The authors wish to acknowledge the support of the National Science Foundation Award 1636933, NIH/NHLBI award K23 127251 and the Georgia Research Alliance.
Keywords:
Heart failure
mobile health (mHealth)
regres- sion
actigraphy
geolocation
social interaction
Personalized heart failure severity estimates using passive smartphone data
Abstract
—Heart failure (HF) is one of the leading causes of
mortality in the United States with a high economic burden
due to readmissions. We present a novel approach to remotely
monitor quality of life in patients with HF using a smartphone
app and a scalable cloud-based architecture. In a preliminary
study, we assess continuous data from 10 HF subjects over a
period of up to a year. Over 680 million samples of physical
movement data, 9,000 geographic location updates, and 11,000
individual contact activity/diversity events in the form of phone
calls were captured from the app. Personalized models were
constructed from these data to estimate self-reported quality
of life using the Kansas City Cardiomyopathy Questionnaire
(KCCQ), which has been shown to be a reliable health status
measure for HF patients. Generalized linear models using only
motion features were shown to reliably estimate the KCCQ
score with an out of sample mean absolute error of 5.71%.
Personalized models for estimating the HF severity as mild or
severe were also built as a proof of concept to detect when a
subject’s data indicated a clinical deterioration. Average out of
sample accuracy was 83% for this binary classification prob-
lem. Creation of personalized models from passive smartphone
data collected ‘in-the-wild’ to identify changes in HF severity
appears possible. This new approach holds promise as a low
burden and accurate method of monitoring HF symptoms,
which could aid clinicians in early assessment and prevention
of adverse outcomes.