The prevalence of sleep disorders and insomnia has surged across diverse age groups [1]. The global sleep dynamics research estimates a 10%–20% occurrence of insomnia disorder within populations, with chronic symptoms in nearly half of the affected individuals [2]. Similarly, over one-fifth of adults experience sleep disorders in Korea [3]. Moreover, the incidence of insomnia has doubled over the last decade, emphasizing the need for effective assessment tools and interventions [4].

Accumulating evidence has underscored a correlation between compromised sleep quality and adverse health outcomes. Cardiovascular mortality, coronary heart disease, stroke, dementia, mental disorders, and metabolic syndrome are among the potential consequences of poor sleep quality [1]. A recent study even highlights associations between sleep disturbances in patients with Parkinson’s disease and alterations in white matter and functional networks, and cognitive impairments [5].

Evaluating sleep quality involves an interplay of factors, including sleep onset, maintenance, duration, and sense of revitalization upon awakening [6]. Sleep quality can be objectively and subjectively measured. Polysomnography, an objective method, is known to accurately collect sleep parameter data [7]. However, it is resource-intensive and has limited accessibility for clinicians, necessitating alternative approaches. Conversely, subjective self-rating questionnaires have the advantages of patient compliance, ease of administration, and reduced demands on healthcare personnel.

The Pittsburgh Sleep Quality Index (PSQI) is a widely utilized subjective sleep quality measure, encompassing seven key facets, including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction [8]. The Korean version of the PSQI (PSQI-K) used in Korea [9] and also in this study assigns scores of 0–3 to each facet, resulting in a total score range of 0–21. Higher scores indicate poorer sleep quality. Notwithstanding its frequent application, discrepancies in interpreting PSQI scores—such as the use of subdomain cutoffs—have yielded suboptimal assessments of sleep disorders.

To address this gap, a previous study examined the reliability and validity of the PSQI-K in a small sample of young adults based on data from Fitbit devices (Fitbit Inc., San Francisco, CA, USA) [10]. However, the study lacks robustness as only a limited number of young adults in their 20s were tested. This study therefore aimed to comprehensively examine the reliability and validity of the PSQI-K through a relatively large-scale analysis of Korean adults. This evaluation was enhanced by objective sleep assessments using Fitbit devices, thus providing a broader perspective on sleep quality.

We verified the reliability and validity of the translated PSQI-K to evaluate subjective sleep quality and ascertain the optimal cutoff score for diagnosing sleep disorders in Korean adults. In this study, the reliability coefficient of the PSQI-K was 0.61, within the 0.6–0.8 standard for an adequate reliability coefficient. Cronbach’s α is a measurement that calculates the internal consistency of an assessment instrument [16]. In this study, the reliability coefficient of the PSQI-K was 0.61, which was lower than that of 0.84 reported by Shon et al. [9]. However, a Cronbach’s alpha value between 0.6 and 0.8 is deemed acceptable [16-18]. Thus, this study confirmed the acceptable reliability of the tool. The difference in Cronbach’s α between this study and that of Shon et al. [9] may be due to the use of polysomnography in their study [9], whereas Fitbit was used in this study.

An exploratory factor analysis was conducted to verify the construct validity of the PSQI-K. Among the seven subfactors, those with an eigenvalue of ≥1.0 and factor loadings of ≥0.60 were categorized into three factors, explaining 48.2% of sleep quality. This finding differs from those of Shon et al. [9] where a two-factor division was obtained. Construct validity pertains to how effectively a set of indicators represents or reflects a concept that is not directly measurable. Our approach involved examining the covariations of factors and their loadings on latent variables through an exploratory factor analysis. The resultant structural model revealed a three-factor structure. By employing factor rotation, the initial matrix was transformed into a simple structure matrix, enhancing interpretability. To this process, the varimax method was applied, assuming independence among factors and, consequently, zero inter-factor correlations. This method has the advantage of clearly distinguishing between factors, rendering interpretation more straightforward. In the evaluation of construct validity, the factors exhibited a distinct and well-defined latent meaning, facilitating a more straightforward interpretation.

The criterion validity of the PSQI-K was verified using indicators of sleep efficiency, total sleep time, sleep latency, and number of midnight awakenings measured using Fitbit trackers. The PSQI-K total score was weakly negatively correlated with Fitbit’s sleep efficiency (r=-0.25), total sleep time (r=-0.024), and number of midnight awakenings (r=-0.016), indicating a lack of validity compared to the study by Shin and Kim [10] This may be due to the differences in the characteristics of the study participants. While the study by Shin and Kim [10] had a small sample size comprising only young adults working in hospitals, the present study had a larger and more diverse study population encompassing various ages.

Fitbit uses accelerometers and heart rate sensors to estimate sleep stages (light, deep, and rapid eye movement sleep). While these can provide a good approximation, they may not be as accurate as data collected through polysomnography, which involves a more extensive array of sensors, including electroencephalography [19]. Moreover, there is a potential for error in the results due to environmental stimuli or user factors; hence, the user regularity with using the Fitbit during sleep should also be considered [12].

However, recent advances in wearable devices and big data analytics have made it possible to collect and analyze lifestyle and environmental data. An integrated analysis of these data can predict individual health risks, based on which appropriate management measures through active participation in healthcare behaviors can be encouraged. These measures are expected to prevent or delay the onset of diseases, thereby minimizing economic losses for individuals and society [20]. Therefore, this study, which evaluated the validity and reliability of the PSQI-K—a diagnostic tool for sleep disorders—using a simple and user-friendly wearable device compared to polysomnography—an existing diagnostic tool—is expected to have positive significance.

The optimal cutoff for distinguishing sleep disorder groups based on the PSQI-K total score was 6 points, which is consistent with previous studies. Thus, Korean adults with a PSQI-K total score of ≥6 can be screened as “poor sleepers,” which may be a useful criterion for identifying sleep disorder groups in research and clinical practice.

This study has several limitations. We did not consider the history of sleep disorders or sleep medication types—two factors that should be accounted for in future studies. In addition, participants were selected from only two hospitals; therefore, the study population may not be representative of the general Korean population. Further reliability and validity should therefore be confirmed through studies involving representative populations. Despite this study’s limitations, we verified the reliability and validity of the PSQI-K using Fitbit for a relatively large number of participants of various ages, As compared to a previous study which included a small sample size comprising only young adults working in hospitals [10].

In conclusion, the PSQI-K, which is frequently used as a screening tool for sleep disorders in Korean adults, was highly correlated with the Fitbit tracker, a simple objective measurement tool, in terms of sleep efficiency, total sleep time, sleep latency, and number of awakenings. The study also verified a cutoff for the PSQI-K to identify “poor sleepers,” which can be used as a more convenient tool for screening patients for sleep interventions.