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Well‐being trajectories in breast cancer and their predictors: A machine‐learning approach

2023Journal article Open access
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dc.contributor.authorKarademas, Evangelos
dc.contributor.authorMylona, Eugenia
dc.contributor.authorMazzocco, Ketti
dc.contributor.authorPat‐Horenczyk, Ruth
dc.contributor.authorSousa, Berta
dc.contributor.authorOliveira‐Maia, Albino J.
dc.contributor.authorOliveira, Jose
dc.contributor.authorRoziner, Ilan
dc.contributor.authorStamatakos, Georgios
dc.contributor.authorCardoso, Fatima
dc.contributor.authorKondylakis, Haridimos
dc.contributor.authorKolokotroni, Eleni
dc.contributor.authorKourou, Konstantina
dc.contributor.authorLemos, Raquel
dc.contributor.authorManica, Isabel
dc.contributor.authorManikis, George
dc.contributor.authorMarzorati, Chiara
dc.contributor.authorMattson, Johanna
dc.contributor.authorTravado, Luzia
dc.contributor.authorTziraki‐Segal, Chariklia
dc.contributor.authorFotiadis, Dimitris
dc.contributor.authorPoikonen‐Saksela, Paula
dc.contributor.authorSimos, Panagiotis
dc.date.accessioned2024-03-14T17:46:42Z
dc.date.available2024-03-14T17:46:42Z
dc.date.issued2023
dc.description.abstractObjective:This study aimed to described istinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months followinga breast cancer diagnosis,and identify the medical, socio-demographic,lifestyle, and psychologica lfactors that predict these trajectories.Methods:474 females (mean age=55.79 years) were enrolled in the first weeksafter surgery or biopsy. Data from seven assessmentpoints over 18 months, at 3-month intervals,were used. The two outcomeswere assessedat all points. Potential predictors were assessed at baseline and the first follow‐up. Machine‐ Learning techniques were used to detect latent patterns of change and identify the most important predictors. Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well‐being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune‐ related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine‐learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well‐beingpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationKarademas, E. C., Mylona, E., Kondylakis, H., Kourou, K., Manikis, G., Fotiadis, D., Simos, P., Mazzocco, K., Marzorati, C., Pat-Horenczyk, R., Sousa, B., Cardoso, F., Oliveira-Maia, A. J., Oliveira, J., Lemos, R., Manica, I., Travado, L., Roziner, I., Stamatakos, G., … Tziraki-Segal, C. (2023). Well-being trajectories in breast cancer and their predictors: A machine-learning approach. Psycho-Oncology, 32(11), 1762–1770. https://doi.org/10.1002/pon.6230pt_PT
dc.identifier.doi10.1002/pon.6230pt_PT
dc.identifier.issn10579249
dc.identifier.urihttp://hdl.handle.net/10400.12/9672
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherJohn Wiley and Sons Ltdpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBreast cancerpt_PT
dc.subjectCancêrpt_PT
dc.subjectOncologypt_PT
dc.subjecttrajectoriespt_PT
dc.subjectTrajectory predictorpt_PT
dc.titleWell‐being trajectories in breast cancer and their predictors: A machine‐learning approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceUnited Kingdompt_PT
oaire.citation.endPage1770pt_PT
oaire.citation.issue11pt_PT
oaire.citation.startPage1762pt_PT
oaire.citation.titlePsycho-Oncologypt_PT
oaire.citation.volume32pt_PT
person.familyNameKarademas
person.familyNameMylona
person.familyNameMazzocco
person.familyNameSousa
person.familyNameRoziner
person.familyNameStamatakos
person.familyNameCardoso
person.familyNameKondylakis
person.familyNameKolokotroni
person.familyNameKourou
person.familyNameBaptista de Lemos Guerra de Oliveira
person.familyNameManica
person.familyNameMarzorati
person.familyNameMattson
person.familyNameTravado
person.familyNameFotiadis
person.familyNameSimos
person.givenNameEvangelos
person.givenNameEugenia
person.givenNameKetti
person.givenNameBerta
person.givenNameIlan
person.givenNameGeorgios
person.givenNameFatima
person.givenNameHaridimos
person.givenNameEleni
person.givenNameKonstantina
person.givenNameRaquel Maria
person.givenNameIsabel
person.givenNameChiara
person.givenNameJohanna
person.givenNameLuzia
person.givenNameDimitris
person.givenNamePanagiotis
person.identifier819494
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person.identifier.orcid0000-0002-0116-8564
person.identifier.ridI-6598-2013
person.identifier.scopus-author-id6701750975
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rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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