## 4 Discussion
In four works [35; 39; 44; 50], algorithms classified as \"Unsupervised learning\" were used, specifically Aged LookBackApriori (ALBA), K-means clustering (K-MC), and genetic algorithm (GA). Another four works [31; 33; 43; 54] employed \"Instance based learning\" algorithms, such as the K-nearest neighbor algorithm (K-NN). In one work, [PERSON] et al. [39], an algorithm classified as \"Ensemble learning\" eXtreme gradient boosting classifier (XGBC) was used. Finally, in eight works [25; 26; 28; 32; 46; 48; 49; 57] \"Neural networks\" were used, specifically, recurrent neural networks (RNNs), convolutional neural networks (CNNs), fully connected neural networks (FCNN), gated recurrent units (GRUs), multilayer perceptron networks (MLPNs), C2R, which a is combination of CNN and RNN, gated recurrent neural networks (GRUNNs), artificial neural networks (ANNs), and long short-term memory (LSTM). There is a significant heterogeneity in the use of different types of algorithms in each work. There is no clearly preferred option in the domain. Moreover, it is observable that the use of one type of algorithm does not exclude the use of others, even within the same proposal, as can be seen in works where up to three different types of ML (supervised, unsupervised, and ensemble learning) algorithm are used [39]. A wide range of studies using various types of algorithms has been noted. This diversity underscores that the field is still in development, with no clear consensus yet on which algorithm is best suited for specific applications. The common practice of using multiple algorithm types within the same study further reflects efforts to compare and assess the differences in performance of the generated recommendations. Additionally, there is a notable presence of works that use recommendation techniques other than ML, such as AHPSort [30], PASP [42], or the \"Fuzzy inference engine\" [41]. RQ3: Is gamification used to encourage healthy practices? Regarding the use of a gamified environment, as shown in Table 2, its implementation is rather limited. Only 4 of the 34 studies analyzed [27; 34; 56; 26] utilize this approach to encourage user engagement and adherence to the healthy practices recommended by the system. Among the various gamification elements available, only missions are used to encourage user participation [27], personalized feedback [56], \"Exergames\" [35], and personalized messages and rewards to motivate users to perform the activities proposed in their system [34]. As noted in some works [60, 61, 62], several elements can be utilized to design a gamification environment that promotes user participation. Key components such as badges, leaderboards, goal setting, challenges, scoreboards, and rewards have been identified as critical for enhancing engagement in PA practices. Furthermore, as discussed in The Physical Activity Messaging Framework [63], following specific guidelines when sending messages is also essential to encourage this practice and effectively engage users. The absence of simpler elements, such as leaderboards, is notable. Also noteworthy is the lack of any type of gamification environment in the two works that use blockchain technology as a support database [53, 54], despite the fact that blockchain technology facilitates proposals such as transparent leaderboards, or the delivery of rewards automatically and without user intervention by means of oracles and smart contracts, due to its characteristics of automation, transparency, reliability, and security. Given the importance attributed to gamification in the literature as a key element for engaging users in various recommendation systems, its implementation would be highly beneficial. However, its absence represents a major shortcoming in the analyzed studies. Regarding the ethical issues related to data protection, the lack of gamification in the two works [53, 54] that use blockchain technology is particularly noteworthy, as there are studies that demonstrate how to comply with data protection laws through the use of this technology [64].