion, through increased exercise capacity as measured by peak V02 improvements over the course of care. These programs have recently been extended to include heart failure with reduced ejection fraction (HFrEF) patients. Despite the reduction in mortality and readmissions, participation and adherence in cardiac rehabilitation programs remains a challenge, especially in underserved communities because of limited program availability, the distance and transportation access to a program, its hours of operation, as well as a lack of diversity and gender-dominated programs. Home-based programs using smartphones have shown to increase adherence and achieve similar outcomes. While home-based programs also improved resting heart rate, systolic blood pressure, and levels of physical activity achieved through metabolic equivalent of tasks and peak V02 at the end of the study, users expressed a desire to have individualized education and treatment. Home-based systems still do not achieve real-time interaction, feedback, and monitoring that center-based rehabilitation does through a lack of feedback and necessity of self-reported exertion values. A system is needed that quantify measures of exercise capacity, which can lead to recovery, dynamically throughout the course of treatment. This proposal develops an unobtrusive system, with new mobile health technology sensors, and trains analytic models that allow for personalized quantification of rehabilitation trajectories in HFrEF patients, which can monitor patient adherence and improvement in measures during exercise as well as while at rest. This system investigates the improvement over the course of a 12-week cardiac rehabilitation study and designs trajectories of recovery to understand improvements in peak V02 and exercise capacity in HFrEF patients by also measuring improvements of measurements of heart rate and blood pressure while at rest. This allows for an investigation of additional measures, over time, that may better quantify recovery in HFrEF patients that can be used for center-based rehabilitation or home-based rehabilitation. This can provide a significant enhancement of metrics that define recovery for HFrEF patients with estimations to metrics that are difficult to collect and evaluate.

项目总结/摘要 我们工作的目标是利用移动的健康技术开发机器学习模型， 纵向恢复轨迹，如心脏康复所需。调查使用移动的 健康技术，以量化恢复措施的轨迹，个性化的运动能力的理解 和心脏功能。以心脏病为基础的心脏康复计划降低了心血管死亡风险， 以这种纵向方式改善患者结果，通过增加运动能力， 在护理过程中的峰值V02改善。这些计划最近已扩大到包括心脏 射血分数降低（HFrEF）患者。尽管死亡率和再入院率有所下降， 参与和坚持心脏康复计划仍然是一个挑战，特别是在服务不足的地区， 社区，因为有限的程序可用性，距离和交通访问的程序，其 工作时间长，缺乏多样性和性别主导的方案。基于家庭的程序使用 智能手机已经显示出增加依从性并实现类似的结果。虽然家庭项目 还改善了静息心率、收缩压和通过以下方式实现的体力活动水平： 在研究结束时，任务的代谢当量和峰值V02，用户表示希望 个性化的教育和治疗。基于家庭的系统仍然没有实现实时交互、反馈， 以及通过缺乏反馈和自我报告的必要性来监测基于中心的康复 发挥价值观。需要一个系统来量化运动能力的测量，这可以导致恢复， 在整个治疗过程中动态变化。该提案开发了一种不引人注目的系统，具有新的移动的 健康技术传感器，并训练分析模型，允许个性化的康复量化 HFrEF患者的轨迹，可以监测患者依从性和治疗期间措施的改善 在休息时也可以锻炼。该系统调查了12周心脏移植过程中的改善情况。 康复研究和设计恢复轨迹，以了解V02峰值和运动的改善 通过测量心率和血压测量值的改善， 在休息的时候。这样就可以随着时间的推移对可能更好地量化恢复的其他措施进行调查 在HFrEF患者中，可用于基于中心的康复或基于家庭的康复。这可以提供 显著增强了定义HFrEF患者恢复的指标， 很难收集和评估。