Abstract

Videogames, besides a form of entertainment, can be of added value to purposes such as physical rehabilitation (serious games). In this context, custom made serious games may be of great value, especially if they combine the necessary actions that the user must perform with engaging and captivating gameplay. Given data protection and ethical constraints, assessing a patient’s gaming preferences and personal traits to predict preferred game design by big data from online social profiles is not possible. In this matter, we developed a questionnaire that tries to infer patients’ gaming preferences from limited personal information, which we denote as “Small Data”. The questionnaire was tested in a pilot study with 17 healthy participants and results suggest that the collected information may help decide what kind of videogame better suits a particular patient, potentially aiding the process of developing Serious Games for healthcare.

Recommended Citation

Vieira, C., Alves, P., Alves, J., & Perrota, A. (2023). Small Data as a Tool to Predict Player Game Design Preferences: A Qualitative Pilot Study. In A. R. da Silva, M. M. da Silva, J. Estima, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development, Organizational Aspects and Societal Trends (ISD2023 Proceedings). Lisbon, Portugal: Instituto Superior Técnico. ISBN: 978-989-33-5509-1. https://doi.org/10.62036/ISD.2023.52

Paper Type

Full Paper

DOI

10.62036/ISD.2023.52

Additional Files
References_DOI_ISD.2023.52.pdf (23 kB)
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Small Data as a Tool to Predict Player Game Design Preferences: A Qualitative Pilot Study

Videogames, besides a form of entertainment, can be of added value to purposes such as physical rehabilitation (serious games). In this context, custom made serious games may be of great value, especially if they combine the necessary actions that the user must perform with engaging and captivating gameplay. Given data protection and ethical constraints, assessing a patient’s gaming preferences and personal traits to predict preferred game design by big data from online social profiles is not possible. In this matter, we developed a questionnaire that tries to infer patients’ gaming preferences from limited personal information, which we denote as “Small Data”. The questionnaire was tested in a pilot study with 17 healthy participants and results suggest that the collected information may help decide what kind of videogame better suits a particular patient, potentially aiding the process of developing Serious Games for healthcare.