Khosravi Ahmad, Emamian Mohammad Hassan, Hashemi Hassan, Fotouhi Akbar
Center for Health Related Social and Behavioral Sciences Research, Shahroud University of Medical Sciences, Shahroud, Iran.
Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran.
Sleep Med X. 2021 Jul 2;3:100038. doi: 10.1016/j.sleepx.2021.100038. eCollection 2021 Dec.
OBJECTIVE/BACKGROUND: There have been conducted few studies in Iran on the quality of sleep in the general population. This study aimed to use the item response theory (IRT) model to examine the accuracy of the seven components of the Pittsburgh Sleep Quality Index (PSQI) and to provide an appropriate cut-off point for population-based studies.
This study was performed using the data of the second phase of the Shahroud Eye Cohort Study (ShECS) in 2014. The sleep quality of 4710 participants was measured through PSQI. Using an IRT model, the seven components of the index are considered as indicators and sleep quality as the latent variable in the measurement model. This model supposed that there is only one hidden component to explain the respondent's behavior to a number of items.
Results of analyzing different components of PSQI showed that component 6 (using sleep medication) and 7 (daytime dysfunction disorder) had the lowest values of discrimination parameter and component 4 (habitual sleep efficiency) and 1 (sleep quality) had the highest value of discrimination parameter. Persons with an expected sleep quality score of less than or equal to 6.5 will be defined as good sleep quality pattern.
Since discrimination values for components 6 and 7 are less than the values for other components, the use of the standardized latent scores is emphasized for assessing the quality of sleep in the population.
目的/背景:伊朗针对普通人群睡眠质量的研究较少。本研究旨在运用项目反应理论(IRT)模型检验匹兹堡睡眠质量指数(PSQI)七个组成部分的准确性,并为基于人群的研究提供合适的截断点。
本研究采用2014年沙赫鲁德眼队列研究(ShECS)第二阶段的数据。通过PSQI对4710名参与者的睡眠质量进行测量。在测量模型中,使用IRT模型将该指数的七个组成部分视为指标,将睡眠质量视为潜在变量。该模型假定只有一个隐藏成分来解释受访者对多个项目的反应。
对PSQI不同组成部分的分析结果显示,第6个组成部分(使用睡眠药物)和第7个组成部分(日间功能障碍)的区分参数值最低,第4个组成部分(习惯性睡眠效率)和第1个组成部分(睡眠质量)的区分参数值最高。预期睡眠质量得分小于或等于6.5的人将被定义为良好睡眠质量模式。
由于第6和第7个组成部分的区分值低于其他组成部分,因此强调使用标准化潜在分数来评估人群的睡眠质量。