A high level of motivation and frequent training are relevant in software-based rehabilitation to improve cognitive functioning after acquired brain injury. We evaluated the benefit of tailored user-centered gamification elements in a clinical study with N=83 outpatients undergoing three weeks of cognitive training in their home environment. The use of gamification in relation to the patient's player type was explored in three steps. First, we determined the individual player types and related requests for specific game elements by means of questionnaires. Afterwards, we examined the effect of gamified training based on a non-player character and training progress within a metaphor. We considered secondly the individual perception and emotional effect and thirdly the performance based on training duration. 37 elements were requested by patients of all types, 18 elements were partially requested, and 4 elements were rejected. A comparison shows that the requested game elements partly differ between healthy persons and patients. Overall, gamification was perceived positively and gamified training leads to an increase in enjoyment compared to non-gamified training. In detail, however, there were different effects on the individual player types: socialisers experienced more enjoyment while achievers perceived higher competence throughout gamified cognitive training. Also, differences in performance in training duration were found. Within gamified training, socialisers trained significantly more than patients not primarily assigned to this type. In contrast, no significant difference was found for achievers. By showing modulating requests and effects in player types, our results support user-centered tailoring of game elements in the development of software-based cognitive training in rehabilitation.
CITATION STYLE
Gabele, M., Weicker, J., Wagner, S., Thoms, A., Huβlein, S., & Hansen, C. (2021). Effects and ways of tailored gamification in software-based training in cognitive rehabilitation. In UMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 158–168). Association for Computing Machinery, Inc. https://doi.org/10.1145/3450613.3456828
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