Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back: Protocol for a Multicenter Clinical Pilot Study

Greta Pettini, Virginia Sanchini, Ruth Pat-Horenczyk, Berta Sousa, Marianna Masiero, Chiara Marzorati, Viviana Enrica Galimberti, Elisabetta Munzone, Johanna Mattson, Leena Vehmanen, Meri Utriainen, Ilan Roziner, Raquel Lemos, Diana Frasquilho, Fatima Cardoso, Albino J. Oliveira-Maia, Eleni Kolokotroni, Georgios Stamatakos, Riikka Leena Leskelä, Ira HaavistoJuha Salonen, Robert Richter, Evangelos Karademas, Paula Poikonen-Saksela, Ketti Mazzocco*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Background: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient's sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient's ability to bounce back from a stressful life event,such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back"or, in short, BOUNCE. Objective: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. Methods: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. Results: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. Conclusions: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients' needs, supported by eHealth technologies.

Original languageAmerican English
Article numbere34564
JournalJMIR Research Protocols
Issue number10
StatePublished - Oct 2022

Bibliographical note

Funding Information:
This work has been funded by the European Commission and the Horizon 2020 framework.

Funding Information:
AJOM was the national coordinator for Portugal of a noninterventional study (EDMS-ERI-143085581, 4.0) to characterize a treatment-resistant depression cohort in Europe, sponsored by Janssen-Cilag, Ltd (2019-2020); is the recipient of a grant from Schuhfried GmbH for the norming and validation of cognitive tests; and is the national coordinator for Portugal of trials related to psilocybin therapy for treatment-resistant depression, sponsored by Compass Pathways, Ltd (EudraCT [European Union Drug Regulating Authorities Clinical Trials Database] Nos. 2017-003288-36 and 2020-001348-25), and esketamine for treatment-resistant depression, sponsored by Janssen-Cilag, Ltd (EudraCT No. 2019-002992-33). JS is employed by Varian Medical System Inc, which is the manufacturer of the Noona patient-reported outcomes platform.

Publisher Copyright:
© 2022 Greta Pettini.


  • cancer
  • coping
  • decision-making
  • personality
  • quality of life
  • resilience


Dive into the research topics of 'Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back: Protocol for a Multicenter Clinical Pilot Study'. Together they form a unique fingerprint.

Cite this