The project is in line with the RIS3 strategy, focusing on interdisciplinary medical innovation and the development of high added-value products for preventive healthcare, thus addressing RIS3 investment priority 1.The project aims to create a patient-centred digital platform for diabetes education that meets the RIS3 priorities in biomedicine, medical technology and pharmacy. The initiative supports the objectives of RIS3 in the development of new products and technologies (#2) and in strengthening Latvia's knowledge base and human capital in key areas such as biomedicine and medical technologies (#6). In addition, it complements the Ministry of Health's Action Plan on Obesity, focusing on proactive and preventive healthcare.NACE Code: 72.19 - Research and development of medical sciences (Other natural sciences and engineering research and experimental development)Proposed project activities include industrial and experimental research.Objective of the project: The project focuses on the development of a digital assistant for the training of diabetic patients intended for adults and children with type 1 diabetes mellitus (T1DM). The assistant will use improved data collection and analysis methods, such as continuous glucose monitoring (CGM) data, patient-reported health outcome measurements (PROMs) and patient-reported experience measurements (PREMs), to provide personalised training on carbohydrate counting, insulin use and diabetes control strategies. The aim is to enable young patients to better understand and control their disease through an attractive and accessible platform.FORD classification: 1.2. Natural Sciences – Computer Science and Informatics 3.5. Medical and health sciences - Other medical sciencesScientific manager of the project: The project is led by Dr. Dmitry Bļizņuks with extensive experience in digital health innovation and project management. Dr. Bļizņuks is a leading expert in the management of interdisciplinary teams and the development of improved health care solutions, approved with international awards and patents. 1. Children's Clinical University Hospital (BKUS) is the largest specialized multi-profile children's medical institution in Latvia.2. Pauls Stradins Clinical University Hospital (PSKUS): The PSCS shall provide clinical experience, patient data and knowledge in the field of healthcare, ensuring the practical application of the digital platform.3. SIA Longenesis: A leading digital health technology company specializing in patient engagement and data management solutions will adapt its existing platform to achieve the project objectives and ensure compliance with GDPR requirements.Problem overview: T1DM control poses unique challenges for both adults and children, as it requires continuous and careful self-care from an early age. For this reason, personalized diabetes training can significantly contribute to children's and adolescents' self-efficacy. However, current personalised diabetes training solutions for adults, children and adolescents face a number of limitations. These limitations underline the need for more accessible, tested and safe tools that are well integrated into traditional healthcare systems, providing a balanced approach to diabetes patient education.Research objectives:Development of a digital platform that includes CGM data and patient-reported indicators for personalised diabetes training.User-specific design, involving focus groups with children and parents to provide age-appropriate content and use experiences.Testing and validating a immersive digital assistant through pilot studies with T1DM patients.Introduction of data analytics to optimise educational interventions based on individual progress and behavioural patterns. Previous work: BKUS specialists developed educational materials tailored to T1DM patients (more information: diabetaapmaciba.lv). SIA Longenesis in collaboration with Children's Clinical University Hospital introduced PROM/PREM tools for T1DM patients to perform quantitative and qualitative quality of life analysis for patients using insulin pumps and those receiving multiple insulin injections per day. At the same time, PSKUS evaluated different machine learning models to classify CGM-based intercellular glucose levels in adult patients.Methodology: The project extends the results of previous studies to include validated survey tools and improved machine learning models for comprehensive data analysis. A GDPR compliant digital platform will safely manage participants' data, providing personalised feedback and engaging users with gamification elements and dynamic health recommendations tailored to individual needs. Real-time integration of CGM data with patient-reported outcomes will create a holistic approach to personalised training. A pilot study in T1DM patients will be conducted to assess the usability and effectiveness of the platform, measuring user involvement, self-efficacy improvements and diabetes self-control. Ad