Latent profile analysis and influencing factors of sleep quality in community perimenopausal women: a cross-sectional study

Background Sleep disorders in perimenopausal women are serious problems, which have a negative impact on women's physical and mental health. However, there are few studies on the potential profile of sleep quality in perimenopausal women in the community. Therefore, this study aims to explore different potential trajectories of sleep quality in perimenopausal women in the community and analyze the influencing factors of different trajectories. Methods A cross-sectional study was conducted from July 2024 to December 2024, and a total of 281 perimenopausal women in the community were recruited from 4 communities in Bengbu. The participants completed the Pittsburgh Sleep Quality Index (PSQI), and self-rating anxiety scale (SAS), self-rating depression scale (SDS) and simplified coping style questionnaire (SCSQ). Latent profile analysis(LPA) was employed to identify latent profiles of sleep quality of perimenopausal women in the community. The predictors of sleep quality in different latent profiles were assessed via multinomial logistic regression analysis. One-way ANOVA, chi-square test or Fisher exact test, and the Kruskal-Walis test were used to compare the PSQI scores of perimenopausal women in the community under different latent profile characteristics. Results In this study, 88 out of 281 perimenopausal women community in the had PSQI scores more than 7 points, and the incidence of sleep disorders was 33.3%. The sleep quality of perimenopausal women in the community could be divided into three different potential trajectories, including 193 cases (68.7%) in the good sleep quality group, 68 cases (24.2%) in the general sleep quality group, and 20 cases (7.1%) in the poor sleep quality group. Taking the good sleep quality group as the reference group, drinking history ( OR  = 2.087), chronic disease history ( OR  = 2.221), spouse health status ( OR  = 1.880) and anxiety ( OR  = 4.358) were risk factors for predicting the general sleep quality of perimenopausal women in the community ( P  < 0.05). Spouse health status ( OR  = 2.130) and anxiety ( OR  = 19.512) were risk factors for poor sleep quality of perimenopausal women in the community ( P  < 0.05). Conclusions There are three qualitatively different potential trajectory categories of sleep quality in perimenopausal women in the community, and drinking history, chronic disease, poor spouse health and anxiety have predictive effects on their trajectory categories. In the future, community nursing staff can take targeted interventions according to different categories of sleep quality in perimenopausal women to improve sleep quality and level of health promotion.