Personalized heart failure severity estimates using passive smartphone data

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 social networking 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 activity 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 problem. 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.