Coping with breast cancer increasingly becomes a major socio-economic challenge not least due to the constantly rising incidence of the disease in the developing world. There is a growing need for novel strategies to improve understanding and capacity to predict the resilience of women to the variety of stressful experiences and practical challenges related to breast cancer. This is a necessary step toward efficient recovery through personalized interventions. BOUNCE has brought together modelling, medical, and social sciences experts to advance current knowledge on the dynamic nature of resilience as it relates to efficient recovery from breast cancer. BOUNCE considers clinical, cancer-related biological, lifestyle, and psychosocial parameters in order to predict individual resilience trajectories throughout the cancer continuum. Ultimately prediction models and related decision support tools to become available to clinicians aspire to support resilience in breast cancer survivors and help them remain in the workforce and enjoy a better quality of life.

The BOUNCE project is currently in it’s 4th and final year. More importantly, the prospective multi-centre clinical pilot, which is at the core of the project, is underway at four major oncology centres (in Italy, Finland, Israel and Portugal). The clinical study is now tracking the medical and psychosocial trajectories of approximately 550 women who were diagnosed with breast cancer 10-16 months earlier. Participating women are assessed every three months on a long list of potentially relevant variables as they bounce back during the highly stressful treatment and recovery period following the diagnosis of breast cancer.

Project information

Duration: 01/11/2017-31/10/2021 (48 months)
Total Budget: 4.999.410 €
Project type: H2020 – SCI-2017-CNECT-2
Topic: SC1-PM-17-2017: Personalized Computer Models and in-silico systems for well-being
Funded: This project has received funding from the European Union’s Horizon 2020 research and innovation programme
Grant Agreement Nr.: 777167


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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777167

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