Personalization in Serious Games and Gamification for Healthcare: A Three-Tiered Review of Models, Methods and Opportunities
For researchers and developers of healthcare serious games and gamification, this review provides a structured overview of personalization strategies and highlights the need for more modular and comparable approaches.
This review of 50 articles on personalized serious games and gamification for healthcare introduces a three-tiered classification model and finds that data-driven approaches are most common (22/50), while knowledge-driven and hybrid methods are more prevalent in rehabilitation. The authors identify limited reusability as a key challenge and propose opportunities for progress including shareable knowledge assets and hybrid approaches.
Serious games and gamification (SGG) have shown to have positive effects on health outcomes of eHealth applications. However, research has shown that a shift towards a personalized approach is needed, considering the diversity of users. This introduces new challenges to the domain of SGG as research is needed on how such personalization is achieved. A literature search was conducted to provide an overview of personalization strategies. In total, 50 articles were identified, 35 reported on a serious game and 15 focused on gamification. We introduce a three-tiered classification model, including a model level, a personalization paradigm level, and algorithmic framework level to synthesize how personalization is implemented. Data-driven approaches are most common overall (22/50), with knowledge-driven and hybrid methods more prevalent in rehabilitation, reflecting safety and explainability requirements. Popular modeling choices include Hexad-based player modeling and ontologies for expert knowledge integration. Despite encouraging results, reusability remains limited, impeding comparison and knowledge transfer. This review outlines opportunities for progress:shareable knowledge assets, swap-friendly personalization engines, and clinically bounded hybrid approaches, alongside cautious use of generative AI to accelerate design while maintaining safety and explainability. This classification framework and synthesis aims to guide more modular, comparable, and clinically aligned personalized SGG.