A latent profile analysis of resilience and their relation to differences in sleep quality in patients with lung cancer
Purpose Sleep problems are a significant issue in patients with lung cancer, and resilience is a closely related factor. However, few studies have identified subgroups of resilience and their relationship with sleep quality. This study aimed to investigate whether there are different profiles of resilience in patients with lung cancer, to determine the sociodemographic characteristics of each subgroup, and to determine the relationship between resilience and sleep quality in different subgroups. Methods A total of 303 patients with lung cancer from four tertiary hospitals in China completed the General Sociodemographic sheet, the Connor-Davidson Resilience Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis was applied to explore the latent profiles of resilience. Multivariate logistic regression was used to analyze the sociodemographic variables in each profile, and ANOVA was used to explore the relationships between resilience profiles and sleep quality. Results The following three latent profiles were identified: the “high-resilience group” (30.2%), the “moderate-resilience group” (46.0%), and the “low-resilience group” (23.8%). Gender, place of residence, and average monthly household income significantly influenced the distribution of resilience in patients with lung cancer. Conclusion The resilience patterns of patients with lung cancer varied. It is suggested that health care providers screen out various types of patients with multiple levels of resilience and pay more attention to female, rural, and poor patients. Additionally, individual differences in resilience may provide an actionable means for addressing sleep problems..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:32 |
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| Enthalten in: |
Supportive care in cancer - 32(2024), 3 vom: 13. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Juan [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: | |
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| Themen: |
Latent profile analysis |
| Anmerkungen: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s00520-024-08337-1 |
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| funding: |
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| 520 | |a Purpose Sleep problems are a significant issue in patients with lung cancer, and resilience is a closely related factor. However, few studies have identified subgroups of resilience and their relationship with sleep quality. This study aimed to investigate whether there are different profiles of resilience in patients with lung cancer, to determine the sociodemographic characteristics of each subgroup, and to determine the relationship between resilience and sleep quality in different subgroups. Methods A total of 303 patients with lung cancer from four tertiary hospitals in China completed the General Sociodemographic sheet, the Connor-Davidson Resilience Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis was applied to explore the latent profiles of resilience. Multivariate logistic regression was used to analyze the sociodemographic variables in each profile, and ANOVA was used to explore the relationships between resilience profiles and sleep quality. Results The following three latent profiles were identified: the “high-resilience group” (30.2%), the “moderate-resilience group” (46.0%), and the “low-resilience group” (23.8%). Gender, place of residence, and average monthly household income significantly influenced the distribution of resilience in patients with lung cancer. Conclusion The resilience patterns of patients with lung cancer varied. It is suggested that health care providers screen out various types of patients with multiple levels of resilience and pay more attention to female, rural, and poor patients. Additionally, individual differences in resilience may provide an actionable means for addressing sleep problems. | ||
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| 700 | 1 | |a Chen, Ou-ying |4 aut | |
| 700 | 1 | |a Zhang, Jing-ping |4 aut | |
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