Finally, T1D patients in collaboration with a physician will design a treatment based on individual patient needs. Insulin doses will be self-administered by the patients according to the treatment plan and self-measured blood sugar concentrations. External insulin therapy aims to keep the blood glucose concentrations within the normoglycemic range between 70 and 180 mg/dL [4; 5]. Regular physical exercise has many proven health benefits and is therefore widely recommended as part of a healthy lifestyle. However, exercise significantly alters glucose homeostasis in patients with T1D. In addition, physical activities increase glucose uptake by muscles leading to a drop in blood glucose concentration, which can reach the hazardous hypoglycemic values. Increased insulin sensitivity, during and several hours or even days after the exercise [6], creates long-lasting effects on daily activities of patients. The common method for preventing hypoglyemia is to reduce insulin doses. However, the slow absorption of insulin from the subcutaneous tissue and the physical limit of insulin reduction make this method insufficient to prevent hypoglyemia. Additional to the insulin treatment, many T1D related conditions can be mitigated by a nutrition therapy, which arises as an important solution to prevent, manage and control diabetes, as well as relieve complications associated with T1D by adjusting the quantity, quality and methods of nutrient intake [7]. Along with a healthy diet, physical activity plays a vital role in diabetes treatment, producing multiple general and diabetes-specific health benefits [8]. Despite the evidenced benefits, many people are physically inactive [9], since exercise is a major source of hypoglycemia in diabetic patients [10], and risk of hypoglycemia is a significant limiting factor of blood glucose regulation in T1D patients [11]. For most diabetic people, exercise has far less adverse health consequences than sedentary lifestyle [12]. Healthcare approaches change from the traditional relationship between providers and patients to a paradigm that gives patients a crucial role in guiding their care [13]. The change emphasizes the importance of self-management, which is considered a necessary part of chronic disease management and secondary prevention [14], especially for diabetes patients. Evidence shows that supporting patients to manage their health will improve clinical outcomes, reduce the economic burden, and improve quality of life [14]. Food recommendation systems emerge as a new self-management solution that can suggest the best diets according to patients' health situation and preferences, solving the physical activity paradigm while following a nutrition therapy for diabetic patients [15]. Among others, [PERSON] et al. [16] used a food-clustering analysis to propose a food recommendation system for patients with diabetes, while [PERSON] et al. [17] developed a smartphone application for managing diabetic patient nutrition using artificial intelligence techniques. In [18], [PERSON] et al. develop a diabetic food recommendation agent that, according to a personal lifestyle and particular health needs, can create a meal plan. [PERSON] and [PERSON] [19] present a type-2 fuzzy logic-based diet recommendation system to help achieve a healthy lifestyle to control diabetes. A complete systematic review of nutrition recommendation systems with a focus on technical aspects can be found in [20]. Previous work from the authors has shown promising results for using machine learning techniques in a food recommendation system, maintaining healthy blood glucose levels on a T1D simulator during exercise [21]. The term machine learning is considered a large family of mathematical and statistical methods that have historically focused on prediction [22]. With the development of new technology, a vast amount of health-related data is continuously generated. However, data availability is varied among various dimensions and quality.