{"paper_id":"011dc97e-e583-4c08-8381-e896a682c38b","body_text":"Psychometric properties of the two-item Pittsburgh Sleep Quality Index (PSQI-2) in a cohort of community-dwelling older men | 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 Article Psychometric properties of the two-item Pittsburgh Sleep Quality Index (PSQI-2) in a cohort of community-dwelling older men Luiz Antônio Alves de Menezes-Júnior This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7526287/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background The full Pittsburgh Sleep Quality Index (PSQI) is a widely used measure of sleep quality, but can be impractical in large studies due to its length. The abbreviated two-item version (PSQI-2) is a promising alternative, yet its longitudinal psychometric properties remain underexplored in community-based cohorts of older adults. Objective To comprehensively evaluate the cross-sectional and longitudinal validity of the PSQI-2 against the full PSQI in a cohort of older men. Methods This longitudinal analysis utilized data from 2,911 participants in the Osteoporotic Fractures in Men (MrOS) Sleep Study with complete sleep data at two visits. Cross-sectional validity was assessed using mixed-effects regression and Bland-Altman analysis. Diagnostic accuracy for poor sleep quality (PSQI > 5, >7, and > 10) was evaluated with Receiver Operating Characteristic (ROC) curves. Longitudinal properties included test-retest reliability (Intraclass Correlation Coefficient, ICC), correlation of change scores (Δ), and the accuracy of the PSQI-2 in detecting clinically meaningful change (ΔPSQI > 3) using the area under the curve (AUC). Results The PSQI-2 showed a strong cross-sectional association with the full PSQI (β = 2.08, p < 0.001), explaining 72% of its variance. For identifying poor sleep quality, the PSQI-2 demonstrated excellent accuracy (AUC = 0.89) with an optimal cutoff of ≥ 2 (sensitivity = 77.5%, specificity = 84.5%). Longitudinally, both instruments showed moderate test-retest reliability (PSQI-2 ICC = 0.579; PSQI ICC = 0.639). The correlation between their change scores was strong (r = 0.682, p < 0.001), and the PSQI-2 showed reasonable accuracy (AUC = 0.716) in detecting clinically meaningful change (PSQI > 3). Conclusion The PSQI-2 is a valid and reliable tool for cross-sectional screening of poor sleep quality in older men at the cutoff of ≥ 2. It is also responsive to directional change over time and can identify individuals with clinically significant changes in sleep. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Sleep Quality Validation Studies Geriatrics Reproducibility of Results Epidemiologic Methods Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Large-scale epidemiological studies are fundamental for investigating multiple health outcomes simultaneously, including cardiovascular diseases, neurodegenerative disorders, cancer incidence, musculoskeletal conditions, and mental health outcomes [ 1 ]. These studies face the significant challenge of collecting comprehensive data across these diverse domains while minimizing participant burden and maintaining high retention rates across multiple follow-ups. The necessity to cover numerous health themes often results in extensive questionnaires, which can lead to respondent fatigue, increased missing data, and reduced overall data quality [ 2 ]. Consequently, researchers are often forced to make difficult choices about which domains to include, and important health aspects, such as sleep quality, are frequently excluded or assessed with insufficient depth due to time and space constraints [ 3 , 4 ]. In this context, the use of short, objective instruments becomes essential to ensure data quality and feasibility without compromising scientific rigor. Sleep quality is a critical determinant of overall health and well-being, particularly in older adults [ 5 ]. Poor sleep is pervasive in this population and is intricately linked to a heightened risk of cognitive decline, cardiovascular disease, impaired physical function, and reduced quality of life [ 6 , 7 ]. The Pittsburgh Sleep Quality Index (PSQI) is a well-validated instrument for assessing sleep quality, widely used [ 8 ], but its length (19 items) makes it impractical for certain large epidemiological investigations where multiple constructs need to be measured. This limitation is particularly relevant in studies involving older adults, where comprehensive assessment batteries can lead to respondent fatigue and increased missing data [ 2 ]. To address this challenge, abbreviated versions like the two-item PSQI (PSQI-2) have been developed, offering a pragmatic solution for sleep quality screening in resource-constrained scenarios [ 9 ]. The PSQI-2, derived from the components of sleep quality and sleep duration, has emerged as a promising short alternative [ 9 ]. Its brevity offers a compelling advantage for rapid screening. An initial cross-sectional study has demonstrated strong correlations between the PSQI-2 and the full PSQI, suggesting good concurrent validity [ 9 ]. However, despite its practical advantages, the psychometric properties of the scales require rigorous validation in different populations and contexts, particularly in large epidemiological studies with longitudinal designs. The MrOS Study provides an ideal platform for this validation, given its comprehensive sleep assessment protocol and well-characterized cohort of older men [ 10 , 11 ]. Therefore, the main objective of this analysis was to conduct a comprehensive evaluation of the psychometric properties of the PSQI-2 in comparison with the full PSQI. Specifically, we aimed to: (1) confirm its cross-sectional concurrent validity and diagnostic accuracy for identifying poor sleep quality; (2) assess its test-retest reliability; and (3) evaluate its longitudinal responsiveness and ability to detect clinically meaningful changes in sleep quality over time. Methods Study population and design This study used data from the Men's Osteoporotic Fracture Sleep Study (MrOS), available from the Sleep Data platform. [ 11 ]. The MrOS Sleep Study was conducted between December 2003 and March 2005, during which 3,135 participants from the original cohort underwent a comprehensive sleep assessment constituting the study baseline (Visit 1). Exclusion criteria included regular use of positive airway pressure devices or supplemental oxygen during sleep. A follow-up examination (Visit 2) was conducted approximately 4.7 years (median) later, when sleep questionnaires were readministered. From the initial sleep study participants, 2,911 men with complete sleep questionnaire (PSQI) information formed our analytical sample. [ 10 , 12 , 13 ]. Variables Sleep Quality The primary variables of interest were sleep quality measures. The PSQI was used to assess overall sleep quality. The global PSQI score, ranging from 0 to 21, was calculated from seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction, with higher scores indicating worse sleep quality [ 14 ]. The abbreviated PSQI-2 score was derived from the subjective sleep quality item (\"During the past month, how would you rate your sleep quality overall?\") and the sleep duration component, based on the average hours of sleep per night. The scores from these two items (each ranging from 0–3) were summed to create a total score ranging from 0 to 6, where higher scores denote poorer sleep quality [ 9 ]. In a validation study, this instrument has demonstrated high internal consistency, with a Cronbach’s alpha of 0.94 (95%CI: 0.93–0.95) and McDonald’s ômega of 0.85 (95%CI: 0.84–0.86) [ 9 ]. Coviariates A comprehensive array of covariates was employed to characterize the cohort and for potential adjustment in analyses. Sociodemographic factors included age, categorized into three groups (67–74, 75–84, and 85–90 years), self-reported race and ethnicity, and the level of educational attainment. Anthropometric measures consisted of body mass index, classified into standard categories of underweight, normal, overweight, and obese, and waist circumference, categorized based on established risk thresholds. Hypertension was defined based on measured blood pressure values and classified as present if systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg [ 15 ]. Additional health status indicators included self-reported, physician-diagnosed morbidities: asthma, congestive heart failure, chronic obstructive pulmonary disease, diabetes, myocardial infarction, osteoarthritis, osteoporosis, and stroke. Sleep-specific characteristics encompassed: (1) self-reported sleep disorders (insomnia, narcolepsy, periodic leg movements, restless legs, sleep apnea, or other sleep disorders); (2) objective polysomnography-derived metrics including apnea-hypopnea index, sleep efficiency, and percentage of sleep time in N3 and REM stages; and (3) validated sleep questionnaire scores assessing daytime sleepiness (ESS) [ 16 ], functional outcomes (FOSQ) [ 17 ], and insomnia severity (ISI) [ 18 ]. Statistical analysis The analytical strategy was comprehensive and multi-staged, incorporating both cross-sectional and longitudinal approaches to evaluate the psychometric properties of the PSQI-2. Data preparation involved cleaning and consistency checks, with the PSQI-2 score (range 0–6) as the index test and the full PSQI score (range 0–21) as the reference standard. The primary cross-sectional analysis pooled data from both visits to maximize statistical power while accounting for within-subject correlation using mixed-effects models. Concurrent validity was assessed through repeated-measures correlation and mixed linear regression of the full PSQI on the PSQI-2. Agreement between the original PSQI and PSQI-2 instruments was then assessed using Bland-Altman analysis for repeated measures. To ensure optimal comparability between instruments, PSQI-2 scores were rescaled to match the PSQI's 0–21 scale using linear regression coefficients derived from the relationship between the original measures. Diagnostic accuracy was assessed for established PSQI cutoffs (> 5, > 7, >10) using mixed-effects logistic regression. The area under the receiver operating characteristic curve (AUC) was calculated with cluster bootstrapping to derive confidence intervals. The optimal cutoff for the PSQI-2 was determined by maximizing Youden’s J index (J = sensitivity + specificity − 1), and sensitivity, specificity, positive and negative predictive values, and likelihood ratios were estimated at this threshold. Calibration was assessed by grouping predicted probabilities into deciles and plotting the mean predicted probability against the observed proportion of individuals with PSQI > 5, >7, and > 10 for each decile, with a nonparametric loess curve added to visualize the agreement relative to the 45-degree line of perfect calibration. Secondary analyses were conducted separately for each visit to assess the temporal consistency of the psychometric properties. The longitudinal analysis focused on reliability and responsiveness. Test-retest reliability was quantified using the intraclass correlation coefficient (ICC) for absolute agreement from mixed models. Responsiveness to change was evaluated by calculating the correlation between change scores (Visit 2 – Visit 1) for the PSQI and PSQI-2 using Pearson coefficients. The standardized response mean (SRM) was calculated for each instrument. Furthermore, the ability of the absolute change in the PSQI-2 to detect a clinically meaningful change in the full PSQI, defined as an absolute change > 3 points, according to the literature [ 19 , 20 ], was analyzed using ROC curves. We also performed a binary logistic regression model to investigate which sociodemographic and health factors were associated with poor sleep quality according to the PSQI and PSQI-2 classifications. All analyses were performed using Stata version 18, employing robust standard errors where appropriate, and were two-sided with significance set at p < 0.05. Results The analysis included 2,911 men aged 67 to 90 years with complete data at both visits. As shown in Table 1 , the cohort was predominantly White (90.7%) and well-distributed across educational, anthropometric, and clinical characteristics. Sleep quality scores remained stable between visits: the full PSQI averaged 5.63 (95% CI: 5.51–5.75) at Visit 1 and 5.48 (5.29–5.67) at Visit 2, while the PSQI-2 averaged 1.66 (1.62–1.71) and 1.52 (1.45–1.58), respectively. The prevalence of poor sleep (PSQI > 5) was consistent at 44.0% (Visit 1) and 43.6% (Visit 2), and for the PSQI-2 (PSQI-2 ≥ 2) was 53.6% (Visit 1) and 50.5% (Visit 2). Higher cutoffs (PSQI > 7 and > 10) also showed minimal change over time (Fig. 1 ). Furthermore, the distribution of PSQI-2 components remained stable across visits. For sleep quality (Fig. 2 A), most participants reported \"good\" sleep (score 1: 54.7% V1, 57.4% V2). For sleep duration (Fig. 2 B), most reported 6-<7 hours (score 1: 56.0% V1, 53.3% V2), with a 5.3% increase in optimal duration (≥ 7 hours) at V2. Confidence intervals overlapped for all categories, indicating no statistically significant changes between visits. Table 1 Sociodemographic, anthropometric, and clinical characteristics of the MrOS Sleep participants according to poor sleep quality defined by the PSQI and the PSQI-2 Total PSQI PSQI 2 n % 95%CI n % 95%CI n % 95%CI Age 67–74 years old 1247 42.8 41.0 44.6 538 43.2 40.4 45.9 673 54.0 51.3 56.8 75–84 years old 1404 48.2 46.4 50.1 620 44.2 41.6 46.8 756 53.9 51.2 56.5 85–90 years old 260 8.9 7.9 10.0 123 47.3 41.2 53.4 130 50.2 44.1 56.3 Race/Ethnicity African American 99 3.4 2.7 4.1 49 49.5 39.7 59.3 61 61.6 52.0 71.2 American Indian or Alaska Native 32 1.1 0.7 1.5 16 50.0 32.7 67.3 14 43.8 26.6 60.9 Asian 84 2.9 2.3 3.5 42 50.0 39.3 60.7 53 63.1 52.8 73.4 Native Hawaiian or Other Pacific Islander 55 1.9 1.4 2.4 29 52.7 39.5 65.9 32 58.2 45.2 71.2 White 2641 90.7 89.7 91.8 1145 43.4 41.5 45.3 1399 53.0 51.1 54.9 Initial education level High school or less 624 21.4 20.0 22.9 311 49.8 45.9 53.8 366 58.7 54.8 62.5 Some college/college graduate 1178 40.5 38.7 42.3 529 44.9 42.1 47.8 640 54.4 51.6 57.3 Graduate degree 1109 38.1 36.3 39.9 441 39.8 36.9 42.7 553 49.9 46.9 52.8 Anthropometry Body mass index Normal 1463 50.3 48.5 52.1 628 42.9 40.4 45.5 771 52.7 50.1 55.3 Underweight 234 8.0 7.1 9.0 99 42.5 36.1 48.8 122 52.6 46.2 59.0 Overweight 704 24.2 22.6 25.8 307 43.6 39.9 47.3 378 53.7 50.0 57.4 Obese 508 17.5 16.1 18.8 245 48.2 43.9 52.6 286 56.3 52.0 60.6 Waist circumference Normal 1827 62.9 61.2 64.7 782 42.8 40.6 45.1 956 52.4 50.1 54.7 Increased 726 25.0 23.4 26.6 312 43.0 39.4 46.6 392 54.0 50.4 57.6 Very increased 351 12.1 10.9 13.3 183 52.1 46.9 57.4 206 58.7 53.5 63.8 Hypertension Normotensive 2333 80.1 78.7 81.6 1019 43.7 41.7 45.7 1258 54.0 51.9 56.0 Hypertensive 578 19.9 18.4 21.3 262 45.3 41.3 49.4 301 52.1 48.0 56.2 Morbidities Asthma 224 7.7 6.7 8.7 106 47.3 40.8 53.9 127 56.7 50.2 63.2 Congestive heart failure or enlarged heart 174 6.0 5.1 6.8 93 53.5 46.0 60.9 110 63.2 56.1 70.4 COPD, chronic obstructive lung disease, or emphysema 151 5.2 4.4 6.0 85 56.3 48.4 64.2 95 62.9 55.2 70.6 Diabetes 387 13.3 12.1 14.5 184 47.6 42.6 52.5 221 57.1 52.2 62.0 Heart attack, coronary or myocardial infarction 508 17.5 16.1 18.8 270 53.2 48.8 57.5 303 59.7 55.4 63.9 Osteoarthritis or degenerative arthritis 701 24.1 22.5 25.7 373 53.2 49.5 56.9 414 59.1 55.4 62.7 Osteoporosis 212 7.3 6.3 8.2 108 50.9 44.2 57.7 125 59.0 52.3 65.6 Stroke, blood clot in the brain or bleeding in the brain 111 3.8 3.1 4.5 61 55.0 45.7 64.2 64 57.7 48.5 66.9 Sleep disorders None 2631 90.4 89.3 91.5 1107 42.1 40.2 44.0 1364 51.9 50.0 53.8 At least one 280 9.6 8.6 10.7 174 62.1 56.5 67.8 195 69.6 64.3 75.0 Sleep architecture Apnea-hypopnea index - 18.3 17.7 18.8 - 18.7 17.8 19.6 - 18.65 17.9 19.5 Sleep efficiency (%) - 76.0 75.6 76.5 - 74.3 73.6 75.0 - 75.03 74.4 75.7 Stage N3 of sleep (%) - 11.3 10.9 11.6 - 11.1 10.6 11.6 - 11.34 10.9 11.8 Stage REM of sleep (%) - 19.3 19.0 19.5 - 19.0 18.6 19.3 - 19.02 18.7 19.4 Sleep questionnaires ESS score - 6.2 6.0 6.3 - 6.6 6.4 6.8 - 6.49 6.3 6.7 FOSQ score - 18.7 18.7 18.8 - 18.2 18.1 18.3 - 18.45 18.4 18.5 ISI score - 4.9 4.7 5.2 - 7.4 7.0 7.9 - 6.84 6.4 7.2 This table presents the distribution of poor sleep quality across subgroups defined by age, race/ethnicity, education, body mass index, waist circumference, hypertension, chronic conditions, and sleep disorders. Poor sleep was defined as PSQI total score > 5 and PSQI-2 score ≥ 2. Prevalence estimates (%) are shown with 95% confidence intervals. The cross-sectional pooled analysis demonstrated a strong concurrent validity between the PSQI-2 and the full PSQI, with the full PSQI, with Pearson correlations exceeding 0.70 across both visits (Fig. 2 ). The mixed-effects model revealed a highly significant association (β = 2.167, p < .001) (Fig. 3 A). The repeated-measures correlation coefficient was 0.786. The Bland-Altman analysis revealed excellent agreement. Linear regression-based rescaling eliminated the systematic bias between the original PSQI and the PSQI-2, resulting in a mean difference of 0.00 points. The agreement limits of -3.91 to 3.91 correspond to approximately 18% of the total scale (0–21) (Fig. 3 B). In terms of diagnostic accuracy, ROC analyses showed that the PSQI-2 achieved good performance across conventional cut-points, with AUC values consistently above 0.80 (Fig. 4 A). At the traditional threshold of PSQI > 5, the optimal PSQI-2 cut-point was ≥ 2, yielding balanced sensitivity (84.5%) and specificity (72.0%). Similar results were observed for the other cutoffs (> 7 and > 10), with detailed metrics provided in Fig. 4 B. The calibration plot for the prediction of PSQI > 5, > 7, and > 10 indicates very good agreement between the predicted probabilities from the model and the observed outcomes. The non-parametric loess curve (representing the observed probabilities) closely follows the ideal 45-degree line of perfect calibration across almost the entire range of predicted probabilities (Fig. 4 C). The longitudinal analysis revealed moderate test-retest reliability for both instruments. The ICC for the full PSQI was 0.639, while for the PSQI-2 it was 0.579, indicating acceptable but not excellent stability over time. The responsiveness analysis showed a strong correlation between the change scores of the two instruments (Pearson's r = 0.682, p < .001; Spearman's rho = 0.652, p < .001), confirming that both scales capture similar directions of change in sleep quality. However, the agreement in categorical classification of change (improved, stable, worsened) was only fair (Kappa = 0.351), despite a 76.2% exact agreement. The SRM was small for both the original PSQI (0.07) and the PSQI-2 (-0.13). The ROC analysis for detecting a clinically significant change (> 3 points on the full PSQI) yielded an AUC of 0.716 for the absolute change in PSQI-2, demonstrating reasonable accuracy (Fig. 5 ). Figure 5 , furthermore, illustrates the relationship between individual changes on both instruments, incorporating clinical thresholds. This scatterplot (Fig. 5 B), with reference lines at ± 3 points for the PSQI (vertical) and ± 1 point for the PSQI-2 (horizontal), shows a general diagonal trend indicating concordance in the direction of change. This interpretation is supported by Fig. 3 C, a boxplot showing the distribution of PSQI-2 change scores stratified by the categorical change in the full PSQI (using the > 3 point threshold). The medians align with expectations, negative for the \"improved\" group, near zero for the \"stable\" group, and positive for the \"worsened\" group, but the variability, especially within the \"improved\" and \"worsened\" categories, underscores the differences in sensitivity between the two measures. Associations between poor sleep quality and sociodemographic/clinical factors are shown in Table 2 . Both instruments identified consistent risk factors, including cardiovascular conditions (congestive heart failure: PSQI OR = 1.68, PSQI-2 OR = 1.60), respiratory disease (COPD: PSQI OR = 1.68, PSQI-2 OR = 1.50), and osteoarthritis (PSQI OR = 1.60, PSQI-2 OR = 1.29). Graduate education was protective on both scales (PSQI OR = 0.69, PSQI-2 OR = 0.72). The overlapping confidence intervals for nearly all estimates indicate no significant differences between instruments in detecting these associations, except FOSQ scores, where the full PSQI showed a stronger association; however, both in the same direction (OR: 0.66; 95%CI: 0.62–0.70 and OR: 0.77; 95%CI: 0.73–0.82, for PSQI and PSQI-2, respectively). Table 2 Associations of sociodemographic, anthropometric, and clinical characteristics with poor sleep quality according to the PSQI and the PSQI-2 PSQI PSQI 2 Difference PSQI vs PSQI2 OR 95%CI p-value OR 95%CI p-value Age 67–74 years old 1.00 - - - 1.00 - - - - 75–84 years old 1.03 0.90 1.18 0.684 0.96 0.84 1.11 0.612 No sig 85–90 years old 1.09 0.88 1.34 0.427 0.78 0.63 0.97 0.022 No sig Race/Ethnicity African American 1.00 - - - 1.00 - - - - American Indian or Alaska Native 1.41 1.00 1.98 0.048 1.49 1.05 2.11 0.024 No sig Asian 1.03 0.72 1.46 0.880 1.41 0.99 2.02 0.056 No sig Native Hawaiian or Other Pacific Islander 1.71 1.10 2.66 0.016 1.39 0.89 2.17 0.144 No sig White 1.11 0.63 1.96 0.722 0.72 0.40 1.27 0.252 No sig Initial education level High school or less 1.00 - - - 1.00 - - - - Some college/college graduate 0.85 0.72 1.01 0.065 0.84 0.71 1.00 0.052 No sig Graduate degree 0.69 0.58 0.82 0.000 0.72 0.61 0.86 0.000 No sig Anthropometry Body mass index Normal 1.00 - - - 1.00 - - - - Underweight 0.92 0.73 1.16 0.470 0.89 0.71 1.12 0.330 No sig Overweight 1.11 0.95 1.30 0.187 1.04 0.89 1.21 0.656 No sig Obese 1.38 1.15 1.64 0.000 1.14 0.96 1.36 0.143 No sig Waist circumference Normal 1.00 - - - 1.00 - - - - Increased 1.06 0.91 1.23 0.451 1.03 0.89 1.19 0.719 No sig Very increased 1.47 1.21 1.79 0.000 1.25 1.03 1.53 0.025 No sig Hypertension Normotensive 1.00 - - - 1.00 - - - - Hypertensive 1.02 0.90 1.16 0.744 0.89 0.78 1.01 0.076 No sig Morbidities Asthma 1.24 0.98 1.57 0.080 1.18 0.93 1.50 0.170 No sig Congestive heart failure or enlarged heart 1.68 1.29 2.18 0.000 1.60 1.22 2.09 0.001 No sig COPD, chronic obstructive lung disease, or emphysema 1.68 1.21 2.34 0.002 1.50 1.07 2.10 0.019 No sig Diabetes 1.21 1.01 1.45 0.041 1.10 0.92 1.32 0.311 No sig Heart attack, coronary or myocardial infarction 1.55 1.31 1.83 0.000 1.30 1.10 1.54 0.002 No sig Osteoarthritis or degenerative arthritis 1.60 1.38 1.86 0.000 1.29 1.11 1.49 0.001 No sig Osteoporosis 1.31 1.04 1.66 0.022 1.20 0.95 1.52 0.125 No sig Stroke, blood clot in the brain or bleeding in the brain 1.40 1.02 1.92 0.040 1.02 0.74 1.40 0.925 No sig Sleep disorders None 1.00 - - - 1.00 - - - - At least one 2.02 1.66 2.47 0.000 1.82 1.49 2.23 0.000 No sig Sleep architecture Apnea-hypopnea index 1.00 1.00 1.01 0.106 1.00 1.00 1.01 0.086 No sig Sleep efficiency (%) 0.98 0.98 0.99 0.000 0.99 0.98 0.99 0.000 No sig Stage N3 of sleep (%) 1.00 0.99 1.00 0.289 1.00 1.00 1.01 0.256 No sig Stage REM of sleep (%) 0.99 0.98 0.99 0.002 0.99 0.98 1.00 0.069 No sig Sleep questionnaires ESS score 1.05 1.03 1.07 0.000 1.05 1.03 1.07 0.000 No sig FOSQ score 0.66 0.62 0.70 0.000 0.77 0.73 0.82 0.000 Sig ISI score* 1.37 1.31 1.44 0.000 1.31 1.26 1.37 0.000 No sig Odds ratios (OR) and 95% confidence intervals from logistic regression models are reported, comparing poor versus good sleepers according to the PSQI (> 5) and PSQI-2 (≥ 2). Discussion This study provides a comprehensive longitudinal evaluation of the abbreviated two-item Pittsburgh Sleep Quality Index (PSQI-2) against the full PSQI in a large cohort of community-dwelling older men. Our findings confirm strong cross-sectional validity and excellent diagnostic accuracy of the PSQI-2, supporting its use as an efficient screening tool. However, the analysis also reveals nuances regarding its longitudinal performance, highlighting both its capabilities and limitations for tracking changes in sleep quality over time. The robust cross-sectional association between the PSQI-2 and the full PSQI demonstrates that the two items, subjective sleep quality and sleep duration, capture the majority of the variance in the global PSQI score. This finding is consistent with previous validation studies in other populations. Famodu et al. (2018) [ 21 ], for example, developed a reduced 13-item version, maintaining a high correlation with the original PSQI (rho = 0.94), while Sancho-Domingo et al. (2021)[ 22 ] Validated a 6-item version with a correlation of 0.93 and high accuracy (AUC = 0.87) for identifying sleep disorders. Furthermore, the study of the proposed and validation of PSQI-2, with adults in a population-based household survey with stratified sampling in Brazil, had excellent internal consistency and known-group validity, with a sensitivity of 77.9% and specificity of 73.8% [ 9 ]. The selection of the two components of the PSQI-2, subjective sleep quality and sleep duration, is supported by guidelines from organizations such as the National Sleep Foundation and the American Academy of Sleep Medicine, which recognize these domains as central to the assessment of sleep health. [ 23 ]. Previous psychometric studies have shown that these items represent the core constructs of the complete PSQI [ 9 ]. Previous factor analyses [ 9 ]Have identified that these components capture essential dimensions of sleep, perceptual, and behavioral, with robust factor loadings and adequate internal consistency. Our results corroborate the validity of this abbreviated approach. The strong correlation between the PSQI-2 and the full instrument, together with its excellent diagnostic accuracy, demonstrates that these two items preserve the screening capacity of the original instrument. Moderate longitudinal stability and the ability to detect clinically significant changes reinforce its usefulness in longitudinal contexts, suggesting that while there is a degree of natural variability in self-reported sleep over one year, the PSQI-2 is reasonably stable. Furthermore, the PSQI-2 demonstrated a reasonable ability to detect those who experienced a clinically meaningful change, defined as a > 3 point shift on the full PSQI [ 19 , 20 ]. This suggests that while the absolute change measured by the PSQI-2 may not directly mirror that of the full version, it retains significant utility for identifying individuals whose sleep has substantively improved or declined, a key requirement for patient management and research outcomes. Although brief instruments offer advantages in terms of practicality and reduced burden on respondents [ 24 ]It is essential to consider their limitations. Abbreviated versions may require context- and population-specific cut-off points, and do not capture multidimensionality, such as sleep disturbances, medication use, and daytime dysfunction [ 14 ], which additionally increase the total score but are not fully captured in the two-item version. However, our results demonstrated that the PSQI-2 maintained consistent performance, supporting its use as a balanced tool between brevity and validity for large-scale studies and clinical screening. Several limitations must be considered. The study cohort consisted exclusively of older men, predominantly white, which may limit the generalizability of our findings to women or more ethnically diverse populations. Furthermore, the definition of \"clinically meaningful change\" was anchored to the full PSQI rather than an external clinical anchor. Future studies would benefit from using such external criteria to further validate the responsiveness of the PSQI-2. Among the strengths, note the longitudinal design with two assessments, which allowed us to analyze not only cross-sectional validity but also the reliability and responsiveness of the instrument over time. The large sample size and comprehensive characterization of the cohort, with detailed data on general health, comorbidities, and objective sleep parameters, allowed for robust analyses. The use of statistical methods appropriate for longitudinal data, such as mixed-effects models and repeated measures correlation analysis, reinforces the validity of the conclusions. Finally, the simultaneous assessment of multiple psychometric properties, including concurrent validity, reliability, diagnostic accuracy, and responsiveness, provides a comprehensive view of the PSQI-2's performance in a real-life context. Conclusion This study demonstrates that the PSQI-2 is a robust and practical instrument for assessing sleep quality in older men. Our findings confirm its strong concurrent validity with the full PSQI, excellent diagnostic accuracy for identifying poor sleep quality (PSQI > 5) at the optimal cutoff of ≥ 2, and moderate test-retest reliability. The full PSQI remains the standard index for a comprehensive, multidimensional assessment. The PSQI-2’s strength lies in its ability to efficiently track directional changes and classify poor sleepers, making it particularly valuable for large-scale epidemiological studies, longitudinal monitoring, and initial clinical screening. Future research should evaluate these findings in more diverse populations, including women and other ethnic groups. Nevertheless, based on our results, the PSQI-2 can be confidently recommended as a valid, reliable, and responsive brief measure of sleep quality in aging populations, in both clinical and research settings where the full PSQI may be impractical. Declarations Funding This study was conducted with data from the Osteoporotic Fractures in Men (MrOS) Study, which is supported by National Institutes of Health (NIH) funding. The National Heart, Lung, and Blood Institute (NHLBI) provides funding for the MrOS Sleep Study under grant numbers R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. The research and authorship of this article were supported by the Federal University of Ouro Preto (UFOP). The lead researcher was also supported by scholarships and funding from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). 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 institutional review boards at all participating institutions. Informed Consent Informed consent was obtained from all individual participants included in the study. Data Availability The datasets analyzed for this study were provided by the National Sleep Research Resource (Sleep Data, https://sleepdata.org). The MrOS 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 MrOS Study and the Sleep Data platform. Authors' Contributions LAAMJ: conception and study design; analysis and interpretation of data; writing the manuscript, critical review, and final approval. Acknowledgements The author would like to express their sincere gratitude to the principal investigators, staff, and participants of the Osteoporotic Fractures in Men (MrOS) Study for their invaluable contribution to this research. Without their dedication and effort, this work would not have been possible. 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. References Thompson A. Thinking big: large-scale collaborative research in observational epidemiology. Eur J Epidemiol 2009;24:727–31. https://doi.org/10.1007/s10654-009-9412-1. Egleston BL, Miller SM, Meropol NJ. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects. Stat Med 2011;30:3560–72. https://doi.org/10.1002/sim.4377. Chen Y, Zhou E, Wang Y, Wu Y, Xu G, Chen L. The past, present, and future of sleep quality assessment and monitoring. 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Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, et al. Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society. Journal of Clinical Sleep Medicine 2015;11:591–2. https://doi.org/10.5664/jcsm.4758. Atkinson EA, Carter CMP, Rohr JLC, Smith GT. Brief instruments and short forms. APA handbook of research methods in psychology: Foundations, planning, measures, and psychometrics (Vol. 1) (2nd ed.)., Washington: American Psychological Association; 2023, p. 451–66. https://doi.org/10.1037/0000318-021. Additional Declarations No competing interests reported. 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13:41:12\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":69757,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of PSQI and PSQI-2 scores in the MrOS Sleep Study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eHistograms display the frequency distribution of the full PSQI total score (0–21) and the abbreviated PSQI-2 score (0–6) across all participants and both visits.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7526287/v1/6dc6d25f59d46bd422c2eed8.png\"},{\"id\":93942602,\"identity\":\"023a354d-e481-402c-add2-37ea5cb8eb28\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 13:49:12\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":87671,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of PSQI-2 component scores by study visit in the MrOS Sleep Study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eHistograms display the frequency distribution of (A) subjective sleep quality (score 0–3: very good to very poor), and (B) sleep duration component (score 0–3: ≥7 hours to \\u0026lt;5 hours) across all participants and both visits.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7526287/v1/8bea5bce5df3025a444ca367.png\"},{\"id\":93941863,\"identity\":\"a29667ad-9590-4cdd-bab8-a3f774614a5f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 13:41:12\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":77515,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation between PSQI-2 and PSQI total scores across both visits of the MrOS Sleep Study and Bland–Altman plots comparing PSQI-2 with PSQI total scores\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePanel A shows a scatterplot of PSQI-2 (x-axis) versus PSQI total (y-axis), with fitted regression line and confidence bands, demonstrating strong concurrent validity. Each dot represents one observation from one visit. Panel B displays Bland–Altman plots, with the mean of the two measures (x-axis) plotted against their difference (y-axis). Solid lines represent mean bias and dashed lines the 95% limits of agreement. The PSQI-2 score was rescaled to the original PSQI metric (0-21) using the linear regression equation derived from the data: PSQI-2 Rescaled = 1.99 + 2.21 × PSQI-2.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7526287/v1/1ce274290164892767700d28.png\"},{\"id\":93941866,\"identity\":\"83adcd04-85d2-4090-a025-a6dadd0d6c63\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 13:41:12\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":120353,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDiagnostic accuracy of the PSQI-2 for detecting poor sleep quality defined by PSQI thresholds\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePanel A presents ROC curves of the PSQI-2 against three PSQI cut-offs (\\u0026gt;5, \\u0026gt;7 and \\u0026gt;10). Area under the curve (AUC) with 95% confidence intervals is provided. Panel B shows sensitivity and specificity curves across different probability thresholds, illustrating the trade-off between these indicators. Panel C displays calibration plots comparing observed versus predicted probabilities for each cut-off, with the diagonal representing perfect calibration. The figure demonstrates that the PSQI-2 achieves good to excellent diagnostic accuracy across cut-offs, with AUC values ranging from 0.85 to 0.92, and shows adequate calibration.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7526287/v1/fc891bc4297d303f39e72618.png\"},{\"id\":93944125,\"identity\":\"f40d4342-7c68-4143-94a1-a56bc13a77eb\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 14:05:12\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":39942,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eResponsiveness and longitudinal validity of the PSQI-2 compared with the PSQI.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePanel A shows ROC curves for the ability of PSQI-2 change scores to detect clinically meaningful improvement in sleep quality, defined as a \\u0026gt;3-point reduction in PSQI total score (AUC=0.75). Panel B illustrates scatterplots of change in PSQI-2 versus change in PSQI, with vertical and horizontal reference lines marking clinical thresholds (±1 for PSQI-2 and ±3 for PSQI), classifying individuals as improved, stable, or worsened. Panel C displays boxplots of PSQI-2 change according to categories of PSQI change (\\u0026lt;5, 6–10, \\u0026gt;10), highlighting expected directional patterns.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7526287/v1/1e306eda2d5f72fb03f8d7e7.png\"},{\"id\":93945305,\"identity\":\"ec22a83f-eac1-405d-a4d4-430909d6dbd1\",\"added_by\":\"auto\",\"created_at\":\"2025-10-20 14:13:13\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1703189,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7526287/v1/a7baf58a-6d73-4b5d-a773-68bd90940131.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Psychometric properties of the two-item Pittsburgh Sleep Quality Index (PSQI-2) in a cohort of community-dwelling older men\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eLarge-scale epidemiological studies are fundamental for investigating multiple health outcomes simultaneously, including cardiovascular diseases, neurodegenerative disorders, cancer incidence, musculoskeletal conditions, and mental health outcomes [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. These studies face the significant challenge of collecting comprehensive data across these diverse domains while minimizing participant burden and maintaining high retention rates across multiple follow-ups. The necessity to cover numerous health themes often results in extensive questionnaires, which can lead to respondent fatigue, increased missing data, and reduced overall data quality [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Consequently, researchers are often forced to make difficult choices about which domains to include, and important health aspects, such as sleep quality, are frequently excluded or assessed with insufficient depth due to time and space constraints [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. In this context, the use of short, objective instruments becomes essential to ensure data quality and feasibility without compromising scientific rigor.\\u003c/p\\u003e\\u003cp\\u003eSleep quality is a critical determinant of overall health and well-being, particularly in older adults [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Poor sleep is pervasive in this population and is intricately linked to a heightened risk of cognitive decline, cardiovascular disease, impaired physical function, and reduced quality of life [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The Pittsburgh Sleep Quality Index (PSQI) is a well-validated instrument for assessing sleep quality, widely used [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], but its length (19 items) makes it impractical for certain large epidemiological investigations where multiple constructs need to be measured. This limitation is particularly relevant in studies involving older adults, where comprehensive assessment batteries can lead to respondent fatigue and increased missing data [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. To address this challenge, abbreviated versions like the two-item PSQI (PSQI-2) have been developed, offering a pragmatic solution for sleep quality screening in resource-constrained scenarios [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe PSQI-2, derived from the components of sleep quality and sleep duration, has emerged as a promising short alternative [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Its brevity offers a compelling advantage for rapid screening. An initial cross-sectional study has demonstrated strong correlations between the PSQI-2 and the full PSQI, suggesting good concurrent validity [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. However, despite its practical advantages, the psychometric properties of the scales require rigorous validation in different populations and contexts, particularly in large epidemiological studies with longitudinal designs. The MrOS Study provides an ideal platform for this validation, given its comprehensive sleep assessment protocol and well-characterized cohort of older men [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTherefore, the main objective of this analysis was to conduct a comprehensive evaluation of the psychometric properties of the PSQI-2 in comparison with the full PSQI. Specifically, we aimed to: (1) confirm its cross-sectional concurrent validity and diagnostic accuracy for identifying poor sleep quality; (2) assess its test-retest reliability; and (3) evaluate its longitudinal responsiveness and ability to detect clinically meaningful changes in sleep quality over time.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy population and design\\u003c/h2\\u003e\\u003cp\\u003eThis study used data from the Men's Osteoporotic Fracture Sleep Study (MrOS), available from the Sleep Data platform. [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. The MrOS Sleep Study was conducted between December 2003 and March 2005, during which 3,135 participants from the original cohort underwent a comprehensive sleep assessment constituting the study baseline (Visit 1). Exclusion criteria included regular use of positive airway pressure devices or supplemental oxygen during sleep. A follow-up examination (Visit 2) was conducted approximately 4.7 years (median) later, when sleep questionnaires were readministered. From the initial sleep study participants, 2,911 men with complete sleep questionnaire (PSQI) information formed our analytical sample. [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\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\\u003cp\\u003eThe primary variables of interest were sleep quality measures. The PSQI was used to assess overall sleep quality. The global PSQI score, ranging from 0 to 21, was calculated from seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction, with higher scores indicating worse sleep quality [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. The abbreviated PSQI-2 score was derived from the subjective sleep quality item (\\\"During the past month, how would you rate your sleep quality overall?\\\") and the sleep duration component, based on the average hours of sleep per night. The scores from these two items (each ranging from 0\\u0026ndash;3) were summed to create a total score ranging from 0 to 6, where higher scores denote poorer sleep quality [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. In a validation study, this instrument has demonstrated high internal consistency, with a Cronbach\\u0026rsquo;s alpha of 0.94 (95%CI: 0.93\\u0026ndash;0.95) and McDonald\\u0026rsquo;s \\u0026ocirc;mega of 0.85 (95%CI: 0.84\\u0026ndash;0.86) [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eCoviariates\\u003c/h3\\u003e\\n\\u003cp\\u003eA comprehensive array of covariates was employed to characterize the cohort and for potential adjustment in analyses. Sociodemographic factors included age, categorized into three groups (67\\u0026ndash;74, 75\\u0026ndash;84, and 85\\u0026ndash;90 years), self-reported race and ethnicity, and the level of educational attainment. Anthropometric measures consisted of body mass index, classified into standard categories of underweight, normal, overweight, and obese, and waist circumference, categorized based on established risk thresholds. Hypertension was defined based on measured blood pressure values and classified as present if systolic blood pressure\\u0026thinsp;\\u0026ge;\\u0026thinsp;140 mmHg or diastolic blood pressure\\u0026thinsp;\\u0026ge;\\u0026thinsp;90 mmHg [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Additional health status indicators included self-reported, physician-diagnosed morbidities: asthma, congestive heart failure, chronic obstructive pulmonary disease, diabetes, myocardial infarction, osteoarthritis, osteoporosis, and stroke.\\u003c/p\\u003e\\u003cp\\u003eSleep-specific characteristics encompassed: (1) self-reported sleep disorders (insomnia, narcolepsy, periodic leg movements, restless legs, sleep apnea, or other sleep disorders); (2) objective polysomnography-derived metrics including apnea-hypopnea index, sleep efficiency, and percentage of sleep time in N3 and REM stages; and (3) validated sleep questionnaire scores assessing daytime sleepiness (ESS) [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], functional outcomes (FOSQ) [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], and insomnia severity (ISI) [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003eThe analytical strategy was comprehensive and multi-staged, incorporating both cross-sectional and longitudinal approaches to evaluate the psychometric properties of the PSQI-2. Data preparation involved cleaning and consistency checks, with the PSQI-2 score (range 0\\u0026ndash;6) as the index test and the full PSQI score (range 0\\u0026ndash;21) as the reference standard.\\u003c/p\\u003e\\u003cp\\u003eThe primary cross-sectional analysis pooled data from both visits to maximize statistical power while accounting for within-subject correlation using mixed-effects models. Concurrent validity was assessed through repeated-measures correlation and mixed linear regression of the full PSQI on the PSQI-2. Agreement between the original PSQI and PSQI-2 instruments was then assessed using Bland-Altman analysis for repeated measures. To ensure optimal comparability between instruments, PSQI-2 scores were rescaled to match the PSQI's 0\\u0026ndash;21 scale using linear regression coefficients derived from the relationship between the original measures. Diagnostic accuracy was assessed for established PSQI cutoffs (\\u0026gt;\\u0026thinsp;5, \\u0026gt;\\u0026thinsp;7, \\u0026gt;10) using mixed-effects logistic regression. The area under the receiver operating characteristic curve (AUC) was calculated with cluster bootstrapping to derive confidence intervals. The optimal cutoff for the PSQI-2 was determined by maximizing Youden\\u0026rsquo;s J index (J\\u0026thinsp;=\\u0026thinsp;sensitivity\\u0026thinsp;+\\u0026thinsp;specificity\\u0026thinsp;\\u0026minus;\\u0026thinsp;1), and sensitivity, specificity, positive and negative predictive values, and likelihood ratios were estimated at this threshold. Calibration was assessed by grouping predicted probabilities into deciles and plotting the mean predicted probability against the observed proportion of individuals with PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5, \\u0026gt;7, and \\u0026gt;\\u0026thinsp;10 for each decile, with a nonparametric loess curve added to visualize the agreement relative to the 45-degree line of perfect calibration.\\u003c/p\\u003e\\u003cp\\u003eSecondary analyses were conducted separately for each visit to assess the temporal consistency of the psychometric properties. The longitudinal analysis focused on reliability and responsiveness. Test-retest reliability was quantified using the intraclass correlation coefficient (ICC) for absolute agreement from mixed models. Responsiveness to change was evaluated by calculating the correlation between change scores (Visit 2 \\u0026ndash; Visit 1) for the PSQI and PSQI-2 using Pearson coefficients. The standardized response mean (SRM) was calculated for each instrument. Furthermore, the ability of the absolute change in the PSQI-2 to detect a clinically meaningful change in the full PSQI, defined as an absolute change\\u0026thinsp;\\u0026gt;\\u0026thinsp;3 points, according to the literature [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], was analyzed using ROC curves.\\u003c/p\\u003e\\u003cp\\u003eWe also performed a binary logistic regression model to investigate which sociodemographic and health factors were associated with poor sleep quality according to the PSQI and PSQI-2 classifications. All analyses were performed using Stata version 18, employing robust standard errors where appropriate, and were two-sided with significance set at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe analysis included 2,911 men aged 67 to 90 years with complete data at both visits. As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, the cohort was predominantly White (90.7%) and well-distributed across educational, anthropometric, and clinical characteristics. Sleep quality scores remained stable between visits: the full PSQI averaged 5.63 (95% CI: 5.51\\u0026ndash;5.75) at Visit 1 and 5.48 (5.29\\u0026ndash;5.67) at Visit 2, while the PSQI-2 averaged 1.66 (1.62\\u0026ndash;1.71) and 1.52 (1.45\\u0026ndash;1.58), respectively. The prevalence of poor sleep (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5) was consistent at 44.0% (Visit 1) and 43.6% (Visit 2), and for the PSQI-2 (PSQI-2\\u0026thinsp;\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u003c/span\\u003e\\u0026thinsp;2) was 53.6% (Visit 1) and 50.5% (Visit 2). Higher cutoffs (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;7 and \\u0026gt;\\u0026thinsp;10) also showed minimal change over time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Furthermore, the distribution of PSQI-2 components remained stable across visits. For sleep quality (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA), most participants reported \\\"good\\\" sleep (score 1: 54.7% V1, 57.4% V2). For sleep duration (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB), most reported 6-\\u0026lt;7 hours (score 1: 56.0% V1, 53.3% V2), with a 5.3% increase in optimal duration (\\u0026ge;\\u0026thinsp;7 hours) at V2. Confidence intervals overlapped for all categories, indicating no statistically significant changes between visits.\\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\\u003eSociodemographic, anthropometric, and clinical characteristics of the MrOS Sleep participants according to poor sleep quality defined by the PSQI and the PSQI-2\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"14\\\"\\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\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c14\\\" colnum=\\\"14\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c9\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003ePSQI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c14\\\" namest=\\\"c10\\\"\\u003e\\u003cp\\u003ePSQI 2\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u003cp\\u003e95%CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e\\u003cp\\u003e95%CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e\\u003cp\\u003e95%CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\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\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e67\\u0026ndash;74 years old\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1247\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e42.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsthma\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e224\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e106\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e47.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e40.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e53.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e127\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e56.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e50.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e63.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCongestive heart failure or enlarged heart\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e174\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e53.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e46.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e60.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e110\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e63.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e56.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e70.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCOPD, chronic obstructive lung disease, or emphysema\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e151\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e56.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e48.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e64.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e62.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e55.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e70.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e387\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e12.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e184\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e47.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e42.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e52.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e221\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e57.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e52.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e62.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHeart attack, coronary or myocardial infarction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e508\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e17.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e16.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e270\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e53.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e48.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e57.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e303\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e59.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e55.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e63.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOsteoarthritis or degenerative arthritis\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e701\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e24.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e22.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e25.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e373\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e53.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e49.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e56.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e414\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e59.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e55.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e62.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOsteoporosis\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e212\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e108\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e50.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e44.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e57.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e125\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e59.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e52.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e65.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke, blood clot in the brain or bleeding in the brain\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e111\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e55.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e45.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e64.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e57.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e48.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e66.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSleep disorders\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNone\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2631\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e90.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e89.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e91.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1107\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e42.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e40.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e44.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1364\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e51.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e50.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e53.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAt least one\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e280\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd 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colname=\\\"c12\\\"\\u003e\\u003cp\\u003e64.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e75.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSleep architecture\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eApnea-hypopnea index\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e18.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e17.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e19.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e18.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e17.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e73.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e75.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e75.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e74.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e75.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage N3 of sleep (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e11.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e11.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e10.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e11.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e11.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e10.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e11.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage REM of sleep (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e19.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e19.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e19.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e18.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e19.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e19.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e18.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e19.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSleep questionnaires\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eESS score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e6.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e6.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e6.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e6.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e6.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e6.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFOSQ score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e18.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e18.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e18.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e18.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e18.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e18.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e18.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eISI score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e7.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e7.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e7.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e6.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e6.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e7.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c14\\\" namest=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"14\\\" nameend=\\\"c14\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eThis table presents the distribution of poor sleep quality across subgroups defined by age, race/ethnicity, education, body mass index, waist circumference, hypertension, chronic conditions, and sleep disorders. Poor sleep was defined as PSQI total score\\u0026thinsp;\\u0026gt;\\u0026thinsp;5 and PSQI-2 score\\u0026thinsp;\\u0026ge;\\u0026thinsp;2. Prevalence estimates (%) are shown with 95% confidence intervals.\\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\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe cross-sectional pooled analysis demonstrated a strong concurrent validity between the PSQI-2 and the full PSQI, with the full PSQI, with Pearson correlations exceeding 0.70 across both visits (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The mixed-effects model revealed a highly significant association (β\\u0026thinsp;=\\u0026thinsp;2.167, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). The repeated-measures correlation coefficient was 0.786. The Bland-Altman analysis revealed excellent agreement. Linear regression-based rescaling eliminated the systematic bias between the original PSQI and the PSQI-2, resulting in a mean difference of 0.00 points. The agreement limits of -3.91 to 3.91 correspond to approximately 18% of the total scale (0\\u0026ndash;21) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn terms of diagnostic accuracy, ROC analyses showed that the PSQI-2 achieved good performance across conventional cut-points, with AUC values consistently above 0.80 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). At the traditional threshold of PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5, the optimal PSQI-2 cut-point was \\u0026ge;\\u0026thinsp;2, yielding balanced sensitivity (84.5%) and specificity (72.0%). Similar results were observed for the other cutoffs (\\u0026gt;\\u0026thinsp;7 and \\u0026gt;\\u0026thinsp;10), with detailed metrics provided in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB. The calibration plot for the prediction of PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5, \\u0026gt;\\u0026thinsp;7, and \\u0026gt;\\u0026thinsp;10 indicates very good agreement between the predicted probabilities from the model and the observed outcomes. The non-parametric loess curve (representing the observed probabilities) closely follows the ideal 45-degree line of perfect calibration across almost the entire range of predicted probabilities (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe longitudinal analysis revealed moderate test-retest reliability for both instruments. The ICC for the full PSQI was 0.639, while for the PSQI-2 it was 0.579, indicating acceptable but not excellent stability over time. The responsiveness analysis showed a strong correlation between the change scores of the two instruments (Pearson's r\\u0026thinsp;=\\u0026thinsp;0.682, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001; Spearman's rho\\u0026thinsp;=\\u0026thinsp;0.652, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001), confirming that both scales capture similar directions of change in sleep quality. However, the agreement in categorical classification of change (improved, stable, worsened) was only fair (Kappa\\u0026thinsp;=\\u0026thinsp;0.351), despite a 76.2% exact agreement. The SRM was small for both the original PSQI (0.07) and the PSQI-2 (-0.13). The ROC analysis for detecting a clinically significant change (\\u0026gt;\\u0026thinsp;3 points on the full PSQI) yielded an AUC of 0.716 for the absolute change in PSQI-2, demonstrating reasonable accuracy (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, furthermore, illustrates the relationship between individual changes on both instruments, incorporating clinical thresholds. This scatterplot (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB), with reference lines at \\u0026plusmn;\\u0026thinsp;3 points for the PSQI (vertical) and \\u0026plusmn;\\u0026thinsp;1 point for the PSQI-2 (horizontal), shows a general diagonal trend indicating concordance in the direction of change. This interpretation is supported by Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC, a boxplot showing the distribution of PSQI-2 change scores stratified by the categorical change in the full PSQI (using the \\u0026gt;\\u0026thinsp;3 point threshold). The medians align with expectations, negative for the \\\"improved\\\" group, near zero for the \\\"stable\\\" group, and positive for the \\\"worsened\\\" group, but the variability, especially within the \\\"improved\\\" and \\\"worsened\\\" categories, underscores the differences in sensitivity between the two measures.\\u003c/p\\u003e\\u003cp\\u003eAssociations between poor sleep quality and sociodemographic/clinical factors are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Both instruments identified consistent risk factors, including cardiovascular conditions (congestive heart failure: PSQI OR\\u0026thinsp;=\\u0026thinsp;1.68, PSQI-2 OR\\u0026thinsp;=\\u0026thinsp;1.60), respiratory disease (COPD: PSQI OR\\u0026thinsp;=\\u0026thinsp;1.68, PSQI-2 OR\\u0026thinsp;=\\u0026thinsp;1.50), and osteoarthritis (PSQI OR\\u0026thinsp;=\\u0026thinsp;1.60, PSQI-2 OR\\u0026thinsp;=\\u0026thinsp;1.29). Graduate education was protective on both scales (PSQI OR\\u0026thinsp;=\\u0026thinsp;0.69, PSQI-2 OR\\u0026thinsp;=\\u0026thinsp;0.72). The overlapping confidence intervals for nearly all estimates indicate no significant differences between instruments in detecting these associations, except FOSQ scores, where the full PSQI showed a stronger association; however, both in the same direction (OR: 0.66; 95%CI: 0.62\\u0026ndash;0.70 and OR: 0.77; 95%CI: 0.73\\u0026ndash;0.82, for PSQI and PSQI-2, respectively).\\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 of sociodemographic, anthropometric, and clinical characteristics with poor sleep quality according to the PSQI and the PSQI-2\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"10\\\"\\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\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003ePSQI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c9\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003ePSQI 2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eDifference\\u003c/p\\u003e\\u003cp\\u003ePSQI vs PSQI2\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOR\\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\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eOR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e95%CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\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\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e67\\u0026ndash;74 years old\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e75\\u0026ndash;84 years old\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.684\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.612\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e85\\u0026ndash;90 years old\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.427\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsthma\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.080\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.170\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCongestive heart failure or enlarged heart\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCOPD, chronic obstructive lung disease, or emphysema\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.041\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.92\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.311\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHeart attack, coronary or myocardial infarction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOsteoarthritis or degenerative arthritis\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.86\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOsteoporosis\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.52\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.125\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke, blood clot in the brain or bleeding in the brain\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.92\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.040\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.925\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSleep disorders\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNone\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAt least one\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSleep architecture\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eApnea-hypopnea index\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.106\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.086\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSleep efficiency (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage N3 of sleep (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.289\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.256\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage REM of sleep (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.069\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSleep questionnaires\\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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eESS score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFOSQ score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.70\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.73\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eSig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eISI score*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.44\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eNo sig\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"10\\\" nameend=\\\"c10\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eOdds ratios (OR) and 95% confidence intervals from logistic regression models are reported, comparing poor versus good sleepers according to the PSQI (\\u0026gt;\\u0026thinsp;5) and PSQI-2 (\\u0026ge;\\u0026thinsp;2).\\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\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study provides a comprehensive longitudinal evaluation of the abbreviated two-item Pittsburgh Sleep Quality Index (PSQI-2) against the full PSQI in a large cohort of community-dwelling older men. Our findings confirm strong cross-sectional validity and excellent diagnostic accuracy of the PSQI-2, supporting its use as an efficient screening tool. However, the analysis also reveals nuances regarding its longitudinal performance, highlighting both its capabilities and limitations for tracking changes in sleep quality over time.\\u003c/p\\u003e\\u003cp\\u003eThe robust cross-sectional association between the PSQI-2 and the full PSQI demonstrates that the two items, subjective sleep quality and sleep duration, capture the majority of the variance in the global PSQI score. This finding is consistent with previous validation studies in other populations. Famodu et al. (2018) [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], for example, developed a reduced 13-item version, maintaining a high correlation with the original PSQI (rho\\u0026thinsp;=\\u0026thinsp;0.94), while Sancho-Domingo et al. (2021)[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e] Validated a 6-item version with a correlation of 0.93 and high accuracy (AUC\\u0026thinsp;=\\u0026thinsp;0.87) for identifying sleep disorders. Furthermore, the study of the proposed and validation of PSQI-2, with adults in a population-based household survey with stratified sampling in Brazil, had excellent internal consistency and known-group validity, with a sensitivity of 77.9% and specificity of 73.8% [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe selection of the two components of the PSQI-2, subjective sleep quality and sleep duration, is supported by guidelines from organizations such as the National Sleep Foundation and the American Academy of Sleep Medicine, which recognize these domains as central to the assessment of sleep health. [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Previous psychometric studies have shown that these items represent the core constructs of the complete PSQI [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Previous factor analyses [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]Have identified that these components capture essential dimensions of sleep, perceptual, and behavioral, with robust factor loadings and adequate internal consistency.\\u003c/p\\u003e\\u003cp\\u003eOur results corroborate the validity of this abbreviated approach. The strong correlation between the PSQI-2 and the full instrument, together with its excellent diagnostic accuracy, demonstrates that these two items preserve the screening capacity of the original instrument. Moderate longitudinal stability and the ability to detect clinically significant changes reinforce its usefulness in longitudinal contexts, suggesting that while there is a degree of natural variability in self-reported sleep over one year, the PSQI-2 is reasonably stable. Furthermore, the PSQI-2 demonstrated a reasonable ability to detect those who experienced a clinically meaningful change, defined as a\\u0026thinsp;\\u0026gt;\\u0026thinsp;3 point shift on the full PSQI [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. This suggests that while the absolute change measured by the PSQI-2 may not directly mirror that of the full version, it retains significant utility for identifying individuals whose sleep has substantively improved or declined, a key requirement for patient management and research outcomes.\\u003c/p\\u003e\\u003cp\\u003eAlthough brief instruments offer advantages in terms of practicality and reduced burden on respondents [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]It is essential to consider their limitations. Abbreviated versions may require context- and population-specific cut-off points, and do not capture multidimensionality, such as sleep disturbances, medication use, and daytime dysfunction [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e], which additionally increase the total score but are not fully captured in the two-item version. However, our results demonstrated that the PSQI-2 maintained consistent performance, supporting its use as a balanced tool between brevity and validity for large-scale studies and clinical screening.\\u003c/p\\u003e\\u003cp\\u003eSeveral limitations must be considered. The study cohort consisted exclusively of older men, predominantly white, which may limit the generalizability of our findings to women or more ethnically diverse populations. Furthermore, the definition of \\\"clinically meaningful change\\\" was anchored to the full PSQI rather than an external clinical anchor. Future studies would benefit from using such external criteria to further validate the responsiveness of the PSQI-2. Among the strengths, note the longitudinal design with two assessments, which allowed us to analyze not only cross-sectional validity but also the reliability and responsiveness of the instrument over time. The large sample size and comprehensive characterization of the cohort, with detailed data on general health, comorbidities, and objective sleep parameters, allowed for robust analyses. The use of statistical methods appropriate for longitudinal data, such as mixed-effects models and repeated measures correlation analysis, reinforces the validity of the conclusions. Finally, the simultaneous assessment of multiple psychometric properties, including concurrent validity, reliability, diagnostic accuracy, and responsiveness, provides a comprehensive view of the PSQI-2's performance in a real-life context.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study demonstrates that the PSQI-2 is a robust and practical instrument for assessing sleep quality in older men. Our findings confirm its strong concurrent validity with the full PSQI, excellent diagnostic accuracy for identifying poor sleep quality (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5) at the optimal cutoff of \\u0026ge;\\u0026thinsp;2, and moderate test-retest reliability. The full PSQI remains the standard index for a comprehensive, multidimensional assessment. The PSQI-2\\u0026rsquo;s strength lies in its ability to efficiently track directional changes and classify poor sleepers, making it particularly valuable for large-scale epidemiological studies, longitudinal monitoring, and initial clinical screening.\\u003c/p\\u003e\\u003cp\\u003eFuture research should evaluate these findings in more diverse populations, including women and other ethnic groups. Nevertheless, based on our results, the PSQI-2 can be confidently recommended as a valid, reliable, and responsive brief measure of sleep quality in aging populations, in both clinical and research settings where the full PSQI may be impractical.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cbr\\u003e\\u003c/strong\\u003eThis study was conducted with data from the Osteoporotic Fractures in Men (MrOS) Study, which is supported by National Institutes of Health (NIH) funding. The National Heart, Lung, and Blood Institute (NHLBI) provides funding for the MrOS Sleep Study under grant numbers R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839.\\u003c/p\\u003e\\n\\u003cp\\u003eThe research and authorship of this article were supported by the\\u0026nbsp;Federal University of Ouro Preto (UFOP). The lead researcher was also supported by scholarships and funding from the\\u0026nbsp;Coordena\\u0026ccedil;\\u0026atilde;o de Aperfei\\u0026ccedil;oamento de Pessoal de N\\u0026iacute;vel Superior (CAPES)\\u0026nbsp;and the\\u0026nbsp;Conselho Nacional de Desenvolvimento Cient\\u0026iacute;fico e Tecnol\\u0026oacute;gico (CNPq).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\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\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study protocols were approved by institutional review boards at all participating institutions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed Consent\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInformed consent was obtained from all individual participants included in the study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets analyzed for this study were provided by the National Sleep Research Resource (Sleep Data, https://sleepdata.org). The MrOS 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 MrOS Study and the Sleep Data platform.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLAAMJ: conception and study design; analysis and interpretation of data; writing the manuscript, critical review, and final approval.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003cbr\\u003e\\u003c/strong\\u003eThe author would like to express their sincere gratitude to the principal investigators, staff, and participants of the Osteoporotic Fractures in Men (MrOS) Study for their invaluable contribution to this research. Without their dedication and effort, this work would not have been possible. 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\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eThompson A. Thinking big: large-scale collaborative research in observational epidemiology. Eur J Epidemiol 2009;24:727\\u0026ndash;31. https://doi.org/10.1007/s10654-009-9412-1.\\u003c/li\\u003e\\n\\u003cli\\u003eEgleston BL, Miller SM, Meropol NJ. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects. Stat Med 2011;30:3560\\u0026ndash;72. https://doi.org/10.1002/sim.4377.\\u003c/li\\u003e\\n\\u003cli\\u003eChen Y, Zhou E, Wang Y, Wu Y, Xu G, Chen L. The past, present, and future of sleep quality assessment and monitoring. Brain Res 2023;1810:148333. https://doi.org/10.1016/j.brainres.2023.148333.\\u003c/li\\u003e\\n\\u003cli\\u003eThe Lancet Diabetes \\u0026amp; Endocrinology. Sleep: a neglected public health issue. Lancet Diabetes Endocrinol 2024;12:365. https://doi.org/10.1016/S2213-8587(24)00132-3.\\u003c/li\\u003e\\n\\u003cli\\u003eHu W, Han Q, Chu J, Sun N, Li T, Feng Z, et al. Mechanism of the association between sleep quality and mortality in middle-aged and older adults: A prospective study analysis of the UK Biobank. Arch Gerontol Geriatr 2023;113:105051. https://doi.org/10.1016/j.archger.2023.105051.\\u003c/li\\u003e\\n\\u003cli\\u003eZimmerman ME, Benasi G, Hale C, Yeung LK, Cochran J, Brickman AM, et al. The effects of insufficient sleep and adequate sleep on cognitive function in healthy adults. Sleep Health 2024:1\\u0026ndash;8. https://doi.org/10.1016/j.sleh.2023.11.011.\\u003c/li\\u003e\\n\\u003cli\\u003eLi J, Cao D, Huang Y, Chen Z, Wang R, Dong Q, et al. Sleep duration and health outcomes: an umbrella review. Sleep and Breathing 2022;26:1479\\u0026ndash;501. https://doi.org/10.1007/s11325-021-02458-1.\\u003c/li\\u003e\\n\\u003cli\\u003eIb\\u0026aacute;\\u0026ntilde;ez V, Silva J, Cauli O. A survey on sleep questionnaires and diaries. Sleep Med 2018;42:90\\u0026ndash;6. https://doi.org/10.1016/j.sleep.2017.08.026.\\u003c/li\\u003e\\n\\u003cli\\u003ede Menezes-J\\u0026uacute;nior LAA, Carraro JCC, Machado-Coelho GLL, Meireles AL. The Pittsburgh sleep quality index-2 (PSQI-2): the validity of a two-item sleep quality screener in Brazilian adults. Sleep and Breathing 2025;29:238. https://doi.org/10.1007/s11325-025-03408-x.\\u003c/li\\u003e\\n\\u003cli\\u003eBlackwell T, Yaffe K, Ancoli-Israel S, Redline S, Ensrud KE, Stefanick ML, et al. Associations between sleep architecture and sleep-disordered breathing and cognition in older community-dwelling men: The osteoporotic fractures in men sleep study. J Am Geriatr Soc 2011;59:2217\\u0026ndash;25. https://doi.org/10.1111/j.1532-5415.2011.03731.x.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, et al. The National Sleep Research Resource: Towards a sleep data commons. Journal of the American Medical Informatics Association 2018;25:1351\\u0026ndash;8. https://doi.org/10.1093/jamia/ocy064.\\u003c/li\\u003e\\n\\u003cli\\u003eOrwoll E, Blank JB, Barrett-Connor E, Cauley J, Cummings S, Ensrud K, et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study \\u0026mdash; A large observational study of the determinants of fracture in older men. Contemp Clin Trials 2005;26:569\\u0026ndash;85. https://doi.org/10.1016/j.cct.2005.05.006.\\u003c/li\\u003e\\n\\u003cli\\u003eMehra R, Stone KL, Blackwell T, Ancoli Israel S, Dam TL, Stefanick ML, et al. Prevalence and Correlates of Sleep‐Disordered Breathing in Older Men: Osteoporotic Fractures in Men Sleep Study. J Am Geriatr Soc 2007;55:1356\\u0026ndash;64. https://doi.org/10.1111/j.1532-5415.2007.01290.x.\\u003c/li\\u003e\\n\\u003cli\\u003eBuysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193\\u0026ndash;213. https://doi.org/10.1016/0165-1781(89)90047-4.\\u003c/li\\u003e\\n\\u003cli\\u003eMancia G, Kreutz R, Brunstr\\u0026ouml;m M, Burnier M, Grassi G, Januszewicz A, et al. 2023 ESH Guidelines for the management of arterial hypertension The Task Force for the management of arterial hypertension of the European Society of Hypertension. J Hypertens 2023;41:1874\\u0026ndash;2071. https://doi.org/10.1097/HJH.0000000000003480.\\u003c/li\\u003e\\n\\u003cli\\u003eJohns MW. A New Method for Measuring Daytime Sleepiness: The Epworth Sleepiness Scale. Sleep 1991;14:540\\u0026ndash;5. https://doi.org/10.1093/sleep/14.6.540.\\u003c/li\\u003e\\n\\u003cli\\u003eWeaver TE, Laizner AM, Evans LK, Maislin G, Chugh DK, Lyon K, et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 1997;20:835\\u0026ndash;43. https://doi.org/10.1093/sleep/20.10.835.\\u003c/li\\u003e\\n\\u003cli\\u003eBastien C. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2001;2:297\\u0026ndash;307. https://doi.org/10.1016/S1389-9457(00)00065-4.\\u003c/li\\u003e\\n\\u003cli\\u003eMcDonnell LM, Hogg L, McDonnell L, White P. Pulmonary rehabilitation and sleep quality: a before and after controlled study of patients with chronic obstructive pulmonary disease. NPJ Prim Care Respir Med 2014;24:14028. https://doi.org/10.1038/npjpcrm.2014.28.\\u003c/li\\u003e\\n\\u003cli\\u003eEadie J, van de Water AT, Lonsdale C, Tully MA, van Mechelen W, Boreham CA, et al. Physiotherapy for Sleep Disturbance in People With Chronic Low Back Pain: Results of a Feasibility Randomized Controlled Trial. Arch Phys Med Rehabil 2013;94:2083\\u0026ndash;92. https://doi.org/10.1016/j.apmr.2013.04.017.\\u003c/li\\u003e\\n\\u003cli\\u003eFamodu OA, Barr ML, Hol\\u0026aacute;skov\\u0026aacute; I, Zhou W, Morrell JS, Colby SE, et al. Shortening of the Pittsburgh Sleep Quality Index Survey Using Factor Analysis. Sleep Disord 2018;2018:1\\u0026ndash;9. https://doi.org/10.1155/2018/9643937.\\u003c/li\\u003e\\n\\u003cli\\u003eSancho-Domingo C, Carballo JL, Coloma-Carmona A, Buysse DJ. Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) and measurement invariance across gender and age in a population-based sample. Psychol Assess 2021;33:111\\u0026ndash;21. https://doi.org/10.1037/pas0000959.\\u003c/li\\u003e\\n\\u003cli\\u003eWatson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, et al. Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society. Journal of Clinical Sleep Medicine 2015;11:591\\u0026ndash;2. https://doi.org/10.5664/jcsm.4758.\\u003c/li\\u003e\\n\\u003cli\\u003eAtkinson EA, Carter CMP, Rohr JLC, Smith GT. Brief instruments and short forms. APA handbook of research methods in psychology: Foundations, planning, measures, and psychometrics (Vol. 1) (2nd ed.)., Washington: American Psychological Association; 2023, p. 451\\u0026ndash;66. https://doi.org/10.1037/0000318-021.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-biological-timing-and-sleep\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [npj Biological Timing and Sleep](https://www.nature.com/npjbts)\",\"snPcode\":\"44323\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44323/3\",\"title\":\"npj Biological Timing and Sleep\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Sleep Quality, Validation Studies, Geriatrics, Reproducibility of Results, Epidemiologic Methods\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7526287/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7526287/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eThe full Pittsburgh Sleep Quality Index (PSQI) is a widely used measure of sleep quality, but can be impractical in large studies due to its length. The abbreviated two-item version (PSQI-2) is a promising alternative, yet its longitudinal psychometric properties remain underexplored in community-based cohorts of older adults.\\u003c/p\\u003e\\u003ch2\\u003eObjective\\u003c/h2\\u003e\\u003cp\\u003eTo comprehensively evaluate the cross-sectional and longitudinal validity of the PSQI-2 against the full PSQI in a cohort of older men.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eThis longitudinal analysis utilized data from 2,911 participants in the Osteoporotic Fractures in Men (MrOS) Sleep Study with complete sleep data at two visits. Cross-sectional validity was assessed using mixed-effects regression and Bland-Altman analysis. Diagnostic accuracy for poor sleep quality (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5, \\u0026gt;7, and \\u0026gt;\\u0026thinsp;10) was evaluated with Receiver Operating Characteristic (ROC) curves. Longitudinal properties included test-retest reliability (Intraclass Correlation Coefficient, ICC), correlation of change scores (Δ), and the accuracy of the PSQI-2 in detecting clinically meaningful change (ΔPSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;3) using the area under the curve (AUC).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eThe PSQI-2 showed a strong cross-sectional association with the full PSQI (β\\u0026thinsp;=\\u0026thinsp;2.08, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), explaining 72% of its variance. For identifying poor sleep quality, the PSQI-2 demonstrated excellent accuracy (AUC\\u0026thinsp;=\\u0026thinsp;0.89) with an optimal cutoff of \\u0026ge;\\u0026thinsp;2 (sensitivity\\u0026thinsp;=\\u0026thinsp;77.5%, specificity\\u0026thinsp;=\\u0026thinsp;84.5%). Longitudinally, both instruments showed moderate test-retest reliability (PSQI-2 ICC\\u0026thinsp;=\\u0026thinsp;0.579; PSQI ICC\\u0026thinsp;=\\u0026thinsp;0.639). The correlation between their change scores was strong (r\\u0026thinsp;=\\u0026thinsp;0.682, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and the PSQI-2 showed reasonable accuracy (AUC\\u0026thinsp;=\\u0026thinsp;0.716) in detecting clinically meaningful change (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;3).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThe PSQI-2 is a valid and reliable tool for cross-sectional screening of poor sleep quality in older men at the cutoff of \\u0026ge;\\u0026thinsp;2. It is also responsive to directional change over time and can identify individuals with clinically significant changes in sleep.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Psychometric properties of the two-item Pittsburgh Sleep Quality Index (PSQI-2) in a cohort of community-dwelling older men\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-20 13:41:07\",\"doi\":\"10.21203/rs.3.rs-7526287/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-12-06T14:16:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-05T22:45:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-10T16:48:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"268967000323851082087139148567913676310\",\"date\":\"2025-10-29T14:42:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"321717245367285528605406830159032517378\",\"date\":\"2025-10-07T13:25:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-07T12:50:33+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-20T11:35:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-15T18:13:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"npj Biological Timing and Sleep\",\"date\":\"2025-09-03T11:03:37+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-biological-timing-and-sleep\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [npj Biological Timing and Sleep](https://www.nature.com/npjbts)\",\"snPcode\":\"44323\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44323/3\",\"title\":\"npj Biological Timing and Sleep\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"6ef0ee67-b748-45e9-8686-412ebef1abc7\",\"owner\":[],\"postedDate\":\"October 20th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":56538369,\"name\":\"Health sciences/Diseases\"},{\"id\":56538370,\"name\":\"Health sciences/Health care\"},{\"id\":56538371,\"name\":\"Health sciences/Medical research\"}],\"tags\":[],\"updatedAt\":\"2025-12-30T19:08:21+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-20 13:41:07\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7526287\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7526287\",\"identity\":\"rs-7526287\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}