## 2 Methods Used for Integrated Monitoring and Management of Diet-Related Diseases
### RHMS-Based Novel Evaluation Method for Diet-Related Disease Management
Personal counselling and RHMS that provide easily available and tailored dietary guidance are examples of educational approaches. Patients who feel empowered are more likely to take an active role in their own care, which improves long-term outcomes;
* _Personalized digital interventions_: Educational interventions are carefully designed for the digital health field, taking into account the technological advances in healthcare [77; 78]. Personalized consultations that make use of AI and personalized algorithms combine easily with RHMS. These solutions, which include web-based interfaces, mobile applications, and virtual learning environments, enable data-driven customisation in addition to accessibility. The efficacy of diet-related disease management is improved in this digitally augmented environment when patients receive personalized nutritional information that corresponds to their individual health profiles;
* _Interdisciplinary collaboration_: Diseases linked to diet may arise from an intricate relationship between environmental, psychological, and physiological variables [79]. Thus, multidisciplinary cooperation is necessary for successful management. To develop comprehensive management plans, psychologists, IT professionals, nutritionists, and healthcare professionals have to collaborate adequately together. Because diet-related disorders are complex, a collaborative approach guarantees a thorough grasp of the patient's medical condition and enables balanced procedures. _Precision in data needs smart technologies for enhanced monitoring_. _Precision data_ are defined as highly precise, reliable, and consistent data [80]. In this sense, precision refersto a high degree of reliability and accuracy in the information gathered. Precision data offer a comprehensive and precise picture of the information being measured or identified while also considering other pertinent factors. They eliminate the possibility of errors and uncertainties to the maximum extent feasible. Precision is essential for data collection and analysis in scientific and technological contexts, because it enhances the validity and reliability of research findings. Precision data in the context of RHMS may include precise assessments of dietary intake, health parameters, or additional relevant variables. The setting up of a smart monitoring environment, which is intended to guarantee the highest level of precision and dependability of data, is a fundamental component of the suggested innovative evaluation approach, namely, integrated monitoring for diet-related management. By using modern sensors and complex algorithms, the RHMS lowers data collecting errors. Using advanced analytics, the proposed evaluation method goes beyond traditional approaches. The assessment framework's integration of predictive modelling and identifying anomalies adds a dynamic and adaptable component to disease management. This approach provides itself as a proactive, data-driven framework that outperforms traditional ones, delivering increased efficacy in healthcare by predicting trends and finding abnormalities in real time. Measurements of health parameters and dietary evaluations are made more accurate by machine learning algorithms that have been trained on a variety of datasets. These algorithms improve the accuracy of dietary assessments and health parameter measurements, and they form a core component of the smart monitoring environment. Machine learning algorithms can adjust to the peculiarities of each patient, leading to a degree of customisation and precision that is not possible with conventional methods. _The comprehensive monitoring provided by RHMS goes beyond conventional metrics and provides a holistic understanding and integrated insights for advanced evaluation_. The proposed novel evaluation method goes beyond traditional measurements in the pursuit of accuracy in diet-related disease management inside the digital health domain, adopting the concept of comprehensive monitoring.