AI Article Synopsis
- The study investigates the ability to use contextual information about participants to predict their responses to Ecological Momentary Assessment (EMA) triggers.
- By analyzing a publicly available dataset, the researchers discover that basic features such as activity, conversation status, audio, and location can increase prediction precision to 0.647, surpassing a baseline of 0.41.
- This finding suggests that researchers can optimize the timing of EMAs to enhance response rates in field studies.
Article Abstract
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) 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5899885 | PMC |
| http://dx.doi.org/10.1145/3123024.3124571 | DOI Listing |
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