## 4 Discussion
The primary aim of any system designed to recommend healthy activities, like the 34 examined in this review, should be to engage users and motivate them to participate in the suggested activities. This participation is key to improving their overall health and well-being. Beyond focusing on immediate benefits, these systems should also promote the development of lifelong healthy habits starting in childhood, with the broader goal of supporting healthy aging. This approach not only improves quality of life but also leads to substantial economic savings by lowering long-term healthcare costs. RQ4: What user data are used to make the recommendation? Regarding the user data used for making recommendations, these are of a very diverse nature, as can be seen in Table 2. The data collected from users to make the recommendation include sociodemographic information, body mass index (BMI), user preferences, and their levels of PA, in general. It is important to note that these data are used simply to determine to which group or cluster the users should be assigned to receive their recommendation. This cluster is determined, as appropriate, by BMI, sociodemographic data, levels of physical activity, or preferences, whether related to activities, PA, or diet. Thus, the recommendation is not overly personalized for each user. By grouping users and making recommendations based on similarities, these suggestions turn out to be more generic and do not take into account all the particularities of each user, unlike what occurs in previous works by the authors [65]. In the referenced work, various dimensions of the subjects' health are considered, such as physical function, mental health, vitality, diet, and sleep, all assessed through validated and standardized questionnaires; injuries, illnesses, available material for training, and available facilities, as well as sociodemographic data. With all this information, it is possible to recommend challenges in a fully personalized and individualized way for each subject. Of all the works present in this review, only one of them [27] uses a questionnaire to collect objective user data, in this case related to dietary habits. It is possible that the absence of comprehensive data collected from validated questionnaires, which would provide complete information about the user, contributes to a discrepancy between the performance of these recommendations in controlled laboratory settings and their effectiveness in real-world scenarios. While these recommendations may perform well in a closed environment, they may fail to fully engage users, meet their expectations, or significantly impact their overall health when applied outside the laboratory. Among the limitations of this study, it is noteworthy that solutions not using new data analysis paradigms such as AI have not been explored. Therefore, solutions opting for simpler models or those that do not require massive computations may have been excluded from this analysis [65]. As another limitation, we lack a description of the tests performed with the algorithms in order to be able to make comparisons on their performance and real utility. This study shows that although there are many proposals, they often lack a systematic process and mechanisms to encourage adherence to them. Following the points raised in this discussion, it would be valuable to conduct further research to explore the effectiveness of the applications in real-world environments. Additionally, to maximize the success of their implementation, it is crucial to use all available methods to engage users effectively, such as incorporating gamification strategies.