Contextual Analysis to Understand Compliance with Smartphone-based Ecological Momentary Assessment

Abstract

Mobile device-based ecological momentary assessment (mobile EMA) is increasingly utilized to capture in situ information about a person's physical and mental health states. Mobile EMA has methodological advantages over traditional survey methods (e.g., decreased recall bias); however, these advantages are reduced by participant noncompliance with EMA protocols. There is a dearth of information about how different participant contexts predict compliance. We examine how different spatiotemporal contexts and participant-phone interactions predict EMA response rate and response latency. Utilizing data from 65 participants during a two-week study, we first extract features from smartphone sensors that characterize participant context (location, social context, activity). We then build and evaluate a classifier to predict participant response rate and response latency for EMA-delivered prompts based on the context features, achieving 78% accuracy. We discuss the implications of our results for improving participant compliance in future health studies that deploy mobile EMAs.