Player-Type-based Personalization of Gamification in Fitness Apps

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.