Τhe proposed research is expected to deliver on all of the following points:

• Enable the understanding of areas of unmet need in NCDs prevention, possibly also addressing disease mechanism, management of disease progression and relapse. Providing new approaches for prevention, focussing on the digitally supported personalised dimension, that can be adopted and scaled up.
• Devise new or improved ambitious policy and intervention options, with expected high population-wide impact on the target groups in question. To be proposed and made available for effective health promotion and disease prevention including targeted communication strategies to successfully reach out to the risk groups.
• Design an integrated, holistic approach that includes several of the following aspects: genetic predisposition to NCDs, meta-genomics, epigenomics, the microbiome, metabolomics, sleep disorders, large cohorts, molecular profiling in longitudinal health screening, impact of lack of physical activity, novel predictive biomarker candidates, diets and nutrition, eating habits for designing customised dietary patterns (geographical variation), and the influence of choice environment on personal choices.
• Study the ethical, legal and social aspects as well as health economics of the personalised prevention tools and programmes being developed. Consider optimal health counselling and communication to the patients/citizens. Address legal aspects of balancing the right not to know and the obligation of helping people in danger.

Furthermore, the proposed research is expected to deliver on several of the following points:

• Develop and validate effective strategies to prevent NCDs and optimise health and well-being of citizens (including the most vulnerable). Propose the strategies to policymakers along with mechanisms to monitor their progress. The strategies need to be aligned with relevant national and European health laws and policies.
• Provide scientific evidence on interactions between the genetic predisposition to multifactorial diseases and environmental factors or environmental triggers. Propose scientifically supported personalised prevention strategies that ensure how to modify the environmental drivers of behavioural risk factors.
• Develop new computational tools combining and analysing comprehensive data with different dimensions to identify risk factors and modifiers. Creating procedures and algorithms to combine information from different sources (with standardised common data models) to generate risk scores for several diseases and provide health promotion recommendations for the individual as advised by healthcare professionals. Furthermore, develop advanced computational modelling techniques for predicting disease risk and predisposition (addressed together in an integrative approach) and identifying the optimal solution/intervention for different target groups and individuals.
• Develop tools and techniques to increase the efficiency and cost- effectiveness of on the one hand interventions, adjusting their scope, characteristics and resources, and on the other hand healthcare infrastructure and how it promotes and delivers health promotion, disease prevention, and care effectively to the different population groups.
• Design tools to collect various data to advance health promotion and disease prevention and strategies for providing omics essays for the general patient with a focus on cost-effectiveness and flexibility.
• Determine how to optimise the benefits of physical activity, smart monitoring of physical activity and sedentary behaviour with measurable data, addressing barriers to uptake and implementation of healthy lifestyles in daily life, understanding what promotion methods work and why, behavioural science to understand healthier choice environments. Balancing the ecosystem associated with the economic, social, and health consequences of NCDs. Affordability related consideration should be taken into account to ensure accessibility of new tools and techniques.
• Conduct data mining of real-world data and develop quantifiable and distinguishable indicators from wearables data, taking into account ‘light-weight’ AI means to ensure patient privacy and short reaction times.
• Demonstrate with a practical prototype on a given health challenge: from multimodal data collection to identification of an effective prevention strategy to be tested and validated for one or several NCDs.