JMIR Publications

ABSTRACT

Background:

Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Early detection of HF symptoms and proactive management may reduce adverse events.

Objective:

In this study, we predict heart failure decompensation events from features derived from passive and active data collected by a smartphone-based framework.

Methods:

Twenty-eight participants were monitored using a smartphone app after discharge from hospitals, and each clinical event during the enrollment (N=110 clinical events) was recorded. Motion, social, location, and clinical survey data collected via the smartphone-based monitoring system were used to develop and validate an algorithm for classifying and predicting HF decompensation events (hospitalizations or clinic visit) versus clinic monitoring visits in which they were determined to be compensated or stable. Models based on single modality as well as early and late fusion approaches combining patient-reported outcomes and passive smartphone data were evaluated.

Results:

The highest area under the precision-recall curve (AUCPr) for classifying decompensation with a late fusion approach was 0.77 using leave one subject out cross-validation. The duration and number of calls were among the most informative features. Predictions two days ahead of the event showed the highest performance with an AUCPr of 0.8 for the late fusion approach.

Conclusions:

Passively collected data from smart-phones, especially when combined with weekly patient-reported outcomes, may reflect behavioral and physiological changes due to HF and thus could enable prediction of HF decompensation.