PREVENT improves upscaling of primary interventions for weight control management during childhood and adolescence to reduce cancer risks in adulthood. This relies on current evidence that relates excess body weight with increased cancer risk. Towards this end, PREVENT applies a series of implementation research actions in the following directions. First, it identifies barriers to current interventions and policies preventing them from upscaling to different geographical, socio-economic, and cultural settings. Then, it introduces new multi-actor and context-aware interventions along with new user engagement strategies to face the current upscaling bottlenecks; multi-actor in the sense that they target different types of users (e.g., students, family, educators, policymakers) and context-aware in the sense that PREVENT interventions are tailored to the specific implementation places (class, canteen, sports fields, labs, outside school). The PREVENT new policies are adapted, piloted, and scaled up within the schools’ communities of three European countries facing different epidemiological settings on childhood obesity, geographic, socio-economic and cultural attributes. The pilots are designed to be holistic end-to-end ecosystems, including users, medical professionals, policymakers, public authorities, and civil communities. They focus on the whole school communities of Greece, Sweden, and Spain-Catalonia, that is, PREVENT outreach to more than 3.3 million students, required for guideline provisioning, large-scale implementation, multi-parameter assessment, and scaling-up. Co-creation, active behavioral change, self-evaluation through user empowerment, motivational interviewing, social innovation, digital-assistive engagement, health apps, and multi-domain assessment are implementation research aspects of PREVENT to advance user acceptability and compatibility with existing policies, and thus improve sustainability and upscaling. This action is part of the Cancer Mission cluster of projects on "Prevention and Early Detection".

Psoriatic Arthritis (PsA) is a chronic, progressive, inflammatory disease affecting 1-2% of the general population, while manifesting in up to 30% of people with psoriasis (PsO). The transition from health to PsA is currently untraceable; diagnosis of early PsA is challenging even in PsO patients. Untimely diagnosis is common and contributes to early deterioration of quality of life, also increasing the burden of the multiple comorbidities associated with PsA. In this vein, iPROLEPSIS aspires to shed light upon the health-to-PsA transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogenous (i.a., in-depth health, environmental, genetic, behavioural) data, digital phenotyping of inflammatory symptoms with emphasis on tracking of motor manifestations using smart devices and wearables, novel optoacoustic imaging-based markers of PsA in the skin and joints, and investigation of the role of mast cells in the PsA transition, to identify key drivers of the disease and support personalized models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment. To ultimately advance PsA diagnosis and care, the models will be translated into a digital health ecosystem comprising dependable tools for supporting healthcare professionals in disease screening, monitoring and treatment via quantitative, explainable evidence, and empowering people with/at risk of PsA with tailored insights and preventive interventions based on actionable factors for educated health management. The project will steer its research and development efforts following a trustworthy framework for ethical, lawful, and robust AI, and a user-centered co-creation approach based on constant involvement of key stakeholders during the design, development, and testing of the digital health ecosystem, securing successful integration of the latter in the continuum of care.

In the EU more than 6 million new cardiovascular disease (CVD) cases are reported yearly, along with 1.8 million related deaths, posing a substantial burden on the national healthcare systems and society in general. Development of effective preventive interventions, adopted for each individual and population, will not only reduce the overall cost of patient management, but will also justify the assertion that CVDs could be ultimately prevented and controlled. The exploration of existing clinical, genetic and real-world data sources can contribute to this direction, however several challenges related to the data availability, management and processing remain open. The 48-month project CVDLINK aims to tackle these challenges by implementing a privacy-by-design European-wide federated platform-as-a-service (PaaS) for the delivery of effective data-driven human-centric interventions and the advancement of research and management in the CVD domain. CVDLINK is based on two major offerings: (1) the seamless and legally compliant linking and integration, secure sharing and automated curation of existing data; and (2) a set of AI and data-enabled precision medicine tools and pipelines, for better diagnosis, risk stratification and treatment. In the context of the project, 7 different cardiovascular conditions will be examined, making use of different retrospective datasets, cohort studies and biobanks from 7 counties, aiming to set a paradigm of the how heterogeneous data sources, can be effectively exploited for building comprehensive AI-driven tools, in order to bring substantial benefits for health systems, patients, industry and EU citizens. The developed tools will be prospectively validated in 5 countries, demonstrating the impact of CVDLINK. Additionally, a set of best practices will be generated, which along with a cost-effectiveness analysis, and systematic raising awareness campaigns will promote its wide adoption in the mid-term.