mHealth for Heart Failure: Predictive Models of Readmission Risk and Self-care Using Consumer Activity Trackers (R01 HL141773; PI: Corey Arnold)
mHealth tracker

Heart failure is a debilitating disease that affects over five million people in the United States and in 2012 had a direct cost of over $30.7 billion annually. Home monitoring of such patients has the potential to reduce costs and improve quality of life by reducing preventable hospital readmissions. The goals of this project are to: 1) demonstrate that patients are adherent to a home monitoring regimen when using minimally-invasive monitoring technologies; 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 a prospective pilot study.