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• W2969591102 startingPage "1206" @default.
• W2969591102 abstract "Ecological Momentary Assessment (EMA) is an in-the-moment data collection method which avoids retrospective biases and maximizes ecological validity. A challenge in designing EMA systems is finding a time to ask EMA questions that increases participant engagement and improves the quality of data collection. In this work, we introduce SEP-EMA, a machine learning-based method for providing transition-based context-aware EMA prompt timings. We compare our proposed technique with traditional time-based prompting for 19 individuals living in smart homes. Results reveal that SEP-EMA increased participant response rate by 7.19% compared to time-based prompting. Our findings suggest that prompting during activity transitions makes the EMA process more usable and effective by increasing EMA response rates and mitigating loss of data due to low response rates." @default.
• W2969591102 created "2019-08-29" @default.
• W2969591102 creator A5048183050 @default.
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• W2969591102 date "2020-04-01" @default.
• W2969591102 modified "2023-10-17" @default.
• W2969591102 title "Context-Aware Delivery of Ecological Momentary Assessment" @default.
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• W2969591102 doi "https://doi.org/10.1109/jbhi.2019.2937116" @default.
• W2969591102 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8059357" @default.
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• W2969591102 hasPublicationYear "2020" @default.
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