## 3 Results  
As mentioned earlier, the objective of this study was to conduct a systematic review using the PRISMA methodology to analyze and synthesize findings from selected studies on the innovative use of recommendation systems. These systems employ various recommendation algorithms, as well as ML, DL, or other AI techniques, in fields such as physical activity, exercise, active aging, health, mental health, dietary habits, and sleep habits. As shown in Figure 2, the initial search identified 3109 articles from the selected databases. Figure 2: Flow diagram of the systematic review according to PRISMA guidelines. Once duplicates were removed, 2026 articles were evaluated by their title and abstract, and assigned a score from 0 to 3 based on their relevance to the research. After scoring, the majority of the articles (1918) received less than 1 point and were directly discarded. Only three articles received 1.5 points, 24 articles scored 2 points, 33 papers were rated with 2.5 points, and 48 received the highest score of 3 points. In total, 108 works advanced to the full-text reading phase. These 108 selected works were thoroughly analyzed. In this analysis, each paper was reviewed by a different pair of reviewers. During this full reading phase, 74 works were excluded for various reasons, as shown in Figure 1, mainly for not meeting all the inclusion/exclusion criteria. Thus, the final corpus for analysis in this systematic review was narrowed down to 34 works [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. As can be seen in Table 2, the included works are grouped according to the domain under ten different domains. Under the \"Diet\" label, seven works are grouped [25; 26; 27; 28; 29; 30; 31]. In the \"PA\" category, another six studies are included [32; 33; 34; 35; 36; 37]. Under the label \"PA, social activities and diet\", one work is found [38]. Four other works are grouped under \"PA and diet\" [39; 40; 41; 42]. Two works are classified under the label \"PA and sleep quality\" [43; 44]. The work by [PERSON] et al. [45] is classified under \"PA, PE and diet\". Four works [46; 47; 48; 49] are grouped under the \"PE\" label. Seven more works are classified under \"PE and diet\" [50; 51; 52; 53; 54; 55; 56]. Another work is classified under \"PE and mental health\" [57]. Finally, one work under the \"Sport\" label [58]. Within the recommendation model, a wide variety of approaches are found, highlighting the well-known CF, content-based filtering, and knowledge-based filtering models. Within these models, various algorithms are developed, specified in Table 1 as \"Technique used\". The most notable data collected from users, for whom the recommendations are intended, include sociodemographic information, Body Mass Index (BMI), user preferences, and physical activity (PA) levels. Regarding the databases used to store these data, most works included in this systematic review do not specify whether they use conventional or distributed databases. However, based on the information provided in the articles, it can be inferred that many use conventional databases. Notably, three studies mention the use of distributed databases. One study utilizes the \"Microsoft Azure\" database [37], and two others use blockchain technology to store their data [53; 54]. Regarding the development level of the included proposals, they are found at TRL-3 (experimental proof of concept), TRL-4 (technology validated in the lab), and TRL-5 (technology validated in a relevant environment (industriality relevant environment in the case of key enabling technologies)) [59]. In the last column of Table 2, there is information related to the use of gamification to engage users and the specific elements used for that purpose. Notably, only four studies utilize this feature: [PERSON] et al. [27] use missions to encourage user participation, [PERSON] et al. [56] aim to engage users through personalized feedback, [PERSON] et al.