Performance of a two-item sleep quality measure (PSQI-2): a comprehensive evaluation in a multiethnic cohort (MESA Study)

Performance of a two-item sleep quality measure (PSQI-2): a comprehensive evaluation in a multiethnic cohort (MESA Study) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance of a two-item sleep quality measure (PSQI-2): a comprehensive evaluation in a multiethnic cohort (MESA Study) Luiz Menezes-Júnior This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8413149/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To evaluated the abbreviated two-item Pittsburgh Sleep Quality Index (PSQI-2) against the full PSQI in a multi-ethnic cohort. Methods We analyzed data from 2,237 participants from the MESA Sleep Ancillary Study. The full PSQI was adapted by integrating actigraphy data for sleep duration, latency, and efficiency components, while maintaining the original seven-component structure scored 0–3. The PSQI-2 was derived from two components: sleep duration (questionnaire-based) and subjective sleep quality. Validation analyses included correlation analysis; ROC curves for three PSQI cutpoints (> 5, > 7, >10) with sensitivity/specificity calculations, Bland-Altman analysis for agreement, bootstrap internal validation, and logistic regression for demographic, clinical, and sleep-related covariates. Results Poor sleep quality was prevalent (65.6% by PSQI > 5; 65.7% by PSQI-2 > 1). The PSQI-2 showed strong correlation with the full PSQI (r = 0.520, p < 0.001), consistent across gender and age subgroups. Both measures identified similar risk patterns: Black and Hispanic participants had higher odds of poor sleep, and obesity, sleep disorders, daytime sleepiness, and evening chronotype consistently increased poor sleep odds. The PSQI-2 demonstrated good discriminant validity across PSQI cutpoints (AUC: 0.785 for > 5, 0.748 for > 7, 0.750 for > 10), with sensitivity ranging from 71.8–86.8% and specificity from 46.3–79.1%. Conclusion The PSQI-2 shows strong validity and consistent performance with the full PSQI, effectively identifying poor sleep quality and associated factors. Its brevity makes it suitable for large-scale studies and clinical screening. Sleep quality PSQI validation epidemiological methods sleep disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Sleep quality is a fundamental pillar of human health, and its impairment is a significant public health concern associated with a wide range of adverse outcomes, including non-communicable chronic diseases, metabolic disorders, and cognitive decline [ 1 , 2 ]. The Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. (1989), has been established as a gold-standard self-report instrument for evaluating sleep quality in both clinical and research settings [ 3 ]. Its reliability and validity have been examined in diverse populations [ 4 – 6 ], confirming its utility while also revealing that its psychometric properties can be optimized in specific groups [ 7 ]. Despite its widespread use, the comprehensive nature of the 19-item PSQI presents practical limitations. In large-scale epidemiological studies that simultaneously investigate multiple health domains, extensive questionnaires can lead to respondent fatigue, increased missing data, and ultimately, constraints on the breadth of research [ 8 , 9 ]. This challenge has spurred the development and validation of abbreviated instruments, such as the two-item PSQI (PSQI-2), which focuses on the core dimensions of sleep duration and subjective sleep quality [ 10 , 11 ]. In a population-based household survey in Brazil, psychometric analyses supported a two-factor structure based on PSQI items, with excellent internal consistency, clear gradients in poor sleep prevalence across score levels, and good concurrent and known-group validity [ 10 ]. More recently, validation in the MrOS Sleep Study extended this evidence to a longitudinal context among community-dwelling older men, showing strong agreement with the full PSQI, excellent discriminatory accuracy, moderate test–retest reliability, and good responsiveness to clinically meaningful changes in sleep over time [ 11 ]. Together, these findings support the PSQI-2 as a valid, reliable, and pragmatic alternative for sleep quality assessment in large-scale epidemiological and longitudinal studies. Therefore, the brevity of such tools makes them particularly suitable for large studies where time and questionnaire space are limited. However, the performance of these short forms, especially in diverse, multi-ethnic populations and against objective sleep measures, requires further robust characterization [ 12 ]. The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary Study provides an ideal platform to address this research gap. MESA itself is a landmark, prospective cohort study initiated by the National Heart, Lung, and Blood Institute (NHLBI) in 1999–2000 to investigate the prevalence, correlates, and progression of subclinical cardiovascular disease in a sex-balanced, multi-ethnic cohort [ 13 ]. The MESA Sleep Ancillary Study augmented this rich dataset with a comprehensive sleep assessment protocol, including 7-day actigraphy, in addition to self-report questionnaires [ 14 ]. This unique combination of subjective and objective sleep data within a large, community-dwelling, multi-ethnic population offers an unparalleled opportunity to validate an abbreviated sleep instrument against a robust criterion standard. Therefore, this study aims to validate an adapted version of the PSQI-2 within the MESA Sleep study. We will examine its psychometric properties, diagnostic accuracy against the full PSQI, and its relationship with key demographic, clinical, and objective sleep measures. Methods Study population and design The Multi-Ethnic Study of Atherosclerosis (MESA) is a prospective cohort study sponsored by the National Heart, Lung, and Blood Institute (NHLBI). Its primary goal is to investigate risk factors for the development and progression of subclinical cardiovascular disease in a diverse population [ 13 ]. The datasets analyzed for this study were provided by the National Sleep Research Resource (Sleep Data, https://sleepdata.org ) [ 15 ]. The study began in 1999–2000, enrolling 6,814 participants aged 45–84 years who were free of clinically diagnosed cardiovascular disease at baseline. Participants were recruited from six field centers across the United States: Baltimore, MD; Chicago, IL; Los Angeles, CA; New York, NY; Saint Paul, MN; and Winston-Salem, NC. A key strength of MESA is its deliberate inclusion of a multi-ethnic population, with the cohort comprising White, Black, Hispanic, and Chinese-American individuals. Participants have undergone serial clinical examinations, with the most recent (the seventh exam) being conducted from 2022 to 2024. Between exams, annual follow-up contacts are conducted to assess clinical cardiovascular events and other health outcomes [ 13 ]. The MESA Sleep Ancillary Study was conducted to examine the relationships between sleep characteristics and cardiovascular disease risk. Data collection for this ancillary study occurred in close temporal proximity to the MESA Exam 5 (2010–2013) [ 14 ]. Out of the 4,077 participants who attended Exam 5, 2,261 individuals were enrolled in the MESA Sleep Ancillary Study and provided objective and subjective sleep data. Our present analysis focuses on the subset of these participants who completed the relevant sleep questionnaires and objective measurements. A major strength of the MESA Sleep study is its multi-method assessment of sleep, which includes for this study: Actigraphy, with participants wore an Actiwatch Spectrum (Philips Respironics) on the non-dominant wrist for 7 consecutive days to objectively estimate habitual sleep patterns, including sleep duration, sleep efficiency, and night-to-night variability in sleep timing, in their home environment [ 14 ]. Furthermore, p articipants completed self-report questionnaires, including the Women's Health Initiative Insomnia Rating Scale (WHIIRS), Epworth Sleepiness Scale (ESS), Modified Horne-Ostberg Morningness-Eveningness Questionnaire (MEQ) and other instruments assessing sleep disorder screening, captures self-reported physician diagnoses of specific sleep disorders. Variables Sleep quality Full sleep quality questionnaire The standard PSQI was not administered in the MESA Sleep Ancillary Study, therefore, an adapted measure of full sleep quality was constructed. This adaptation leveraged core sleep domains assessed by the study's existing validated questionnaires. Thus, the questionnaire was developed to mirror the structure and scoring of the original instrument while also leveraging the unique strengths of the dataset, which included both self-reported questionnaire data and objective actigraphy measures. The primary goal was to create a composite sleep quality score that integrated subjective perceptions with behavioral sleep patterns. The full PSQI was constructed to comprise the same seven components as the original PSQI, each scored on a 0–3 scale, where 0 indicates no difficulty and 3 indicates severe difficulty. Subjective sleep quality was directly derived from the question assessing overall typical night's sleep (typicalslp5). The original 5-point scale was recoded to the PSQI's 4-point scale (0–3), consolidating the two poorest categories into a single top score. Sleep latency was calculated by integrating both questionnaire and actigraphy data to capture the multifaceted nature of sleep onset difficulty. Initially, a weighted average of weekly sleep onset latency was calculated from weekday (avgonsetlatencywd5) and weekend (avgonsetlatencywe5) data. This continuous measure (in minutes) was then categorized into PSQI scoring bands. Furthermore, the frequency of self-reported trouble falling asleep (trbleslpng5) was scored on a 0–3 scale. The final sleep latency component score was generated by summing the actigraphy and questionnaire scores and recategorizing the combined value. For sleep duration, objective actigraphy data was prioritized for this component to reflect actual sleep time rather than time in bed. Therefore, a weighted average of weekly total sleep time was calculated from weekday (avgmainsleepwd5) and weekend (avgmainsleepwe5) data. This value was then scored based on the established PSQI criteria. Sleep efficiency, defined as the ratio of total sleep time to total time in bed multiplied by 100, was directly obtained from the actigraphy-derived variable (slp_eff5). This objective measure was then scored using the standard PSQI thresholds. Sleep disturbances was constructed from the frequency of six specific sleep problems reported in the questionnaire: trouble falling asleep (trbleslpng5); waking up in the middle of the night (wakeup5); waking up too early (wakeearly5); trouble getting back to sleep after waking up (bcksleep5); snoring (snored5); and stopping breathing during sleep (stpbrthng5). Each disturbance was scored on a 0–3 scale based on its frequency. The sum of these six scores was calculated, and since the adapted scale had a lower maximum (18) than the original PSQI (27), the scoring thresholds were proportionally adjusted to maintain a 0–3 component score. Use of sleep medication, was evaluated with the frequency of sleeping pill use (slpngpills5) was directly scored on the 0–3 scale. Finally, the daytime dysfunction and sleepiness, was evaluated by combining daytime sleepiness, with the frequency of feeling overly sleepy during the day (sleepy5); and frequency of sleep difficulties causing irritability (irritable5). Each was scored on a 0–3 scale. The sum of these two scores was then recategorized to generate the final component score, ensuring it remained on the standard 0–3 scale. The global full PSQI score was computed as the sum of the seven component scores, with a higher score indicating worse sleep quality. The traditional cut-off of > 5 was used to classify participants as having "poor" sleep quality. PSQI-2 The PSQI-2 development followed the conceptual framework proposed by Menezes-Júnior et al. (2025). The two-component structure comprised sleep duration (identical to component 1 of the full adapted PSQI) and subjective sleep quality (derived from typicalslp5, identical to component 4 of the full PSQI). This approach aligns with the theoretical foundation that these two domains capture the essential elements of sleep quality assessment. The PSQI-2 score ranged from 0–6, with the established cutoff of ≥ 2 indicating poor sleep quality, consistent with validation studies in other populations [ 10 ] Coviariates The analysis adjusted for a comprehensive set of covariates known or suspected to be associated with sleep quality and health outcomes. Sociodemographic characteristics included sex (female, male), age group (54–64 years, 65–74 years, 75 + years), and self-reported race/ethnicity (White, Black, Hispanic, Chinese-American). Employment status was detailed by assessing whether participants were currently working and, if so, their predominant work shift (daytime, night/rotating, irregular), with a distinct category for those not in the workforce. Behavioral and health-related factors accounted for were current smoking status (yes, no), categorized further into never, former, or current smoker, and body mass index (BMI) categorized as normal, overweight, or obese. Additional behavioral covariates included the habit of taking regular naps (yes, no) and the use of a CPAP or BiPAP machine for sleep-disordered breathing (yes, no). Furthermore, the model adjusted for several key sleep-related conditions and traits, namely the presence of clinically significant insomnia, restless legs syndrome, a prior diagnosis of sleep apnea, and excessive daytime sleepiness. Finally, to account for individual differences in circadian preference, chronotype was also included as a covariate, classified as morning, intermediate, or evening type. Statistical analysis Our validation approach employed comprehensive psychometric analyses following established guidelines for instrument validation. All analyses were conducted using Stata version 17 (StataCorp, College Station, TX). The analysis proceeded in two sequential phases. The first phase focused on descriptive characterization, data preparation, and internal consistency assessment. We examined distributional properties of both PSQI instruments using histograms and formal normality testing (Shapiro-Wilk tests). Prevalence estimates of poor sleep quality according to various definitions provided context for the clinical relevance of findings. Component-level analyses included examination of the distribution and intercorrelation of PSQI-2 components, assessing the fundamental structure of the abbreviated instrument. Internal consistency reliability was evaluated using Cronbach’s alpha and McDonald’s omega coefficients for the full PSQI, with values ≥ 0.70 considered indicative of acceptable reliability. The second phase encompassed psychometric validation. Concurrent validity assessment included Pearson correlations between full PSQI and PSQI-2 scores, with supplementary linear regression modeling the functional relationship between instruments. Scatter plots with regression lines provided visual representation of this relationship. Agreement between instruments was quantified using Bland-Altman analysis with regression-based rescaling of the PSQI-2 to the PSQI metric, calculating mean bias, standard deviation of differences, and 95% limits of agreement. This approach acknowledges the different scaling of the two instruments while enabling direct comparison. Diagnostic performance evaluation employed receiver operating characteristic (ROC) analysis using the full PSQI cutoffs (> 5, > 7, >10) using logistic regression. The area under the receiver operating characteristic curve (AUC) was calculated with cluster bootstrapping to derive confidence intervals. Recognizing the importance of instrument performance across demographic groups, we conducted stratified analyses by gender and age (using median split). Results 3.1 Sample characteristics Table 1 presents the prevalence and 95% confidence intervals (CI) for sociodemographic, health, and sleep-related characteristics of the MESA Sleep cohort (n = 2,237), stratified by sleep quality according to the PSQI (> 5) and PSQI-2 (> 1) thresholds. The sample was composed predominantly of women (53.6%), participants aged 54–64 years (36.2%), and racially diverse groups including 37.1% White, 27.5% Black, 23.5% Hispanic, and 11.9% Chinese-American. Obesity affected 35.5% of the sample, and 6.7% were current smokers. Sleep disorders were frequently self-reported: 9.0% had a diagnosis of sleep apnea, 6.6% insomnia, and 4.7% restless legs syndrome. Excessive daytime sleepiness was reported by 13.8%, and 7.4% identified as having an evening chronotype. Approximately 59.9% of participants reported regular napping (Table 1 ). Table 1 Prevalence and 95% confidence intervals of sociodemographic, health, and sleep-related characteristics according to sleep quality measures (MESA, n = 2,237) Variable Category Total % (95% CI) PSQI > 5 % (95% CI) PSQI-2 > 1 % (95% CI) Sociodemographic characteristics Sex Female 53.6 (49.8;57.2) 52.8 (46.7;58.8) 54.4 (50.1;58.7) Male 46.4 (42.8;50.2) 47.2 (41.2;53.3) 45.6 (41.4;49.9) Age group 54;64 years 36.2 (31.6;41.1.0) 35.5 (31.8;39.3) 39.4 (35.3;43.7) 65;74 years 30.8 (28.9;32.7) 30.7 (27.7;33.8) 30.2 (28.1;32.5) 75 + years 33.0 (29.5;36.8) 33.9 (30.4;37.5) 30.4 (26.8;34.3) Race/ethnicity White 37.1 (20.4;57.6) 34.9 (19.8;53.8) 36.5 (20.5;56.1.0) Black 27.5 (11.2;53.5) 29.5 (12.5;55.1.0) 28.7 (11.2;56.2) Hispanic 23.5 (6.7;56.7) 24.8 (7.1;58.7) 23.5 (6.7;56.9) Chinese-American 11.9 (2.2;45.0) 10.8 (2.0;42.1.0) 11.4 (2.0;45.0) Currently working No 57.3 (55.3;59.4) 59.0 (56.4;61.5) 55.4 (52.9;58) Yes 42.7 (40.6;44.7) 41.0 (38.5;43.6) 44.6 (42;47.1.0) Work shift Daytime 29.9 (28.1;31.9) 27.4 (25.1;29.7) 31.1 (28.8;33.5) Irregular 7.3 (6.3;8.5) 7.6 (6.3;9.1.0) 7.7 (6.5;9.2) Nighttime/rotating shifts 5.4 (4.5;6.4) 6.0 (4.9;7.4) 5.7 (4.6;7) Does not work 57.3 (55.3;59.4) 59 (56.4;61.5) 55.4 (52.9;58) Behavioral and health characteristics Current smoker No 93.3 (92.2;94.3) 92.7 (91.3;93.9) 92.8 (91.4;94) Yes 6.7 (5.7;7.8) 7.3 (6.1;8.7) 7.2 (6;8.6) Smoking status Former smoker 38.4 (36.4;40.4) 39.7 (37.2;42.2) 39.8 (37.3;42.3) Current smoker 6.7 (5.7;7.8) 7.3 (6.1;8.7) 7.2 (6;8.6) Never smoked 54.3 (52.3;56.4) 52.6 (50;55.1.0) 52.5 (50;55.1.0) Does not know 0.5 (0.3;1;0) 0.5 (0.2;1.0) 0.5 (0.2;1.0) BMI category Normal 27.3 (18.3;38.7) 24.2 (15.9;35.0) 25.6 (17.3;36.2) Overweight 37.2 (34.9;39.5) 37.6 (34.0;41.4) 37.4 (34.0;41.1.0) Obese 35.5 (25.3;47.2) 38.2 (27.6;50.1.0) 36.9 (26.0;49.4) Regular naps No 40.1 (38;42.2) 37.4 (34.9;39.9) 40.0 (37.5;42.6) Yes 59.9 (57.8;62) 62.6 (60.1;65.1.0) 60.0 (57.4;62.5) Uses CPAP/BiPAP No 94.2 (93.1;95.1.0) 27.4 (25.1;29.7) 93.4 (92;94.5) Yes 5.8 (4.9;6.9) 7.6 (6.3;9.1.0) 6.6 (5.5;8) Sleep-related conditions and components Clinically significant insomnia No 64.3 (62.3;66.3) 48.7 (46.1;51.3) 52.1 (49.6;54.7) Yes 35.7 (33.7;37.7) 51.3 (48.7;53.9) 47.9 (45.3;50.4) Diagnosed sleep apnea No 91.0 (89.7;92.1.0) 90.0 (88.4;91.5) 89.9 (88.3;91.3) Yes 9.0 (7.9;10.3) 10.0 (8.5;11.6) 10.1 (8.7;11.7) Diagnosed insomnia No 93.4 (92.3;94.4) 90.6 (89;92) 91.2 (89.7;92.6) Yes 6.6 (5.6;7.7) 9.4 (8;11.0) 8.8 (7.4;10.3) Diagnosed restless legs syndrome No 95.3 (94.4;96.1.0) 94.2 (92.9;95.3) 94.7 (93.4;95.7) Yes 4.7 (3.9;5.6) 5.8 (4.7;7.1.0) 5.3 (4.3;6.6) Excessive daytime sleepiness No 86.2 (84.7;87.6) 82.5 (80.4;84.4) 94.7 (93.4;95.7) Yes 13.8 (12.4;15.3) 17.5 (15.6;19.6) 5.3 (4.3;6.6) Chronotype Morning 41.6 (35.5;47.9) 40.0 (33.5;46.7) 40.7 (35.0;46.7) Intermediate 51.1 (45.3;56.8) 51.2 (44.9;57.5) 51.1 (45.3;57.0) Evening 7.4 (6.3;8.6) 8.9 (8.1;9.7) 8.1 (7.4;8.9) Prevalence and 95% confidence intervals were estimated from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary Study. Poor sleep quality was defined as PSQI ≥ 10, and reduced sleep quality by PSQI-2 as scores ≥ 3. WHIIRS: Women’s Health Initiative Insomnia Rating Scale; PSG: polysomnography. 3.2 Sleep quality Distributions of PSQI components and total scores are illustrated in Figs. 1 – 2 . The mean full PSQI score was 7.1 (SD = 3.2, range: 0–19), and the abbreviated PSQI-2 showed a mean score of 2.0 (SD = 1.1, range: 0–6). Overall, 65.6% of participants had poor sleep quality by the PSQI (> 5), while 65.7% met the cutoff for reduced sleep quality by the PSQI-2 (> 1) (Fig. 1 ). Overall, patterns of association between sociodemographic and health variables were consistent for both PSQI and PSQI-2 classifications (Table 1 ). Sleep disturbances were the most prevalent PSQI component (94.5% any impairment, 40.7% moderate-severe), followed by daytime dysfunction (48.7% any, 23.3% moderate-severe) and sleep latency problems (45.8% any, 18.8% moderate-severe). Sleep efficiency impairments affected 39.4% of participants, while 31.0% had problematic sleep duration. Only 23.2% of participants had no impaired components, whereas 21.5% had three or more impaired components. Sleep medication use was least common (15.6% any use, 9.4% regular use) (Fig. 2 ). The PSQI-2 showed strong correlation with the full PSQI (r = 0.520, p 69 years: r = 0.510). Furthermore, logistic regression analyses revealed similar association patterns across PSQI measures. Significant racial disparities existed, with Black and Hispanic participants showing 40–47% higher odds of poor sleep. Obesity, sleep disorders (insomnia, restless legs, apnea), daytime sleepiness, and evening chronotype consistently increased poor sleep odds, with generally stronger effects for the full PSQI. Only age associations diverged between measures (Table 2 ). Table 2 Associations between participant characteristics and poor sleep quality according to PSQI (> 5) and PSQI-2 (> 2) thresholds (MESA Sleep Study) Variable Category OR (PSQI > 5) (95% CI) OR (PSQI-2 > 1) (95% CI) CI overlap Sociodemographic characteristics Sex Male (ref: Female) 1.10 (0.92;1.31.0) 0.90 (0.76;1.08) * Race/Ethnicity Chinese-American (ref: White) 0.92 (0.69;1.22) 0.93 (0.70;1.24) * Black, African-American 1.47 (1.18;1.84) 1.20 (0.96;1.50) * Hispanic 1.40 (1.11;1.76) 1.05 (0.83;1.33) * Age group 65;74 years (ref: 54;64) 1.05 (0.85;1.30) 0.72 (0.58;0.90) * 75 + years 1.15 (0.93;1.41.0) 0.60 (0.48;0.74) + Health and behavioral characteristics BMI category Overweight (ref: Normal) 1.42 (1.15;1.76) 1.22 (0.98;1.52) * Obese 1.73 (1.38;2.15) 1.36 (1.09;1.70) * Current smoker Yes 1.32 (0.91;1.90) 1.28 (0.89;1.85) * Work shift Night/rotating (ref: Daytime) 1.84 (1.19;2.84) 1.06 (0.69;1.63) * Irregular 1.39 (0.97;2.00) 1.05 (0.72;1.53) * Diagnosed sleep apnea Yes 1.44 (1.04;1.99) 1.55 (1.11;2.16) * Diagnosed insomnia Yes 7.15 (3.84;13.30) 4.07 (2.44;6.81.0) * Diagnosed restless legs syndrome Yes 2.30 (1.40;3.79) 1.60 (1.01;2.53) * CPAP/BiPAP use Yes 1.40 (0.95;2.08) 1.62 (1.07;2.45) * Regular naps Yes 1.39 (1.16;1.66) 1.01 (0.84;1.21.0) * Sleep-related scales and components Insomnia severity (WHIIRS) Moderate (ref: None) 13.04 (9.55;17.81.0) 6.54 (5.04;8.49) Severe 26.42 (11.61;60.12) 10.57 (5.81;19.20) * Sleepiness severity (Epworth) Mild;moderate (ref: Normal) 2.82 (2.02;3.94) 1.75 (1.29;2.38) * Excessive 3.77 (1.98;7.18) 2.33 (1.32;4.11.0) * Excessive daytime sleepiness Yes 2.86 (2.10;3.90) 1.80 (1.36;2.39) * Chronotype Intermediate (ref: Morning) 1.13 (0.94;1.35) 1.07 (0.89;1.28) * Evening 2.18 (1.47;3.24) 1.49 (1.02;2.16) * Note : Logistic regression models were estimated separately for PSQI (> 5) and PSQI-2 (> 2) as dependent variables. OR = Odds Ratio; CI = Confidence Interval; CI overlap = * indicates overlapping 95% CIs between models; + indicates non-overlapping intervals. 3.3 Reliability statistics Continuous performance metrics assessing the predictive correspondence between PSQI-2 and the full PSQI are displayed in Table 3 . The PSQI-2 achieved a Brier Score of 0.185 (95% CI: 0.006–0.188), indicating low overall prediction error. The Mean Absolute Error (MAE = 0.371, 95% CI: 0.019–0.377) and Root Mean Square Error (RMSE = 0.430, 95% CI: 0.075–0.434) also reflected good agreement and stability. The Integrated Discrimination Improvement (IDI = 0.187, 95% CI: 0.002–0.332) supported a high degree of discrimination overlap between the PSQI-2 and full PSQI (Table 3 ). Table 3 Continuous accuracy metrics for PSQI-2 model against the full PSQI. Model Estimate 95%CI Brier Score 0.185 0.006 0.188 MAE 0.371 0.019 0.377 RMSE 0.430 0.075 0.434 IDI 0.187 0.002 0.332 Performance of PSQI-2 in predicting the continuous probability score of the full PSQI. The Brier Score measures overall prediction. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) measure the average magnitude of prediction errors. The Integrated Discrimination Improvement (IDI) approximates the difference in discrimination performance between models, calculated as the mean difference in predicted probabilities between PSQI-2 and the full PSQI. A Bland–Altman plot (Fig. 4 ) demonstrated a negligible mean bias (–0.012) and limits of agreement within ± 1.96 SD, indicating no systematic bias and good concordance across the score range. Furthermore, the ROC curve analysis (Fig. 5 ) demonstrated that the PSQI-2 showed good discriminant validity for identifying poor sleep quality across different cutpoints of the full PSQI. The area under the curve (AUC) was 0.785 for PSQI > 5, 0.748 for PSQI > 7, and 0.750 for PSQI > 10. At the traditional PSQI > 5 cutpoint, the PSQI-2 > 1 showed sensitivity of 80.4% and specificity of 59.3%, correctly classifying 73.0% of cases. For the PSQI > 7 cutpoint, sensitivity increased to 86.8% while specificity decreased to 46.3%, with 62.1% correct classification. For the most stringent PSQI > 10 cutpoint, the optimal PSQI-2 threshold shifted to ≥ 3, achieving balanced performance with 71.8% sensitivity and 79.1% specificity, correctly classifying 78.0% of cases. Internal validation by bootstrap resampling (n = 1,000) shows that original Youden Index (0.399 for PSQI-2 > 1) was slightly reduced after bias correction (bootstrap = 0.442), yielding a small bias (0.042) and 95% bias-corrected confidence interval (0.341–0.452) (Table 4 ). Table 4 Internal validation of model-specific risk thresholds using bootstrap resampling (n = 1,000 repetitions). Cutoff PSQI Cutoff PSQI-2 Original Youden Bootstrap mean Bias 95% CI (BC) > 5 > 1 0.399 0.442 0.042 0.341; 0.452 > 2 0.315 0.330 0.015 0.266; 0.361 > 3 0.104 0.119 0.015 0.074; 0.135 Discussion The present study demonstrates that the abbreviated two-item Pittsburgh Sleep Quality Index (PSQI-2) shows strong validity and consistent performance with the full questionnaire of sleep quality in a large, multi-ethnic cohort of middle-aged and older adults. Overall, the PSQI-2 effectively captured poor sleep quality and reproduced key association patterns observed with the full instrument, supporting its utility as a parsimonious measure in both research and clinical contexts where time and respondent burden are critical considerations. The strong conceptual alignment between the PSQI-2 and the full PSQI supports the premise that subjective sleep quality and sleep duration represent core dimensions of the broader sleep quality construct. This overlap is reinforced by the consistency of associations observed across both instruments with major sociodemographic, clinical, and behavioral factors. Both measures identified higher likelihood of poor sleep among Black and Hispanic participants, corroborating prior evidence of racial and ethnic disparities in sleep health reported in MESA and other population-based studies [ 16 ]. Similarly, expected associations with obesity, diagnosed sleep disorders, daytime sleepiness, and evening chronotype were consistently observed, with slightly stronger effects for the full PSQI, as anticipated given its broader scope [ 1 , 2 ]. These findings support the construct validity of the PSQI-2 as a concise yet informative indicator of sleep quality. Our findings align with a growing body of literature supporting the use of abbreviated sleep measures tailored to specific research and clinical needs. Since its original development by Buysse et al. [ 3 , 17 ] the PSQI has become one of the most widely used subjective sleep instruments worldwide. However, accumulating evidence suggests that the PSQI global score may not be strictly unidimensional, with several studies proposing two- or three-factor structures across different populations [ 7 , 17 – 21 ]. These observations provide a strong theoretical basis for the PSQI-2, as a carefully selected subset of items focusing on sleep quality and duration may adequately capture essential aspects of perceived sleep disturbance. Furthermore, the validation of the PSQI-2 has important implications for both epidemiological research and clinical practice. In large-scale cohort studies such as MESA, where extensive phenotyping must be balanced against feasibility and participant burden, the PSQI-2 provides a pragmatic solution for incorporating sleep quality assessment without substantially increasing survey length or respondent fatigue. This is particularly relevant given the growing body of evidence linking poor sleep quality and short sleep duration to adverse cardiometabolic, cognitive, and mental health outcomes, including hypertension, diabetes, cardiovascular disease, depression, and cognitive decline [ 22 , 23 ]. The use of brief, validated instruments has been repeatedly emphasized as a key strategy to improve sleep surveillance in population-based studies and public health monitoring. In clinical settings, the PSQI-2 may function as an efficient first-line screening tool for poor perceived sleep quality, enabling early identification of individuals who may benefit from further diagnostic evaluation or targeted interventions [ 24 , 25 ]. Its brevity and ease of administration make it especially suitable for primary care, geriatric assessments, and outpatient clinics, where time constraints often limit the use of longer instruments. Prior studies have shown that brief sleep screeners can meaningfully improve detection of sleep problems in routine care and support clinical decision-making without compromising validity [ 25 ]. Thus, the PSQI-2 bridges an important gap between comprehensive sleep assessment and real-world clinical and epidemiological feasibility, facilitating broader integration of sleep health into research and practice. Several limitations should be acknowledged. First, the study relied on subjective sleep measures, which may not fully align with objective sleep parameters, although subjective perception remains clinically meaningful. The MESA study collected extensive objective sleep data through polysomnography and actigraphy[ 22 , 23 ] and future research could explore how the PSQI-2 correlates with these objective measures. Second, the cross-sectional nature of our analysis precludes assessment of test-retest reliability, which would strengthen the validation of the PSQI-2. Third, while the PSQI-2 showed good performance in this multi-ethnic cohort, its performance in other populations should be verified, as sleep perceptions and reporting may vary across different cultural and clinical contexts. Furthermore, the PSQI-2 evaluated in this study is an adaptation derived from the full PSQI rather than an independently administered instrument, which may influence item interpretation. Nevertheless, this also represents a strength, as sleep duration and subjective sleep quality are commonly assessed across epidemiological studies, allowing the PSQI-2 framework to be tested and replicated in diverse datasets. This flexibility also opens opportunities for future research to evaluate alternative PSQI-2 adaptations using the same core items across different study designs and populations. Despite these limitations, this study has notable strengths, including the large, well-characterized multi-ethnic cohort, the comprehensive psychometric evaluation using multiple complementary methods, and the consistency of findings across key demographic subgroups. The integration of actigraphy data for certain components of the full PSQI adaptation also strengthens the original measure's objectivity against which the PSQI-2 was validated. Collectively, these results support the PSQI-2 as a valid, efficient, and scalable measure of sleep quality, particularly well-suited for epidemiological studies and clinical contexts where the full PSQI may be impractical. Conclusion The PSQI-2 demonstrates strong validity as an abbreviated alternative to the full PSQI in a multi-ethnic cohort, effectively identifying poor sleep quality and maintaining consistent associations with key demographic, clinical, and sleep-related factors. Its brevity and favorable psychometric properties make it suitable for large-scale studies and clinical screening where the full PSQI may be impractical. Future research should explore the longitudinal performance of the PSQI-2, its responsiveness to interventions, and its validity in specific clinical populations. Additionally, comparison with objective sleep measures could further illuminate what aspects of sleep quality are captured by this abbreviated instrument. As sleep health continues to be recognized as essential to overall well-being, efficient and valid assessment tools like the PSQI-2 will play an increasingly important role in both research and clinical practice. Declarations Conflict of Interest All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript. Ethical Approval The study protocols were approved by six fields institutional review boards at all participating institutions. Further study design details have been previously published [ 26 ] Informed Consent Informed consent was obtained from all individual participants included in the study. Funding The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary study was supported via the National Heart, Lung, and Blood Institute (NHLBI) and by cooperative agreements from the National Center for Advancing Translational Sciences (NCATS). The corresponding author was supported from the Federal University of Ouro Preto (UFOP), Coordination for the Improvement of Higher Education Personnel (CAPES) and National Council for Scientific and Technological Development (CNPq). Author Contribution LAAMJ: conception and study design; analysis and interpretation of data; writing the manuscript, critical review, and final approval. Acknowledgement The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary study was funded by NIH-NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups (RO1 HL098433). MESA is supported by NHLBI funded contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by cooperative agreements UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 funded by NCATS. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002). We are also deeply grateful to the National Sleep Research Resource (Sleep Data, https://sleepdata.org) for providing access to the datasets and the computational tools that were essential for this analysis. Furthermore, acknowledge the support of the Federal University of Ouro Preto (UFOP) and the Group for Research and Education in Nutrition and Collective Health (GPENSC) for their support and encouragement. Data Availability The datasets analyzed for this study were provided by the National Sleep Research Resource (Sleep Data, https:/sleepdata.org ) . The MESA data are publicly available for researchers upon request. Access to the data requires approval of a data use agreement and compliance with the terms and conditions set by the MESA Study and the Sleep Data platform. References Philippens N, Janssen E, Kremers S, Crutzen R. Determinants of natural adult sleep: An umbrella review. PLoS ONE. 2022;17:1–30. https://doi.org/10.1371/journal.pone.0277323 . Li J, Cao D, Huang Y, Chen Z, Wang R, Dong Q, et al. Sleep duration and health outcomes: an umbrella review. Sleep Breath. 2022;26:1479–501. https://doi.org/10.1007/s11325-021-02458-1 . 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Criterion validity of the Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale for the diagnosis of sleep disorders. Sleep Med. 2014;15:422–9. https://doi.org/10.1016/j.sleep.2013.12.015 . Blaha MJ, DeFilippis AP. Multi-Ethnic Study of Atherosclerosis (MESA). J Am Coll Cardiol. 2021;77:3195–216. https://doi.org/10.1016/j.jacc.2021.05.006 . Ogilvie RP, Redline S, Bertoni AG, Chen X, Ouyang P, Szklo M, et al. Actigraphy Measured Sleep Indices and Adiposity: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep. 2016;39:1701–8. https://doi.org/10.5665/sleep.6096 . Zhang G-Q, Cui L, Mueller R, Tao S, Kim M, Rueschman M, et al. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc. 2018;25:1351–8. https://doi.org/10.1093/jamia/ocy064 . Egan KJ, Knutson KL, Pereira AC, von Schantz M. The role of race and ethnicity in sleep, circadian rhythms and cardiovascular health Short title: Sleep and cardiovascular health across ethnicities. Sleep Med Rev. 2017;33:70. https://doi.org/10.1016/J.SMRV.2016.05.004 . Carpi M. The Pittsburgh Sleep Quality Index: a brief review. Occup Med (Chic Ill). 2025;75:14–5. https://doi.org/10.1093/occmed/kqae121 . Helles M, Fletcher R, Münch M, Gibson R. Examining the structure validity of the Pittsburgh Sleep Quality Index among female workers during New Zealand’s initial COVID-19 lockdown. Sleep Biol Rhythms. 2024;22:217–25. https://doi.org/10.1007/s41105-023-00509-6 . Zhong Q-Y, Gelaye B, Sánchez SE, Williams MA. Psychometric Properties of the Pittsburgh Sleep Quality Index (PSQI) in a Cohort of Peruvian Pregnant Women. J Clin Sleep Med. 2015;11:869–77. https://doi.org/10.5664/jcsm.4936 . Yang D, Li Y, Jia J, Li H, Wang R, Zhu J, et al. Construction and validation of a predictive model for sleep disorders among pregnant women. BMC Pregnancy Childbirth. 2025;25:242. https://doi.org/10.1186/s12884-025-07197-9 . Mariman A, Vogelaers D, Hanoulle I, Delesie L, Tobback E, Pevernagie D. Validation of the three-factor model of the PSQI in a large sample of chronic fatigue syndrome (CFS) patients. J Psychosom Res. 2012;72:111–3. https://doi.org/10.1016/j.jpsychores.2011.11.004 . Chen X, Wang R, Zee P, Lutsey PL, Javaheri S, Alcántara C, et al. Racial/Ethnic Differences in Sleep Disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep. 2015. https://doi.org/10.5665/sleep.4732 . Dean DA, Wang R, Jacobs DR, Duprez D, Punjabi NM, Zee PC, et al. A Systematic Assessment of the Association of Polysomnographic Indices with Blood Pressure: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep. 2015;38:587–96. https://doi.org/10.5665/sleep.4576 . Schiza SE, Randerath W, Drummond M. Screening with limited sleep tests to increase pre-test probability. ERS Handbook of Respiratory Sleep Medicine. European Respiratory Society; 2023. https://doi.org/10.1183/9781849841641.009322 . Luyster FS, Choi J, Yeh C-H, Imes CC, Johansson AEE, Chasens ER. Screening and evaluation tools for sleep disorders in older adults. Appl Nurs Res. 2015;28:334–40. https://doi.org/10.1016/j.apnr.2014.12.007 . Bild DE. Multi-Ethnic Study of Atherosclerosis: Objectives and Design. Am J Epidemiol. 2002;156:871–81. https://doi.org/10.1093/aje/kwf113 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Feb, 2026 Editor invited by journal 25 Dec, 2025 Editor assigned by journal 22 Dec, 2025 Submission checks completed at journal 22 Dec, 2025 First submitted to journal 20 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8413149","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584386020,"identity":"627f885d-682a-4bcd-a8ba-80c9fe4da8be","order_by":0,"name":"Luiz Menezes-Júnior","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACPgbGBgTvA4McmJZgYDiAUwsbshbGGQzGxGhBAsw8RGmRSG7+wJhjk8fPf/jwZ9s2AzlzBuaDt3kY7uTj1pLYYMC4La1YsuFYmnRum4GxZQNbsjUPwzPLBjxaEhi3HU7ccLDHjDm37U/ihgM8ZtI8DIcN8NlygHHb/8T9h/k/f7ZsMwBq4f9GSEtjA+O2A4kb2HgYpBnBWnjY8GvhedjMkLgtOXHGGTYzyZ5zQL80sxlbzjF4hlMLP3v64w8ft9kl9vcffvzhRxkwxNibH954U3EHpxYwSEDmGDCDSbwa0ABJikfBKBgFo2BEAAC0nFD5XXs0uQAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Ouro Preto","correspondingAuthor":true,"prefix":"","firstName":"Luiz","middleName":"","lastName":"Menezes-Júnior","suffix":""}],"badges":[],"createdAt":"2025-12-20 15:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8413149/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8413149/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101792302,"identity":"353aaef4-d780-4fcc-a7ce-6a85c3776984","added_by":"auto","created_at":"2026-02-03 16:11:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51192,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PSQI and PSQI-2 scores, and prevalence of poor sleep quality according PSQI scale and cutoff values in the MESA Sleep cohort.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8413149/v1/17fe71951ea9c3417e1f2b7b.png"},{"id":101792479,"identity":"4c6e3882-69dd-488a-aae8-46e947fddcf4","added_by":"auto","created_at":"2026-02-03 16:12:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66370,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PSQI components in the MESA Sleep cohort.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8413149/v1/a3aac769487dd3dcd937dc08.png"},{"id":101792391,"identity":"4fbe479b-bdee-48c9-bc06-4fb4109557c4","added_by":"auto","created_at":"2026-02-03 16:12:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50329,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between PSQI and PSQI-2 total scores in the MESA Sleep cohort.\u003cbr\u003e\n \u003cem\u003eScatterplot with regression line (r = 0.520, p \u0026lt; 0.001) showing linear association and consistency across score ranges.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8413149/v1/3dd4c48c52e3ef3dc90e8fc4.png"},{"id":101792428,"identity":"fe94dc2a-91e3-46fa-89f5-25bc7adba806","added_by":"auto","created_at":"2026-02-03 16:12:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59150,"visible":true,"origin":"","legend":"\u003cp\u003eBland–Altman plot comparing PSQI and PSQI-2 scores in the MESA Sleep cohort.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMean difference close to zero (bias = –0.012), with limits of agreement within ±1.96 SD indicating no systematic bias.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8413149/v1/51bcbe42c12328683f62edbe.png"},{"id":101792401,"identity":"87465c30-b31d-46df-8a00-e9d887ba90a5","added_by":"auto","created_at":"2026-02-03 16:12:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53343,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve for PSQI-2 predicting full PSQI in the MESA Sleep cohort.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8413149/v1/2fb73bf2c9d67be3191a7197.png"},{"id":101792538,"identity":"17946075-2ff4-458f-955e-0e053f37ff89","added_by":"auto","created_at":"2026-02-03 16:12:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1328353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8413149/v1/301a9ed1-c8b3-49f4-a2a2-397e743eda00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance of a two-item sleep quality measure (PSQI-2): a comprehensive evaluation in a multiethnic cohort (MESA Study)","fulltext":[{"header":"Background","content":"\u003cp\u003eSleep quality is a fundamental pillar of human health, and its impairment is a significant public health concern associated with a wide range of adverse outcomes, including non-communicable chronic diseases, metabolic disorders, and cognitive decline [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. (1989), has been established as a gold-standard self-report instrument for evaluating sleep quality in both clinical and research settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Its reliability and validity have been examined in diverse populations [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], confirming its utility while also revealing that its psychometric properties can be optimized in specific groups [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite its widespread use, the comprehensive nature of the 19-item PSQI presents practical limitations. In large-scale epidemiological studies that simultaneously investigate multiple health domains, extensive questionnaires can lead to respondent fatigue, increased missing data, and ultimately, constraints on the breadth of research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This challenge has spurred the development and validation of abbreviated instruments, such as the two-item PSQI (PSQI-2), which focuses on the core dimensions of sleep duration and subjective sleep quality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In a population-based household survey in Brazil, psychometric analyses supported a two-factor structure based on PSQI items, with excellent internal consistency, clear gradients in poor sleep prevalence across score levels, and good concurrent and known-group validity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. More recently, validation in the MrOS Sleep Study extended this evidence to a longitudinal context among community-dwelling older men, showing strong agreement with the full PSQI, excellent discriminatory accuracy, moderate test\u0026ndash;retest reliability, and good responsiveness to clinically meaningful changes in sleep over time [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Together, these findings support the PSQI-2 as a valid, reliable, and pragmatic alternative for sleep quality assessment in large-scale epidemiological and longitudinal studies.\u003c/p\u003e \u003cp\u003eTherefore, the brevity of such tools makes them particularly suitable for large studies where time and questionnaire space are limited. However, the performance of these short forms, especially in diverse, multi-ethnic populations and against objective sleep measures, requires further robust characterization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary Study provides an ideal platform to address this research gap. MESA itself is a landmark, prospective cohort study initiated by the National Heart, Lung, and Blood Institute (NHLBI) in 1999\u0026ndash;2000 to investigate the prevalence, correlates, and progression of subclinical cardiovascular disease in a sex-balanced, multi-ethnic cohort [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The MESA Sleep Ancillary Study augmented this rich dataset with a comprehensive sleep assessment protocol, including 7-day actigraphy, in addition to self-report questionnaires [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This unique combination of subjective and objective sleep data within a large, community-dwelling, multi-ethnic population offers an unparalleled opportunity to validate an abbreviated sleep instrument against a robust criterion standard.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to validate an adapted version of the PSQI-2 within the MESA Sleep study. We will examine its psychometric properties, diagnostic accuracy against the full PSQI, and its relationship with key demographic, clinical, and objective sleep measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and design\u003c/h2\u003e \u003cp\u003eThe Multi-Ethnic Study of Atherosclerosis (MESA) is a prospective cohort study sponsored by the National Heart, Lung, and Blood Institute (NHLBI). Its primary goal is to investigate risk factors for the development and progression of subclinical cardiovascular disease in a diverse population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The datasets analyzed for this study were provided by the National Sleep Research Resource (Sleep Data, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sleepdata.org\u003c/span\u003e\u003cspan address=\"https://sleepdata.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study began in 1999\u0026ndash;2000, enrolling 6,814 participants aged 45\u0026ndash;84 years who were free of clinically diagnosed cardiovascular disease at baseline. Participants were recruited from six field centers across the United States: Baltimore, MD; Chicago, IL; Los Angeles, CA; New York, NY; Saint Paul, MN; and Winston-Salem, NC. A key strength of MESA is its deliberate inclusion of a multi-ethnic population, with the cohort comprising White, Black, Hispanic, and Chinese-American individuals. Participants have undergone serial clinical examinations, with the most recent (the seventh exam) being conducted from 2022 to 2024. Between exams, annual follow-up contacts are conducted to assess clinical cardiovascular events and other health outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe MESA Sleep Ancillary Study was conducted to examine the relationships between sleep characteristics and cardiovascular disease risk. Data collection for this ancillary study occurred in close temporal proximity to the MESA Exam 5 (2010\u0026ndash;2013) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Out of the 4,077 participants who attended Exam 5, 2,261 individuals were enrolled in the MESA Sleep Ancillary Study and provided objective and subjective sleep data. Our present analysis focuses on the subset of these participants who completed the relevant sleep questionnaires and objective measurements.\u003c/p\u003e \u003cp\u003eA major strength of the MESA Sleep study is its multi-method assessment of sleep, which includes for this study: Actigraphy, with participants wore an Actiwatch Spectrum (Philips Respironics) on the non-dominant wrist for 7 consecutive days to objectively estimate habitual sleep patterns, including sleep duration, sleep efficiency, and night-to-night variability in sleep timing, in their home environment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, \u003cb\u003ep\u003c/b\u003earticipants completed self-report questionnaires, including the Women's Health Initiative Insomnia Rating Scale (WHIIRS), Epworth Sleepiness Scale (ESS), Modified Horne-Ostberg Morningness-Eveningness Questionnaire (MEQ) and other instruments assessing sleep disorder screening, captures self-reported physician diagnoses of specific sleep disorders.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSleep quality\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eFull sleep quality questionnaire\u003c/h2\u003e \u003cp\u003eThe standard PSQI was not administered in the MESA Sleep Ancillary Study, therefore, an adapted measure of full sleep quality was constructed. This adaptation leveraged core sleep domains assessed by the study's existing validated questionnaires. Thus, the questionnaire was developed to mirror the structure and scoring of the original instrument while also leveraging the unique strengths of the dataset, which included both self-reported questionnaire data and objective actigraphy measures. The primary goal was to create a composite sleep quality score that integrated subjective perceptions with behavioral sleep patterns.\u003c/p\u003e \u003cp\u003eThe full PSQI was constructed to comprise the same seven components as the original PSQI, each scored on a 0\u0026ndash;3 scale, where 0 indicates no difficulty and 3 indicates severe difficulty.\u003c/p\u003e \u003cp\u003eSubjective sleep quality was directly derived from the question assessing overall typical night's sleep (typicalslp5). The original 5-point scale was recoded to the PSQI's 4-point scale (0\u0026ndash;3), consolidating the two poorest categories into a single top score. Sleep latency was calculated by integrating both questionnaire and actigraphy data to capture the multifaceted nature of sleep onset difficulty. Initially, a weighted average of weekly sleep onset latency was calculated from weekday (avgonsetlatencywd5) and weekend (avgonsetlatencywe5) data. This continuous measure (in minutes) was then categorized into PSQI scoring bands. Furthermore, the frequency of self-reported trouble falling asleep (trbleslpng5) was scored on a 0\u0026ndash;3 scale. The final sleep latency component score was generated by summing the actigraphy and questionnaire scores and recategorizing the combined value.\u003c/p\u003e \u003cp\u003eFor sleep duration, objective actigraphy data was prioritized for this component to reflect actual sleep time rather than time in bed. Therefore, a weighted average of weekly total sleep time was calculated from weekday (avgmainsleepwd5) and weekend (avgmainsleepwe5) data. This value was then scored based on the established PSQI criteria. Sleep efficiency, defined as the ratio of total sleep time to total time in bed multiplied by 100, was directly obtained from the actigraphy-derived variable (slp_eff5). This objective measure was then scored using the standard PSQI thresholds.\u003c/p\u003e \u003cp\u003eSleep disturbances was constructed from the frequency of six specific sleep problems reported in the questionnaire: trouble falling asleep (trbleslpng5); waking up in the middle of the night (wakeup5); waking up too early (wakeearly5); trouble getting back to sleep after waking up (bcksleep5); snoring (snored5); and stopping breathing during sleep (stpbrthng5).\u003c/p\u003e \u003cp\u003eEach disturbance was scored on a 0\u0026ndash;3 scale based on its frequency. The sum of these six scores was calculated, and since the adapted scale had a lower maximum (18) than the original PSQI (27), the scoring thresholds were proportionally adjusted to maintain a 0\u0026ndash;3 component score.\u003c/p\u003e \u003cp\u003eUse of sleep medication, was evaluated with the frequency of sleeping pill use (slpngpills5) was directly scored on the 0\u0026ndash;3 scale. Finally, the daytime dysfunction and sleepiness, was evaluated by combining daytime sleepiness, with the frequency of feeling overly sleepy during the day (sleepy5); and frequency of sleep difficulties causing irritability (irritable5).\u003c/p\u003e \u003cp\u003eEach was scored on a 0\u0026ndash;3 scale. The sum of these two scores was then recategorized to generate the final component score, ensuring it remained on the standard 0\u0026ndash;3 scale.\u003c/p\u003e \u003cp\u003eThe global full PSQI score was computed as the sum of the seven component scores, with a higher score indicating worse sleep quality. The traditional cut-off of \u0026gt;\u0026thinsp;5 was used to classify participants as having \"poor\" sleep quality.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePSQI-2\u003c/h3\u003e\n\u003cp\u003eThe PSQI-2 development followed the conceptual framework proposed by Menezes-J\u0026uacute;nior et al. (2025). The two-component structure comprised sleep duration (identical to component 1 of the full adapted PSQI) and subjective sleep quality (derived from typicalslp5, identical to component 4 of the full PSQI). This approach aligns with the theoretical foundation that these two domains capture the essential elements of sleep quality assessment. The PSQI-2 score ranged from 0\u0026ndash;6, with the established cutoff of \u0026ge;\u0026thinsp;2 indicating poor sleep quality, consistent with validation studies in other populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCoviariates\u003c/h2\u003e \u003cp\u003eThe analysis adjusted for a comprehensive set of covariates known or suspected to be associated with sleep quality and health outcomes. Sociodemographic characteristics included sex (female, male), age group (54\u0026ndash;64 years, 65\u0026ndash;74 years, 75\u0026thinsp;+\u0026thinsp;years), and self-reported race/ethnicity (White, Black, Hispanic, Chinese-American). Employment status was detailed by assessing whether participants were currently working and, if so, their predominant work shift (daytime, night/rotating, irregular), with a distinct category for those not in the workforce. Behavioral and health-related factors accounted for were current smoking status (yes, no), categorized further into never, former, or current smoker, and body mass index (BMI) categorized as normal, overweight, or obese. Additional behavioral covariates included the habit of taking regular naps (yes, no) and the use of a CPAP or BiPAP machine for sleep-disordered breathing (yes, no). Furthermore, the model adjusted for several key sleep-related conditions and traits, namely the presence of clinically significant insomnia, restless legs syndrome, a prior diagnosis of sleep apnea, and excessive daytime sleepiness. Finally, to account for individual differences in circadian preference, chronotype was also included as a covariate, classified as morning, intermediate, or evening type.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e Our validation approach employed comprehensive psychometric analyses following established guidelines for instrument validation. All analyses were conducted using Stata version 17 (StataCorp, College Station, TX).\u003c/p\u003e \u003cp\u003eThe analysis proceeded in two sequential phases. The first phase focused on descriptive characterization, data preparation, and internal consistency assessment. We examined distributional properties of both PSQI instruments using histograms and formal normality testing (Shapiro-Wilk tests). Prevalence estimates of poor sleep quality according to various definitions provided context for the clinical relevance of findings. Component-level analyses included examination of the distribution and intercorrelation of PSQI-2 components, assessing the fundamental structure of the abbreviated instrument. Internal consistency reliability was evaluated using Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega coefficients for the full PSQI, with values\u0026thinsp;\u0026ge;\u0026thinsp;0.70 considered indicative of acceptable reliability.\u003c/p\u003e \u003cp\u003eThe second phase encompassed psychometric validation. Concurrent validity assessment included Pearson correlations between full PSQI and PSQI-2 scores, with supplementary linear regression modeling the functional relationship between instruments. Scatter plots with regression lines provided visual representation of this relationship. Agreement between instruments was quantified using Bland-Altman analysis with regression-based rescaling of the PSQI-2 to the PSQI metric, calculating mean bias, standard deviation of differences, and 95% limits of agreement. This approach acknowledges the different scaling of the two instruments while enabling direct comparison.\u003c/p\u003e \u003cp\u003eDiagnostic performance evaluation employed receiver operating characteristic (ROC) analysis using the full PSQI cutoffs (\u0026gt;\u0026thinsp;5, \u0026gt;\u0026thinsp;7, \u0026gt;10) using logistic regression. The area under the receiver operating characteristic curve (AUC) was calculated with cluster bootstrapping to derive confidence intervals. Recognizing the importance of instrument performance across demographic groups, we conducted stratified analyses by gender and age (using median split).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e3.1 Sample characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the prevalence and 95% confidence intervals (CI) for sociodemographic, health, and sleep-related characteristics of the MESA Sleep cohort (n\u0026thinsp;=\u0026thinsp;2,237), stratified by sleep quality according to the PSQI (\u0026gt;\u0026thinsp;5) and PSQI-2 (\u0026gt;\u0026thinsp;1) thresholds. The sample was composed predominantly of women (53.6%), participants aged 54\u0026ndash;64 years (36.2%), and racially diverse groups including 37.1% White, 27.5% Black, 23.5% Hispanic, and 11.9% Chinese-American. Obesity affected 35.5% of the sample, and 6.7% were current smokers. Sleep disorders were frequently self-reported: 9.0% had a diagnosis of sleep apnea, 6.6% insomnia, and 4.7% restless legs syndrome. Excessive daytime sleepiness was reported by 13.8%, and 7.4% identified as having an evening chronotype. Approximately 59.9% of participants reported regular napping (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence and 95% confidence intervals of sociodemographic, health, and sleep-related characteristics according to sleep quality measures (MESA, n\u0026thinsp;=\u0026thinsp;2,237)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePSQI\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePSQI-2\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.6 (49.8;57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.8 (46.7;58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.4 (50.1;58.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.4 (42.8;50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.2 (41.2;53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.6 (41.4;49.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54;64 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.2 (31.6;41.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.5 (31.8;39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.4 (35.3;43.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.8 (28.9;32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.7 (27.7;33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.2 (28.1;32.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.0 (29.5;36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.9 (30.4;37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.4 (26.8;34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.1 (20.4;57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.9 (19.8;53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.5 (20.5;56.1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.5 (11.2;53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.5 (12.5;55.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.7 (11.2;56.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.5 (6.7;56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8 (7.1;58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.5 (6.7;56.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese-American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.9 (2.2;45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8 (2.0;42.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.4 (2.0;45.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently working\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.3 (55.3;59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.0 (56.4;61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.4 (52.9;58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.7 (40.6;44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.0 (38.5;43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.6 (42;47.1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork shift\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaytime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.9 (28.1;31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.4 (25.1;29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.1 (28.8;33.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3 (6.3;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6 (6.3;9.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.7 (6.5;9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNighttime/rotating shifts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4 (4.5;6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.9;7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.7 (4.6;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.3 (55.3;59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (56.4;61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.4 (52.9;58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBehavioral and health characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.3 (92.2;94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.7 (91.3;93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.8 (91.4;94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 (5.7;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3 (6.1;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.2 (6;8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.4 (36.4;40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.7 (37.2;42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.8 (37.3;42.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 (5.7;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3 (6.1;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.2 (6;8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.3 (52.3;56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.6 (50;55.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.5 (50;55.1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.3;1;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5 (0.2;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5 (0.2;1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.3 (18.3;38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.2 (15.9;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.6 (17.3;36.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.2 (34.9;39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.6 (34.0;41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.4 (34.0;41.1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.5 (25.3;47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.2 (27.6;50.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.9 (26.0;49.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular naps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.1 (38;42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.4 (34.9;39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.0 (37.5;42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.9 (57.8;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.6 (60.1;65.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.0 (57.4;62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUses CPAP/BiPAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.2 (93.1;95.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.4 (25.1;29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.4 (92;94.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8 (4.9;6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6 (6.3;9.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.6 (5.5;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep-related conditions and components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinically significant insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.3 (62.3;66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.7 (46.1;51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.1 (49.6;54.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.7 (33.7;37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.3 (48.7;53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.9 (45.3;50.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed sleep apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.0 (89.7;92.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.0 (88.4;91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.9 (88.3;91.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.0 (7.9;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0 (8.5;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.1 (8.7;11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.4 (92.3;94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.6 (89;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.2 (89.7;92.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6 (5.6;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.4 (8;11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.8 (7.4;10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed restless legs syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.3 (94.4;96.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.2 (92.9;95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.7 (93.4;95.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7 (3.9;5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8 (4.7;7.1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3 (4.3;6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive daytime sleepiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.2 (84.7;87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.5 (80.4;84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.7 (93.4;95.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.8 (12.4;15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.5 (15.6;19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3 (4.3;6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.6 (35.5;47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0 (33.5;46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.7 (35.0;46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.1 (45.3;56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.2 (44.9;57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.1 (45.3;57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4 (6.3;8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.9 (8.1;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.1 (7.4;8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003ePrevalence and 95% confidence intervals were estimated from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary Study. Poor sleep quality was defined as PSQI\u0026thinsp;\u0026ge;\u0026thinsp;10, and reduced sleep quality by PSQI-2 as scores\u0026thinsp;\u0026ge;\u0026thinsp;3. WHIIRS: Women\u0026rsquo;s Health Initiative Insomnia Rating Scale; PSG: polysomnography.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Sleep quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDistributions of PSQI components and total scores are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean full PSQI score was 7.1 (SD\u0026thinsp;=\u0026thinsp;3.2, range: 0\u0026ndash;19), and the abbreviated PSQI-2 showed a mean score of 2.0 (SD\u0026thinsp;=\u0026thinsp;1.1, range: 0\u0026ndash;6). Overall, 65.6% of participants had poor sleep quality by the PSQI (\u0026gt;\u0026thinsp;5), while 65.7% met the cutoff for reduced sleep quality by the PSQI-2 (\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, patterns of association between sociodemographic and health variables were consistent for both PSQI and PSQI-2 classifications (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSleep disturbances were the most prevalent PSQI component (94.5% any impairment, 40.7% moderate-severe), followed by daytime dysfunction (48.7% any, 23.3% moderate-severe) and sleep latency problems (45.8% any, 18.8% moderate-severe). Sleep efficiency impairments affected 39.4% of participants, while 31.0% had problematic sleep duration. Only 23.2% of participants had no impaired components, whereas 21.5% had three or more impaired components. Sleep medication use was least common (15.6% any use, 9.4% regular use) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PSQI-2 showed strong correlation with the full PSQI (r\u0026thinsp;=\u0026thinsp;0.520, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which remained consistent across gender (women: r\u0026thinsp;=\u0026thinsp;0.523; men: r\u0026thinsp;=\u0026thinsp;0.516) and age groups (\u0026le;\u0026thinsp;69 years: r\u0026thinsp;=\u0026thinsp;0.534; \u0026gt;69 years: r\u0026thinsp;=\u0026thinsp;0.510). Furthermore, logistic regression analyses revealed similar association patterns across PSQI measures. Significant racial disparities existed, with Black and Hispanic participants showing 40\u0026ndash;47% higher odds of poor sleep. Obesity, sleep disorders (insomnia, restless legs, apnea), daytime sleepiness, and evening chronotype consistently increased poor sleep odds, with generally stronger effects for the full PSQI. Only age associations diverged between measures (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between participant characteristics and poor sleep quality according to PSQI (\u0026gt;\u0026thinsp;5) and PSQI-2 (\u0026gt;\u0026thinsp;2) thresholds (MESA Sleep Study)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (PSQI\u0026thinsp;\u0026gt;\u0026thinsp;5) (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (PSQI-2\u0026thinsp;\u0026gt;\u0026thinsp;1) (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI overlap\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (ref: Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.92;1.31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.76;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese-American (ref: White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.69;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.70;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack, African-American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.18;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20 (0.96;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.11;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.83;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65;74 years (ref: 54;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.85;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.58;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.93;1.41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.48;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth and behavioral characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight (ref: Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.15;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22 (0.98;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.73 (1.38;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36 (1.09;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.91;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28 (0.89;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNight/rotating (ref: Daytime)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.84 (1.19;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.69;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39 (0.97;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.72;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed sleep apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44 (1.04;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.55 (1.11;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.15 (3.84;13.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.07 (2.44;6.81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed restless legs syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.30 (1.40;3.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60 (1.01;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPAP/BiPAP use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.95;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.62 (1.07;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular naps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.16;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.84;1.21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep-related scales and components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia severity (WHIIRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (ref: None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.04 (9.55;17.81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.54 (5.04;8.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.42 (11.61;60.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.57 (5.81;19.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleepiness severity (Epworth)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild;moderate (ref: Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.82 (2.02;3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.75 (1.29;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.77 (1.98;7.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.33 (1.32;4.11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive daytime sleepiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.86 (2.10;3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.80 (1.36;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate (ref: Morning)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.94;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.89;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.47;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.02;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eLogistic regression models were estimated separately for PSQI (\u0026gt;\u0026thinsp;5) and PSQI-2 (\u0026gt;\u0026thinsp;2) as dependent variables. OR\u0026thinsp;=\u0026thinsp;Odds Ratio; CI\u0026thinsp;=\u0026thinsp;Confidence Interval; CI overlap = * indicates overlapping 95% CIs between models; + indicates non-overlapping intervals.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Reliability statistics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eContinuous performance metrics assessing the predictive correspondence between PSQI-2 and the full PSQI are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The PSQI-2 achieved a Brier Score of 0.185 (95% CI: 0.006\u0026ndash;0.188), indicating low overall prediction error. The Mean Absolute Error (MAE\u0026thinsp;=\u0026thinsp;0.371, 95% CI: 0.019\u0026ndash;0.377) and Root Mean Square Error (RMSE\u0026thinsp;=\u0026thinsp;0.430, 95% CI: 0.075\u0026ndash;0.434) also reflected good agreement and stability. The Integrated Discrimination Improvement (IDI\u0026thinsp;=\u0026thinsp;0.187, 95% CI: 0.002\u0026ndash;0.332) supported a high degree of discrimination overlap between the PSQI-2 and full PSQI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinuous accuracy metrics for PSQI-2 model against the full PSQI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrier Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePerformance of PSQI-2 in predicting the continuous probability score of the full PSQI. The Brier Score measures overall prediction. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) measure the average magnitude of prediction errors. The Integrated Discrimination Improvement (IDI) approximates the difference in discrimination performance between models, calculated as the mean difference in predicted probabilities between PSQI-2 and the full PSQI.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA Bland\u0026ndash;Altman plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrated a negligible mean bias (\u0026ndash;0.012) and limits of agreement within \u0026plusmn;\u0026thinsp;1.96 SD, indicating no systematic bias and good concordance across the score range. Furthermore, the ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated that the PSQI-2 showed good discriminant validity for identifying poor sleep quality across different cutpoints of the full PSQI. The area under the curve (AUC) was 0.785 for PSQI\u0026thinsp;\u0026gt;\u0026thinsp;5, 0.748 for PSQI\u0026thinsp;\u0026gt;\u0026thinsp;7, and 0.750 for PSQI\u0026thinsp;\u0026gt;\u0026thinsp;10. At the traditional PSQI\u0026thinsp;\u0026gt;\u0026thinsp;5 cutpoint, the PSQI-2\u0026thinsp;\u0026gt;\u0026thinsp;1 showed sensitivity of 80.4% and specificity of 59.3%, correctly classifying 73.0% of cases. For the PSQI\u0026thinsp;\u0026gt;\u0026thinsp;7 cutpoint, sensitivity increased to 86.8% while specificity decreased to 46.3%, with 62.1% correct classification. For the most stringent PSQI\u0026thinsp;\u0026gt;\u0026thinsp;10 cutpoint, the optimal PSQI-2 threshold shifted to \u0026ge;\u0026thinsp;3, achieving balanced performance with 71.8% sensitivity and 79.1% specificity, correctly classifying 78.0% of cases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInternal validation by bootstrap resampling (n\u0026thinsp;=\u0026thinsp;1,000) shows that original Youden Index (0.399 for PSQI-2\u0026thinsp;\u0026gt;\u0026thinsp;1) was slightly reduced after bias correction (bootstrap\u0026thinsp;=\u0026thinsp;0.442), yielding a small bias (0.042) and 95% bias-corrected confidence interval (0.341\u0026ndash;0.452) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInternal validation of model-specific risk thresholds using bootstrap resampling (n\u0026thinsp;=\u0026thinsp;1,000 repetitions).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCutoff PSQI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff PSQI-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOriginal Youden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBootstrap mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI (BC)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.341; 0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.266; 0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.074; 0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates that the abbreviated two-item Pittsburgh Sleep Quality Index (PSQI-2) shows strong validity and consistent performance with the full questionnaire of sleep quality in a large, multi-ethnic cohort of middle-aged and older adults. Overall, the PSQI-2 effectively captured poor sleep quality and reproduced key association patterns observed with the full instrument, supporting its utility as a parsimonious measure in both research and clinical contexts where time and respondent burden are critical considerations.\u003c/p\u003e \u003cp\u003eThe strong conceptual alignment between the PSQI-2 and the full PSQI supports the premise that subjective sleep quality and sleep duration represent core dimensions of the broader sleep quality construct. This overlap is reinforced by the consistency of associations observed across both instruments with major sociodemographic, clinical, and behavioral factors. Both measures identified higher likelihood of poor sleep among Black and Hispanic participants, corroborating prior evidence of racial and ethnic disparities in sleep health reported in MESA and other population-based studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, expected associations with obesity, diagnosed sleep disorders, daytime sleepiness, and evening chronotype were consistently observed, with slightly stronger effects for the full PSQI, as anticipated given its broader scope [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These findings support the construct validity of the PSQI-2 as a concise yet informative indicator of sleep quality.\u003c/p\u003e \u003cp\u003eOur findings align with a growing body of literature supporting the use of abbreviated sleep measures tailored to specific research and clinical needs. Since its original development by Buysse et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] the PSQI has become one of the most widely used subjective sleep instruments worldwide. However, accumulating evidence suggests that the PSQI global score may not be strictly unidimensional, with several studies proposing two- or three-factor structures across different populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These observations provide a strong theoretical basis for the PSQI-2, as a carefully selected subset of items focusing on sleep quality and duration may adequately capture essential aspects of perceived sleep disturbance.\u003c/p\u003e \u003cp\u003eFurthermore, the validation of the PSQI-2 has important implications for both epidemiological research and clinical practice. In large-scale cohort studies such as MESA, where extensive phenotyping must be balanced against feasibility and participant burden, the PSQI-2 provides a pragmatic solution for incorporating sleep quality assessment without substantially increasing survey length or respondent fatigue. This is particularly relevant given the growing body of evidence linking poor sleep quality and short sleep duration to adverse cardiometabolic, cognitive, and mental health outcomes, including hypertension, diabetes, cardiovascular disease, depression, and cognitive decline [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The use of brief, validated instruments has been repeatedly emphasized as a key strategy to improve sleep surveillance in population-based studies and public health monitoring.\u003c/p\u003e \u003cp\u003eIn clinical settings, the PSQI-2 may function as an efficient first-line screening tool for poor perceived sleep quality, enabling early identification of individuals who may benefit from further diagnostic evaluation or targeted interventions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Its brevity and ease of administration make it especially suitable for primary care, geriatric assessments, and outpatient clinics, where time constraints often limit the use of longer instruments. Prior studies have shown that brief sleep screeners can meaningfully improve detection of sleep problems in routine care and support clinical decision-making without compromising validity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, the PSQI-2 bridges an important gap between comprehensive sleep assessment and real-world clinical and epidemiological feasibility, facilitating broader integration of sleep health into research and practice.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the study relied on subjective sleep measures, which may not fully align with objective sleep parameters, although subjective perception remains clinically meaningful. The MESA study collected extensive objective sleep data through polysomnography and actigraphy[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and future research could explore how the PSQI-2 correlates with these objective measures. Second, the cross-sectional nature of our analysis precludes assessment of test-retest reliability, which would strengthen the validation of the PSQI-2. Third, while the PSQI-2 showed good performance in this multi-ethnic cohort, its performance in other populations should be verified, as sleep perceptions and reporting may vary across different cultural and clinical contexts. Furthermore, the PSQI-2 evaluated in this study is an adaptation derived from the full PSQI rather than an independently administered instrument, which may influence item interpretation. Nevertheless, this also represents a strength, as sleep duration and subjective sleep quality are commonly assessed across epidemiological studies, allowing the PSQI-2 framework to be tested and replicated in diverse datasets. This flexibility also opens opportunities for future research to evaluate alternative PSQI-2 adaptations using the same core items across different study designs and populations.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study has notable strengths, including the large, well-characterized multi-ethnic cohort, the comprehensive psychometric evaluation using multiple complementary methods, and the consistency of findings across key demographic subgroups. The integration of actigraphy data for certain components of the full PSQI adaptation also strengthens the original measure's objectivity against which the PSQI-2 was validated. Collectively, these results support the PSQI-2 as a valid, efficient, and scalable measure of sleep quality, particularly well-suited for epidemiological studies and clinical contexts where the full PSQI may be impractical.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe PSQI-2 demonstrates strong validity as an abbreviated alternative to the full PSQI in a multi-ethnic cohort, effectively identifying poor sleep quality and maintaining consistent associations with key demographic, clinical, and sleep-related factors. Its brevity and favorable psychometric properties make it suitable for large-scale studies and clinical screening where the full PSQI may be impractical. Future research should explore the longitudinal performance of the PSQI-2, its responsiveness to interventions, and its validity in specific clinical populations. Additionally, comparison with objective sleep measures could further illuminate what aspects of sleep quality are captured by this abbreviated instrument. As sleep health continues to be recognized as essential to overall well-being, efficient and valid assessment tools like the PSQI-2 will play an increasingly important role in both research and clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e The study protocols were approved by six fields institutional review boards at all participating institutions. Further study design details have been previously published [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary study was supported via the National Heart, Lung, and Blood Institute (NHLBI) and by cooperative agreements from the National Center for Advancing Translational Sciences (NCATS). The corresponding author was supported from the Federal University of Ouro Preto (UFOP), Coordination for the Improvement of Higher Education Personnel (CAPES) and National Council for Scientific and Technological Development (CNPq).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLAAMJ: conception and study design; analysis and interpretation of data; writing the manuscript, critical review, and final approval.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Ancillary study was funded by NIH-NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups (RO1 HL098433). MESA is supported by NHLBI funded contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by cooperative agreements UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 funded by NCATS. The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002). We are also deeply grateful to the National Sleep Research Resource (Sleep Data, https://sleepdata.org) for providing access to the datasets and the computational tools that were essential for this analysis. Furthermore, acknowledge the support of the Federal University of Ouro Preto (UFOP) and the Group for Research and Education in Nutrition and Collective Health (GPENSC) for their support and encouragement.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed for this study were provided by the National Sleep Research Resource (Sleep Data, https:/sleepdata.org ) . The MESA data are publicly available for researchers upon request. Access to the data requires approval of a data use agreement and compliance with the terms and conditions set by the MESA Study and the Sleep Data platform.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePhilippens N, Janssen E, Kremers S, Crutzen R. Determinants of natural adult sleep: An umbrella review. 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Am J Epidemiol. 2002;156:871\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwf113\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwf113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sleep quality, PSQI, validation, epidemiological methods, sleep disorders","lastPublishedDoi":"10.21203/rs.3.rs-8413149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8413149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo evaluated the abbreviated two-item Pittsburgh Sleep Quality Index (PSQI-2) against the full PSQI in a multi-ethnic cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from 2,237 participants from the MESA Sleep Ancillary Study. The full PSQI was adapted by integrating actigraphy data for sleep duration, latency, and efficiency components, while maintaining the original seven-component structure scored 0\u0026ndash;3. The PSQI-2 was derived from two components: sleep duration (questionnaire-based) and subjective sleep quality. Validation analyses included correlation analysis; ROC curves for three PSQI cutpoints (\u0026gt;\u0026thinsp;5, \u0026gt;\u0026thinsp;7, \u0026gt;10) with sensitivity/specificity calculations, Bland-Altman analysis for agreement, bootstrap internal validation, and logistic regression for demographic, clinical, and sleep-related covariates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePoor sleep quality was prevalent (65.6% by PSQI\u0026thinsp;\u0026gt;\u0026thinsp;5; 65.7% by PSQI-2\u0026thinsp;\u0026gt;\u0026thinsp;1). The PSQI-2 showed strong correlation with the full PSQI (r\u0026thinsp;=\u0026thinsp;0.520, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent across gender and age subgroups. Both measures identified similar risk patterns: Black and Hispanic participants had higher odds of poor sleep, and obesity, sleep disorders, daytime sleepiness, and evening chronotype consistently increased poor sleep odds. The PSQI-2 demonstrated good discriminant validity across PSQI cutpoints (AUC: 0.785 for \u0026gt;\u0026thinsp;5, 0.748 for \u0026gt;\u0026thinsp;7, 0.750 for \u0026gt;\u0026thinsp;10), with sensitivity ranging from 71.8\u0026ndash;86.8% and specificity from 46.3\u0026ndash;79.1%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe PSQI-2 shows strong validity and consistent performance with the full PSQI, effectively identifying poor sleep quality and associated factors. Its brevity makes it suitable for large-scale studies and clinical screening.\u003c/p\u003e","manuscriptTitle":"Performance of a two-item sleep quality measure (PSQI-2): a comprehensive evaluation in a multiethnic cohort (MESA Study)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:10:42","doi":"10.21203/rs.3.rs-8413149/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-02T06:52:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-25T18:11:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-23T04:33:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-23T04:31:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-20T15:35:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8347b6ba-f5c0-46c0-bbdb-a2cca6da9c49","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T16:10:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:10:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8413149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8413149","identity":"rs-8413149","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}