## 4 Discussion  
In this section, we present an analysis of the data obtained from the 34 publications analyzed. We proposed four research questions (Table 1), which are discussed here:  
RQ1: To what extent have personalized healthy activity recommendation systems been developed? In this systematic review, we examined publications up to 18 August 2024, from major databases using the search query mentioned in Section 1. As illustrated in Figure 3, there is an upward trend in the number of publications in this field. This suggests that the relationship between recommendation systems utilizing AI or ML in areas such as physical activity, exercise, active aging, mental health, eating habits, and sleep habits is still in its early stages of development, with a growing number of research studies on the topic. Regarding the level of development and use of AI, as shown in Table 2, it can be seen that it is in an early phase of development, as indicated by the Technology Readiness Level (TRL) (between 3 and 5) of the publications included in this work. Three works are at TRL-3 (Experimental proof of concept) [39; 54; 57], 22 are at TRL-4 (Technology validated in lab) [25; 26; 28; 31; 33; 34; 35; 36; 40; 43; 44; 45; 50; 51; 53; 55; 56; 58], and eight are at TRL-5 (Technology validated in relevant environment) [27; 29; 30; 32; 41; 42; 47; 49; 52]. Many of the studies we examined lacked real-world testing. Instead of evaluating recommendations with actual users, they relied on calculations and comparisons of different recommendation methods using datasets on very limited testing scenarios. Therefore, it is not possible to obtain a clear evaluation of the actual performance in real world scenarios. As the reader may note, there is not a comparable set of parameters to establish a fair comparison or even a common (and mature) TLR level for all the evaluated options. Figure 3: Evolution of the publication year of the included studies. Only in two studies [41; 42] included validation with real users performed. In [PERSON] et al. [42], they tested the functioning of their recommendations with a real user in four different scenarios with satisfactory results. In [PERSON] et al. [41], they conducted an online survey with 117 subjects who were asked three questions about their satisfaction with the recommendation made. According to this survey, 63.53% of the participants showed a preference for the more diverse PA recommendation made over a less advanced one. Given the current state of technological advancement and the limited validation in real-world environments observed in the proposals reviewed in this study, it is clear that while these proposals hold significant societal value, they are still in the early stages of development. It is important to highlight that these tools have great potential to improve quality of life, particularly in terms of physical, mental, and behavioral health. Once fully developed and applied in real-world settings, these systems can encourage the adoption of healthy habits through various strategic approaches, underscoring their potential as valuable interventions. RQ2: What types of algorithms do they use? Do they use ML? Or other AI techniques? In the analyzed works, a wide range of algorithms are used, as shown in Table 2. In terms of recommendation techniques, these vary from CF, content-based filtering, rule-based filtering, knowledge-based filtering, to various hybrid systems. The latter are present in 16 of the 34 works analyzed. It is important to note that in 17 of the works, the implemented recommendation model is not mentioned. Works employing \"Supervised learning\" use algorithms such as decision tree classifier (DTC), random forest classifier (RFC), support vector machine (SVM), support vector classifier (SVC), Naive Bayes algorithm (NB), Gaussian Naive Bayes algorithm (GNB), linear regression (LR), and support vector regression (SVR). At least one of these algorithms was used in nine works [32; 33; 35; 39; 40; 45; 48; 50; 54].