## 3 Results
It focuses on personalizing diet plans based on age, height, and weight to enhance performance and recovery, using Indian cuisine for culturally relevant suggestions. And finally, [PERSON] et al. [25] discussed the development of a food recommendation system using a MLPN. The system helps individuals and caregivers make informed meal choices based on nutritional goals, personal preferences, and daily needs. The model achieves over 60% accuracy in predicting complete meals, highlighting the importance of personalized nutrition plans. * Physical Activity. Under this domain, we can find another six works. The first one belongs to [PERSON] et al. [37], who developed a multimodal hybrid reasoning methodology integrating rule-based, case-based, and preference-based reasoning to offer personalized physical activity recommendations. The system, validated in a weight management scenario, significantly improves recommendation accuracy by tailoring advice to individual user profiles, preferences, and goals. In the work of [PERSON] et al. [36], an adaptive, data-driven system designed to provide personalized activity recommendations using fitness tracker data is presented. The system generates customized hour-by-hour PA plans, adjusts recommendations based on real-time data, and aims to improve user adherence to daily PA targets by dynamically updating goals throughout the day. [PERSON] et al. [35] developed an ML-based approach to recommend personalized PA for exergame players. It emphasizes the importance of player modeling to enhance engagement and effectiveness in promoting PA. The study demonstrates improved accuracy in PA recommendations by tailoring them to individual preferences and behaviors. In [PERSON], [PERSON] et al. [33], an eCoaching system that uses machine learning and semantic ontology to monitor PA and generate personalized recommendations was introduced. By integrating sensor data and personal preferences, the system tailors lifestyle advice to help users achieve their health goals, with improved performance through transfer and incremental learning methods. [PERSON], [PERSON] et al. [34] detailed the development of the ProHealth eCoach app, designed to promote a healthy lifestyle through personalized activity recommendations. Using an iterative user-centereddesign approach, the app integrates user preferences, feedback, and AI-driven insights to deliver tailored coaching, aiming to enhance user engagement and motivation in achieving health goals. And finally, in [PERSON] et al. [32], a deep learning-based system for dynamically recommending personalized PA through a mobile fitness app is proposed. By clustering users based on physical and physiological data, the system tailors daily activity plans, adjusting them in real-time to help users achieve their fitness goals, showing improved accuracy over traditional methods. * Physical Activity, Social Activities and Diet. Under this label, only one work is placed. The mentioned work belongs to [PERSON] et al. [38], who introduced a personalized recommendation algorithm for elderly diets and physical and social exercises using CF techniques. By combining user and item-based CF, the system offers tailored suggestions, achieving 63.1% accuracy. The fusion algorithm outperforms traditional methods, enhancing user satisfaction and supporting healthy aging by adapting recommendations to individual preferences and needs. * Physical Activity and Diet. Placed within this domain, there are another four works. The work of [PERSON] et al. [42] proposed a context-aware adaptive recommendation system for personal well-being services. It integrates machine learning models and an ontology-based approach to recognize human activities and contexts. The system uses probabilistic reasoning to provide personalized, adaptive recommendations, aiming to promote healthy lifestyles by adjusting to user behaviors and feedback. Other work from [PERSON] et al.