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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiya Tao reports financial support was provided by Chinese Scholarship Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Analysis of institutional authors

Tao, XiyaAuthorSaenz-Lechon, NicolasAuthorEckert, MartinaCorresponding Author

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June 3, 2025
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Mapping the landscape of Artificial intelligence for serious games in Health: An enhanced meta review

Publicated to:Computers In Human Behavior Reports. 18 100696- - 2025-05-01 18(), DOI: 10.1016/j.chbr.2025.100696

Authors: Tao, Xiya; Saenz-Lechon, Nicolas; Eckert, Martina

Affiliations

Univ Politecn Madrid, Calle Alan Turing 4, Madrid 28031, Spain - Author

Abstract

This paper presents an enhanced meta-review of artificial intelligence (AI) applications in serious games (SGs) for health, focusing on adaptation, personalisation, and real-time data processing to improve rehabilitation outcomes. A systematic review methodology, following PRISMA-ScR guidelines, was employed to analyse studies from 2017 to 2025, identifying the AI algorithms most frequently used to personalise gaming experiences, improve patient engagement, and increase treatment efficacy. Our proposal categorises the algorithms based on their role in the game environment, which can either be game control or user assessment. Game control algorithms adapt the game environment and difficulty, while user assessment algorithms gather information about the player's state, such as performance, mood, or physiological data, to evaluate the treatment progress. The review also examines the growing trend of using multimodal data as input for machine learning models. Results show that a few well-known algorithms, such as Decision Trees (DT), Artificial Neural Networks (ANN), Fuzzy Logic (FL), Na & iuml;ve Bayes (NB), and Support Vector Machines (SVM), are frequently used. However, there is a clear distinction in their purposes: while FL is typically applied to game control tasks, SVMs are mainly used for user assessment. This review offers valuable insights for researchers in the field, providing a comprehensive overview of the suitability of different AI algorithms for various tasks in SGs, with a particular focus on personalisation and motivation.

Keywords

AdaptationArtificial intelligenceExergamesGamificationGamingHealthIndividualizationMachine learningMultimodal datPersonalisationSerious games

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Computers In Human Behavior Reports due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 13/221, thus managing to position itself as a Q1 (Primer Cuartil), in the category Psychology, Multidisciplinary. Notably, the journal is positioned above the 90th percentile.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-21:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 21 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/89440/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 36
  • Downloads: 63

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (TAO, XIYA) and Last Author (ECKERT, MARTINA).

the author responsible for correspondence tasks has been ECKERT, MARTINA.