Constraint-Based Verification of a Mobile App Game Designed for Nudging People to Attend Cancer Screening

Authors

  • Arnaud Gotlieb SIMULA
  • Marine Louarn SIMULA
  • Mari Nygard Cancer Registry of Norway
  • Tomas Ruiz-Lopez Cancer Registry of Norway
  • Sagar Sen SIMULA
  • Roberta Gori University of Pisa

DOI:

https://doi.org/10.1609/aaai.v31i2.19094

Abstract

In Norway, cervical cancer prevention involves the participation of as many eligible women aged 25-69 years as possible. However, reaching and inviting every eligible women to attend cervical cancer screening and HPV vaccination is difficult. Using social nudging and gamification in modern means of communication can encourage the participation of unscreened people. Simula Research Laboratory together with the Cancer Registry of Norway have developed FightHPV, a mobile app game intended to inform adolescent and eligible women about cervical cancer screening and HPV vaccination while they play and, to facilitate their further participation to prevention campaigns. However, game design and health information transfer can be hard to reconcile, as the design of each game episode is more guided by the release of information than gameplay and playing difficulty. In this paper, we propose a constraint-based model of FightHPV to evaluate the difficulty of each episode and to help the game designer in improving the player experience. This approach is relevant to facilitate social nudging of eligible women to participate to cervical cancer screening and HPV vaccination, as shown by the initial deployment of FightHPV and tests performed in focus groups. The design of this mobile app can thus be regarded as a new application case of Artificial Intelligence techniques such as gamification and constraint programming.

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Published

2017-02-11

How to Cite

Gotlieb, A., Louarn, M., Nygard, M., Ruiz-Lopez, T., Sen, S., & Gori, R. (2017). Constraint-Based Verification of a Mobile App Game Designed for Nudging People to Attend Cancer Screening. Proceedings of the AAAI Conference on Artificial Intelligence, 31(2), 4678-4685. https://doi.org/10.1609/aaai.v31i2.19094