ADS Capstone Chronicles Revised

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while still focusing on small but doable changes in a patient's diet (Reynolds & Mitri, 2024). Reynolds and Mitri (2024) emphasized manageable dietary changes, recommending nutrient-dense, high-fiber foods like non-starchy vegetables, legumes, whole grains, and low-fat dairy as foundational for diabetic meals. High-fiber fruits, which help minimize post meal glucose spikes, are preferred over processed carbs. Reynolds and Mitri (2024) advised minimizing added sugars and refined grains, which elevate blood glucose rapidly. Instead, balanced meals with lean proteins and healthy fats support diabetes goals of stable glucose levels, weight maintenance, and reduced complications. Reynolds and Mitri (2024) developed these dietary recommendations by analyzing the current scientific guidelines and clinical research on nutritional strategies for diabetes management. Reynolds and Mitri’s approach involves a review of evidence-based practices and guidelines from reputable organizations. These organizations, including the American Diabetes Association, identify specific foods as well as dietary patterns that would support stable glucose levels as well as improve metabolic health in diabetic individuals. By emphasizing nutrient-dense foods and balanced meals, the recommendations offer generalized practical and sustainable guidelines for a personalized diabetes care plan that may be difficult for individuals to find elsewhere. 3.2 Quality and accuracy of online nutrition-related information: a systematic review of content analysis studies Individuals are often taught at a young age how to understand nutritional labeling, but the severity of its importance is likely never

stressed. In today’s world, all diabetic patients have the gift of online nutritional labels and information in their cellular device. However, a systematic review of the quality and accuracy of nutrition related information on websites and social media published by the Cambridge University Press reported low accuracies (Dennis et al., 2023). This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to accurately assess these publications of nutritional data. Data extracted in this systematic review included studies from 1996-2021, the online environment investigated, nutrition-related topic of interest, inclusion and exclusion criteria, method of quality and/or accuracy evaluation, results, and conflicts of interest. This systematic review found that historically published studies had compared nutritional information against authoritative guidelines, academic literature, or national dietary guidelines. Overall, almost 50% of the studies reviewed in Quality and accuracy of online nutrition-related information: a systematic review of content analysis studies were labeled as poor when it came to recognizing the nutritional value of different foods. These results heavily support the coming project, suggesting that personalized recommendations for diabetic patients are necessary, as other resources are not properly labeling nutritional value. 3.3 Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A review Using artificial intelligence and machine learning in nutrition is essential in developing an effective personalized food recommendation system for diabetic patients. Tsoliakidis et al. (2024) explained a comprehensive evaluation of machine learning’s potential to strengthen

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