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

> **NIH NIH R21** · TEXAS ENGINEERING EXPERIMENT STATION · 2020 · $177,045

## Abstract

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.

## Key facts

- **NIH application ID:** 10018016
- **Project number:** 5R21EB028486-02
- **Recipient organization:** TEXAS ENGINEERING EXPERIMENT STATION
- **Principal Investigator:** Bobak Jack Mortazavi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $177,045
- **Award type:** 5
- **Project period:** 2019-09-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10018016

## Citation

> US National Institutes of Health, RePORTER application 10018016, Estimating Trajectory of Recovery in Cardiac Rehabilitation using Mobile Health Technology and Personalized Machine Learning (5R21EB028486-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10018016. Licensed CC0.

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