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Published on: December 16, 2022
Identification of sleep quality clusters among stroke patients: A multi-center Latent Profile Analysis study
Lina Guo1, Mengyv Zhang2, Genoosha Namassevayam3
1Department of Neurology, National Advanced Stroke Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Background:
Quality sleep plays a crucial role in maintaining good health. Nevertheless, sleep disruption is a common and complex issue after a stroke. It can increase the likelihood of stroke recurrence by influencing modifiable risk factors. However, there is currently a lack of research on the latent classes or clusters of sleep quality and its predictive factors among stroke patients.
Objectives:
This study aims to identify latent classes of sleep quality and explore the predictive factors associated with different sleep quality clusters among stroke patients.
Methods:
A total of 500 participants were recruited through cluster random sampling from January 2023 to May 2023. Latent profile analysis was conducted to identify latent classes of sleep quality within the sample of stroke patients. Additionally, multinomial regression analyses were employed to investigate the predictors associated with the different latent classes identified in the analysis.
Results:
Out of the 500 participants, 458 (91.6 %) completed the survey, and 71 % of them reported experiencing sleep problems. The analysis revealed three latent profile classes: the "good sleep quality-deficient duration" group (65.4 %), the "moderate sleep quality-more disturbances" group (14.1 %), and the "poor sleep quality-low efficiency" group (20.5 %). Factors associated with sleep quality were identified. Protective factors for sleep quality included being male, having the TOAST type of large-artery atherosclerosis, having a good education, high household income, no family history of stroke, residing in rural areas, and having better environmental and social support (all p < 0.05). Risk factors for sleep quality included smoking, high perceived stress, and a greater number of comorbidities (all p < 0.05).
Conclusions:
This study has successfully identified three distinct latent profile classes of sleep quality and their associated predictors among stroke patients in China. The findings offer both theoretical guidance and practical insights for the development of targeted intervention programs aimed at enhancing the sleep quality of stroke patients.
