ArticleBMC nursing2025
Analysis of sleep patterns among clinical nurses: a latent profile and association rule mining approach.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Latent profiles and characteristic networks of second victim experiences among junior nurses: a latent profile analysis and association rule mining study.BMC nursing · 2026Article
- Latent profile analysis of sleep quality and its association with psychological resilience among emergency department nurses.Frontiers in psychology · 2026Article
- Turnover Intention and Associated Factors Among Night-Shift Midwives in China: A Cross-Sectional Study.Journal of nursing management · 2026Article
- Latent profiles of professional identity and sleep quality and associations with anxiety symptoms among emergency nurses.Frontiers in public health · 2026Article
- Latent profile analysis of sleep quality and associated factors on sleep patterns in night-shift nurses: a cross-sectional study.Frontiers in public health · 2026Article
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4 authors.
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Abstract
backgroundTraditional approaches to assessing sleep quality in clinical nurses often overlook population heterogeneity and the complex interplay of influencing factors. This study employs Latent Profile Analysis (LPA) and Association Rule Mining (ARM) to identify distinct sleep quality subgroups and uncover key factor combinations, thereby informing targeted intervention strategies.
methodsA total of 1,686 nurses from 123 hospitals in Shandong Province were recruited through multistage stratified sampling. LPA was used to classify participants based on seven sleep dimensions from the Pittsburgh Sleep Quality Index (PSQI), while ARM was applied to identify frequent itemsets of sleep disorder triggers. Key influencing factors were further examined using univariate analysis and multivariate logistic regression.
resultsThree latent sleep profiles were identified: high (63.11%), moderate (34.10%), and low (2.79%) sleep quality. The low-sleep subgroup was characterized by higher proportions of being unmarried/divorced (42.55%), low monthly income (≤ 3,000 CNY, 42.55%), non-permanent employment (76.60%), and severe psychological distress (44.68%). In contrast, the high-sleep subgroup featured higher rates of being married (85.62%), moderate income (3,001–7,000 CNY, 73.03%), and low psychological distress (51.32%). Key determinants included marital status (OR = 2.153/2.252), income (OR = 9.098), employment type (OR = 1.475), and psychological state (OR = 0.060–0.555). ARM revealed distinct risk combinations: “low income + non-permanent employment” (lift = 3.895) for the low-sleep group; “married + moderate income + non-permanent employment + patient conflict” for the moderate group; and “high income + low psychological distress” buffering night-shift effects in the high-sleep group.
conclusionBy integrating LPA and ARM, this study reveals the multidimensional heterogeneity and interactive mechanisms underlying clinical nurses’ sleep quality. The findings support a stratified intervention framework combining institutional safeguards with precision strategies to enhance sleep health management in nursing populations.
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