Investigating Contextual Cues as Indicators for Ema Delivery

Varun Mishra, Dartmouth College
Byron Lowens, Clemson University
Sarah Lord, Dartmouth College
Kelly Caine, Clemson University
David Kotz, Dartmouth College

report by Dartmouth Department of Computer Science


In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) prompt. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant's activity, conversation status, audio, and location, we can predict whether an EMA prompt triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.410. Using this knowledge, the researchers conducting field studies can efficiently schedule EMA prompts and achieve higher response rates.