Player-Type-based Personalization of Gamification in Fitness Apps

Nadine Sienel, Patrick Münster, Gottfried Zimmermann

2021

Abstract

This paper examines the effect of personalized gamification on an individual’s motivation in the context of fitness apps. In a first study, we evaluate the four categorization models "Bartle Player Types", "Big Five", "Hexad User Types", and "BrainHex" on their ability to predict individual gamification preferences of users and develop a new prediction model called “MoMo”. Bartle, BrainHex, and MoMo are validated empirically in a second study, employing off-the-shelf fitness apps with gamification elements. The results of both studies indicate that a prediction is possible using the categorization models. Among all models, MoMo performs best in predicting individual gamification preferences, followed by BrainHex. Results of the second study indicate that, although the models MoMo and BrainHex perform better in predicting the theoretical rating of gamification elements than the random model, the prediction of the real motivation value in a specific fitness app is more difficult. This may be due to the concrete implementation of the elements in the second study, and due to the general problem of (theoretically) rating gamification elements without having experienced them in a real application.

Paper Citation

in Harvard Style

Sienel N., Münster P. and Zimmermann G. (2021). Player-Type-based Personalization of Gamification in Fitness Apps. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF; ISBN 978-989-758-490-9, SciTePress, pages 361-368. DOI: 10.5220/0010230603610368

in Bibtex Style

@conference{healthinf21,
author={Nadine Sienel and Patrick Münster and Gottfried Zimmermann},
title={Player-Type-based Personalization of Gamification in Fitness Apps},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF},
year={2021},
pages={361-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010230603610368},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF
TI - Player-Type-based Personalization of Gamification in Fitness Apps
SN - 978-989-758-490-9
AU - Sienel N.
AU - Münster P.
AU - Zimmermann G.
PY - 2021
SP - 361
EP - 368
DO - 10.5220/0010230603610368
PB - SciTePress