## 3 Results
It offers personalized health recommendations, such as meal plans and exercise routines, tailored to users' vital signs, medical history, and preferences, aiming to improve overall well-being through a user-friendly interface. [PERSON] et al. [55] presented \"The Runner\", an RS designed for runners to provide personalized workout and nutrition plans based on user profiles, preferences, and goals. It combines expert knowledge with social dimensions, leveraging semantic web technologies to offer tailored recommendations and adapt to users' evolving needs. [PERSON], [PERSON] et al. [53] proposed an intelligent microservice architecture based on blockchain technology for healthcare applications, specifically focusing on predictive analytics for personalized fitness and diet data in an IoT environment. It integrates machine learning with blockchain to enhance data security, scalability, and performance, using Hyperledger Fabric to ensure secure and efficient data management and analysis. In [PERSON], [PERSON] et al. [54], a secure fitness framework combining IoT-enabled blockchain technology with machine learning was implemented. It aims to enhance data security, privacy, and real-time analysis in fitness applications. The framework utilizes smart contracts and an inference engine to provide personalized workout and diet recommendations, ensuring efficient data management and improved user experience in fitness environments. [PERSON] et al. [52] introduced an AI-based gym trainer and diet recommendation system designed to offer personalized PE routines and diet plans. It utilizes ML algorithms for workout monitoring and posture analysis, while also providing customized diet recommendations based on user inputs, aiming to enhance fitness and health outcomes efficiently. Ultimately, [PERSON] et al. [50] presented a ML-based system for personalized diet and workout recommendations. By analyzing user inputs like height, weight, and age, the system provides tailored dietary plans and PE routines. It employs K-MC and RFC to classify users' needs and suggest appropriate strategies for weight loss, weight gain, or maintenance. * Physical Exercise and Mental Health. In this domain, only one work was found. It belongs to [PERSON] and [PERSON] [57], who discussed the implementation of a dual-RNN system designed to recommend personalized PE routines and predict the likelihood of successful completion for each activity and mental health. By analyzing users' PE history and performance data, the system aims to offer tailored recommendations, enhancing users' adherence to fitness goals and improving health outcomes. Sport. Once again, only one research study was included within this domain. This is the work by [PERSON] and [PERSON] [58], which created an improved CF algorithm for sports training analysis. By optimizing traditional CF techniques, the proposed method enhances the efficiency and accuracy of personalized training recommendations for athletes. It uses data mining to analyze historical data, identifying athletes' preferences to suggest suitable training activities, ultimately improving training outcomes. After summarizing the characteristics of the articles that progressed to the final phase, in Figure 3, the reader can observe that the research related to the areas mentioned above follows an upward tendency.