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Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level

Fatigue is the most prevalent symptom in breast cancer. It might be perceived differently among patients over time as a consequence of the differing patients’ adaptation and psychological adjustment to their cancer experience which can be related to response shift (RS). RS analyses can provide impor...

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Autores principales: Salmon, Maxime, Blanchin, Myriam, Rotonda, Christine, Guillemin, Francis, Sébille, Véronique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5673928/
https://www.ncbi.nlm.nih.gov/pubmed/28994209
http://dx.doi.org/10.1002/cam4.1219
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author Salmon, Maxime
Blanchin, Myriam
Rotonda, Christine
Guillemin, Francis
Sébille, Véronique
author_facet Salmon, Maxime
Blanchin, Myriam
Rotonda, Christine
Guillemin, Francis
Sébille, Véronique
author_sort Salmon, Maxime
collection PubMed
description Fatigue is the most prevalent symptom in breast cancer. It might be perceived differently among patients over time as a consequence of the differing patients’ adaptation and psychological adjustment to their cancer experience which can be related to response shift (RS). RS analyses can provide important insights on patients’ adaptation to cancer but it is usually assumed that RS occurs in the same way in all individuals which is unrealistic. This study aimed to identify patients’ subgroups in which different RS effects on self‐reported fatigue could occur over time using a combination of methods for manifest and latent variables. The FATSEIN study comprised 466 breast cancer patients followed over a 2‐year period. Fatigue was measured with the Multidimensional Fatigue Inventory questionnaire (MFI‐20) during 10 visits. A novel combination of Mixed Models, Growth Mixture Modeling, and Structural Equation Modeling was used to assess the occurrence of RS in fatigue changes to identify subgroups displaying different RS patterns over time. An increase in fatigue was evidenced over the 8‐month follow‐up, followed by a decrease between the 8‐ and 24‐month. Four latent classes of patients were identified. Different RS patterns were detected in all latent classes between the inclusion and 8 months (last cycle of chemotherapy). No RS was evidenced between 8‐ and 24‐month. Several RS effects were evidenced in different groups of patients. Women seemed to adapt differently to their treatment and breast cancer experience possibly indicating differing needs for medical/psychological support.
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spelling pubmed-56739282017-11-15 Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level Salmon, Maxime Blanchin, Myriam Rotonda, Christine Guillemin, Francis Sébille, Véronique Cancer Med Clinical Cancer Research Fatigue is the most prevalent symptom in breast cancer. It might be perceived differently among patients over time as a consequence of the differing patients’ adaptation and psychological adjustment to their cancer experience which can be related to response shift (RS). RS analyses can provide important insights on patients’ adaptation to cancer but it is usually assumed that RS occurs in the same way in all individuals which is unrealistic. This study aimed to identify patients’ subgroups in which different RS effects on self‐reported fatigue could occur over time using a combination of methods for manifest and latent variables. The FATSEIN study comprised 466 breast cancer patients followed over a 2‐year period. Fatigue was measured with the Multidimensional Fatigue Inventory questionnaire (MFI‐20) during 10 visits. A novel combination of Mixed Models, Growth Mixture Modeling, and Structural Equation Modeling was used to assess the occurrence of RS in fatigue changes to identify subgroups displaying different RS patterns over time. An increase in fatigue was evidenced over the 8‐month follow‐up, followed by a decrease between the 8‐ and 24‐month. Four latent classes of patients were identified. Different RS patterns were detected in all latent classes between the inclusion and 8 months (last cycle of chemotherapy). No RS was evidenced between 8‐ and 24‐month. Several RS effects were evidenced in different groups of patients. Women seemed to adapt differently to their treatment and breast cancer experience possibly indicating differing needs for medical/psychological support. John Wiley and Sons Inc. 2017-10-10 /pmc/articles/PMC5673928/ /pubmed/28994209 http://dx.doi.org/10.1002/cam4.1219 Text en © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Salmon, Maxime
Blanchin, Myriam
Rotonda, Christine
Guillemin, Francis
Sébille, Véronique
Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
title Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
title_full Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
title_fullStr Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
title_full_unstemmed Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
title_short Identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
title_sort identifying patterns of adaptation in breast cancer patients with cancer‐related fatigue using response shift analyses at subgroup level
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5673928/
https://www.ncbi.nlm.nih.gov/pubmed/28994209
http://dx.doi.org/10.1002/cam4.1219
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