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Mood modeling: accuracy depends on active logging and reflection

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Abstract

Current behavior change systems often demand extremely advanced sensemaking skills, requiring users to interpret personal datasets in order to understand and change behavior. We describe EmotiCal, a system to help people better manage their emotions, that finesses such complex sensemaking by directly recommending specific mood-boosting behaviors to users. This paper first describes how we develop the accurate mood models that underlie these mood-boosting recommendations. We go on to analyze what types of information contribute most to the predictive power of such models, and how we might design systems to reliably collect such predictive information. Our results show that we can derive very accurate mood models with relatively small samples of just 70 users. These models explain 61% of variance by combining: (a) user reflection about the effects of different activities on mood, (b) user explanations of how different activities affect mood, and (c) individual differences. We discuss the implications of these findings for the design of behavior change systems, as well as for theory and practice. Contrary to many recent approaches, our findings argue for the importance of active user reflection rather than passive sensing.

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  1. University of California Santa Cruz, 1156 High St, Santa Cruz, CA, 95060, USA

    Aaron Springer, Victoria Hollis & Steve Whittaker

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  1. Aaron Springer
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  2. Victoria Hollis
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  3. Steve Whittaker
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Correspondence to Aaron Springer.

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Springer, A., Hollis, V. & Whittaker, S. Mood modeling: accuracy depends on active logging and reflection. Pers Ubiquit Comput 22, 723–737 (2018). https://doi.org/10.1007/s00779-018-1123-8

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