Original ArticleAssociations between Pittsburgh Sleep Quality Index factors and health outcomes in women with posttraumatic stress disorder
Introduction
The Pittsburgh Sleep Quality Index (PSQI) [1] is considered an essential measure of sleep and insomnia symptoms in treatment research, and it is a recommended assessment tool for both epidemiological studies and studies that address the mechanisms of sleep disorders [2]. It was developed, in part, to provide a user-friendly and clinically-oriented measure of sleep quality. The 18-items on the PSQI index seven clinically-formulated sleep domains: sleep duration, sleep disturbance, sleep latency, daytime disturbance, habitual sleep efficiency, sleep quality, and use of sleep medications. Scores on each of the seven PSQI subscales are used to calculate a global score that ranges from zero to 21. Global scores above five distinguish poor sleepers from good sleepers with high sensitivity (90–99%) and specificity (84–87%) [1], [3].
Despite broad use of the PSQI in clinical and research settings, relatively few studies have examined the factor structure of the instrument [4], [5], [6], [7]. The few that have indicated that the PSQI is better represented by two- or three-factor models than a single factor [4], [5], [6], [7]. For example, Cole et al. [4] examined the factor structure of the PSQI using a combination of exploratory factor analysis and confirmatory factor analysis in a large sample of older adults. Results suggested that the seven PSQI subscales are best represented by three latent factors: Sleep Efficiency, Perceived Sleep Quality, and Daily Disturbances. The Sleep Efficiency factor included the sleep duration and habitual sleep efficiency subscales of the PSQI; the Perceived Sleep Quality factor included the sleep quality, sleep latency, and sleep medication subscales of the PSQI; and the Daily Disturbances factor included the sleep disturbance and daytime dysfunction subscales of the PSQI. Cole et al. [4] also found marginal fit for a two-factor model that combined Perceived Sleep Quality and Daily Disturbances into a single factor. These two- and three-factor models were later replicated by Magee et al. [6] using exploratory and confirmatory factor analysis in a sample of Australian adults. However, Magee et al. [6] argued for the two-factor model rather than the three-factor model due to evidence of overlap between the Perceived Sleep Quality and Daily Disturbances factors. The multidimensionality of the PSQI has also been supported by principal component analysis [5], [7]. For example, a principal component analysis performed by Kotronoulas et al. [5] supported a two-factor model of the PSQI in which Factor 1 included all of the PSQI component scores with the exception of sleep medication and daytime dysfunction. Each of these studies suggests that a multidimensional PSQI scoring system may be preferable to the traditional PSQI global score. However, the best representation of the PSQI factor structure remains unclear. Structural models of the PSQI need to be replicated and extended to a range of patient populations before they can be broadly applied. It is also important to consider whether PSQI factor scores provide incremental value beyond the PSQI global score. Clinicians and researchers who use the PSQI as a screening instrument for sleep disorders and as a tool for case conceptualization may well wonder whether using a multifactorial scoring system will improve the PSQI’s convergent or discriminant validity. However, the relationship between PSQI factor scores and other mental and physical health outcomes has not been established.
The primary aim of the present study was to compare the relative fit of alternative two- and three-factor PSQI structural models in data from a sample of individuals with posttraumatic stress disorder (PTSD). Understanding the performance of the measure in a sample with PTSD is important because of the high prevalence and wide variety of sleep problems in this patient population. Of individuals diagnosed with PTSD in the general population, 44% report difficulty initiating sleep, 91% report difficulty maintaining sleep, and 52% report nightmares [8]. This represents a two- to fivefold increase in the rate of these sleep complaints relative to healthy individuals [8]. Moreover, the concurrence of sleep disruption and PTSD predicts poor clinical outcomes [9], [10]. Sleep disruptions in PTSD need to be accurately characterized in order for basic research and clinical interventions to progress, and the PSQI is often the primary measure of sleep in descriptive studies of PTSD [11], [12] and PTSD intervention trials [13], [14].
The secondary aim of the present study was to evaluate the convergent and discriminant validity of the best-fitting PSQI factor model. We accomplished this by comparing patterns of association between observed PSQI factor scores and observed scores on measures of PTSD, depression, anger, and physical symptoms. Given prior evidence that poor sleep quality assessed with the PSQI is associated with increased negative affect (e.g., anxiety, depression, anger) [15], [16], [17] and physical health concerns [16], [18], [19], [20], we anticipated a moderate to high degree of association between PSQI factor scores and external health outcomes. We also expected to find evidence of discriminant associations between PSQI factor scores and these outcomes. Specifically, based on evidence that sleep quality is more predictive of negative affect and physical health than sleep quantity [16], we predicted that mental and physical health outcomes would have a stronger correlation with the Perceived Sleep Quality factor score than the Sleep Efficiency factor score.
Our third and final aim was to evaluate the incremental value of the PSQI factor scores in relation to the global PSQI score. To accomplish this, we compared the patterns of association between observed PSQI factor scores and observed scores on mental and physical health indices to the pattern of association between the PSQI global score and the same external symptom measures. We anticipated that some external symptoms would have stronger correlations with certain PSQI factor scores than with the PSQI global score. More specifically, because the Daily Disturbances factor includes a PSQI subscale with several questions about physical health complaints, we expected scores on our external measure of physical health complaints to have a stronger correlation with the Daily Disturbances factor score than the PSQI global score.
Section snippets
Participants
Participants were 319 females enrolled in one of two clinical trials of trauma-focused cognitive behavioral therapy that were conducted sequentially in the same location [21], [22]. All participants were victims of sexual assault (86%) or physical assault (14%) and had a current diagnosis of PTSD. Participants were at least three months post-trauma at the time of assessment. About half of the participants (44%) met criteria for current major depressive disorder at the time of initial assessment
Sample characteristics
A t-test indicated that PSQI scores did not differ significantly between samples in the two intervention trials included in study analyses, t(288)= 0.89, p = .38. Therefore, data were pooled across samples for all analyses. As expected, most participants (85%) reported poor sleep quality (PSQI scores > 5). Table 1 presents mean scores on the PSQI, CAPS, BDI, STAXI Trait Anger scale, and PILL for the combined sample. Regular use of sleep medications (scores of two or three on the PSQI sleep
Discussion
The present study compared the relative fit of one-, two-, and three-factor measurement models of the PSQI. It also examined the convergent and discriminant validity of each PSQI factor score and the PSQI global score in relation to measures of PTSD, depression, anger, and physical symptoms. The results provide additional evidence that sleep characteristics assessed by the PSQI are best-represented by three latent factors: Sleep Efficiency, Perceived Sleep Quality, and Daily Disturbances [4].
Conflict of interest
The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: http://dx.doi.org/10.1016/j.sleep.2012.02.014.
Acknowledgements
This research was supported in part by National Institute of Mental Health Grant R01-MH51509, awarded to Patricia A. Resick at the University of Missouri, St. Louis. Support for the first and second authors was provided by National Institute of Mental Health Institutional Training Grant T32-MH019836, awarded to Terence M. Keane at the National Center for PTSD, Boston, MA. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official
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