Anticipating habit formation: a psychological computing approach to behavior change support

C. Zhang, A. van Wissen, D. Lakens, J. Vanschoren, B.E.R. Ruyter, de, W.A. IJsselsteijn

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Abstract

Mobile computing systems hold the promise of becoming a cost-effective solution for supporting behavior change towards more healthy lifestyles. We present here an approach where the system implements a formal model of habit formation based on psychology theories, anticipates the behaviors and cognitive states of the users, and picks interventions based on model predictions. First, we discuss the motivation and system requirements for the approach. Next, we propose in detail an underlying computational model of habit formation which constitutes the key component of the system. Finally, future work and challenges will be discussed, focusing on the empirical validation of the model and the mapping between the model and intervention techniques.
Original languageEnglish
Title of host publicationProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1247-1254
ISBN (Electronic)978-1-4503-4462-3
DOIs
Publication statusPublished - 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016
http://ubicomp.org/ubicomp2016/index.php

Conference

Conference2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016)
Abbreviated titleUbiComp2016
CountryGermany
CityHeidelberg
Period12/09/1616/09/16
Internet address

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  • Cite this

    Zhang, C., van Wissen, A., Lakens, D., Vanschoren, J., Ruyter, de, B. E. R., & IJsselsteijn, W. A. (2016). Anticipating habit formation: a psychological computing approach to behavior change support. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1247-1254). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2968439