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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $733,331

## Abstract

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 tailo...

## Key facts

- **NIH application ID:** 10122982
- **Project number:** 5R01HL141773-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Corey Wells Arnold
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $733,331
- **Award type:** 5
- **Project period:** 2019-04-05 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10122982

## Citation

> US National Institutes of Health, RePORTER application 10122982, mHealth for Heart Failure: Predictive Models of Readmission Risk and Self-care Using Consumer Activity Trackers (5R01HL141773-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10122982. Licensed CC0.

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