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 EMA trigger. 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 if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.
Direct link to paper

Related Publications

Exploring the State-of-Receptivity for mHealth Interventions
Florian Kunzler, Varun Mishra, Jan-Niklas Kramer, Davis Kotz, Elgar Fleisch, Tobias Kowatsch
In this work, we explore the factors affecting users’ receptivity towards Just-In-Time Adaptive Interventions (JITAI).

Our Supporters