## 3 Results

Table 2: Synthesis of the studies. Ordered in chronological order and according to domain. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ashothes/Tuer** & **Domain** & **Item Recommended** & **Recommendation Model** & **Data from User** & **Support Database** & **TRL** & **Confication (Greenus)** \\ \hline ([PERSON] et al., 2018)[6] & & Workout routines & NEM Techniquesweet ENN, C2R & HealthStatus, super performances and & & NEM (conventional) & 5 & NEM \\ ([PERSON] et al., 2019)[40] & & Workout routines & NEM Techniquesweet ANN and C1R & User preferences and user daily activity & & NEM (conventional) & 4 & NEM \\ ([PERSON] et al., 2019)[40] & PE & Play event queries, blocking, multiple & & Expert system using Outsology & & & & \\ ([PERSON] et al., 2021)[40] & & Working, and the intensity of the activity & Expert system using Outsology & & & & & \\  & ([PERSON] et al., 2021)[40] & Running, hiding and indoor service & A four-layer neural network & Socialomy and and real heart rate & & Conventional & 4 & NEM \\ \hline ([PERSON] et al., 2020)[40] & & Healthier diet and FE & NEM & General links based on diet and & NEM (conventional) & 4 & Yue Personalized technology \\ \hline ([PERSON] et al., 2020)[40] & & Healthier diet and VR & NEM & Physical information, labels, & & & & \\  & ([PERSON] et al., 2013)[50] & Daily diet and VR & NEM & Operational and cellular power & & & & \\  & ([PERSON] et al., 2017)[40] & & Diet plans and vascular networks & NEM Techniques used: NEM & Socialomy/engold-and PI levels & & & \\ ([PERSON] et al., 2021)[40] & & Pre-and diet & Diet plans and vascular networks & KGR Techniques and DTC, LLS, PMA, & & & & \\  & ([PERSON] et al., 2021)[40] & & Workout routines and & and KNN & NEM & Distributed (blockchain) & 3 & NEM \\ ([PERSON] et al., 2021)[40] &  
* Diet. Under this label, six works are placed. [PERSON] et al. [31] proposed a smart recommender system, called a smart recommender system of hybrid learning, which uses hybrid learning methods to improve personal well-being services, particularly in health food recommendations. The system addresses challenges like the cold start problem and scalability in recommender systems by combining content-based and collaborative filtering experimental results show that SRHL improves recommendation accuracy by 14.61% compared to traditional methods. [PERSON] et al. [30] presented a food recommender system to generate personalized daily meal plans. It employs a multi-criteria decision-making approach and optimization techniques. Experimental results demonstrate its effectiveness in providing personalized, nutritionally balanced recommendations. In [PERSON] et al. [29], a collaborative filtering-based recommender system for personalized dietary advice using data from the ELSA-Brasil study was evaluated. It compares user-based and item-based algorithms, finding both effective but with slight advantages for user-based filtering. [PERSON] and [PERSON] [28] developed FoodRecNet, a deep learning-based food recommender system that personalizes recommendations using a wide range of user and food characteristics, including preferences, health conditions, and food images. Evaluations show FoodRecNet significantly improves recommendation accuracy compared to existing methods, addressing challenges like the cold-start problem and enhancing user experience. In [PERSON] et al. [27], the CarpeDiem app was developed, a mobile-health framework designed to promote nutritional behavior change through gamification. It uses personalized dietary missions, based on key food groups, and advanced AI techniques to foster healthier eating habits and prevent chronic diseases. The app emphasizes gradual, sustainable changes and personalization to improve long-term adherence. [PERSON] et al. [26] presented a model that recommends nutritious meals for Indian women athletes and active youth using LSTM and LSTM with GRU.