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A latent profile analysis of resilience and their relation to differences in sleep quality in patients with lung cancer.
Li, Juan; Yin, Yi-Zhen; Zhang, Jie; Puts, Martine; Li, Hui; Lyu, Meng-Meng; Wang, An-Ni; Chen, Ou-Ying; Zhang, Jing-Ping.
Afiliação
  • Li J; Xiangya School of Nursing, Central South University, Changsha, 410013, China.
  • Yin YZ; Xiangya School of Nursing, Central South University, Changsha, 410013, China.
  • Zhang J; School of Nursing, Hunan University of Chinese Medicine, Changsha, 410208, China.
  • Puts M; Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, M5T1P8, Canada.
  • Li H; Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Lyu MM; West China School of Nursing, Sichuan University, Chengdu, 610041, China.
  • Wang AN; Alice Lee Center for Nursing Studies, Yong Loo School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
  • Chen OY; School of Nursing, Fudan University, Shanghai, 200032, China.
  • Zhang JP; School of Nursing, Hunan University of Chinese Medicine, Changsha, 410208, China. chenouying2008@126.com.
Support Care Cancer ; 32(3): 155, 2024 Feb 13.
Article em En | MEDLINE | ID: mdl-38347229
ABSTRACT

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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Testes Psicológicos / Transtornos do Sono-Vigília / Resiliência Psicológica / Neoplasias Pulmonares Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Female / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Testes Psicológicos / Transtornos do Sono-Vigília / Resiliência Psicológica / Neoplasias Pulmonares Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Female / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article