A Framework for Predicting Adherence in Remote Health Monitoring Systems

Abstract

Remote health monitoring (RHM) systems have shown potential effectiveness in disease management and prevention. In several studies RHM systems have been shown to reduce risk factors for cardiovascular disease (CVD) for a subset of the study participants. However, many RHM study participants fail to adhere to the prescribed study protocol or end up dropping from the study prior to its completion. In a recent Women's Heart Health study of 90 individuals in the community, we developed Wanda-CVD, an enhancement to our previous RHM system. Wanda-CVD is a smartphone-based RHM system designed to assist participants to reduce identified CVD risk factors by motivating participants through wireless coaching using feedback and prompts as social support. Many participants adhered to the study protocol, however, many did not completely adhere, and some even dropped prior to study completion. In this paper, we present a framework for analyzing baseline features to predict adherence to prescribed medical protocols that can be applied to other RHM systems. Such a prediction tool can aid study coordinators and clinicians in identifying participants who will need further study support, leading potentially to participants deriving maximal benefit from the RHM system, potentially saving healthcare costs, clinician and participant time and resources. We analyze key contextual features that predict with an accuracy of 85.2% which participants are more likely to adhere to the study protocol. Results from the Women's Heart Health study demonstrate that factors such as perceived health threat of heart disease, and perceived social support are among the factors that aid in predicting patient RHM protocol adherence in a group of African American women ages 25-45.