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spelling Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE BackProtocol for a Multicenter Clinical Pilot StudySDG 3 - Good Health and Well-beingFunding: This work has been funded by the European Commission and the Horizon 2020 framework.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. TRIAL REGISTRATION: ClinicalTrials.gov NCT05095675; https://clinicaltrials.gov/ct2/show/NCT05095675. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34564.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNPettini, GretaSanchini, VirginiaPat-Horenczyk, RuthSousa, BertaMasiero, MariannaMarzorati, ChiaraGalimberti, Viviana EnricaMunzone, ElisabettaMattson, JohannaVehmanen, LeenaUtriainen, MeriRoziner, IlanLemos, RaquelFrasquilho, DianaCardoso, FatimaOliveira-Maia, Albino JKolokotroni, EleniStamatakos, GeorgiosLeskelä, Riikka-LeenaHaavisto, IraSalonen, JuhaRichter, RobertKarademas, EvangelosPoikonen-Saksela, PaulaMazzocco, Ketti2022-11-04T22:11:16Z2022-10-122022-10-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/145249eng1929-0748PURE: 47465865https://doi.org/10.2196/34564info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:25:26Zoai:run.unl.pt:10362/145249Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:58.912478Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
Protocol for a Multicenter Clinical Pilot Study
title Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
spellingShingle Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
Pettini, Greta
SDG 3 - Good Health and Well-being
title_short Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
title_full Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
title_fullStr Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
title_full_unstemmed Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
title_sort Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back
author Pettini, Greta
author_facet Pettini, Greta
Sanchini, Virginia
Pat-Horenczyk, Ruth
Sousa, Berta
Masiero, Marianna
Marzorati, Chiara
Galimberti, Viviana Enrica
Munzone, Elisabetta
Mattson, Johanna
Vehmanen, Leena
Utriainen, Meri
Roziner, Ilan
Lemos, Raquel
Frasquilho, Diana
Cardoso, Fatima
Oliveira-Maia, Albino J
Kolokotroni, Eleni
Stamatakos, Georgios
Leskelä, Riikka-Leena
Haavisto, Ira
Salonen, Juha
Richter, Robert
Karademas, Evangelos
Poikonen-Saksela, Paula
Mazzocco, Ketti
author_role author
author2 Sanchini, Virginia
Pat-Horenczyk, Ruth
Sousa, Berta
Masiero, Marianna
Marzorati, Chiara
Galimberti, Viviana Enrica
Munzone, Elisabetta
Mattson, Johanna
Vehmanen, Leena
Utriainen, Meri
Roziner, Ilan
Lemos, Raquel
Frasquilho, Diana
Cardoso, Fatima
Oliveira-Maia, Albino J
Kolokotroni, Eleni
Stamatakos, Georgios
Leskelä, Riikka-Leena
Haavisto, Ira
Salonen, Juha
Richter, Robert
Karademas, Evangelos
Poikonen-Saksela, Paula
Mazzocco, Ketti
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
RUN
dc.contributor.author.fl_str_mv Pettini, Greta
Sanchini, Virginia
Pat-Horenczyk, Ruth
Sousa, Berta
Masiero, Marianna
Marzorati, Chiara
Galimberti, Viviana Enrica
Munzone, Elisabetta
Mattson, Johanna
Vehmanen, Leena
Utriainen, Meri
Roziner, Ilan
Lemos, Raquel
Frasquilho, Diana
Cardoso, Fatima
Oliveira-Maia, Albino J
Kolokotroni, Eleni
Stamatakos, Georgios
Leskelä, Riikka-Leena
Haavisto, Ira
Salonen, Juha
Richter, Robert
Karademas, Evangelos
Poikonen-Saksela, Paula
Mazzocco, Ketti
dc.subject.por.fl_str_mv SDG 3 - Good Health and Well-being
topic SDG 3 - Good Health and Well-being
description Funding: This work has been funded by the European Commission and the Horizon 2020 framework.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-04T22:11:16Z
2022-10-12
2022-10-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145249
url http://hdl.handle.net/10362/145249
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1929-0748
PURE: 47465865
https://doi.org/10.2196/34564
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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