Xiang Yingjun, Wei Shujuan, Sun Xiaoya, Yang Weiting, Han Yaohui, Wu Xuanzhen
Shenzhen Futian Center for Chronic Disease Control, Shenzhen, Guangdong, China.
Front Psychol. 2024 Oct 15;15:1481580. doi: 10.3389/fpsyg.2024.1481580. eCollection 2024.
Healthcare workers often encounter inadequate sleep conditions. However, limited research has examined the underlying sleep patterns among healthcare workers. This study aimed to identify sleep patterns in healthcare workers, explore predictors associated with various sleep patterns, and investigate the relationship between sleep patterns and psychiatric symptoms.
This cross-sectional study was conducted in Shenzhen, China, from April 2023 to June 2023. In total, data from 1,292 participants were included using a convenience sampling method. A latent profile analysis was conducted to identify sleep patterns based on the seven dimensions of the Pittsburgh Sleep Quality Index. Multinomial logistic regression analysis was conducted to investigate the influence of socio-demographic variables on each profile. A one-way ANOVA test was employed to examine the relationships between sleep patterns and psychiatric symptoms.
Three distinct profiles were identified: good sleepers (63.9%), inefficient sleepers (30.3%), and poor sleepers (5.7%). Multinomial logistic regression analysis indicated that gender and marital status were predictors of various sleep patterns. The ANOVA revealed significant differences in psychiatric symptoms scores among the three sleep patterns; poor sleepers exhibited the highest levels of mental distress.
This study identified three distinct sleep patterns in healthcare workers and their significant associations with psychiatric symptoms. These findings contribute to the development of targeted intervention strategies aimed at improving sleep and reducing psychiatric symptoms among healthcare workers.
医护人员经常面临睡眠条件不足的情况。然而，针对医护人员潜在睡眠模式的研究有限。本研究旨在识别医护人员的睡眠模式，探索与各种睡眠模式相关的预测因素，并调查睡眠模式与精神症状之间的关系。
本横断面研究于2023年4月至2023年6月在中国深圳进行。采用便利抽样方法，共纳入1292名参与者的数据。基于匹兹堡睡眠质量指数的七个维度进行潜在类别分析，以识别睡眠模式。进行多项逻辑回归分析，以研究社会人口学变量对每种睡眠模式的影响。采用单因素方差分析来检验睡眠模式与精神症状之间的关系。
识别出三种不同的睡眠模式：睡眠良好者（63.9%）、睡眠效率低下者（30.3%）和睡眠不佳者（5.7%）。多项逻辑回归分析表明，性别和婚姻状况是各种睡眠模式的预测因素。方差分析显示，三种睡眠模式的精神症状评分存在显著差异；睡眠不佳者的精神痛苦程度最高。
本研究识别出医护人员的三种不同睡眠模式及其与精神症状的显著关联。这些发现有助于制定有针对性的干预策略，旨在改善医护人员的睡眠并减轻其精神症状。
