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

Aug 4, 2026

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This study describes distinct anxiety/depression symptom trajectories and changes in overall health status and quality of life across 18 months after a breast cancer diagnosis. Using machine-learning methods on repeated assessments, it analyzes how medical, socio-demographic, lifestyle, and psychological variables relate to latent patterns of change. The sample includes 474 women followed at seven time points, with outcomes measured at each assessment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Objective

Methods

Results

Conclusions

Background

Breast cancer distress over time

Dynamic trajectories of adaptation

Received: 2 June 2023 Revised: 29 September 2023 Accepted: 2 October 2023 DOI: 10. 1002/pon.6230 ORIGINAL ARTICLE Well-being trajectories in breast cancer and their predictors: A machine-learning approach Evangelos C. Karademas1,2 | Eugenia Mylona2 | Ketti Mazzocco3,4 | Ruth Pat-Horenczyk5 | Berta Sousa6 | Albino J. Oliveira-Maia7,8 | Jose Oliveira7,8 | Ilan Roziner9 | Georgios Stamatakos10 | Fatima Cardoso6 | Haridimos Kondylakis2 | Eleni Kolokotroni10 | Konstantina Kourou2 | Raquel Lemos7,11 | Isabel Manica7 | George Manikis2 | Chiara Marzorati4 | Johanna Mattson12,13 | Luzia Travado7 | Chariklia Tziraki-Segal14 | Dimitris Fotiadis2,15 | Paula Poikonen-Saksela12,13 | Panagiotis Simos2,16 | on behalf of the BOUNCE consortium 1Department of Psychology, University of Crete, Rethymnon, Greece 2Foundation for Research and Technology—Hellas, Heraklion, Greece 3Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy 4Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy 5School of Social Work and Social Welfare, Hebrew University of Jerusalem, Jerusalem, Israel 6Breast Unit, Champalimaud Clinical Centre, Champalimaud Foundation, Lisboa, Portugal 7Champalimaud Research and Clinical Centre, Champalimaud Foundation, Lisboa, Portugal 8NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisboa, Portugal 9Department of Communication Disorders, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel 10Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece 11ISPA—Instituto Universitário de Ciências Psicológicas, Sociais e da Vida, Lisboa, Portugal 12Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland 13University of Helsinki, Helsinki, Finland 14Hebrew University of Jerusalem Center for Sustainability, Jerusalem, Israel 15Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece 16Medical School, University of Crete, Rethymnon, Greece Correspondence Evangelos C. Karademas, Department of Psychology, University of Crete, Rethymno, Greece. Email: [karademas@uoc.gr](karademas@uoc.gr) Funding information Horizon 2020 Framework Program Abstract Objective: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. Methods: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. Psycho-Oncology published by John Wiley & Sons Ltd. KARADEMAS ET AL. - 1763 Potential predictors were assessed at baseline and the first follow-up. MachineLearning 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, immunerelated inflammation) predicted patients' participation in the delayed resp

FAQs

What is the study’s main objective?

To describe distinct well-being and anxiety/depression symptom trajectories over 18 months after breast cancer diagnosis and identify predictors for those trajectories.

How was the study conducted and analyzed?

A cohort of 474 women was assessed at seven time points over 18 months. Machine-learning techniques were used to detect latent patterns of change and determine key predictors.

What trajectories were identified for each outcome?

Five trajectories were found: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress).

Which factors predicted participation in delayed response and chronic distress trajectories?

Psychological factors such as negative affect, coping, sense of control, and social support, along with age and some medical variables (including symptoms and immunerelated inflammation), predicted delayed response and chronic distress compared with other trajectories.

Aug 4, 2026

Upload by Quinn