A prospective study on Sleep Patterns and the Risk of Chronic pain in Middle-aged and Elderly People in China

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Methods The main cohort (n = 10,580) was constructed based on baseline data from the China Health and Retirement Longitudinal Survey (CHARLS) in 2011. Sleep patterns were evaluated using the Sleep Health Index (SMPI), and chronic pain outcomes were evaluated using follow-up data from 2013 and 2015. External validation cohorts (n = 12,615) were constructed using follow-up data from 2015 to 2018. The Cox proportional hazards regression model was used to analyze the association between sleep patterns and the risk of chronic pain onset, and the robustness of the results was verified through subgroup analysis and sensitivity analysis. Results A total of 1075 new cases of chronic pain occurred in the main cohort during the 4-year follow-up period. For every 1-point increase in the sleep health index, the risk of chronic pain in middle-aged and elderly individuals decreased significantly by 9% (fully adjusted model HR = 0.91, 95%CI:0.88–0.94, P < 0.001). Subgroup analysis showed that the protective effect of sleep patterns was significant in age < 60 years, 60–69 years, male, female, and urban population, with only marginal insignificance in age ≥ 70 years. Sensitivity analysis (excluding onset within 2 years of follow-up, complete case analysis) results were highly consistent with the main analysis (HR = 0.91–0.92, all P < 0.001). In the external validation cohort, after using the simplified covariation-adjusted strategy (adjusted only for age and gender), for every 1-point increase in the sleep health index, the risk of chronic pain onset decreased by 7% (HR = 0.93, 95%CI:0.91–0.96, P < 0.001), consistent and significant with the results of the main cohort. Conclusion Healthy sleep patterns are significantly associated with a reduced risk of chronic pain in middle-aged and elderly people in China, and improving sleep quality may be an important public health strategy for preventing chronic pain in middle-aged and elderly people. Middle-aged and elderly people Sleep patterns Chronic pain Risk of onset Figures Figure 1 Figure 2 Figure 3 1 Introduction Chronic pain is a common health problem among middle-aged and elderly people, characterized by persistent pain and recurrent episodes, which seriously affects physical function, sleep quality and quality of life of middle-aged and elderly people, and also increases the medical burden on families and society [ 1 , 2 ]. [ 5 , 6 ] With the acceleration of the aging process, the prevalence of chronic pain among middle-aged and elderly people is on the rise. The pathogenesis is complex and is influenced by various factors such as physiology, psychology and lifestyle. Among them, the association between sleep disorders and chronic pain has become a research hotspot in recent years [ 3 , 4 ][ 7 ]. Sleep is an important physiological repair process in the human body. Poor sleep quality and disrupted sleep structure may increase the risk of chronic pain by influencing mechanisms such as neurotransmitter regulation and inflammatory responses [ 8 , 9 ]. Existing studies have mostly focused on the relationship between a single sleep indicator and chronic pain, lacking a comprehensive assessment of sleep patterns, while the comprehensive sleep Pattern Index (SMPI) covers multiple core dimensions such as nocturnal sleep, napping, and sleep patterns. It provides a more comprehensive reflection of an individual's sleep health [ 10 , 11 ]. CHARLS is a national longitudinal survey of Chinese residents aged 45 and above, aiming to establish a high-quality, representative and publicly accessible microdatabase. The database provides comprehensive information on middle-aged and elderly people. The subjects were sampled using the multi-stage probability Proportional scale (PPS) sampling technique from 28 provinces in China. The data for this study were derived from the China Health and Retirement Tracking Survey (CHARLS). The initial sample was obtained by combining data from nine core modules, including blood tests, health status and function, demographic background, work retirement and pensions, health insurance, family members, biomarkers, family information, and family transfers. The data included a wealth of sleep-related indicators, pain-related information, and covariates such as demographics, lifestyle, and chronic diseases. It provides good data support for exploring the relationship between SMPI and chronic pain in middle-aged and elderly people. Construct SMPI, systematically analyze its correlation with chronic pain in middle-aged and elderly people and population heterogeneity, and combine sample screening logic, mechanism exploration, methodological discussion and research limitations to provide a scientific basis for precise prevention and individualized intervention of chronic pain in middle-aged and elderly people [ 12 ]. 2 Research Methods 2.1 Research Subjects Data from the China Health and Retirement Longitudinal Survey (CHARLS). CHARLS is a national longitudinal survey of Chinese residents aged 45 and above, designed to generate high-quality, representative and publicly accessible microdatabases. The database provides comprehensive information on middle-aged and elderly people. The subjects were sampled using the multi-stage probability Proportional scale (PPS) sampling technique from 28 provinces in China. The data for this study were derived from the 2015 China Health and Retirement Follow-up Survey (CHARLS). The initial sample was obtained by combining data from nine core modules, including blood tests, health status and function, demographic background, work retirement and pensions, health insurance, family members, biomarkers, family information, and family transfers. A total of 10,580 valid samples that met the study criteria were obtained through subsequent data merging, variable construction, and rigorous screening [13]. 2.2 Data sources and processing 2.2.1 Data Merging and variable Construction This study constructed two cohorts respectively: ① Main analysis cohort: With 2011 as the baseline investigation period and 2013 and 2015 as the follow-up periods, the research subjects who had no chronic pain at baseline, completed the follow-up and had complete core variables were included; ② External validation cohort: With 2015 as the baseline survey period and 2018 as the follow-up period, using the same inclusion and exclusion criteria as the main cohort [14]. 2.2 Data sources and processing Data processing was carried out using R software (version 4.3.0), and data standardization and variable construction were completed through custom functions as follows: ① Outcome variable: Chronic pain was standardized based on the characteristics of CHARLS survey data from different years: The 2011 and 2015 surveys included the variable of duration of pain (da041), and chronic pain was defined as "Do you often suffer from body pain? 1.Yes 2.No and which parts of your body do you feel pain? Please list all parts. 1.(Headache) head 2. Shoulder 3. Arm 4. Wrist 5. Finger 6. Chest 7. Stomach 8. Back 9. Waist 10. Hip 11. Leg 12. Knee 13. Ankle 14. Toe 15. Neck 16. Other areas with ≥1 pain site" Assign values of 1 (with chronic pain) and 0 (without chronic pain), where the number of pain sites is calculated by 16 pain site variables (da042_s1 to da042_s16); In the 2013 and 2018 surveys, there was no distress due to physical pain (da041), which was uniformly broadened to "the presence of ≥1 pain site" and assigned values of 1 (with chronic pain) and 0 (without chronic pain). The outcomes of the primary cohort were evaluated in 2013 (with a 2-year follow-up) and 2015 (with a 4-year follow-up), while those of the external validation cohort were evaluated in 2018 (with a 3-year follow-up). ② Exposure variables: The Comprehensive Sleep Pattern Index is a multi-dimensional quantitative assessment tool developed for sleep health in middle-aged and elderly people, aiming to break through the limitations of a single sleep indicator and comprehensively reflect the overall health level of an individual's sleep pattern. According to CHARLS '2015 data, how many hours did you sleep on average each night in the past month? ; How long have you typically napped over the past month? Based on these questions [15]. The index is epidemiological evidence of the physiological characteristics of sleep and the relationship between sleep and health in middle-aged and elderly people. It integrates three core sleep dimensions for scoring on a scale of 0 to 7, with higher scores indicating healthier sleep patterns. The specific dimensions and scoring logic are as follows: Nighttime sleep score (1-3 points), with 7-8 hours as optimal state (3 points), 6-7 hours or 8-9 hours as appropriate state (2 points), and less than 6 hours or more than 9 hours as abnormal state (1 point); Nap score (0-2 points), 15-60 minutes is appropriate duration (2 points), less than 15 minutes or more than 60 minutes is abnormal duration (1 point), no nap is 0 points; Sleep regularity score (0-2) as a derivative dimension is calculated based on the synergy of nighttime sleep and nap scores (2 points if both are satisfied, 1 point if only one is satisfied, 0 points if neither is satisfied). The SMPI total score is derived from the sum of the scores of the three dimensions and can be further divided into the low healthy sleep group (0-2), the moderate healthy sleep group (3-5), and the high healthy sleep group (≥6). ③ Covariates according to the study design and data availability, the following covariates were included and uniformly coded and standardized: demographic characteristics (age, gender, age group, educational attainment, urban and rural, marital status); Lifestyle factors (smoking, drinking); Chronic diseases (hypertension, diabetes, heart disease, stroke); Functional status indicators (impaired activities of daily living (ADL), impaired instrumental activities of daily living (IADL)); Mental state (depressive symptoms). All covariates were standardized through custom functions, the core variable missing values were handled by direct deletion, and memory optimization strategies were used to retain only the variables needed throughout the analysis and remove the useless original variables to free up memory. 2.2.2 Data filtering To ensure the validity of the study sample and the reliability of the analysis results, this study included all subjects from the 2011 CHARLS baseline survey, totaling 17,705, based on data from multiple CHARLS surveys. ID format uniform: The ID format was standardized through a custom function to ensure the accuracy of interperiod matching. This step did not exclude the study subjects. According to the study inclusion criteria, only the study subjects aged ≥45 years and ≤100 years were retained, and samples with ages 100 years, and missing birth years were excluded. A total of 424 people were excluded. Remaining 17,281, excluding baseline chronic pain cases: Constructing chronic pain outcome variables (using the standard definition in 2011: frequent pain distress and pain location ≥1), excluding baseline diagnosed chronic pain studies, excluding 5,699, remaining 11,582, excluding missing core exposure variables: Construct the Comprehensive Sleep Pattern Index (SMPI) and exclude 995 subjects with missing sleep index, leaving 10,587; Match follow-up outcomes and exclude missing covariates: Match follow-up outcomes in 2013 and 2015, construct survival analysis variables (follow-up time, onset outcome), and exclude samples with missing covariates. A total of 7 individuals were excluded, and the final effective sample size included in the main analysis cohort was 10,580. The data flow chart is shown in Figure 1 below. 3 Results 3.1 Basic characteristics of the research subjects A total of 10,580 participants were included in this study and were classified into unhealthy sleep, moderately healthy sleep, and healthy sleep based on a comprehensive index of sleep patterns. In terms of baseline characteristics, unhealthy sleep presented more adverse situations, such as higher age, higher proportion of women and non-marital status, lower proportion of highly educated people, and significantly increased prevalence of heart disease, impaired activities of daily living and instrumental activities of daily living, and incidence of depressive symptoms. Shorter sleep duration and naps at night, poorer sleep quality; The healthy sleep group showed better basic characteristics, including age structure, educational attainment ratio and sleep duration, as well as a lower proportion of impaired function and a lower incidence of depressive symptoms. The characteristics of the moderately healthy sleep group were mostly between the two groups. There were no significant differences among the three groups in terms of urban-rural distribution, smoking status, and prevalence of stroke, although there were differences in prevalence of hypertension, diabetes, and drinking status. Overall, sleep health was closely related to the baseline health status of the study subjects. The less healthy the sleep, The demographic structure, physical function, mental state and sleep-related indicators of the healthy sleep group were more significant, while the overall baseline health level was better, as shown in Table 1 below. Overall Unhealthy sleep Moderately healthy sleep Healthy sleep p n 10580 2171 5921 2488 age (median [IQR]) 58.00 [51.00, 65.00] 60.00 [54.00, 68.00] 57.00 [51.00, 65.00] 57.00 [50.00, 65.00] <0.001 sex = female (%) 5042 (47.7) 1124 (51.8) 2862 (48.3) 1056 (42.4) <0.001 age_group (%) <0.001 <60 years old 6374 (60.2) 1145 (52.7) 3680 (62.2) 1549 (62.3) 60-69 years old 2697 (25.5) 618 (28.5) 1477 (24.9) 602 (24.2) ≥70 years old 1509 (14.3) 408 (18.8) 764 (12.9) 337 (13.5) education = 1 (%) 1628 (15.4) 205 (9.4) 849 (14.3) 574 (23.1) <0.001 urban = 1 (%) 10569 (99.9) 2167 (99.8) 5917 (99.9) 2485 (99.9) 0.338 marriage = 1 (%) 9322 (88.1) 1818 (83.7) 5238 (88.5) 2266 (91.1) <0.001 smoking = 1 (%) 4380 (41.4) 872 (40.2) 2456 (41.5) 1052 (42.3) 0.33 drinking = 1 (%) 3699 (35.0) 690 (31.8) 2077 (35.1) 932 (37.5) <0.001 hypertension = 1 (%) 2428 (23.1) 530 (24.5) 1284 (21.8) 614 (24.8) 0.002 diabetes = 1 (%) 540 (5.1) 105 (4.9) 263 (4.5) 172 (7.0) <0.001 heart_disease = 1 (%) 1023 (9.7) 252 (11.7) 527 (8.9) 244 (9.9) 0.001 stroke = 1 (%) 187 (1.8) 44 (2.0) 96 (1.6) 47 (1.9) 0.41 adl_impair = TRUE (%) 300 (2.8) 95 (4.4) 161 (2.7) 44 (1.8) <0.001 iadl_impair = TRUE (%) 398 (3.8) 148 (6.8) 199 (3.4) 51 (2.0) <0.001 depression = 1 (%) 2814 (26.6) 826 (38.0) 1455 (24.6) 533 (21.4) <0.001 night_sleep_h (median [IQR]) 7.00 [6.00, 8.00] 5.00 [4.00, 5.00] 7.00 [6.00, 8.00] 7.00 [6.00, 8.00] <0.001 nap_min (median [IQR]) 1.00 [0.00, 60.00] 0.00 [0.00, 2.00] 0.00 [0.00, 60.00] 60.00 [30.00, 60.00] <0.001 sleep_index_total (median [IQR]) 4.00 [3.00, 5.00] 1.00 [1.00, 2.00] 4.00 [4.00, 4.00] 7.00 [6.00, 7.00] <0.001 Table1 Baseline Feature Table 3.2 Association between sleep patterns in the main queue and the risk of chronic pain onset During the 4-year follow-up of the main team, a total of 1,075 new cases of chronic pain occurred, with a cumulative incidence rate of 10.16%, including 440 new cases at 2-year follow-up and 635 new cases at 4-year follow-up. Cox proportional hazards regression analysis showed a significant negative correlation between the sleep health index and the risk of chronic pain onset, and this association remained stable after adjusting for confounding factors. In Model 1 (unadjusted), for every 1-point increase in the sleep health index, the risk of chronic pain decreased by 13% (HR=0.87, 95% CI:0.84-0.90, P<0.001); In Model 2 (adjusted for demographic characteristics), HR=0.90 (95% CI:0.87-0.93, P<0.001); In Model 3 (adjusted for demographic + lifestyle + chronic disease), HR=0.90 (95% CI:0.87-0.93, P<0.001); In Model 4 (fully adjusted model), for every 1-point increase in the sleep health index, the risk of chronic pain onset decreased by 9% (HR=0.91, 95% CI:0.88-0.94, P<0.001), and the model C index was 0.675 (as shown in Chart 2 below). The KM survival curve showed that there were significant differences in chronic pain-free survival among the three groups of subjects. The healthy sleep group had the highest chronic pain-free survival rate, followed by the moderately healthy sleep group, and the unhealthy sleep group had the lowest. The differences between the groups gradually increased with the extension of follow-up time as shown in Figure 2. Restrictive cubic spline analysis revealed a nonlinear dose-response relationship between SMPI and chronic pain in middle-aged and elderly people (P nonlinearity < 0.05) : as the total SMPI score increased from 0 to 5, the risk of chronic pain continued to decrease, and the downward trend was obvious; When the SMPI score was above 5, the risk of chronic pain tended to stabilize and no longer decreased significantly, as shown in Figure 3 [16]. Model Name HR value HR_95CI lower limit HR_95CI upper limit P value Model C-index Model 1: Unadjusted 0.87 0.84 0.9 0 0.572 Model 2: Adjusted demographic characteristics 0.9 0.87 0.93 0 0.646 Model 3: Adjusted demographics + lifestyle + chronic diseases 0.9 0.87 0.93 0 0.656 Model 4: Fully Adjusted Model 0.91 0.88 0.94 0 0.675 Table 2 Cox regression results of sleep patterns and chronic pain 3.3 Subgroup analysis results Subgroup analyses showed that the protective effect of sleep patterns varied among different subgroups, but the overall trend was consistent. In age < 60 years (HR=0.91, 95% CI:0.87-0.95, P<0.001), 60-69 years (HR=0.92, 95% CI:0.86-0.98, P=0.006), male (HR=0.94, 95% CI:0.89-0.99, For every 1-point increase in the sleep health index, the risk of chronic pain onset was significantly reduced in the urban population (HR=0.91, 95% CI:0.88-0.94, P<0.001); The protective effect was marginally insignificant only in people aged 70 and above (HR=0.94, 95% CI:0.86-1.01, P=0.101); The rural population was not included in the subgroup analysis due to insufficient sample size (<100) (Table 3 below). Subgroups Sample size Number of events HR Lower limit of CI Upper limit of CI P value Age <60 5984 592 0.91 0.87 0.95 0 Age: 60-69 2853 301 0.92 0.86 0.98 0.006 Age ≥70 1743 182 0.94 0.86 1.01 0.101 Male 5538 364 0.94 0.89 0.99 0.026 female 5042 711 0.9 0.86 0.94 0 Urban population 10569 1075 0.91 0.88 0.94 0 Table 3 subgroup analysis 3.4 Sensitivity analysis results The sensitivity analysis results showed that after excluding those who developed the disease within 2 years of follow-up, for every 1-point increase in the sleep health index, the risk of chronic pain onset decreased by 8% (HR=0.92, 95% CI:0.88-0.96, P<0.001); The results of the complete case analysis (excluding all covariates missing) were highly consistent with those of the main analysis (HR=0.91, 95% CI:0.88-0.94, P<0.001), indicating good robustness of the study results as shown in Table 4. Analysis Type HR CI_Lower CI_Upper P Principal analysis (fully adjusted model) 0.91 0.88 0.94 0 Exclusion of onset within 2 years of follow-up 0.92 0.88 0.96 0 Complete case analysis 0.91 0.88 0.94 0 Table 4 sensitivity analysis 3.5 External validation results A total of 12,615 research subjects were included in the external validation cohort. During the 3-year follow-up period, 1,968 new cases of chronic pain occurred, with a cumulative incidence rate of 15.60%. The risk of chronic pain onset showed a significant dose-response trend in different sleep pattern groups: 493 cases in the unhealthy sleep group, with a 3-year cumulative incidence rate of 19.3%; There were 1,021 cases in the moderately healthy sleep group, with a cumulative incidence rate of 15.3%; There were 454 cases in the healthy sleep group, with a cumulative incidence rate of 13.4%. In the fully adjusted model, the sample size was sharply reduced from 12,615 to 322 due to significant deficiencies in some covariates, such as functional status and depression. The statistical power was insufficient and the results did not reach statistical significance (HR=0.93, 95% CI:0.77-1.13, P=0.465). With the simplified covariation-adjusted strategy (adjusted only for age and gender), the sample size was retained at 1,968, and for every 1-point increase in the sleep health index, the risk of chronic pain was reduced by 7% (HR=0.93, 95% CI:0.91-0.96, P<0.001), consistent and significant with the main queue results. The robustness of the primary queue results was validated (see Tables 1, 2, and 3 in the annex for specific results). 4 Discussion Chronic pain is a major public health problem affecting the healthy lifespan and quality of life of middle-aged and elderly people. With the accelerating aging process in our country, the disease burden continues to rise. It is necessary to identify the modifiable risk factors in order to build a national primary prevention strategy. Based on the nationally representative CHARLS database[ 18 , 19 ], this study constructed a comprehensive sleep health index (SMPI) covering three core dimensions of nocturnal sleep duration, nap duration, and sleep regularity through a design combining a large sample prospective primary cohort with an external validation cohort, The prospective association between sleep patterns and the risk of chronic pain onset in middle-aged and elderly people in China was systematically explored. The study results showed a significant dose-response relationship between healthy sleep patterns and a reduced risk of chronic pain in middle-aged and elderly people. For every 1-point increase in SMPI, the risk of chronic pain in middle-aged and elderly people in the main cohort decreased significantly by 9%, and in the external validation cohort decreased by 7%. The association was stable in age < 60 years, 60–69 years, male, female and urban populations, and the results remained robust after multiple rounds of sensitivity analysis, suggesting that the combined sleep pattern is an independent protective factor for chronic pain in middle-aged and elderly Chinese people, and improving sleep health is expected to become an important target for precise prevention of chronic pain in middle-aged and elderly people [ 20 , 21 ]. The core findings of this study further complement and expand the existing research evidence in the field of sleep and chronic pain. Previous studies at home and abroad have mostly focused on the association between a single sleep indicator and chronic pain. Multiple cross-sectional studies have confirmed that insomnia, insufficient or excessive sleep duration, and fragmented sleep are all associated with an increased risk of chronic pain, but a single sleep indicator is difficult to fully reflect the overall characteristics of an individual's sleep health, and the cross-sectional design cannot clearly define the temporal association between the two. The strength of causal inferences is limited. Unlike previous studies, this study uses a multi-dimensional comprehensive sleep index to assess sleep patterns, breaking through the limitations of a single indicator and better fitting the essential characteristics of sleep as a multi-dimensional physiological behavior; At the same time, a prospective cohort design was adopted, with the population without chronic pain at baseline as the study subjects. Through follow-up to determine the outcome of the disease, a temporal relationship was established where sleep patterns occurred first and chronic pain occurred later, providing stronger evidence for the causal association between the two. In addition, this study repeated the main analysis results through an independent external validation cohort, further confirming the robustness of the association and filling the domestic gap in prospective studies on the relationship between comprehensive sleep patterns and the risk of chronic pain in middle-aged and elderly people [ 22 , 23 ]. Subgroup analyses showed a certain population heterogeneity in the protective effect of healthy sleep patterns on chronic pain. [ 24 , 25 ] In age stratification, the protective effect of sleep patterns was significant in the population < 60 years old and 60–69 years old, but marginally insignificant in the population ≥ 70 years old, a result consistent with previous relevant studies. The underlying reason may be that older people are often accompanied by multi-system comorbidity, physical function decline, neurodegenerative changes and physiological aging of pain regulatory pathways, chronic pain onset is driven by multiple pathological factors, and the independent protective effect of sleep patterns is diluted by other strong risk factors; [ 26 , 27 ] At the same time, physiological changes in the sleep structure of the elderly, such as reduced slow-wave sleep and increased nocturnal awakenings, and relatively limited intervention space for sleep patterns may also lead to weakened protective effects. In gender stratification, healthy sleep patterns have a slightly stronger protective effect on women than on men, which may be related to gender differences in pain perception, neuroendocrine regulation, and inflammatory response between men and women. [ 28 , 29 ] Women are more sensitive to changes in the endogenous pain suppression system, and the activation of the hypothalamic-pituitary-adrenal (HPA) axis mediated by sleep disorders and the release of pro-inflammatory factors are more likely to trigger pain sensitization. Therefore, the preventive value of healthy sleep patterns for chronic pain in women is more prominent. It is notable that the rural population was not included in the subgroup analysis due to insufficient sample size in this study. Further attention should be paid to the heterogeneity between urban and rural populations in the future to provide a basis for formulating differentiated sleep intervention strategies for different populations [ 30 , 31 ]. The association between sleep patterns and chronic pain identified in this study is supported by multi-dimensional underlying biological mechanisms. [ 32 , 33 ] First of all, sleep is a core physiological process for the repair and homeostasis maintenance of the central pain regulatory system. Healthy sleep patterns, especially adequate slow-wave sleep, can maintain the normal function of the downward pain suppression pathway, upregulate the activity of the endogenous opioid system, and maintain normal pain thresholds; Sleep disorders can directly disrupt the pain regulatory pathway, induce central sensitization, lower the pain tolerance threshold, and increase the risk of chronic pain. Secondly, the inflammatory response is an important mediator of the association between the two. [ 34 , 35 ] Insufficient sleep or disrupted sleep rhythms can activate the HPA axis and the sympathetic nervous system, inducing the continuous release of proinflammatory cytokines such as IL-1β, IL-6, TNF-α, and chronic low-grade inflammation is the core pathological basis for the occurrence and development of various chronic pains such as musculoskeletal pain and neuropathic pain. Healthy sleep patterns can reduce the risk of chronic pain by suppressing the inflammatory cascade. Third, the maintenance of circadian rhythm homeostasis. Appropriate sleep duration, regular naps and a stable schedule can maintain the stability of the body's circadian rhythm, while circadian rhythm disorders have been proven to directly exacerbate hyperalgesia and disrupt pain-related physiological rhythms. Healthy sleep patterns reduce functional disorders of the pain regulation system by stabilizing the circadian rhythm. In addition, this study further controlled for depressive symptoms in the fully adjusted model, and the association between sleep patterns and chronic pain remained significant, suggesting that the protective effect of healthy sleep patterns on chronic pain is independent of psychological factors and has a direct physiological and pathological pathway [ 36 , 37 ]. This research holds significant public health and clinical practice significance. [ 38 , 39 ] At present, the prevention of chronic pain in middle-aged and elderly people in China is still mainly symptomatic treatment, lacking effective primary prevention means, while sleep patterns are controllable risk factors that can be improved through health education and behavioral intervention. The SMPI scoring system constructed in this study is simple and easy to operate. It can be evaluated only by the duration of sleep at night, nap duration and sleep regularity, and is suitable for promotion and application in health screening of middle-aged and elderly people in the community. Based on the results of this study, conducting sleep health interventions for middle-aged and elderly people aged 45 to 69, guiding them to maintain 7 to 8 hours of nighttime sleep, 15 to 60 minutes of regular naps, and stable sleep schedules to improve SMPI scores, is expected to become a low-cost, wide-coverage primary prevention strategy for chronic pain It can not only reduce the risk of chronic pain in middle-aged and elderly people, but also improve their physical function and quality of life, and reduce the medical burden on families and society [ 40 – 45 ]. 5 Limitations of Studies Although this study was based on a large national sample cohort and ensured the robustness of the results through prospective design and external validation, the following limitations still exist: First, there is heterogeneity in the definition of chronic pain outcomes across different follow-up periods. Due to the different questionnaire Settings of the CHARLS database for different survey periods, chronic pain was defined as "frequent pain distress + ≥ 1 pain site" in the baseline and follow-up surveys in 2011 and 2015, while there were no pain distress related items in the follow-up surveys in 2013 and 2018. Only the relaxed definition of "having ≥ 1 pain site" could be used, which might lead to non-differentiated misclassification of outcomes, including some cases of acute pain in the chronic pain outcome to some extent diluted the true association strength between sleep patterns and chronic pain. However, this study, through a sensitivity analysis excluding cases that developed within 2 years of follow-up, found results that were highly consistent with the main analysis, to some extent alleviating the impact of this bias on the study results [ 46 , 47 ]. Second, the dimension coverage of the comprehensive sleep health index was still not comprehensive enough. The SMPI constructed in this study only included three core dimensions: nocturnal sleep duration, nap duration, and sleep regularity. Due to the consistency of questionnaire items in each period of the CHARLS database, sleep indicators closely related to chronic pain such as difficulty falling asleep, number of nocturnal awakenings, sleep apnea, severity of insomnia, and subjective score of sleep quality were not included. It may not be able to fully capture all the characteristics of an individual's sleep pattern, and there is a certain measurement bias in the assessment of sleep health. Future studies could further incorporate multi-dimensional sleep indicators to improve the comprehensive sleep health assessment system applicable to middle-aged and elderly people in China. Third, observational studies cannot completely rule out the influence of residual confounding. Although this study adjusted for a variety of potential confounding factors such as demographic characteristics, lifestyle, chronic comorbidity, physical functional status, and depressive symptoms in the analysis, it was still unable to fully control for unmeasured confounding factors such as physical activity level, dietary pattern, occupational exposure, history of analgesic use, history of previous acute pain attacks, etc. These factors may simultaneously affect sleep patterns and the risk of chronic pain onset, potentially causing residual confounding in the study results. Fourth, there are limitations in the representativeness of the study population and subgroup analysis. The sample size of the rural population in this study was very small, and stratified analysis of the urban-rural subgroups could not be completed. There are significant differences in sleep patterns, exposure to pain risk factors, and access to medical resources among the urban and rural middle-aged and elderly population. The existing research results are mainly applicable to the urban middle-aged and elderly population, and extrapolation is somewhat limited. At the same time, the subjects of this study were only middle-aged and elderly people aged 45 and above in China, and the results could not be directly generalized to younger people, people of other races and countries. Fifth, the validation power of the fully adjusted model of the external validation cohort was insufficient. In the external validation cohort, there were significant deficiencies in covariates such as activities of daily living, instrumental activities of daily living, and depressive symptoms. If a fully adjusted strategy consistent with the main cohort was adopted, the sample size would sharply decrease, resulting in a significant deficiency in statistical power. Therefore, only a simplified covariate strategy adjusted for age and gender was used, which verified the direction and significance of the association. But the full adjustment analysis of the main queue could not be fully replicated, reducing the power of external validation to some extent [ 48 , 49 ]. Sixth, the study did not delve into the intrinsic mechanisms by which sleep patterns affect chronic pain. Existing studies have only validated the prospective association between sleep patterns and the risk of chronic pain onset, without incorporating biomarkers such as inflammatory factors, neuroendocrine markers, and pain pathway-related indicators, thus failing to clarify the mediating pathways and intrinsic mechanisms of the association. Cohort studies and mechanism studies based on biological samples are needed in the future to further reveal the molecular mechanisms by which sleep patterns affect the occurrence of chronic pain [ 50 – 52 ]. Declarations Conflict of Interest The authors declare no competing financial interests. Acknowledgments We sincerely appreciate the contributions of all participants in the China Health and Retirement Longitudinal Study (CHARLS). We also extend our gratitude to the CHARLS research team at Peking University for providing the data and to the Institutional Review Board for its ethical approval (IRB00001052-11015).Clinical trial number: not applicable. Author Contributions Data Availability Statement The raw data supporting the conclusions of this article are publicly available from the original cohort repositories:CHARLS(https://charls.pku.edu.cn/). The processed datasets (including subset data from CHARLS used for statistical analyses) and analytical code will be made available by the corresponding author (Kai Wang), without undue reservation, upon reasonable request. Availability of Data and Materials The China Health and Retirement Longitudinal Study (CHARLS) data can be obtained from the Open Data Platform of Peking University (https://charls.pku.edu.cn/, Research No. 2025-042). Ethical Approval and Consent to Participate The studies involving human participants were approved by the following institutional ethics committees: CHARLS: Institutional Review Board of Peking University (IRB00001052-11015); All studies were conducted in accordance with local legislation, institutional requirements, and the principles of the Declaration of Helsinki. All participants provided written informed consent at the time of enrollment in the respective cohorts. Funding The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. Clinical trial number Not applicable References Wooden S. Multimodal Approach to Chronic Pain Management. Nurs Clin North Am. 2025 Dec; 60 (4) : 619-629. The doi: 10.1016 / j.carol carroll nur 2024.12.012. Epub 2025 Feb 4. PMID: 41136123. Agbor FBAT, Vance DE, Odii CO, Jones AR, Aroke EN. Healthy Diet Consumption Among Chronic Pain Populations: A Concept Analysis. Pain Manag Nurs. 2025 Oct; 26 (5) : e476-e488. Doi: 10.1016 / j.mn 2025.02.013.Epub 2025 Mar 15.PMID: 40090774. Sikandar S, Ackland GL. Chronic pain: a modifiable target to reduce perioperative cardiovascular morbidity. Br J Anaesth.2025 Mar; 134 (3) : 627-631. The doi: 10.1016 / j.b.Ja 2024.11.019.Epub 2024 Dec12 PMID: 39668055; PMCID: PMC11867062. Wager TD, Sutherland SP, Lindquist MA, Sluka KA; A2CPS Consortium. 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09:55:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":808395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9412531/v1/0bccfe14-1132-46ef-872a-29842a61ac7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A prospective study on Sleep Patterns and the Risk of Chronic pain in Middle-aged and Elderly People in China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChronic pain is a common health problem among middle-aged and elderly people, characterized by persistent pain and recurrent episodes, which seriously affects physical function, sleep quality and quality of life of middle-aged and elderly people, and also increases the medical burden on families and society [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] With the acceleration of the aging process, the prevalence of chronic pain among middle-aged and elderly people is on the rise. The pathogenesis is complex and is influenced by various factors such as physiology, psychology and lifestyle. Among them, the association between sleep disorders and chronic pain has become a research hotspot in recent years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSleep is an important physiological repair process in the human body. Poor sleep quality and disrupted sleep structure may increase the risk of chronic pain by influencing mechanisms such as neurotransmitter regulation and inflammatory responses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Existing studies have mostly focused on the relationship between a single sleep indicator and chronic pain, lacking a comprehensive assessment of sleep patterns, while the comprehensive sleep Pattern Index (SMPI) covers multiple core dimensions such as nocturnal sleep, napping, and sleep patterns. It provides a more comprehensive reflection of an individual's sleep health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCHARLS is a national longitudinal survey of Chinese residents aged 45 and above, aiming to establish a high-quality, representative and publicly accessible microdatabase. The database provides comprehensive information on middle-aged and elderly people. The subjects were sampled using the multi-stage probability Proportional scale (PPS) sampling technique from 28 provinces in China. The data for this study were derived from the China Health and Retirement Tracking Survey (CHARLS). The initial sample was obtained by combining data from nine core modules, including blood tests, health status and function, demographic background, work retirement and pensions, health insurance, family members, biomarkers, family information, and family transfers. The data included a wealth of sleep-related indicators, pain-related information, and covariates such as demographics, lifestyle, and chronic diseases. It provides good data support for exploring the relationship between SMPI and chronic pain in middle-aged and elderly people. Construct SMPI, systematically analyze its correlation with chronic pain in middle-aged and elderly people and population heterogeneity, and combine sample screening logic, mechanism exploration, methodological discussion and research limitations to provide a scientific basis for precise prevention and individualized intervention of chronic pain in middle-aged and elderly people [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e"},{"header":"2 Research Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Research Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the China Health and Retirement Longitudinal Survey (CHARLS). CHARLS is a national longitudinal survey of Chinese residents aged 45 and above, designed to generate high-quality, representative and publicly accessible microdatabases. The database provides comprehensive information on middle-aged and elderly people. The subjects were sampled using the multi-stage probability Proportional scale (PPS) sampling technique from 28 provinces in China. The data for this study were derived from the 2015 China Health and Retirement Follow-up Survey (CHARLS). The initial sample was obtained by combining data from nine core modules, including blood tests, health status and function, demographic background, work retirement and pensions, health insurance, family members, biomarkers, family information, and family transfers. A total of 10,580 valid samples that met the study criteria were obtained through subsequent data merging, variable construction, and rigorous screening [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data sources and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Data Merging and variable Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study constructed two cohorts respectively: ① Main analysis cohort: With 2011 as the baseline investigation period and 2013 and 2015 as the follow-up periods, the research subjects who had no chronic pain at baseline, completed the follow-up and had complete core variables were included; ② External validation cohort: With 2015 as the baseline survey period and 2018 as the follow-up period, using the same inclusion and exclusion criteria as the main cohort [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data sources and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData processing was carried out using R software (version 4.3.0), and data standardization and variable construction were completed through custom functions as follows:\u003c/p\u003e\n\u003cp\u003e① Outcome variable: Chronic pain was standardized based on the characteristics of CHARLS survey data from different years: The 2011 and 2015 surveys included the variable of duration of pain (da041), and chronic pain was defined as \u0026quot;Do you often suffer from body pain? 1.Yes 2.No and which parts of your body do you feel pain? Please list all parts. 1.(Headache) head 2. Shoulder 3. Arm 4. Wrist 5. Finger 6. Chest 7. Stomach 8. Back 9. Waist 10. Hip 11. Leg 12. Knee 13. Ankle 14. Toe 15. Neck 16. Other areas with \u0026ge;1 pain site\u0026quot; Assign values of 1 (with chronic pain) and 0 (without chronic pain), where the number of pain sites is calculated by 16 pain site variables (da042_s1 to da042_s16); In the 2013 and 2018 surveys, there was no distress due to physical pain (da041), which was uniformly broadened to \u0026quot;the presence of \u0026ge;1 pain site\u0026quot; and assigned values of 1 (with chronic pain) and 0 (without chronic pain). The outcomes of the primary cohort were evaluated in 2013 (with a 2-year follow-up) and 2015 (with a 4-year follow-up), while those of the external validation cohort were evaluated in 2018 (with a 3-year follow-up).\u003c/p\u003e\n\u003cp\u003e② Exposure variables: The Comprehensive Sleep Pattern Index is a multi-dimensional quantitative assessment tool developed for sleep health in middle-aged and elderly people, aiming to break through the limitations of a single sleep indicator and comprehensively reflect the overall health level of an individual\u0026apos;s sleep pattern. According to CHARLS \u0026apos;2015 data, how many hours did you sleep on average each night in the past month? ; How long have you typically napped over the past month? Based on these questions [15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe index is epidemiological evidence of the physiological characteristics of sleep and the relationship between sleep and health in middle-aged and elderly people. It integrates three core sleep dimensions for scoring on a scale of 0 to 7, with higher scores indicating healthier sleep patterns. The specific dimensions and scoring logic are as follows: Nighttime sleep score (1-3 points), with 7-8 hours as optimal state (3 points), 6-7 hours or 8-9 hours as appropriate state (2 points), and less than 6 hours or more than 9 hours as abnormal state (1 point); Nap score (0-2 points), 15-60 minutes is appropriate duration (2 points), less than 15 minutes or more than 60 minutes is abnormal duration (1 point), no nap is 0 points; Sleep regularity score (0-2) as a derivative dimension is calculated based on the synergy of nighttime sleep and nap scores (2 points if both are satisfied, 1 point if only one is satisfied, 0 points if neither is satisfied). The SMPI total score is derived from the sum of the scores of the three dimensions and can be further divided into the low healthy sleep group (0-2), the moderate healthy sleep group (3-5), and the high healthy sleep group (\u0026ge;6).\u003c/p\u003e\n\u003cp\u003e③ Covariates according to the study design and data availability, the following covariates were included and uniformly coded and standardized: demographic characteristics (age, gender, age group, educational attainment, urban and rural, marital status); Lifestyle factors (smoking, drinking); Chronic diseases (hypertension, diabetes, heart disease, stroke); Functional status indicators (impaired activities of daily living (ADL), impaired instrumental activities of daily living (IADL)); Mental state (depressive symptoms). All covariates were standardized through custom functions, the core variable missing values were handled by direct deletion, and memory optimization strategies were used to retain only the variables needed throughout the analysis and remove the useless original variables to free up memory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Data filtering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the validity of the study sample and the reliability of the analysis results, this study included all subjects from the 2011 CHARLS baseline survey, totaling 17,705, based on data from multiple CHARLS surveys. ID format uniform: The ID format was standardized through a custom function to ensure the accuracy of interperiod matching. This step did not exclude the study subjects. According to the study inclusion criteria, only the study subjects aged \u0026ge;45 years and \u0026le;100 years were retained, and samples with ages \u0026lt;45 years, \u0026gt;100 years, and missing birth years were excluded. A total of 424 people were excluded. Remaining 17,281, excluding baseline chronic pain cases: Constructing chronic pain outcome variables (using the standard definition in 2011: frequent pain distress and pain location \u0026ge;1), excluding baseline diagnosed chronic pain studies, excluding 5,699, remaining 11,582, excluding missing core exposure variables: Construct the Comprehensive Sleep Pattern Index (SMPI) and exclude 995 subjects with missing sleep index, leaving 10,587; Match follow-up outcomes and exclude missing covariates: Match follow-up outcomes in 2013 and 2015, construct survival analysis variables (follow-up time, onset outcome), and exclude samples with missing covariates. A total of 7 individuals were excluded, and the final effective sample size included in the main analysis cohort was 10,580. The data flow chart is shown in Figure 1 below.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Basic characteristics of the research subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10,580 participants were included in this study and were classified into unhealthy sleep, moderately healthy sleep, and healthy sleep based on a comprehensive index of sleep patterns. In terms of baseline characteristics, unhealthy sleep presented more adverse situations, such as higher age, higher proportion of women and non-marital status, lower proportion of highly educated people, and significantly increased prevalence of heart disease, impaired activities of daily living and instrumental activities of daily living, and incidence of depressive symptoms. Shorter sleep duration and naps at night, poorer sleep quality; The healthy sleep group showed better basic characteristics, including age structure, educational attainment ratio and sleep duration, as well as a lower proportion of impaired function and a lower incidence of depressive symptoms. The characteristics of the moderately healthy sleep group were mostly between the two groups. There were no significant differences among the three groups in terms of urban-rural distribution, smoking status, and prevalence of stroke, although there were differences in prevalence of hypertension, diabetes, and drinking status. Overall, sleep health was closely related to the baseline health status of the study subjects. The less healthy the sleep, The demographic structure, physical function, mental state and sleep-related indicators of the healthy sleep group were more significant, while the overall baseline health level was better, as shown in Table 1 below.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnhealthy sleep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModerately healthy sleep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy sleep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eage (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.00 [51.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.00 [54.00, 68.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.00 [51.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.00 [50.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esex = female (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5042 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1124 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2862 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1056 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eage_group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;60 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6374 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1145 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3680 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1549 (62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e60-69 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2697 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e618 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1477 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e602 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;70 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1509 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e408 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e764 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e337 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eeducation = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1628 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e205 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e849 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e574 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eurban = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10569 (99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2167 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5917 (99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2485 (99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emarriage = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9322 (88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1818 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5238 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2266 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esmoking = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4380 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e872 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2456 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1052 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edrinking = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3699 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e690 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2077 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e932 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehypertension = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2428 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e530 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1284 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e614 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ediabetes = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e540 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e263 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eheart_disease = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1023 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e252 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e527 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e244 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003estroke = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eadl_impair = TRUE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eiadl_impair = TRUE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e398 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e148 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e199 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edepression = 1 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2814 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e826 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1455 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e533 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003enight_sleep_h (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.00 [6.00, 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00 [4.00, 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.00 [6.00, 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.00 [6.00, 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003enap_min (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 [0.00, 60.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00 [0.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00 [0.00, 60.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.00 [30.00, 60.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 214px;\"\u003e\n \u003cp\u003esleep_index_total (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4.00 [3.00, 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.00 [1.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.00 [4.00, 4.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e7.00 [6.00, 7.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable1 Baseline Feature Table\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 Association between sleep patterns in the main queue and the risk of chronic pain onset\u003c/h2\u003e\n\u003cp\u003eDuring the 4-year follow-up of the main team, a total of 1,075 new cases of chronic pain occurred, with a cumulative incidence rate of 10.16%, including 440 new cases at 2-year follow-up and 635 new cases at 4-year follow-up. Cox proportional hazards regression analysis showed a significant negative correlation between the sleep health index and the risk of chronic pain onset, and this association remained stable after adjusting for confounding factors. In Model 1 (unadjusted), for every 1-point increase in the sleep health index, the risk of chronic pain decreased by 13% (HR=0.87, 95% CI:0.84-0.90, P\u0026lt;0.001); In Model 2 (adjusted for demographic characteristics), HR=0.90 (95% CI:0.87-0.93, P\u0026lt;0.001); In Model 3 (adjusted for demographic + lifestyle + chronic disease), HR=0.90 (95% CI:0.87-0.93, P\u0026lt;0.001); In Model 4 (fully adjusted model), for every 1-point increase in the sleep health index, the risk of chronic pain onset decreased by 9% (HR=0.91, 95% CI:0.88-0.94, P\u0026lt;0.001), and the model C index was 0.675 (as shown in Chart 2 below). The KM survival curve showed that there were significant differences in chronic pain-free survival among the three groups of subjects. The healthy sleep group had the highest chronic pain-free survival rate, followed by the moderately healthy sleep group, and the unhealthy sleep group had the lowest. The differences between the groups gradually increased with the extension of follow-up time as shown in Figure 2. Restrictive cubic spline analysis revealed a nonlinear dose-response relationship between SMPI and chronic pain in middle-aged and elderly people (P nonlinearity \u0026lt; 0.05) : as the total SMPI score increased from 0 to 5, the risk of chronic pain continued to decrease, and the downward trend was obvious; When the SMPI score was above 5, the risk of chronic pain tended to stabilize and no longer decreased significantly, as shown in Figure 3 [16].\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR_95CI lower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR_95CI upper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel C-index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 214px;\"\u003e\n \u003cp\u003eModel 1: Unadjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 214px;\"\u003e\n \u003cp\u003eModel 2: Adjusted demographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 214px;\"\u003e\n \u003cp\u003eModel 3: Adjusted demographics + lifestyle + chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 214px;\"\u003e\n \u003cp\u003eModel 4: Fully Adjusted Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2 Cox regression results of sleep patterns and chronic pain\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroup analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses showed that the protective effect of sleep patterns varied among different subgroups, but the overall trend was consistent. In age \u0026lt; 60 years (HR=0.91, 95% CI:0.87-0.95, P\u0026lt;0.001), 60-69 years (HR=0.92, 95% CI:0.86-0.98, P=0.006), male (HR=0.94, 95% CI:0.89-0.99, For every 1-point increase in the sleep health index, the risk of chronic pain onset was significantly reduced in the urban population (HR=0.91, 95% CI:0.88-0.94, P\u0026lt;0.001); The protective effect was marginally insignificant only in people aged 70 and above (HR=0.94, 95% CI:0.86-1.01, P=0.101); The rural population was not included in the subgroup analysis due to insufficient sample size (\u0026lt;100) (Table 3 below).\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit of CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit of CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAge \u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAge: 60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAge \u0026ge;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eUrban population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e10569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 3 subgroup analysis\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3.4 Sensitivity analysis results\u003c/h3\u003e\n\u003cp\u003eThe sensitivity analysis results showed that after excluding those who developed the disease within 2 years of follow-up, for every 1-point increase in the sleep health index, the risk of chronic pain onset decreased by 8% (HR=0.92, 95% CI:0.88-0.96, P\u0026lt;0.001); The results of the complete case analysis (excluding all covariates missing) were highly consistent with those of the main analysis (HR=0.91, 95% CI:0.88-0.94, P\u0026lt;0.001), indicating good robustness of the study results as shown in Table 4.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI_Lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI_Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003ePrincipal analysis (fully adjusted model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eExclusion of onset within 2 years of follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eComplete case analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 4 sensitivity analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 External validation results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 12,615 research subjects were included in the external validation cohort. During the 3-year follow-up period, 1,968 new cases of chronic pain occurred, with a cumulative incidence rate of 15.60%. The risk of chronic pain onset showed a significant dose-response trend in different sleep pattern groups: 493 cases in the unhealthy sleep group, with a 3-year cumulative incidence rate of 19.3%; There were 1,021 cases in the moderately healthy sleep group, with a cumulative incidence rate of 15.3%; There were 454 cases in the healthy sleep group, with a cumulative incidence rate of 13.4%.\u003c/p\u003e\n\u003cp\u003eIn the fully adjusted model, the sample size was sharply reduced from 12,615 to 322 due to significant deficiencies in some covariates, such as functional status and depression. The statistical power was insufficient and the results did not reach statistical significance (HR=0.93, 95% CI:0.77-1.13, P=0.465). With the simplified covariation-adjusted strategy (adjusted only for age and gender), the sample size was retained at 1,968, and for every 1-point increase in the sleep health index, the risk of chronic pain was reduced by 7% (HR=0.93, 95% CI:0.91-0.96, P\u0026lt;0.001), consistent and significant with the main queue results. The robustness of the primary queue results was validated (see Tables 1, 2, and 3 in the annex for specific results).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eChronic pain is a major public health problem affecting the healthy lifespan and quality of life of middle-aged and elderly people. With the accelerating aging process in our country, the disease burden continues to rise. It is necessary to identify the modifiable risk factors in order to build a national primary prevention strategy. Based on the nationally representative CHARLS database[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], this study constructed a comprehensive sleep health index (SMPI) covering three core dimensions of nocturnal sleep duration, nap duration, and sleep regularity through a design combining a large sample prospective primary cohort with an external validation cohort, The prospective association between sleep patterns and the risk of chronic pain onset in middle-aged and elderly people in China was systematically explored. The study results showed a significant dose-response relationship between healthy sleep patterns and a reduced risk of chronic pain in middle-aged and elderly people. For every 1-point increase in SMPI, the risk of chronic pain in middle-aged and elderly people in the main cohort decreased significantly by 9%, and in the external validation cohort decreased by 7%. The association was stable in age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, 60\u0026ndash;69 years, male, female and urban populations, and the results remained robust after multiple rounds of sensitivity analysis, suggesting that the combined sleep pattern is an independent protective factor for chronic pain in middle-aged and elderly Chinese people, and improving sleep health is expected to become an important target for precise prevention of chronic pain in middle-aged and elderly people [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe core findings of this study further complement and expand the existing research evidence in the field of sleep and chronic pain. Previous studies at home and abroad have mostly focused on the association between a single sleep indicator and chronic pain. Multiple cross-sectional studies have confirmed that insomnia, insufficient or excessive sleep duration, and fragmented sleep are all associated with an increased risk of chronic pain, but a single sleep indicator is difficult to fully reflect the overall characteristics of an individual's sleep health, and the cross-sectional design cannot clearly define the temporal association between the two. The strength of causal inferences is limited. Unlike previous studies, this study uses a multi-dimensional comprehensive sleep index to assess sleep patterns, breaking through the limitations of a single indicator and better fitting the essential characteristics of sleep as a multi-dimensional physiological behavior; At the same time, a prospective cohort design was adopted, with the population without chronic pain at baseline as the study subjects. Through follow-up to determine the outcome of the disease, a temporal relationship was established where sleep patterns occurred first and chronic pain occurred later, providing stronger evidence for the causal association between the two. In addition, this study repeated the main analysis results through an independent external validation cohort, further confirming the robustness of the association and filling the domestic gap in prospective studies on the relationship between comprehensive sleep patterns and the risk of chronic pain in middle-aged and elderly people [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubgroup analyses showed a certain population heterogeneity in the protective effect of healthy sleep patterns on chronic pain. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] In age stratification, the protective effect of sleep patterns was significant in the population\u0026thinsp;\u0026lt;\u0026thinsp;60 years old and 60\u0026ndash;69 years old, but marginally insignificant in the population\u0026thinsp;\u0026ge;\u0026thinsp;70 years old, a result consistent with previous relevant studies. The underlying reason may be that older people are often accompanied by multi-system comorbidity, physical function decline, neurodegenerative changes and physiological aging of pain regulatory pathways, chronic pain onset is driven by multiple pathological factors, and the independent protective effect of sleep patterns is diluted by other strong risk factors; [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] At the same time, physiological changes in the sleep structure of the elderly, such as reduced slow-wave sleep and increased nocturnal awakenings, and relatively limited intervention space for sleep patterns may also lead to weakened protective effects. In gender stratification, healthy sleep patterns have a slightly stronger protective effect on women than on men, which may be related to gender differences in pain perception, neuroendocrine regulation, and inflammatory response between men and women. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Women are more sensitive to changes in the endogenous pain suppression system, and the activation of the hypothalamic-pituitary-adrenal (HPA) axis mediated by sleep disorders and the release of pro-inflammatory factors are more likely to trigger pain sensitization. Therefore, the preventive value of healthy sleep patterns for chronic pain in women is more prominent. It is notable that the rural population was not included in the subgroup analysis due to insufficient sample size in this study. Further attention should be paid to the heterogeneity between urban and rural populations in the future to provide a basis for formulating differentiated sleep intervention strategies for different populations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between sleep patterns and chronic pain identified in this study is supported by multi-dimensional underlying biological mechanisms. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] First of all, sleep is a core physiological process for the repair and homeostasis maintenance of the central pain regulatory system. Healthy sleep patterns, especially adequate slow-wave sleep, can maintain the normal function of the downward pain suppression pathway, upregulate the activity of the endogenous opioid system, and maintain normal pain thresholds; Sleep disorders can directly disrupt the pain regulatory pathway, induce central sensitization, lower the pain tolerance threshold, and increase the risk of chronic pain. Secondly, the inflammatory response is an important mediator of the association between the two. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Insufficient sleep or disrupted sleep rhythms can activate the HPA axis and the sympathetic nervous system, inducing the continuous release of proinflammatory cytokines such as IL-1β, IL-6, TNF-α, and chronic low-grade inflammation is the core pathological basis for the occurrence and development of various chronic pains such as musculoskeletal pain and neuropathic pain. Healthy sleep patterns can reduce the risk of chronic pain by suppressing the inflammatory cascade. Third, the maintenance of circadian rhythm homeostasis. Appropriate sleep duration, regular naps and a stable schedule can maintain the stability of the body's circadian rhythm, while circadian rhythm disorders have been proven to directly exacerbate hyperalgesia and disrupt pain-related physiological rhythms. Healthy sleep patterns reduce functional disorders of the pain regulation system by stabilizing the circadian rhythm. In addition, this study further controlled for depressive symptoms in the fully adjusted model, and the association between sleep patterns and chronic pain remained significant, suggesting that the protective effect of healthy sleep patterns on chronic pain is independent of psychological factors and has a direct physiological and pathological pathway [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research holds significant public health and clinical practice significance. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] At present, the prevention of chronic pain in middle-aged and elderly people in China is still mainly symptomatic treatment, lacking effective primary prevention means, while sleep patterns are controllable risk factors that can be improved through health education and behavioral intervention. The SMPI scoring system constructed in this study is simple and easy to operate. It can be evaluated only by the duration of sleep at night, nap duration and sleep regularity, and is suitable for promotion and application in health screening of middle-aged and elderly people in the community. Based on the results of this study, conducting sleep health interventions for middle-aged and elderly people aged 45 to 69, guiding them to maintain 7 to 8 hours of nighttime sleep, 15 to 60 minutes of regular naps, and stable sleep schedules to improve SMPI scores, is expected to become a low-cost, wide-coverage primary prevention strategy for chronic pain It can not only reduce the risk of chronic pain in middle-aged and elderly people, but also improve their physical function and quality of life, and reduce the medical burden on families and society [\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e"},{"header":"5 Limitations of Studies","content":"\u003cp\u003eAlthough this study was based on a large national sample cohort and ensured the robustness of the results through prospective design and external validation, the following limitations still exist:\u003c/p\u003e \u003cp\u003eFirst, there is heterogeneity in the definition of chronic pain outcomes across different follow-up periods. Due to the different questionnaire Settings of the CHARLS database for different survey periods, chronic pain was defined as \"frequent pain distress\u0026thinsp;+\u0026thinsp;\u0026ge;\u0026thinsp;1 pain site\" in the baseline and follow-up surveys in 2011 and 2015, while there were no pain distress related items in the follow-up surveys in 2013 and 2018. Only the relaxed definition of \"having\u0026thinsp;\u0026ge;\u0026thinsp;1 pain site\" could be used, which might lead to non-differentiated misclassification of outcomes, including some cases of acute pain in the chronic pain outcome to some extent diluted the true association strength between sleep patterns and chronic pain. However, this study, through a sensitivity analysis excluding cases that developed within 2 years of follow-up, found results that were highly consistent with the main analysis, to some extent alleviating the impact of this bias on the study results [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, the dimension coverage of the comprehensive sleep health index was still not comprehensive enough. The SMPI constructed in this study only included three core dimensions: nocturnal sleep duration, nap duration, and sleep regularity. Due to the consistency of questionnaire items in each period of the CHARLS database, sleep indicators closely related to chronic pain such as difficulty falling asleep, number of nocturnal awakenings, sleep apnea, severity of insomnia, and subjective score of sleep quality were not included. It may not be able to fully capture all the characteristics of an individual's sleep pattern, and there is a certain measurement bias in the assessment of sleep health. Future studies could further incorporate multi-dimensional sleep indicators to improve the comprehensive sleep health assessment system applicable to middle-aged and elderly people in China.\u003c/p\u003e \u003cp\u003eThird, observational studies cannot completely rule out the influence of residual confounding. Although this study adjusted for a variety of potential confounding factors such as demographic characteristics, lifestyle, chronic comorbidity, physical functional status, and depressive symptoms in the analysis, it was still unable to fully control for unmeasured confounding factors such as physical activity level, dietary pattern, occupational exposure, history of analgesic use, history of previous acute pain attacks, etc. These factors may simultaneously affect sleep patterns and the risk of chronic pain onset, potentially causing residual confounding in the study results.\u003c/p\u003e \u003cp\u003eFourth, there are limitations in the representativeness of the study population and subgroup analysis. The sample size of the rural population in this study was very small, and stratified analysis of the urban-rural subgroups could not be completed. There are significant differences in sleep patterns, exposure to pain risk factors, and access to medical resources among the urban and rural middle-aged and elderly population. The existing research results are mainly applicable to the urban middle-aged and elderly population, and extrapolation is somewhat limited. At the same time, the subjects of this study were only middle-aged and elderly people aged 45 and above in China, and the results could not be directly generalized to younger people, people of other races and countries.\u003c/p\u003e \u003cp\u003eFifth, the validation power of the fully adjusted model of the external validation cohort was insufficient. In the external validation cohort, there were significant deficiencies in covariates such as activities of daily living, instrumental activities of daily living, and depressive symptoms. If a fully adjusted strategy consistent with the main cohort was adopted, the sample size would sharply decrease, resulting in a significant deficiency in statistical power. Therefore, only a simplified covariate strategy adjusted for age and gender was used, which verified the direction and significance of the association. But the full adjustment analysis of the main queue could not be fully replicated, reducing the power of external validation to some extent [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSixth, the study did not delve into the intrinsic mechanisms by which sleep patterns affect chronic pain. Existing studies have only validated the prospective association between sleep patterns and the risk of chronic pain onset, without incorporating biomarkers such as inflammatory factors, neuroendocrine markers, and pain pathway-related indicators, thus failing to clarify the mediating pathways and intrinsic mechanisms of the association. Cohort studies and mechanism studies based on biological samples are needed in the future to further reveal the molecular mechanisms by which sleep patterns affect the occurrence of chronic pain [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate the contributions of all participants in the China Health and Retirement Longitudinal Study (CHARLS). We also extend our gratitude to the CHARLS research team at Peking University for providing the data and to the Institutional Review Board for its ethical approval (IRB00001052-11015).Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article are publicly available from the original cohort repositories:CHARLS(https://charls.pku.edu.cn/). The processed datasets (including subset data from CHARLS used for statistical analyses) and analytical code will be made available by the corresponding author (Kai Wang), without undue reservation, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS) data can be obtained from the Open Data Platform of Peking University (https://charls.pku.edu.cn/, Research No. 2025-042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were approved by the following institutional ethics committees:\u003c/p\u003e\n\u003cp\u003eCHARLS: Institutional Review Board of Peking University (IRB00001052-11015);\u003c/p\u003e\n\u003cp\u003eAll studies were conducted in accordance with local legislation, institutional requirements, and the principles of the Declaration of Helsinki. All participants provided written informed consent at the time of enrollment in the respective cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWooden S. Multimodal Approach to Chronic Pain Management. Nurs Clin North Am. 2025 Dec; 60 (4) : 619-629. The doi: 10.1016 / j.carol carroll nur 2024.12.012. Epub 2025 Feb 4. PMID: 41136123.\u003c/li\u003e\n \u003cli\u003eAgbor FBAT, Vance DE, Odii CO, Jones AR, Aroke EN. Healthy Diet Consumption Among Chronic Pain Populations: A Concept Analysis. Pain Manag Nurs. 2025 Oct; 26 (5) : e476-e488. 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PMID: 40592708; PMCID: PMC12856107.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Middle-aged and elderly people, Sleep patterns, Chronic pain, Risk of onset","lastPublishedDoi":"10.21203/rs.3.rs-9412531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9412531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the prospective association between sleep patterns and the risk of chronic pain onset in middle-aged and elderly Chinese individuals, and to validate the robustness of the results.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe main cohort (n\u0026thinsp;=\u0026thinsp;10,580) was constructed based on baseline data from the China Health and Retirement Longitudinal Survey (CHARLS) in 2011. Sleep patterns were evaluated using the Sleep Health Index (SMPI), and chronic pain outcomes were evaluated using follow-up data from 2013 and 2015. External validation cohorts (n\u0026thinsp;=\u0026thinsp;12,615) were constructed using follow-up data from 2015 to 2018. The Cox proportional hazards regression model was used to analyze the association between sleep patterns and the risk of chronic pain onset, and the robustness of the results was verified through subgroup analysis and sensitivity analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1075 new cases of chronic pain occurred in the main cohort during the 4-year follow-up period. For every 1-point increase in the sleep health index, the risk of chronic pain in middle-aged and elderly individuals decreased significantly by 9% (fully adjusted model HR\u0026thinsp;=\u0026thinsp;0.91, 95%CI:0.88\u0026ndash;0.94, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis showed that the protective effect of sleep patterns was significant in age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, 60\u0026ndash;69 years, male, female, and urban population, with only marginal insignificance in age\u0026thinsp;\u0026ge;\u0026thinsp;70 years. Sensitivity analysis (excluding onset within 2 years of follow-up, complete case analysis) results were highly consistent with the main analysis (HR\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.92, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the external validation cohort, after using the simplified covariation-adjusted strategy (adjusted only for age and gender), for every 1-point increase in the sleep health index, the risk of chronic pain onset decreased by 7% (HR\u0026thinsp;=\u0026thinsp;0.93, 95%CI:0.91\u0026ndash;0.96, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent and significant with the results of the main cohort.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHealthy sleep patterns are significantly associated with a reduced risk of chronic pain in middle-aged and elderly people in China, and improving sleep quality may be an important public health strategy for preventing chronic pain in middle-aged and elderly people.\u003c/p\u003e","manuscriptTitle":"A prospective study on Sleep Patterns and the Risk of Chronic pain in Middle-aged and Elderly People in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 00:19:39","doi":"10.21203/rs.3.rs-9412531/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-18T15:08:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-17T07:51:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T01:49:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T01:49:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-14T08:16:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d94a9d5b-97c8-4ef4-90a2-e9f7b7b1bf31","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T00:19:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 00:19:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9412531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9412531","identity":"rs-9412531","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}