JMIR Publications

ABSTRACT

Background:

The use of serious games in healthcare is on the rise, as they motivate treatment adherence, reduce treatment costs and educate patients and families. However, current serious games fail to offer personalized interventions, ignoring the need to abandon the “one-size-fits-all” approach. Moreover, these games, with a primary objective other than pure entertainment, are costly and complex to develop and require the constant involvement of a multidisciplinary team. Furthermore, no standardized approach exists on how serious games can be personalized as existing literature focuses on specific use cases and scenarios. Finally, the domain of serious game development fails to consider any transfer of domain knowledge, which means this labor-intensive process has to be repeated for each serious game.

Objective:

This paper proposes a framework that aims to streamline the multidisciplinary design process of personalized serious games in healthcare and facilitates the reuse of domain knowledge and personalization algorithms. By focusing on the transfer of knowledge to new serious games by reusing components and personalization algorithms, the comparison and evaluation of different personalization strategies can be simplified and expedited. In doing so, the first steps are taken in advancing the state-of-the-art of knowledge regarding personalized serious games in healthcare.

Methods:

The proposed aims to answer three questions that need to be asked when designing serious games: (1) Why is the game personalized? (2) What parameters can be used for personalization? And (3) How is the personalization achieved? Respectively, the three involved stakeholders, namely, the domain expert, the (game) developer and the software engineer are each assigned a question and, following from this, their responsibilities regarding the design of the serious game. The (game) developer is responsible for all the game-related components; the domain expert is in charge of the modelling of the domain knowledge using simple or complex concepts, such as ontologies. Finally, the software engineer manages the personalization algorithms or models, integrated into the system. The framework is illustrated by developing and evaluating a proof of concept.

Results:

The proof of concept, a serious game for shoulder rehabilitation has been evaluated using simulations of heart rate and game scores, to assess how personalization is achieved. The simulations indicate the value of both online personalization, i.e. real-time personalization and offline personalization. The proof of concept illustrates how the interaction between the different components works and how the framework is used to simplify the design process.

Conclusions:

The proposed framework for personalized serious games in healthcare identifies the responsibilities of the involved stakeholders in the design process, using three key questions for personalization. The framework focuses on the transferability of knowledge and reusability of personalization algorithms to simplify the design process of personalized serious games.