Cargando…

Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach

BACKGROUND: Learned helplessness (LH) is an essential psychological factor influencing maintenance haemodialysis (MHD) patients’ health behaviour and is closely related to prognosis of the disease. This study aimed to identify potential trajectories of LH in MHD patients and assess their predictive...

Descripción completa

Detalles Bibliográficos
Autores principales: Xie, Chunyan, Li, Li, Li, Yamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926927/
https://www.ncbi.nlm.nih.gov/pubmed/36798876
http://dx.doi.org/10.2147/PRBM.S401380
_version_ 1784888377361825792
author Xie, Chunyan
Li, Li
Li, Yamin
author_facet Xie, Chunyan
Li, Li
Li, Yamin
author_sort Xie, Chunyan
collection PubMed
description BACKGROUND: Learned helplessness (LH) is an essential psychological factor influencing maintenance haemodialysis (MHD) patients’ health behaviour and is closely related to prognosis of the disease. This study aimed to identify potential trajectories of LH in MHD patients and assess their predictive role in self-management. METHODS: This study was conducted in strict compliance with national laws, the Declaration of Istanbul, and the Declaration of Helsinki. A total of 347 MHD patients at a blood purification centre in Hunan Province, China, were selected as the study population. Four longitudinal surveys (baseline and second/fourth/sixth month after baseline) were conducted using the General Information Questionnaire for MHD patients, the Chinese version of the Learned Helplessness Scale for MHD patients, and the Self-Management Scale for Haemodialysis. Latent growth mixture model (LGMM) analysis was used to identify LH trajectories, and their predictors were analysed using multinomial logistic regression. The predictive role of LH trajectory on self-management was analysed using linear regression. RESULTS: This study identified three LH trajectories in MHD patients, named the “high-decreasing group” (57.9%), “low-increasing group” (21.3%), and “low-stability group” (20.7%). The results of the univariate analysis showed that sex (χ(2)=33.777, P < 0.001), age (χ(2)=10.605, P<0.05), and subjective social status (SSS) (χ(2)=12.43, P<0.01) were associated with LH trajectory classes. Multinomial logistic regression further demonstrated that gender, age, and SSS were predictors of different LH trajectories. The intercept and slope of the overall LH trajectory were negatively correlated with self-management (β=−0.273, P<0.001; β=−0.234, P<0.01). CONCLUSION: MHD patients show three different LH trajectories. The initial level and developmental rate of LH can negatively predict future self-management. It is necessary to screen MHD patients’ LH and develop targeted interventions for them with different LH trajectories at specific stages.
format Online
Article
Text
id pubmed-9926927
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Dove
record_format MEDLINE/PubMed
spelling pubmed-99269272023-02-15 Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach Xie, Chunyan Li, Li Li, Yamin Psychol Res Behav Manag Original Research BACKGROUND: Learned helplessness (LH) is an essential psychological factor influencing maintenance haemodialysis (MHD) patients’ health behaviour and is closely related to prognosis of the disease. This study aimed to identify potential trajectories of LH in MHD patients and assess their predictive role in self-management. METHODS: This study was conducted in strict compliance with national laws, the Declaration of Istanbul, and the Declaration of Helsinki. A total of 347 MHD patients at a blood purification centre in Hunan Province, China, were selected as the study population. Four longitudinal surveys (baseline and second/fourth/sixth month after baseline) were conducted using the General Information Questionnaire for MHD patients, the Chinese version of the Learned Helplessness Scale for MHD patients, and the Self-Management Scale for Haemodialysis. Latent growth mixture model (LGMM) analysis was used to identify LH trajectories, and their predictors were analysed using multinomial logistic regression. The predictive role of LH trajectory on self-management was analysed using linear regression. RESULTS: This study identified three LH trajectories in MHD patients, named the “high-decreasing group” (57.9%), “low-increasing group” (21.3%), and “low-stability group” (20.7%). The results of the univariate analysis showed that sex (χ(2)=33.777, P < 0.001), age (χ(2)=10.605, P<0.05), and subjective social status (SSS) (χ(2)=12.43, P<0.01) were associated with LH trajectory classes. Multinomial logistic regression further demonstrated that gender, age, and SSS were predictors of different LH trajectories. The intercept and slope of the overall LH trajectory were negatively correlated with self-management (β=−0.273, P<0.001; β=−0.234, P<0.01). CONCLUSION: MHD patients show three different LH trajectories. The initial level and developmental rate of LH can negatively predict future self-management. It is necessary to screen MHD patients’ LH and develop targeted interventions for them with different LH trajectories at specific stages. Dove 2023-02-10 /pmc/articles/PMC9926927/ /pubmed/36798876 http://dx.doi.org/10.2147/PRBM.S401380 Text en © 2023 Xie et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Xie, Chunyan
Li, Li
Li, Yamin
Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach
title Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach
title_full Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach
title_fullStr Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach
title_full_unstemmed Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach
title_short Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach
title_sort trajectories of learned helplessness in maintenance haemodialysis patients and their predictive effects on self-management: a latent growth mixture modeling approach
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926927/
https://www.ncbi.nlm.nih.gov/pubmed/36798876
http://dx.doi.org/10.2147/PRBM.S401380
work_keys_str_mv AT xiechunyan trajectoriesoflearnedhelplessnessinmaintenancehaemodialysispatientsandtheirpredictiveeffectsonselfmanagementalatentgrowthmixturemodelingapproach
AT lili trajectoriesoflearnedhelplessnessinmaintenancehaemodialysispatientsandtheirpredictiveeffectsonselfmanagementalatentgrowthmixturemodelingapproach
AT liyamin trajectoriesoflearnedhelplessnessinmaintenancehaemodialysispatientsandtheirpredictiveeffectsonselfmanagementalatentgrowthmixturemodelingapproach