REBECCA – REsearch on BrEast Cancer induced chronic conditions supported by Causal Analysis of multi-source data
Project Status: ONGOING (April 2021 – March 2025)
What aims and scope
To tap into the potential of real-world data, combining it with an advanced data management platform and innovative causal data modelling methodologies, so as to improve clinical research on complex chronic conditions and thereby, health outcomes for patients who have undergone or remain in primary breast cancer treatment.
How it will become possible
Twelve partners from seven European countries comprising clinical and technological universities, industry leaders, public health experts and patient associations will join force to drive impact in breast cancer care.
The project consortium will combine clinical data with that describing patients’ real-life behaviour, including physical activity, eating habits, sleep and online interactions. These new forms of real-world data will be collected via mobile and wearable devices.
The consortium will then develop novel functional and emotional indices for each individual to examine patient status and quality of life. REBECCA will use these indices for optimising patient care and as complementary outcomes during clinical research.
The project will validate its findings and progress by applying such observations in three studies on breast cancer comorbidities and exploring its impact on clinical decision-making processes, outcomes and quality of life.
Where EHMA will deliver
EHMA leads the dissemination and communications activities and actions.
We will develop and implement a communication strategy that reaches different target audience segments of REBECCA to raise awareness about the project and its results. We will also build and engage a network of public health stakeholders to promote the value of real-world data in research and clinical practice in the breast cancer domain.
For more information about the project and the possibilities for involvement, check this poster.
The REBECCA project is funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 965231. This page and the publications contained herein only reflects the author’s views. The European Union is not liable for any use that might be made of the information contained in this page and publications.
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