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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.
Expanded version of the UbiTtention 2017 paper by the same title.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
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Varun Mishra, Byron Lowens, Sarah Lord, Kelly Caine, and David Kotz, "Investigating Contextual Cues as Indicators for EMA Delivery." Dartmouth Computer Science Technical Report TR2018-842, April 2018.
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