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.
Quantification of trajectories of recovery for patients through wearable mobile health technologies can provide personal understanding of improvements and challenges faced in recovery by patients with heart failure with reduced ejection fraction. Such a system will indicate to each patient the improvement in exercise capacity needed to continue preventing any recurrent events, allowing participants to have the understanding and individualized feeling they get from clinic visits and center-based cardiac rehabilitation at home or any other location they wish to participate in recovery activities. The exploration of additional markers of recovery can continue to provide quantification of heart failure conditions useful for clinicians and patients alike.
