Pittsburgh Sleep Quality Index Download

The PSQI was developed in 1988, by Buysse and his colleagues, to create a standardized measure designed to gather consistent information about the subjective nature of people's sleep habits and provide a clear index that both clinicians and patients can use.[1][4][5] It gained popularity as a measure that could be used in research that looks at how sleep might be associated with sleep disorders, depression, and bipolar disorder.

Consisting of 19 items, the PSQI measures several different aspects of sleep, offering seven component scores and one composite score. The component scores consist of subjective sleep quality, sleep latency (i.e., how long it takes to fall asleep), sleep duration, habitual sleep efficiency (i.e., the percentage of time in bed that one is asleep), sleep disturbances, use of sleeping medication, and daytime dysfunction.

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Traditionally, the items from the PSQI have been summed to create a total score to measure overall sleep quality. Statistical analyses also support looking at three factors, which include sleep efficiency (using sleep duration and sleep efficiency variables), perceived sleep quality (using subjective sleep quality, sleep latency, and sleep medication variables), and daily disturbances (using sleep disturbances and daytime dysfunctions variables).[6][7]

Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.

The ROC results showed that the PSQI with a critical value of 6.5 could well screen those with good or bad sleep quality among the frontline medical staff fighting the novel coronavirus. Since the PSQI score was an integer, the critical value was rounded to 7. Those with scores less than 7 have normal sleep, and those with scores of more than 7 experience sleep disorders. The higher the score is, the worse sleep quality is, which is consistent with previous reports.62,63

Similar to the conclusions of CTT, the results of Rasch analysis showed that the PSQI had good adaptability and reliability for frontline COVID-19 health care workers. However, our study and similar studies mainly adopted the self-reported indicators for sleep quality evaluation instead of objective indicators, such as sleep electroencephalogram. Thus, sleep scales with objective indexes remain to be developed.

Regardless of sex, the longer the sedentary time, the stronger the association with poor sleep quality. Nationwide efforts are required to recommend standards for sedentary time and develop evidenced-based healthy behavior guidelines.

The Pittsburgh Sleep Quality Index (PSQI) is a widely used tools for measuring SQ [11, 18]. The PSQI, developed in 1989 by Buysse [2] and colleagues, is a self-reporting tool used for evaluating the quality and patterns of sleep over a month [3]. Existing literature on the PSQI provides information on its psychometric properties, internal consistency, test-retest reliability, validity, and factor structure [19, 20].

Figure 1 shows the subgroup analyses of the association between ST and poor SQ stratified by dependent variables. As shown in Fig. 1, greater sedentary time as associated with higher odds of poor SQ across each of the components of the PSQI such as sleep quality, sleep latency, sleep disturbance, use of sleeping medication, and daytime dysfunction.

Our study results suggest there is a positive association between sedentary time and sleep problems. Several studies also demonstrate this association [30]. Furthermore, several mechanisms according to these associations have also been proposed [16, 33,34,35,36]. A potential mechanism for this is that sleep time can be impaired by the amount of time spent exercising certain behaviors, given that ST is usually associated with watching television or computer use [16, 33]. Insufficient sleep time can be a factor that naturally lowers the quality of sleep [30]. Increased ST, such as from watching television and computer use, may increase the risk of mental health problems by promoting social isolation and limiting development of social network [34, 37]. Consequently, increased ST is associated with mental health problems such as depression, which may contribute to poor SQ [38,39,40]. Furthermore, light-emitting diode (LED)-backlit displays are increasingly used in TV and computer screens. The LED-backlit display may cause significant suppression of melatonin, thus affecting the biological clock and possibly resulting in sleep problems [35]. In addition, sedentary behavior also contributes to the onset and progression of metabolic syndrome, which can cause sleep problems [41]. Moreover, compared to sedentary behavior, nonsedentary behavior is associated with increased energy expenditure/metabolic rate and fatigue, which may reduce the risk of sleep problems [42, 43]. Regardless of achieving sufficient physical activity, standing behavior alone can cope with this [2, 41].

Eight studies found that a 3-factor model best explained the data [29, 35,36,37, 41, 45, 52, 53], Thirteen reported a 2-factor model [22, 24, 26, 32, 34, 38, 40, 50,51,52, 58, 60], and 9 reported a 1-factor model [15,16,17, 42, 46, 48, 49, 59, 64]. One study reported both 2-factor and 3-factor models, but in separate sample populations [44] (Table 3). Two studies reported second-order models; i.e., 2 or 3 first-order latent factors loaded on a higher-order factor [23, 33] (Table 3). Seven studies found the same PSQI structure with both EFA and CFA [40,41,42, 44,45,46, 58], while 3 derived different models from EFA and CFA [17, 22, 24] (Table 3). The medicine component of the PSQI was removed from the final models in some studies [23, 36, 38], while sleep quality component of the PSQI was not reported in the final model by 2 studies [23, 40] (Table 3). Two studies reported finding a 2-factor model with just 5 PSQI components [16, 23], while 1 study reported a model with only three PSQI components [48] (Table 3). Three studies reported final models with cross-loads [29, 45, 63]. Two studies reported non-standardized factor loadings, while 2 studies did not report the factor loadings (Table 3) [26, 29, 46, 63]. The studies showed little variation in number, types, and limit values of the fit indices used.

The Pittsburgh Sleep Quality Index (PSQI) is a measure of self-reported sleep quality and sleep disturbance. Though the PSQI is widely used, it is unclear if it adequately assesses self-reported sleep disturbance in people with schizophrenia spectrum disorders. We used mixed methods to examine the relationship between scores on the PSQI and qualitative self-report during in-depth interview in a group of participants diagnosed with schizophrenia spectrum disorders (N = 15). Although the PSQI appears to accurately capture issues related to sleep initiation, average duration, and interruption by physical complaints, it did not adequately assess other salient issues including irregularity in sleep duration and timing, shallow unrefreshing sleep, prolonged sleep inertia, hypersomnia, and sleep interrupted by mental or psychological complaints. In interview by contrast these types of problems were readily reported and described as important by participants. Our findings suggest that using the PSQI summary score as a measurement of general sleep disturbance in this population may be misleading, as this failed to capture some of the types of sleep problems that are particularly common in this group.

The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) are questionnaires used to assess sleep quality and excessive daytime sleepiness in clinical and population-based studies. The present study aimed to evaluate the construct validity and factor structure of the PSQI and ESS questionnaires among young adults in four countries (Chile, Ethiopia, Peru and Thailand).

Overall, we documented cross-cultural comparability of sleep quality and excessive daytime sleepiness measures using the PSQI and ESS questionnaires among Asian, South American and African young adults. Although both the PSQI and ESS were originally developed as single-factor questionnaires, the results of our EFA and CFA revealed the multi- dimensionality of the scales suggesting limited usefulness of the global PSQI and ESS scores to assess sleep quality and excessive daytime sleepiness.

Through examination of eigenvalues, factor loadings and the scree plot for PSQI subscales, a two-factor model was extracted for Chile, Ethiopia and Thailand (Fig. 1). However, a three-factor model provided a better fit for the data from Peru (Fig. 1). Table 3 shows the principal component analysis (PCA) results for PSQI and ESS subscales according to county. Similar to the scree plots, a two-factor model of the PSQI was extracted in Chile, Ethiopia and Thailand. The first factor (quality) consisted of sleep disturbance, sleep latency, daytime dysfunction, sleep medication and sleep quality. The second factor (efficiency) consists of sleep duration and sleep efficiency. For Peru, the better fitting three-factor model was as follows: first factor (sleep quality) consists of sleep disturbance, daytime dysfunction and sleep quality; second factor (sleep efficiency) consists of sleep duration and sleep efficiency; and third factor (medication) consists of sleep latency and sleep medication. The two-factor model explained approximately 46% of the total variance in Thailand, 48% in Ethiopia, and 49% in Chile while the three factor model in Peru explained approximately 59% of the total variance. 006ab0faaa