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 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=33.777, < 0.001), age (χ=10.605, <0.05), and subjective social status (SSS) (χ=12.43, <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, <0.001; β=-0.234, <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.