Depressive symptoms mediate the association between sleep duration and chronic low back pain in US adults: evidence from the 2009-2010 NHANES

Depressive symptoms mediate the association between sleep duration and chronic low back pain in US adults: evidence from the 2009-2010 NHANES | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Depressive symptoms mediate the association between sleep duration and chronic low back pain in US adults: evidence from the 2009-2010 NHANES Haibo Gong, Xiao Chen, Jing Chen, Yuanhe Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6311945/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between sleep duration and the risk of developing chronic low back pain (CLBP) remains unclear. This study aimed to investigate the association between sleep duration and CLBP, as well as the mediating effect of depressive symptoms. Methods Data from the 2009-2010 US National Health and Nutrition Examination Survey (NHANES) were used, including 4807 adults aged ≥18 years. Multivariable logistic regression analysis was performed to assess the relationship between sleep duration and CLBP. Mediation analysis was conducted to quantify the effect of depressive symptoms on this association. Additionally, restricted cubic splines (RCS) were used to evaluate potential nonlinear relationships. Results A total of 4807 participants were recruited, with a mean age of 49.43 ± 17.75 years, and a CLBP prevalence of 10.56% (508/4,807). Multivariable logistic regression analysis revealed that compared to the reference group, the first and third tertiles of sleep duration were associated with an increased incidence of CLBP. A nonlinear U-shaped association was found between sleep duration and CLBP, with an inflection point at 8 hours of sleep. The odds ratios (95% confidence intervals) for CLBP were 0.75 (0.70, 0.81) and 1.37 (1.14, 1.65) below and above this inflection point, respectively. Mediation analysis indicated that depressive symptoms mediated 30.56% of the association between sleep duration and CLBP. Subgroup analysis identified physical activity (interaction P = 0.007) and cardiovascular diseases (interaction P = 0.009) as interaction factors. Conclusion This study demonstrates that both short and long sleep duration are associated with an increased incidence of CLBP, following a nonlinear U-shaped pattern. This association is partially mediated by depressive symptoms, suggesting that sleep duration may be a valuable predictor of adverse prognosis for CLBP in adults. Chronic low back pain sleep duration depressive symptoms mediating effect NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chronic low back pain (CLBP) is defined as low back pain lasting more than three months, located between the lower edge of the thorax and the horizontal gluteal fold, possibly with lower limb radiation or restricted movement [1]. Approximately 20% of the global population suffers from CLBP, a leading cause of disability [2, 3]. Projections suggest that by 2050, over 800 million people worldwide will be affected by low back pain. CLBP not only impacts individual quality of life but also imposes significant economic and healthcare burdens on society [4, 5]. Traditional biomedical models have long focused on structural or biomechanical factors in CLBP etiology. However, emerging evidence highlights the crucial role of psychosocial determinants in the development and persistence of CLBP [6, 7]. Among these psychosocial determinants, sleep disturbances and depressive symptoms are increasingly recognized as relevant risk factors [8] Research indicates that short or disrupted sleep is associated with increased pain sensitivity and potential recovery delays. The underlying mechanisms may involve complex neuroinflammatory pathways and central sensitization [9, 10]. Depressive symptoms are prevalent in CLBP populations and may exacerbate pain perception through dysregulation of the serotonin and norepinephrine systems [11]. Despite these insights, the complex interplay among sleep duration, depressive symptoms, and CLBP remains poorly understood, particularly in population-based cohorts. Existing studies report inconsistent associations between sleep duration and CLBP. Some suggest a U-shaped relationship, indicating that both very short and very long sleep durations may be associated with increased CLBP risk [12, 13]. Others have identified insufficient sleep as an independent risk factor for CLBP [10, 14]. Notably, few studies have thoroughly examined whether depressive symptoms mediate this relationship, a significant research gap considering sleep deprivation often precedes depressive states, which in turn may worsen pain-related disability. This study examined the relationship between objectively measured sleep duration and CLBP, and whether depressive symptoms mediate this association. Using activity recorder data from the 2009–2010 NHANES cycle, this study addresses the limitations of self-reported sleep measures and validates the depression screening tool, providing a unique opportunity to analyse these factors in a nationally representative sample of American adults. A causal mediation analysis will quantify the contribution of depressive symptoms to the relationship between sleep and CLBP. These findings will inform targeted interventions to address sleep and mental health problems to reduce the burden of CLBP. Methods Study Design and Participants This cross-sectional study used data from the US NHANES, which assesses the health and nutritional status of the US population [15]. The study protocol was approved by the US National Center for Health Statistics Ethics Review Committee and received its latest approval in August 2022. This study adhered to the principles outlined in the Declaration of Helsinki. All participants provided written informed consent before participation. We analyzed data from the 2009–2010 NHANES cycle. Of the initial 10,537 participants, we excluded those under 18 (n = 4,010), those with missing CLBP data (n = 3), missing PHQ-9 questionnaire data (n = 981), missing sleep duration data (n = 7), and those with incomplete data for other variables (n = 729). After applying these criteria, 4807 participants were included in the final analysis. The participant selection process is illustrated in Fig. 1 . Assessment of CLBP CLBP was identified among participants who reported current pain between the lower edge of the thorax and the horizontal gluteal fold, with a pain history of almost daily occurrence for at least three months. CLBP was defined by affirmative answers to two questions: “Have you ever had low back pain, ache, or stiffness that occurred almost every day and lasted for three months or longer?” and “Do you still have low back pain, ache, or stiffness?” [8]. Assessment of sleep duration Sleep duration was assessed using the "Sleep Disorders" data from the NHANES questionnaire. Question SLQ010H asked, "How many hours of sleep do you usually get on workdays or workday nights?" with responses in hours. Participants reported sleep duration of 2–11 hours, with reports of 12 + hours recorded as 12 hours. Sleep duration was analyzed as both a continuous and categorical variable. Based on National Sleep Foundation recommendations and consensus statements from the American Academy of Sleep Medicine and the Sleep Research Society for optimal adult health, sleep duration was categorized into three groups: <7 hours, 7–9 hours, and ≥ 9 hours [16, 17]. Assessment of Depressive Symptoms The Patient Health Questionnaire-9 (PHQ-9) was used for depression diagnosis and symptom severity assessment. Comprising nine questions scored 0–3, total scores ranged from 0 to 27, with higher scores indicating more severe symptoms. Each item was scored from "0" (not at all) to "3" (nearly every day). For analyzing depressive symptom severity, scores were categorized as follows: 0–4 (no symptoms), 5–9 (mild symptoms), 10–14 (moderate symptoms), and ≥ 15 (severe symptoms) [18]. Assessment of Covariates Sociodemographic and health - related behaviors (age, sex, race, education, marital status, poverty-income ratio (PIR), alcohol use, smoking, physical activity, and sedentary time) were obtained via interview. BMI (kg/m²) was calculated from measured weight and height during the physical examination. C - reactive protein (CRP) (mg/dL) was obtained from laboratory tests. Hypertension was defined by self - reported history, antihypertensive medication use, systolic blood pressure ≥ 140 mmHg, or diastolic blood pressure ≥ 90 mmHg [19]. Diabetes was determined by self - reported diagnosis, insulin/oral hypoglycemic use, fasting plasma glucose ≥ 126 mg/dL, 2 - hour oral glucose tolerance test glucose ≥ 200 mg/dL, or hemoglobin A1c ≥ 6.5% [20]. History of cardiovascular disease (congestive heart failure, coronary heart disease, angina, heart attack, and stroke) and cancer was self - reported by participants diagnosed by health professionals. Smoking status was categorized into never (< 100 cigarettes), former (100 cigarettes and quit), and current (100 cigarettes and still smoking). Alcohol consumption was divided into drinker (≥ 12 drinks/year) and non - drinker (< 12 drinks/year). Detailed information on each variable is publicly available at www.cdc.gov/nchs/nhanes/ . Statistical Analysis All statistical analyses were performed using R software (version 4.2.2) and EmpowerStats (version 6.0). Data are presented as means with 95% confidence intervals (CI) or percentages with 95% CI. Continuous variables were expressed as means and standard deviations, while categorical variables were presented as frequencies or percentages. Missing values for the variables are listed in Table SI . Multiple imputation handled missing data for continuous variables, and mode imputation was used for categorical variables. No significant differences existed between pre - and post - imputation data, indicating the imputation procedure did not alter variable distributions. Statistical significance was set at a two - sided p - value < 0.05. The Kruskal-Wallis rank-sum test analyzed continuous variables, while Fisher's exact probability test assessed categorical variables. Multivariable linear regression across three models examined the relationships between sleep duration and CLBP, depressive symptoms and CLBP, and depressive symptoms and sleep duration. Model 1 was unadjusted; Model 2 adjusted for sex, age, and race; Model 3 included additional adjustments for marital status, education, PIR, BMI, smoking, alcohol use, physical activity, sedentary time, hypertension, diabetes, cardiovascular disease, cancer, and CRP. To explore causal pathways and provide clinical insights, the R "mediation" package was used to study how depressive symptoms potentially mediate the relationship between sleep duration and CLBP. Subgroup analyses were conducted by age, sex, race, marital status, education level, PIR, BMI, physical activity, smoking status, hypertension, diabetes, cardiovascular disease, and cancer. These stratification factors were also studied as potential effect modifiers. Finally, restricted cubic spline (RCS) models were used to study the potential dose-response relationships between sleep duration and CLBP, depressive symptoms and CLBP, and depressive symptoms and sleep duration. Results Baseline characteristics A total of 4807 participants were included and divided into two groups according to whether they had CLBP. Table 1 shows significant differences between these groups in sociodemographic, lifestyle, and health - related characteristics. Patients with CLBP were more likely to be non - Hispanic white, under 60 years old, married/living with partner, with higher education and a medium PIR. They had a higher BMI, were more likely to be current smokers, drink less than 12 cups of alcohol per year, and have no history of hypertension, diabetes, cardiovascular disease, or cancer (all P < 0.001). Additionally, CLBP patients had shorter sleep duration (P < 0.001) and a significantly higher PHQ score (P < 0.001). Table 1 Characteristics of patients with and without CLBP. Variable Chronic low back pain P-value Overall (n = 4807) No (n = 4299) Yes (n = 508) Gender,n(%) 0.076 Male 2384 (49.59%) 2151 (50.03%) 233 (45.87%) Female 2423 (50.41%) 2148 (49.97%) 275 (54.13%) Age(year),Mean (SD) 49.43 ± 17.75 49.66 ± 18.20 47.51 ± 13.27 0.035 Age,n(%) < 0.001 < 60 years 3220 (66.99%) 2837 (65.99%) 383 (75.39%) ≥ 60 years 1587 (33.01%) 1462 (34.01%) 125 (24.61%) Race,n(%) 0.093 Mexican American 829 (17.25%) 742 (17.26%) 87 (17.13%) Other Hispanic 448 (9.32%) 407 (9.47%) 41 (8.07%) Non-Hispanic White 2469 (51.36%) 2186 (50.85%) 283 (55.71%) Non-Hispanic Black 835 (17.37%) 752 (17.49%) 83 (16.34%) Other Race 226 (4.70%) 212 (4.93%) 14 (2.76%) Education level,n(%) 0.003 Below high school 1260 (26.21%) 1115 (25.94%) 145 (28.54%) High school 1121 (23.32%) 979 (22.77%) 142 (27.95%) Above high school 2426 (50.47%) 2205 (51.29%) 221 (43.50%) Marital status,n(%) 0.048 Married/living with partner 2906 (60.45%) 2609 (60.69%) 297 (58.46%) Widowed/divorced/separated 1081 (22.49%) 946 (22.01%) 135 (26.57%) Never married 820 (17.06%) 744 (17.31%) 76 (14.96%) PIR,n(%) < 0.001 < 1 1024 (21.30%) 875 (20.35%) 149 (29.33%) ≥ 1, <4 2563 (53.32%) 2303 (53.57%) 260 (51.18%) ≥ 4 1220 (25.38%) 1121 (26.08%) 99 (19.49%) BMI,n(%) < 0.001 < 25 1317 (27.40%) 1215 (28.26%) 102 (20.08%) ≥ 25, < 30 1636 (34.03%) 1479 (34.40%) 157 (30.91%) ≥ 30 1854 (38.57%) 1605 (37.33%) 249 (49.02%) Smoking status,n(%) < 0.001 Never 1046 (21.76%) 862 (20.05%) 184 (36.22%) Former 1208 (25.13%) 1075 (25.01%) 133 (26.18%) Current 2553 (53.11%) 2362 (54.94%) 191 (37.60%) Drinking status,n(%) 0.023 < 12 drinks/year 3556 (73.98%) 3159 (73.48%) 397 (78.15%) ≥ 12 drinks/year 1251 (26.02%) 1140 (26.52%) 111 (21.85%) Physical activity,n(%) 0.151 Inactive 2860 (59.50%) 2578 (59.97%) 282 (55.51%) Moderate 344 (7.16%) 305 (7.09%) 39 (7.68%) Vigorous 1603 (33.35%) 1416 (32.94%) 187 (36.81%) Sleep duration(hours), Mean (SD) 6.83 ± 1.45 6.88 ± 1.40 6.40 ± 1.76 < 0.001 Sleep duration categorization,n(%) < 0.001 < 7 hours 1897 (39.46%) 1626 (37.82%) 271 (53.35%) ≥ 7, < 9 hours 2530 (52.63%) 2336 (54.34%) 194 (38.19%) ≥ 9 hours 380 (7.91%) 337 (7.84%) 43 (8.46%) PHQ-9 score, Mean (SD) 3.32 ± 4.35 2.96 ± 3.96 6.31 ± 6.07 < 0.001 PHQ-9 score categorization,n(%) < 0.001 < 5 3585 (74.58%) 3324 (77.32%) 261 (51.38%) ≥ 5, < 10 765 (15.91%) 646 (15.03%) 119 (23.43%) ≥ 10, < 15 285 (5.93%) 226 (5.26%) 59 (11.61%) ≥ 15 172 (3.58%) 103 (2.40%) 69 (13.58%) Sitting time(hours), Mean (SD) 5.46 ± 3.23 5.45 ± 3.21 5.58 ± 3.36 0.368 CRP(mg/dL), Mean (SD) 0.41 ± 0.73 0.39 ± 0.67 0.54 ± 1.15 < 0.001 Hypertension,n(%) 0.001 Yes 2007 (41.75%) 1761 (40.96%) 246 (48.43%) No 2800 (58.25%) 2538 (59.04%) 262 (51.57%) Diabetes,n(%) 0.040 Yes 854 (17.77%) 747 (17.38%) 107 (21.06%) No 3953 (82.23%) 3552 (82.62%) 401 (78.94%) Cardiovascular disease,n(%) 0.004 Yes 513 (10.67%) 440 (10.23%) 73 (14.37%) No 4294 (89.33%) 3859 (89.77%) 435 (85.63%) Cancer,n(%) 0.910 Yes 504 (10.48%) 450 (10.47%) 54 (10.63%) No 4303 (89.52%) 3849 (89.53%) 454 (89.37%) Data are expressed as means ± SD or percentages (%). Abbreviations: SD, standard deviation; CLBP, chronic low back pain; PIR, Poverty-income ratio; BMI, Body mass index; PHQ−9, Patient Health Questionnaire−9; CRP, C-reactive protein. Association between sleep duration and CLBP We constructed three models to assess the independent effect of sleep duration on the incidence of CLBP. The OR and 95% CI for these three models are presented in Table 2 . In the fully adjusted model (Model 3), a significant negative correlation was observed (OR = 0.83, 95% CI: 0.78–0.88, p < 0.0001), indicating that every 1-hour increase in sleep duration was associated with a 17% reduction in the incidence of CLBP. Further, sleep duration was transformed from a continuous variable to a categorical variable (tertiles). Compared with patients in the T2 sleep duration group (≥ 7 h, < 9 h), the incidence of CLBP was significantly higher in both the T1 group (< 7 h) and the T3 group (≥ 9 h), with increases of 85% (OR = 1.85, 95% CI: 1.51–2.26) and 49% (OR = 1.49, 95% CI: 1.04–2.14), respectively. Threshold effect analysis showed an inflection point at 8 h, with OR values for CLBP below and above this threshold at 0.75 (95% CI: 0.70–0.81) and 1.37 (95% CI: 1.14–1.65), respectively (Table 3 ). These results suggest a significant association between sleep duration and CLBP, highlighting the potential role of both short and long sleep durations in CLBP. Table 2 The relationship between sleep duration and CLBP. Exposure Model 1 Model 2 Model 3 OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value Sleep duration 0.80 (0.75,0.85) < 0.0001 0.79(0.74,0.84) < 0.0001 0.83(0.78,0.88) < 0.0001 Sleep duration categorical T1 (< 7h) 2.01 (1.65, 2.44) < 0.0001 2.12(1.74,2.58) < 0.0001 1.85(1.51,2.26) = 7h, = 9h) 1.54 (1.08, 2.18) 0.0160 1.59(1.12,2.25) 0.0100 1.49(1.04,2.14) 0.0291 Model 1 adjust for: none Model 2 adjust for: gender,age, race Model 3 adjust for: gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP Table 3 Analysis of the threshold effect of sleep duration on CLBP. Exposure Adjusted OR (95% CI) P-value Sleep duration Fitting by the standard linear model 0.83 (0.78, 0.88) < 0.0001 Fitting by the two-piecewise linear model Inflection point 8 Sleep duration < 8 0.75 (0.70, 0.81) 8 1.37 (1.14, 1.65) 0.0008 P for Log-likelihood ratio < 0.001 Adjusted for:gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP Association between depressive symptoms and sleep duration Table 4 shows the multiple regression analysis results for the relationship between depressive symptoms (PHQ-9 score) and sleep duration. Our analysis showed a significant negative correlation between PHQ-9 scores and sleep duration. In Model 3, a 1-point increase in the PHQ-9 score was associated with a 0.06 h decrease in sleep duration (OR = -0.06, 95% CI: -0.07 to -0.05, p < 0.0001). Compared with participants in the T1 group for PHQ-9 scores (< 5 points), those in the T2 group (≥ 5 points, < 10 points), T3 group (≥ 10 points, < 15 points), and T4 group (≥ 15 points) had significantly shorter sleep duration, by 0.37 h (OR = -0.37, 95% CI: -0.49 to -0.26, p < 0.0001), 0.59 h (OR = -0.59, 95% CI: -0.76 to -0.41, p < 0.0001), and 0.80 h (OR = -0.80, 95% CI: -1.02 to -0.58, p < 0.0001), respectively. This finding suggests a strong inverse relationship between depressive symptoms (PHQ-9 scores) and sleep duration, emphasizing the potential role of high PHQ-9 scores in the risk of reduced sleep duration. Table 4 The correlation between depressive symptoms(PHQ-9 score) and sleep duration. Exposure Model 1 Model 2 Model 3 OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value PHQ-9 score -0.06 (-0.07, -0.05) < 0.0001 -0.06 (-0.07, -0.05) < 0.0001 -0.06 (-0.07, -0.05) < 0.0001 PHQ-9 score categorical T1 ( = 5, < 10) -0.39 (-0.50, -0.28) < 0.0001 -0.39 (-0.51, -0.28) < 0.0001 -0.37 (-0.49, -0.26) < 0.0001 T3(≥ 10, < 15) -0.63(-0.81, -0.46) < 0.0001 -0.64 (-0.81, -0.46) < 0.0001 -0.59 (-0.76, -0.41) = 15) -0.85 (-1.07, -0.63) < 0.0001 -0.84 (-1.06, -0.62) < 0.0001 -0.80 (-1.02, -0.58) < 0.0001 Model 1 adjust for: none Model 2 adjust for: gender,age, race Model 3 adjust for: gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP Association between depressive symptoms and CLBP Table 5 presents the multiple regression analysis results for the relationship between depressive symptoms (PHQ-9 score) and CLBP. In all models, PHQ-9 scores were significantly associated with CLBP. In Model 3, a 1-point increase in PHQ-9 score was associated with a significant 12% increase in CLBP incidence (OR = 1.12, 95% CI: 1.10–1.14, p < 0.0001). Compared with the T1 group (< 5 points), the T2 group (≥ 5 points, < 10 points), T3 group (≥ 10 points, < 15 points), and T4 group (≥ 15 points) had a significantly higher CLBP incidence, with increases of 97% (OR = 1.97, 95% CI: 1.54–2.50, p < 0.0001), 171% (OR = 2.71, 95% CI: 1.95–3.77, p < 0.0001), and 550% (OR = 6.55, 95% CI: 4.59–9.34, p < 0.0001), respectively. This strong positive correlation emphasizes the potential role of depressive symptoms in CLBP. Table 5 The relationship between depressive symptoms(PHQ-9 score) and CLBP. Exposure Model 1 Model 2 Model 3 OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value PHQ-9 score 1.14 (1.12,1.16) < 0.0001 1.14 (1.12, 1.16) < 0.0001 1.12 (1.10, 1.14) < 0.0001 PHQ-9 score categorical =5, < 10 2.35 (1.86, 2.96) < 0.0001 2.32 (1.84,2.94) < 0.0001 1.97 (1.54,2.50) < 0.0001 ≥ 10, < 15 3.32 (2.43,4.55) < 0.0001 3.34 (2.44, 4.59) < 0.0001 2.71 (1.95, 3.77) =15 8.53(6.13,11.87) < 0.0001 8.69(6.21,12.14) < 0.0001 6.55 (4.59, 9.34) < 0.0001 Model 1 adjust for: none Model 2 adjust for: gender,age, race Model 3 adjust for: gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP Mediation analyses Mediation analysis showed that depressive symptoms (PHQ-9 score) significantly mediated the relationship between sleep duration and CLBP, with a mediation proportion of 30.56% (Fig. 2). This suggests that depressive symptoms may serve as a key mechanism linking reduced sleep duration to increased CLBP risk. These findings emphasize the potential importance of alleviating depressive symptoms in reducing health risks associated with sleep duration and CLBP. Subgroup After stratification by age, sex, race, BMI, education level, marital status, PIR, smoking status, physical activity, hypertension, diabetes, cancer, and cardiovascular disease, we used univariate analysis to assess the associations between sleep duration and CLBP, as well as between depressive symptoms and CLBP. The correlations were still significant across different subgroups. Moreover, these data suggest that physical activity (interaction P = 0.007) and cardiovascular disease (interaction P = 0.009) might be important confounding variables that modify the relationship between sleep duration and CLBP risk (Fig. 3), while cardiovascular disease (interaction P < 0.0001) might be a key confounder in the relationship between depressive symptoms and CLBP (Fig. 4). Restricted cubic spline After adjusting for multiple covariates, sleep duration showed a nonlinear U-shaped relationship with CLBP (Fig. 5 -A ), with an inflection point at 8 hours of sleep. Below and above this threshold, the OR values for CLBP were 0.75 (95% CI: 0.70–0.81) and 1.37 (95% CI: 1.14–1.65), respectively. In contrast, no nonlinear associations were observed between depressive symptoms (PHQ-9 score) and either sleep duration or CLBP (all p-values for nonlinearity > 0.05). As PHQ-9 scores increased, sleep duration exhibited a gradual downward trend (Fig. 5 -B ), while CLBP incidence showed a gradual upward trend (Fig. 5 -C ). Discussion This study utilized nationally representative data from the 2009–2010 NHANES. The primary aim was to comprehensively examine the mediating role of depressive symptoms in the link between sleep duration and CLBP. Specifically, this study investigated how these factors interact and what this means for understanding the complex relationships between sleep, mental health, and chronic pain in the context of lower back pain. Our results demonstrate that both short and long sleep durations are significantly associated with an increased incidence of CLBP. This suggests a clear connection between one's amount of sleep and the risk of developing chronic low back pain. Moreover, depressive symptoms partly mediated this relationship. This means that depressive symptoms are associated with both sleep duration and CLBP and also act as a bridge, influencing how changes in sleep duration lead to increased CLBP risk. These results are consistent with growing evidence emphasizing the complex interplay between sleep disturbances, psychological distress, and chronic pain. They also offer new perspectives on the potential link between sleep patterns and CLBP. This knowledge may enhance our understanding of the underlying mechanisms of CLBP development and persistence. Our observation of a U - shaped relationship between sleep duration and CLBP aligns with prior studies reporting nonlinear associations between sleep duration and various chronic pain conditions [12]. For example, previous studies have demonstrated that both excessive and insufficient sleep are associated with elevated levels of inflammatory markers such as interleukin − 6 (IL − 6) and CRP. These markers play a role in the body's inflammatory response, which may contribute to pain sensitivity and persistence [21–25]. Additionally, sleep abnormalities or deprivation are associated with dysregulation of the hypothalamic - pituitary - adrenal (HPA) axis. As a key component of the body's stress response system, HPA axis dysregulation may contribute to increased pain sensitivity and maintenance of chronic pain conditions [26, 27]. These associations have been well - documented in the studies by Heffner et al, further supporting our findings [28]. The partial mediating effect of depressive symptoms was responsible for 30.56% of the total effect, indicating that psychological mechanisms and biological pathways are significant in the relationship between sleep duration and CLBP. This aligns with the "central sensitization" model [10, 29, 30]. According to this model, sleep deprivation worsens emotional dysregulation. When emotions are not properly regulated due to insufficient sleep, pain thresholds decrease, increasing pain sensitivity and perpetuating chronic pain [31]. Additionally, depressive symptoms may have an indirect effect on CLBP through behavioral changes [32–34]. For instance, individuals with depressive symptoms might reduce physical activity or withdraw socially. These behavioral changes have a negative impact on the musculoskeletal system. Reduced physical activity results in muscle weakness and decreased flexibility, while social withdrawal restricts opportunities for positive psychosomatic stimulation. Over time, these factors worsen musculoskeletal dysfunction, increases the risk of CLBP development or worsening [35, 36]. Notably, our findings differ from those of Nicassio et al, who found a complete moderating effect of depression on the sleep-pain relationship [37–39]. This discrepancy emphasizes the potential impact of various factors such as population characteristics or pain localization. The partial mediation observed in our study suggests that sleep disorders may contribute independently to CLBP through mechanisms beyond emotional changes [40–43]. For example, during sleep, the body goes through tissue repair processes. Impaired tissue repair during sleep due to sleep disorders might result in the development or persistence of CLBP [44, 45]. Additionally, alterations in the balance of neurotransmitters like serotonin and dopamine, which are crucial for pain regulation and might be caused by sleep disorders, can affect CLBP independently [11, 39]. This emphasizes the importance of developing comprehensive interventions that address both sleep hygiene and mental health issues in CLBP management. A holistic approach that considers both physical and mental well-being might be more effective in treating and preventing chronic low back pain [8, 42, 46]. However, several limitations should be considered when interpreting our results. First, the cross-sectional design of this study has a significant drawback as it precludes causal inferences about the temporal relationships among sleep duration, depressive symptoms, and CLBP. Although our mediation analysis followed established statistical guidelines, longitudinal studies are needed to confirm the proposed pathways. Longitudinal research would allow researchers to observe changes in these variables over time, thereby more accurately determining their causal relationships. Second, the reliance on self-reported measures of sleep duration and CLBP can introduce recall bias. Self-reported data depend on individuals' memory and perception, which can be inaccurate or influenced by various factors. However, the use of a validated NHANES questionnaire helps enhance data reliability to some degree. Third, there may be unmeasured confounding factors which may affect the observed associations. For example, occupational physical demands vary among individuals and can influence both sleep quality and CLBP development. Genetic predispositions also play a role in individuals' susceptibility to pain and mental health issues, which were not adequately considered in our study. Finally, the use of single-wave NHANES data limits our ability to assess dynamic interactions across time. Changes in sleep duration, depressive symptoms, and CLBP may occur at different times, and single-wave datasets cannot capture these complex temporal relationships. Despite these limitations, our findings have important clinical implications. Identifying depressive symptoms as a modifiable mediator indicates that an integrated care model combining cognitive-behavioral therapy for insomnia (CBT-I) with depression management may optimize outcomes for CLBP patients. By simultaneously targeting sleep problems and depressive symptoms, these integrated care models can effectively reduce the severity and impact of chronic low back pain. Future research should employ longitudinal designs to explore bidirectional relationships between these variables. This would involve tracking individuals over extended periods to understand how changes in sleep duration affect depressive symptoms and CLBP as well as the reverse. Additionally, research should investigate whether optimizing sleep duration through behavioral or pharmacological interventions could reduce the incidence of CLBP, particularly in individuals with comorbid depression. This might lead to more targeted and effective preventive and treatment strategies for chronic low back pain. In conclusion, this study improves our understanding of the psychobiological pathways linking sleep patterns to CLBP. By clarifying the mediating role of depressive symptoms, it emphasizes the need for a multidisciplinary approach in chronic pain management. This emphasizes the crucial interplay between physical and mental health in chronic pain conditions, especially chronic low back pain. The results can guide targeted interventions that address both sleep and mental health issues to alleviate the burden of CLBP. This approach is consistent with the biopsychosocial framework, which emphasizes the importance of comprehensive strategies in managing chronic pain. Conclusion This study demonstrates that both short and long sleep durations are associated with an increased incidence of CLBP, following a non-linear U-shaped pattern. This relationship is partially mediated by depressive symptoms, suggesting that sleep duration may serve as a valuable predictor of adverse CLBP prognosis in adults. Clinical practice would benefit from integrating sleep optimization and psychological interventions to improve chronic low back pain outcomes. Declarations Institutional review board statement Not applicable. Authors’ contributions XC, HBG, and JC conceptualized and designed the study. XC, HBG, and JC managed, analyzed and verified the data. XC, HBG, and JC prepared the first draft. XC, HBG, and JC interpreted the data, and XC, HBG, and JC were responsible for editing and proofreading the manuscript. YHF supervised the study. All authors contributed to the critical revision of the manuscript. All authors read and approved the final manuscript. Funding No funding. Availability of data and materials The data from the National Health and Nutrition Examination Survey is openly available at https://www.cdc.gov/nchs/nhanes/index.htm. Ethics approval and consent to participate The NHANES study protocol was approved by the NCHS Research Ethics Review Board, and written informed consent was provided by all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Rehabilitation, the First People's Hospital of Neijiang, Neijiang 641000, China. 2 Department of Orthopedics, the First People's Hospital of Neijiang, Neijiang 641000, China. 3 Department of Pediatrics, The First People’s Hospital of Neijiang, Neijiang, Sichuan, China. References Urits I, Burshtein A, Sharma M, Testa L, Gold PA, Orhurhu V, et al. Low Back Pain, a Comprehensive Review: Pathophysiology, Diagnosis, and Treatment. Current pain and headache reports. 2019;23(3):23. 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Physiology & behavior. 2022;254:113880. Epub 2022/06/16. doi: 10.1016/j.physbeh.2022.113880. PubMed PMID: 35705156. Barazzetti L, Garcez A, Freitas Sant'Anna PC, Souza de Bairros F, Dias-da-Costa JS, Anselmo Olinto MT. Does sleep quality modify the relationship between common mental disorders and chronic low back pain in adult women? Sleep medicine. 2022;96:132-9. Epub 2022/06/07. doi: 10.1016/j.sleep.2022.05.006. PubMed PMID: 35661055. Zambelli Z, Halstead EJ, Fidalgo AR, Dimitriou D. Good Sleep Quality Improves the Relationship Between Pain and Depression Among Individuals With Chronic Pain. Frontiers in psychology. 2021;12:668930. Epub 2021/05/25. doi: 10.3389/fpsyg.2021.668930. PubMed PMID: 34025533; PubMed Central PMCID: PMCPmc8138032. Kelly GA, Blake C, Power CK, O'Keeffe D, Fullen BM. The association between chronic low back pain and sleep: a systematic review. The Clinical journal of pain. 2011;27(2):169-81. Epub 2010/09/16. doi: 10.1097/AJP.0b013e3181f3bdd5. PubMed PMID: 20842008. Tong Y, Zhang XQ, Zhou HY. Chronic Low Back Pain and Sleep Disturbance in Adults in the US: The NHANES 2009-2010 Study. Pain physician. 2024;27(2):E255-e62. Epub 2024/02/07. PubMed PMID: 38324791. Kao YC, Chen JY, Chen HH, Liao KW, Huang SS. The association between depression and chronic lower back pain from disc degeneration and herniation of the lumbar spine. 2022;57(2):165-77. doi: 10.1177/00912174211003760. PubMed PMID: 33840233. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:46:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3952036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6311945/v1/4ae0fa9a-5f20-45d8-aa90-6457bb6af658.pdf"},{"id":83772594,"identity":"e8ae2673-32a1-4e18-af3f-e8c52868825f","added_by":"auto","created_at":"2025-06-02 12:53:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14130,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6311945/v1/cee51ebcee09b67fbe15f2da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Depressive symptoms mediate the association between sleep duration and chronic low back pain in US adults: evidence from the 2009-2010 NHANES","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic low back pain (CLBP) is defined as low back pain lasting more than three months, located between the lower edge of the thorax and the horizontal gluteal fold, possibly with lower limb radiation or restricted movement [1]. Approximately 20% of the global population suffers from CLBP, a leading cause of disability [2, 3]. Projections suggest that by 2050, over 800\u0026nbsp;million people worldwide will be affected by low back pain. CLBP not only impacts individual quality of life but also imposes significant economic and healthcare burdens on society [4, 5]. Traditional biomedical models have long focused on structural or biomechanical factors in CLBP etiology. However, emerging evidence highlights the crucial role of psychosocial determinants in the development and persistence of CLBP [6, 7].\u003c/p\u003e \u003cp\u003eAmong these psychosocial determinants, sleep disturbances and depressive symptoms are increasingly recognized as relevant risk factors [8] Research indicates that short or disrupted sleep is associated with increased pain sensitivity and potential recovery delays. The underlying mechanisms may involve complex neuroinflammatory pathways and central sensitization [9, 10]. Depressive symptoms are prevalent in CLBP populations and may exacerbate pain perception through dysregulation of the serotonin and norepinephrine systems [11]. Despite these insights, the complex interplay among sleep duration, depressive symptoms, and CLBP remains poorly understood, particularly in population-based cohorts. Existing studies report inconsistent associations between sleep duration and CLBP. Some suggest a U-shaped relationship, indicating that both very short and very long sleep durations may be associated with increased CLBP risk [12, 13]. Others have identified insufficient sleep as an independent risk factor for CLBP [10, 14]. Notably, few studies have thoroughly examined whether depressive symptoms mediate this relationship, a significant research gap considering sleep deprivation often precedes depressive states, which in turn may worsen pain-related disability.\u003c/p\u003e \u003cp\u003eThis study examined the relationship between objectively measured sleep duration and CLBP, and whether depressive symptoms mediate this association. Using activity recorder data from the 2009\u0026ndash;2010 NHANES cycle, this study addresses the limitations of self-reported sleep measures and validates the depression screening tool, providing a unique opportunity to analyse these factors in a nationally representative sample of American adults. A causal mediation analysis will quantify the contribution of depressive symptoms to the relationship between sleep and CLBP. These findings will inform targeted interventions to address sleep and mental health problems to reduce the burden of CLBP.\u003c/p\u003e"},{"header":"Methods","content":" \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used data from the US NHANES, which assesses the health and nutritional status of the US population [15]. The study protocol was approved by the US National Center for Health Statistics Ethics Review Committee and received its latest approval in August 2022. This study adhered to the principles outlined in the Declaration of Helsinki. All participants provided written informed consent before participation. We analyzed data from the 2009\u0026ndash;2010 NHANES cycle. Of the initial 10,537 participants, we excluded those under 18 (n\u0026thinsp;=\u0026thinsp;4,010), those with missing CLBP data (n\u0026thinsp;=\u0026thinsp;3), missing PHQ-9 questionnaire data (n\u0026thinsp;=\u0026thinsp;981), missing sleep duration data (n\u0026thinsp;=\u0026thinsp;7), and those with incomplete data for other variables (n\u0026thinsp;=\u0026thinsp;729). After applying these criteria, 4807 participants were included in the final analysis. The participant selection process is illustrated in \u003cb\u003eFig. 1\u003c/b\u003e.\u003c/p\u003e \n\u003ch3\u003eAssessment of CLBP\u003c/h3\u003e\n\u003cp\u003eCLBP was identified among participants who reported current pain between the lower edge of the thorax and the horizontal gluteal fold, with a pain history of almost daily occurrence for at least three months. CLBP was defined by affirmative answers to two questions: \u0026ldquo;Have you ever had low back pain, ache, or stiffness that occurred almost every day and lasted for three months or longer?\u0026rdquo; and \u0026ldquo;Do you still have low back pain, ache, or stiffness?\u0026rdquo; [8].\u003c/p\u003e\n\u003ch3\u003eAssessment of sleep duration\u003c/h3\u003e\n\u003cp\u003eSleep duration was assessed using the \"Sleep Disorders\" data from the NHANES questionnaire. Question SLQ010H asked, \"How many hours of sleep do you usually get on workdays or workday nights?\" with responses in hours. Participants reported sleep duration of 2\u0026ndash;11 hours, with reports of 12\u0026thinsp;+\u0026thinsp;hours recorded as 12 hours. Sleep duration was analyzed as both a continuous and categorical variable. Based on National Sleep Foundation recommendations and consensus statements from the American Academy of Sleep Medicine and the Sleep Research Society for optimal adult health, sleep duration was categorized into three groups: \u0026lt;7 hours, 7\u0026ndash;9 hours, and \u0026ge;\u0026thinsp;9 hours [16, 17].\u003c/p\u003e\n\u003ch3\u003eAssessment of Depressive Symptoms\u003c/h3\u003e\n\u003cp\u003eThe Patient Health Questionnaire-9 (PHQ-9) was used for depression diagnosis and symptom severity assessment. Comprising nine questions scored 0\u0026ndash;3, total scores ranged from 0 to 27, with higher scores indicating more severe symptoms. Each item was scored from \"0\" (not at all) to \"3\" (nearly every day). For analyzing depressive symptom severity, scores were categorized as follows: 0\u0026ndash;4 (no symptoms), 5\u0026ndash;9 (mild symptoms), 10\u0026ndash;14 (moderate symptoms), and \u0026ge;\u0026thinsp;15 (severe symptoms) [18].\u003c/p\u003e\n\u003ch3\u003eAssessment of Covariates\u003c/h3\u003e\n\u003cp\u003eSociodemographic and health - related behaviors (age, sex, race, education, marital status, poverty-income ratio (PIR), alcohol use, smoking, physical activity, and sedentary time) were obtained via interview. BMI (kg/m\u0026sup2;) was calculated from measured weight and height during the physical examination. C - reactive protein (CRP) (mg/dL) was obtained from laboratory tests. Hypertension was defined by self - reported history, antihypertensive medication use, systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg [19]. Diabetes was determined by self - reported diagnosis, insulin/oral hypoglycemic use, fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, 2 - hour oral glucose tolerance test glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, or hemoglobin A1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% [20]. History of cardiovascular disease (congestive heart failure, coronary heart disease, angina, heart attack, and stroke) and cancer was self - reported by participants diagnosed by health professionals. Smoking status was categorized into never (\u0026lt;\u0026thinsp;100 cigarettes), former (100 cigarettes and quit), and current (100 cigarettes and still smoking). Alcohol consumption was divided into drinker (\u0026ge;\u0026thinsp;12 drinks/year) and non - drinker (\u0026lt;\u0026thinsp;12 drinks/year). Detailed information on each variable is publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.2.2) and EmpowerStats (version 6.0). Data are presented as means with 95% confidence intervals (CI) or percentages with 95% CI. Continuous variables were expressed as means and standard deviations, while categorical variables were presented as frequencies or percentages. Missing values for the variables are listed in \u003cb\u003eTable SI\u003c/b\u003e. Multiple imputation handled missing data for continuous variables, and mode imputation was used for categorical variables. No significant differences existed between pre - and post - imputation data, indicating the imputation procedure did not alter variable distributions. Statistical significance was set at a two - sided p - value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe Kruskal-Wallis rank-sum test analyzed continuous variables, while Fisher's exact probability test assessed categorical variables. Multivariable linear regression across three models examined the relationships between sleep duration and CLBP, depressive symptoms and CLBP, and depressive symptoms and sleep duration. Model 1 was unadjusted; Model 2 adjusted for sex, age, and race; Model 3 included additional adjustments for marital status, education, PIR, BMI, smoking, alcohol use, physical activity, sedentary time, hypertension, diabetes, cardiovascular disease, cancer, and CRP.\u003c/p\u003e \u003cp\u003eTo explore causal pathways and provide clinical insights, the R \"mediation\" package was used to study how depressive symptoms potentially mediate the relationship between sleep duration and CLBP. Subgroup analyses were conducted by age, sex, race, marital status, education level, PIR, BMI, physical activity, smoking status, hypertension, diabetes, cardiovascular disease, and cancer. These stratification factors were also studied as potential effect modifiers. Finally, restricted cubic spline (RCS) models were used to study the potential dose-response relationships between sleep duration and CLBP, depressive symptoms and CLBP, and depressive symptoms and sleep duration.\u003c/p\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 4807 participants were included and divided into two groups according to whether they had CLBP. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows significant differences between these groups in sociodemographic, lifestyle, and health - related characteristics. Patients with CLBP were more likely to be non - Hispanic white, under 60 years old, married/living with partner, with higher education and a medium PIR. They had a higher BMI, were more likely to be current smokers, drink less than 12 cups of alcohol per year, and have no history of hypertension, diabetes, cardiovascular disease, or cancer (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, CLBP patients had shorter sleep duration (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significantly higher PHQ score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of patients with and without CLBP.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChronic low back pain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;4807)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4299)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;508)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2384 (49.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2151 (50.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233 (45.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2423 (50.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2148 (49.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275 (54.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(year),Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.43\u0026thinsp;\u0026plusmn;\u0026thinsp;17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.66\u0026thinsp;\u0026plusmn;\u0026thinsp;18.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.51\u0026thinsp;\u0026plusmn;\u0026thinsp;13.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3220 (66.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2837 (65.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e383 (75.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1587 (33.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1462 (34.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125 (24.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e829 (17.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e742 (17.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87 (17.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448 (9.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e407 (9.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (8.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2469 (51.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2186 (50.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e283 (55.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e835 (17.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e752 (17.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (16.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226 (4.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212 (4.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (2.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1260 (26.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1115 (25.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145 (28.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1121 (23.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e979 (22.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142 (27.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2426 (50.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2205 (51.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221 (43.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2906 (60.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2609 (60.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e297 (58.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1081 (22.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e946 (22.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135 (26.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e820 (17.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e744 (17.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76 (14.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1024 (21.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e875 (20.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149 (29.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1, \u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2563 (53.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2303 (53.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260 (51.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1220 (25.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1121 (26.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99 (19.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1317 (27.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1215 (28.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 (20.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;25, \u0026lt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1636 (34.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1479 (34.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157 (30.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1854 (38.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1605 (37.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249 (49.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1046 (21.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e862 (20.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184 (36.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1208 (25.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1075 (25.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133 (26.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2553 (53.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2362 (54.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191 (37.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking status,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;12 drinks/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3556 (73.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3159 (73.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e397 (78.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;12 drinks/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1251 (26.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1140 (26.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111 (21.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical activity,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2860 (59.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2578 (59.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282 (55.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344 (7.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e305 (7.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (7.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1603 (33.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1416 (32.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187 (36.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration(hours), Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration categorization,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;7 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1897 (39.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1626 (37.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271 (53.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;7, \u0026lt;\u0026thinsp;9 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2530 (52.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2336 (54.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194 (38.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;9 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e380 (7.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337 (7.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (8.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9 score, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9 score categorization,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3585 (74.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3324 (77.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261 (51.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5, \u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e765 (15.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e646 (15.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119 (23.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10, \u0026lt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285 (5.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226 (5.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 (11.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172 (3.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103 (2.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (13.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSitting time(hours), Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP(mg/dL),\u003c/p\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2007 (41.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1761 (40.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246 (48.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2800 (58.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2538 (59.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262 (51.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e854 (17.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e747 (17.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107 (21.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3953 (82.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3552 (82.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e401 (78.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiovascular disease,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e513 (10.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e440 (10.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (14.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4294 (89.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3859 (89.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e435 (85.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504 (10.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e450 (10.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (10.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4303 (89.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3849 (89.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454 (89.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eData are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or percentages (%).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: SD, standard deviation; CLBP, chronic low back pain; PIR, Poverty-income ratio; BMI, Body mass index; PHQ\u0026minus;9, Patient Health Questionnaire\u0026minus;9; CRP, C-reactive protein.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between sleep duration and CLBP\u003c/h2\u003e\n \u003cp\u003eWe constructed three models to assess the independent effect of sleep duration on the incidence of CLBP. The OR and 95% CI for these three models are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. In the fully adjusted model (Model 3), a significant negative correlation was observed (OR\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.78\u0026ndash;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that every 1-hour increase in sleep duration was associated with a 17% reduction in the incidence of CLBP. Further, sleep duration was transformed from a continuous variable to a categorical variable (tertiles). Compared with patients in the T2 sleep duration group (\u0026ge;\u0026thinsp;7 h, \u0026lt;\u0026thinsp;9 h), the incidence of CLBP was significantly higher in both the T1 group (\u0026lt;\u0026thinsp;7 h) and the T3 group (\u0026ge;\u0026thinsp;9 h), with increases of 85% (OR\u0026thinsp;=\u0026thinsp;1.85, 95% CI: 1.51\u0026ndash;2.26) and 49% (OR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.04\u0026ndash;2.14), respectively. Threshold effect analysis showed an inflection point at 8 h, with OR values for CLBP below and above this threshold at 0.75 (95% CI: 0.70\u0026ndash;0.81) and 1.37 (95% CI: 1.14\u0026ndash;1.65), respectively (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These results suggest a significant association between sleep duration and CLBP, highlighting the potential role of both short and long sleep durations in CLBP.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relationship between sleep duration and CLBP.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80 (0.75,0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79(0.74,0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83(0.78,0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration categorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 (\u0026lt;\u0026thinsp;7h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01 (1.65, 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12(1.74,2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85(1.51,2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2 (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;7h, \u0026lt;9h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3 (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;9h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54 (1.08, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59(1.12,2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49(1.04,2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 1 adjust for: none\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 2 adjust for: gender,age, race\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 3 adjust for: gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of the threshold effect of sleep duration on CLBP.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting by the standard linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.78, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting by the two-piecewise linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration\u0026thinsp;\u0026lt;\u0026thinsp;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.70, 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration\u0026thinsp;\u0026gt;\u0026thinsp;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37 (1.14, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026nbsp;for Log-likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eAdjusted for:gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between depressive symptoms and sleep duration\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the multiple regression analysis results for the relationship between depressive symptoms (PHQ-9 score) and sleep duration. Our analysis showed a significant negative correlation between PHQ-9 scores and sleep duration. In Model 3, a 1-point increase in the PHQ-9 score was associated with a 0.06 h decrease in sleep duration (OR = -0.06, 95% CI: -0.07 to -0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Compared with participants in the T1 group for PHQ-9 scores (\u0026lt;\u0026thinsp;5 points), those in the T2 group (\u0026ge;\u0026thinsp;5 points, \u0026lt;\u0026thinsp;10 points), T3 group (\u0026ge;\u0026thinsp;10 points, \u0026lt;\u0026thinsp;15 points), and T4 group (\u0026ge;\u0026thinsp;15 points) had significantly shorter sleep duration, by 0.37 h (OR = -0.37, 95% CI: -0.49 to -0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), 0.59 h (OR = -0.59, 95% CI: -0.76 to -0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and 0.80 h (OR = -0.80, 95% CI: -1.02 to -0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), respectively. This finding suggests a strong inverse relationship between depressive symptoms (PHQ-9 scores) and sleep duration, emphasizing the potential role of high PHQ-9 scores in the risk of reduced sleep duration.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe correlation between depressive symptoms(PHQ-9 score) and sleep duration.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.07, -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.07, -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.07, -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9 score\u003c/p\u003e\n \u003cp\u003ecategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 (\u0026lt;\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2 (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5, \u0026lt;\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39 (-0.50, -0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39 (-0.51, -0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37 (-0.49, -0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3(\u0026ge;\u0026thinsp;10, \u0026lt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.63(-0.81, -0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.64 (-0.81, -0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.59 (-0.76, -0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.85 (-1.07, -0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.84 (-1.06, -0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.80 (-1.02, -0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 1 adjust for: none\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 2 adjust for: gender,age, race\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 3 adjust for: gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between depressive symptoms and CLBP\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the multiple regression analysis results for the relationship between depressive symptoms (PHQ-9 score) and CLBP. In all models, PHQ-9 scores were significantly associated with CLBP. In Model 3, a 1-point increase in PHQ-9 score was associated with a significant 12% increase in CLBP incidence (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.10\u0026ndash;1.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Compared with the T1 group (\u0026lt;\u0026thinsp;5 points), the T2 group (\u0026ge;\u0026thinsp;5 points, \u0026lt;\u0026thinsp;10 points), T3 group (\u0026ge;\u0026thinsp;10 points, \u0026lt;\u0026thinsp;15 points), and T4 group (\u0026ge;\u0026thinsp;15 points) had a significantly higher CLBP incidence, with increases of 97% (OR\u0026thinsp;=\u0026thinsp;1.97, 95% CI: 1.54\u0026ndash;2.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), 171% (OR\u0026thinsp;=\u0026thinsp;2.71, 95% CI: 1.95\u0026ndash;3.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and 550% (OR\u0026thinsp;=\u0026thinsp;6.55, 95% CI: 4.59\u0026ndash;9.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), respectively. This strong positive correlation emphasizes the potential role of depressive symptoms in CLBP.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relationship between depressive symptoms(PHQ-9 score) and CLBP.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (1.12,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (1.12, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (1.10, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ-9 score categorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=5, \u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35 (1.86, 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32 (1.84,2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97 (1.54,2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10, \u0026lt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.32 (2.43,4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34 (2.44, 4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71 (1.95, 3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.53(6.13,11.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.69(6.21,12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.55 (4.59, 9.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 1 adjust for: none\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 2 adjust for: gender,age, race\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel 3 adjust for: gender; age; race; education; marital.status; PIR; drinking.status; BMI; hypertension; diabetes; cardiovascular disease; cancer; smoking.status; sitting time; physical activity; CRP\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eMediation analyses\u003c/h2\u003e\n \u003cp\u003eMediation analysis showed that depressive symptoms (PHQ-9 score) significantly mediated the relationship between sleep duration and CLBP, with a mediation proportion of 30.56% (Fig.\u0026nbsp;2). This suggests that depressive symptoms may serve as a key mechanism linking reduced sleep duration to increased CLBP risk. These findings emphasize the potential importance of alleviating depressive symptoms in reducing health risks associated with sleep duration and CLBP.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup\u003c/h2\u003e\n \u003cp\u003eAfter stratification by age, sex, race, BMI, education level, marital status, PIR, smoking status, physical activity, hypertension, diabetes, cancer, and cardiovascular disease, we used univariate analysis to assess the associations between sleep duration and CLBP, as well as between depressive symptoms and CLBP. The correlations were still significant across different subgroups. Moreover, these data suggest that physical activity (interaction P\u0026thinsp;=\u0026thinsp;0.007) and cardiovascular disease (interaction P\u0026thinsp;=\u0026thinsp;0.009) might be important confounding variables that modify the relationship between sleep duration and CLBP risk (Fig.\u0026nbsp;3), while cardiovascular disease (interaction P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) might be a key confounder in the relationship between depressive symptoms and CLBP (Fig.\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eRestricted cubic spline\u003c/h2\u003e\n \u003cp\u003eAfter adjusting for multiple covariates, sleep duration showed a nonlinear U-shaped relationship with CLBP (Fig.\u0026nbsp;5\u003cstrong\u003e-A\u003c/strong\u003e), with an inflection point at 8 hours of sleep. Below and above this threshold, the OR values for CLBP were 0.75 (95% CI: 0.70\u0026ndash;0.81) and 1.37 (95% CI: 1.14\u0026ndash;1.65), respectively. In contrast, no nonlinear associations were observed between depressive symptoms (PHQ-9 score) and either sleep duration or CLBP (all p-values for nonlinearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05). As PHQ-9 scores increased, sleep duration exhibited a gradual downward trend (Fig.\u0026nbsp;5\u003cstrong\u003e-B\u003c/strong\u003e), while CLBP incidence showed a gradual upward trend (Fig.\u0026nbsp;5\u003cstrong\u003e-C\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized nationally representative data from the 2009\u0026ndash;2010 NHANES. The primary aim was to comprehensively examine the mediating role of depressive symptoms in the link between sleep duration and CLBP. Specifically, this study investigated how these factors interact and what this means for understanding the complex relationships between sleep, mental health, and chronic pain in the context of lower back pain. Our results demonstrate that both short and long sleep durations are significantly associated with an increased incidence of CLBP. This suggests a clear connection between one's amount of sleep and the risk of developing chronic low back pain. Moreover, depressive symptoms partly mediated this relationship. This means that depressive symptoms are associated with both sleep duration and CLBP and also act as a bridge, influencing how changes in sleep duration lead to increased CLBP risk. These results are consistent with growing evidence emphasizing the complex interplay between sleep disturbances, psychological distress, and chronic pain. They also offer new perspectives on the potential link between sleep patterns and CLBP. This knowledge may enhance our understanding of the underlying mechanisms of CLBP development and persistence.\u003c/p\u003e \u003cp\u003eOur observation of a U - shaped relationship between sleep duration and CLBP aligns with prior studies reporting nonlinear associations between sleep duration and various chronic pain conditions [12]. For example, previous studies have demonstrated that both excessive and insufficient sleep are associated with elevated levels of inflammatory markers such as interleukin \u0026minus;\u0026thinsp;6 (IL \u0026minus;\u0026thinsp;6) and CRP. These markers play a role in the body's inflammatory response, which may contribute to pain sensitivity and persistence [21\u0026ndash;25]. Additionally, sleep abnormalities or deprivation are associated with dysregulation of the hypothalamic - pituitary - adrenal (HPA) axis. As a key component of the body's stress response system, HPA axis dysregulation may contribute to increased pain sensitivity and maintenance of chronic pain conditions [26, 27]. These associations have been well - documented in the studies by Heffner et al, further supporting our findings [28].\u003c/p\u003e \u003cp\u003eThe partial mediating effect of depressive symptoms was responsible for 30.56% of the total effect, indicating that psychological mechanisms and biological pathways are significant in the relationship between sleep duration and CLBP. This aligns with the \"central sensitization\" model [10, 29, 30]. According to this model, sleep deprivation worsens emotional dysregulation. When emotions are not properly regulated due to insufficient sleep, pain thresholds decrease, increasing pain sensitivity and perpetuating chronic pain [31]. Additionally, depressive symptoms may have an indirect effect on CLBP through behavioral changes [32\u0026ndash;34]. For instance, individuals with depressive symptoms might reduce physical activity or withdraw socially. These behavioral changes have a negative impact on the musculoskeletal system. Reduced physical activity results in muscle weakness and decreased flexibility, while social withdrawal restricts opportunities for positive psychosomatic stimulation. Over time, these factors worsen musculoskeletal dysfunction, increases the risk of CLBP development or worsening [35, 36].\u003c/p\u003e \u003cp\u003eNotably, our findings differ from those of Nicassio et al, who found a complete moderating effect of depression on the sleep-pain relationship [37\u0026ndash;39]. This discrepancy emphasizes the potential impact of various factors such as population characteristics or pain localization. The partial mediation observed in our study suggests that sleep disorders may contribute independently to CLBP through mechanisms beyond emotional changes [40\u0026ndash;43]. For example, during sleep, the body goes through tissue repair processes. Impaired tissue repair during sleep due to sleep disorders might result in the development or persistence of CLBP [44, 45]. Additionally, alterations in the balance of neurotransmitters like serotonin and dopamine, which are crucial for pain regulation and might be caused by sleep disorders, can affect CLBP independently [11, 39]. This emphasizes the importance of developing comprehensive interventions that address both sleep hygiene and mental health issues in CLBP management. A holistic approach that considers both physical and mental well-being might be more effective in treating and preventing chronic low back pain [8, 42, 46].\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered when interpreting our results. First, the cross-sectional design of this study has a significant drawback as it precludes causal inferences about the temporal relationships among sleep duration, depressive symptoms, and CLBP. Although our mediation analysis followed established statistical guidelines, longitudinal studies are needed to confirm the proposed pathways. Longitudinal research would allow researchers to observe changes in these variables over time, thereby more accurately determining their causal relationships. Second, the reliance on self-reported measures of sleep duration and CLBP can introduce recall bias. Self-reported data depend on individuals' memory and perception, which can be inaccurate or influenced by various factors. However, the use of a validated NHANES questionnaire helps enhance data reliability to some degree. Third, there may be unmeasured confounding factors which may affect the observed associations. For example, occupational physical demands vary among individuals and can influence both sleep quality and CLBP development. Genetic predispositions also play a role in individuals' susceptibility to pain and mental health issues, which were not adequately considered in our study. Finally, the use of single-wave NHANES data limits our ability to assess dynamic interactions across time. Changes in sleep duration, depressive symptoms, and CLBP may occur at different times, and single-wave datasets cannot capture these complex temporal relationships.\u003c/p\u003e \u003cp\u003eDespite these limitations, our findings have important clinical implications. Identifying depressive symptoms as a modifiable mediator indicates that an integrated care model combining cognitive-behavioral therapy for insomnia (CBT-I) with depression management may optimize outcomes for CLBP patients. By simultaneously targeting sleep problems and depressive symptoms, these integrated care models can effectively reduce the severity and impact of chronic low back pain. Future research should employ longitudinal designs to explore bidirectional relationships between these variables. This would involve tracking individuals over extended periods to understand how changes in sleep duration affect depressive symptoms and CLBP as well as the reverse. Additionally, research should investigate whether optimizing sleep duration through behavioral or pharmacological interventions could reduce the incidence of CLBP, particularly in individuals with comorbid depression. This might lead to more targeted and effective preventive and treatment strategies for chronic low back pain. In conclusion, this study improves our understanding of the psychobiological pathways linking sleep patterns to CLBP. By clarifying the mediating role of depressive symptoms, it emphasizes the need for a multidisciplinary approach in chronic pain management. This emphasizes the crucial interplay between physical and mental health in chronic pain conditions, especially chronic low back pain. The results can guide targeted interventions that address both sleep and mental health issues to alleviate the burden of CLBP. This approach is consistent with the biopsychosocial framework, which emphasizes the importance of comprehensive strategies in managing chronic pain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that both short and long sleep durations are associated with an increased incidence of CLBP, following a non-linear U-shaped pattern. \u0026nbsp; This relationship is partially mediated by depressive symptoms, suggesting that sleep duration may serve as a valuable predictor of adverse CLBP prognosis in adults. Clinical practice would benefit from integrating sleep optimization and psychological interventions to improve chronic low back pain outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitutional review board statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXC, HBG, and JC conceptualized and designed the study. XC, HBG, and JC managed, analyzed and verified the data. XC, HBG, and JC prepared the first draft. XC, HBG, and JC interpreted the data, and XC, HBG, and JC were responsible for editing and proofreading the manuscript. YHF supervised the study. All authors contributed to the critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from the National Health and Nutrition Examination Survey is openly available at https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES study protocol was approved by the NCHS Research Ethics Review Board, and written informed consent was provided by all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Rehabilitation, the First People\u0026apos;s Hospital of Neijiang, Neijiang 641000, China. \u003csup\u003e2\u003c/sup\u003eDepartment of Orthopedics, the First People\u0026apos;s Hospital of Neijiang, Neijiang 641000, China. \u003csup\u003e3\u003c/sup\u003eDepartment of Pediatrics, The First People\u0026rsquo;s Hospital of Neijiang, Neijiang, Sichuan, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eUrits I, Burshtein A, Sharma M, Testa L, Gold PA, Orhurhu V, et al. 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PubMed PMID: 33840233.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic low back pain, sleep duration, depressive symptoms, mediating effect, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6311945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6311945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between sleep duration and the risk of developing chronic low back pain (CLBP) remains unclear. This study aimed to investigate the association between sleep duration and CLBP, as well as the mediating effect of depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the 2009-2010 US National Health and Nutrition Examination Survey (NHANES) were used, including 4807 adults aged ≥18 years. Multivariable logistic regression analysis was performed to assess the relationship between sleep duration and CLBP. Mediation analysis was conducted to quantify the effect of depressive symptoms on this association. Additionally, restricted cubic splines (RCS) were used to evaluate potential nonlinear relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 4807 participants were recruited, with a mean age of 49.43 ± 17.75 years, and a CLBP prevalence of 10.56% (508/4,807). Multivariable logistic regression analysis revealed that compared to the reference group, the first and third tertiles of sleep duration were associated with an increased incidence of CLBP. A nonlinear U-shaped association was found between sleep duration and CLBP, with an inflection point at 8 hours of sleep. The odds ratios (95% confidence intervals) for CLBP were 0.75 (0.70, 0.81) and 1.37 (1.14, 1.65) below and above this inflection point, respectively. Mediation analysis indicated that depressive symptoms mediated 30.56% of the association between sleep duration and CLBP. Subgroup analysis identified physical activity (interaction P = 0.007) and cardiovascular diseases (interaction P = 0.009) as interaction factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrates that both short and long sleep duration are associated with an increased incidence of CLBP, following a nonlinear U-shaped pattern. This association is partially mediated by depressive symptoms, suggesting that sleep duration may be a valuable predictor of adverse prognosis for CLBP in adults.\u003c/p\u003e","manuscriptTitle":"Depressive symptoms mediate the association between sleep duration and chronic low back pain in US adults: evidence from the 2009-2010 NHANES","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 12:53:36","doi":"10.21203/rs.3.rs-6311945/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22fe44c1-d3f8-4a34-b9d7-55c9e05ee5b6","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-26T14:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 12:53:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6311945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6311945","identity":"rs-6311945","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}