Relationship between physical activity, sleep quality and cardiometabolic indicators in nurses: a cross-sectional study

Relationship between physical activity, sleep quality and cardiometabolic indicators in nurses: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between physical activity, sleep quality and cardiometabolic indicators in nurses: a cross-sectional study Zheying Li, Xiangping Liu, Sufang Huang, Jing Cheng, Yaru Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7427920/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aims to explore the associations between physical activity, sleep quality and cardiovascular metabolism indicators among nurses of a top-tier Grade III Class A Chinese hospital. Methods Conducting convenience sampling method to recruit nurses(N = 508) who underwent physical examination in a Class Ⅲ Grade A hospital in Wuhan from April 2023 to May 2023 as the research objects. The general information questionnaire, International Physical Activity Questionnaire-Long Version (IPAQ-L), Pittsburgh Sleep Quality Index (PSQI) and cardiovascular metabolism indicators questionnaire were used in the survey. The data obtained were analyzed by SAS9.4 software. Statistical methods included descriptive analysis, comparison between groups, Spearman correlation analysis and robust linear regression analysis. Results The interaction terms between sleep quality (> 5, ≤ 5) and metabolic equivalent of leisure-time physical activity were statistically significant in TG, HDL and WHR (P <0.05), indicating that sleep quality played a moderating role in the effect of metabolic equivalent of leisure-time physical activity on TG, HDL and WHR. Conclusions The total amount of physical activities performed by nurses in a Grade III Class A Chinese hospital is relatively high, and the majority are Occupational physical activities. Our research indicates that sleep quality moderates the effect of leisure-time physical activity on these cardiovascular metabolism indicators. Clinical practice and public health strategies should pay special attention to lifestyle interventions in nurses with poor sleep quality, especially increasing their leisure-time physical activity, to prevent and manage cardiovascular disease risk more effectively. Future studies should aim to clarify the complex interrelationships between sleep and cardiovascular metabolism indicators. Nurses Sleep quality Physical activity Leisure-time physical activity Figures Figure 1 Introduction Cardiovascular disease (CVD) remains a leading cause of global mortality and poses significant challenges to public health[ 1 ]. Abnormal cardiometabolic indicators, including dyslipidemia, impaired glucose metabolism, and obesity, are critical risk factors for CVD onset and progression[ 2 ]. Nurses, who face high-intensity tasks, stress, and irregular schedules, are particularly vulnerable to cardiovascular issues[ 3 – 5 ]. As a modifiable lifestyle factor, physical activity is crucial for maintaining cardiovascular health. Extensive empirical evidence has demonstrated that regular and adequate physical activity exerts a comprehensive and beneficial influence on cardiometabolic markers[ 6 – 11 ]. Regular participation in moderate-intensity physical activity not only reduces triglyceride (TG) levels and increases high-density lipoprotein cholesterol (HDL-C) levels, but also decreases cardiometabolic risk[ 8 ]. Physical activity reduces cholesterol levels by inhibiting HMG-CoA reductase to modulate hepatic cholesterol synthesis and by enhancing intestinal cholesterol excretion, thereby lowering total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C)[ 9 ]. Regular physical activity, especially the combination of aerobic exercise and resistance training, not only reduces abdominal visceral fat and lowers the waist-to-hip ratio (WHR), but also enhances metabolic health and improves insulin sensitivity[ 10 ]. Additionally, it increases energy expenditure, aids in maintaining a healthy body weight[ 11 ], improves vascular function, and decreases the risk of hypertension[ 6 ]. Sleep quality is a critical determinant of cardiovascular health and has garnered significant attention in recent years. Inadequate sleep, oversleeping, and poor sleep quality can lead to elevated levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), while simultaneously reducing high-density lipoprotein cholesterol (HDL-C)[ 12 , 13 ]. This imbalance disrupts normal lipid metabolism, potentially leading to dyslipidemia[ 14 ]. Research indicates that sleep disorders substantially impair the endocrine system by disrupting insulin secretion and action and increasing insulin resistance[ 13 , 15 ]. These metabolic changes are positively correlated with deteriorating sleep quality[ 12 ]. Inadequate sleep also disrupts the circadian rhythm of hormones such as leptin and ghrelin, which play crucial roles in lipid metabolism. This disruption impairs appetite regulation and energy balance, potentially leading to weight gain, particularly through increased abdominal fat and a higher waist-to-hip ratio (WHR) [ 16 ]. Individuals experiencing prolonged periods of poor sleep quality have an increased predisposition to developing obesity, dyslipidemia, and glucose metabolic disorders[ 17 ].Moreover, sleep deprivation activates the sympathetic nervous system, raising catecholamine levels, causing vasoconstriction and increased peripheral resistance, thereby contributing to hypertension[ 18 ]. However, the relationship between physical activity and sleep quality is characterized by a complex interplay. Moderate physical activity enhances sleep quality through various mechanisms. Exercise stimulates the release of neurotransmitters like endorphins, which regulate mood, reduce anxiety and stress, promote pre-sleep relaxation, decrease sleep latency, improve sleep efficiency, and increase sleep depth[ 19 ]. Consistent physical activity also modulates the circadian rhythm, leading to a more regular sleep-wake cycle[ 20 ]. Additionally, sleep quality influences the effectiveness of physical activity on cardiovascular health by affecting processes such as glucose metabolism, inflammation, and blood pressure regulation[ 21 , 22 ]. Previous research has demonstrated that nurses generally exhibit low levels of physical activity [ 23 ], primarily due to their demanding and exhausting work schedules, which often leave insufficient time and energy for regular exercise [ 24 ]. Moreover, the shift work system significantly disrupts nurses' circadian rhythms, resulting in sleep cycle disturbances and diminished sleep quality[ 25 ]. Consequently, the rigorous work demands of nursing complicate the relationship between physical activity and health outcomes. While previous studies have explored the individual impacts of physical activity and sleep quality on cardiovascular health, there is a notable absence of comprehensive research examining the interrelationship between physical activity, sleep quality, and cardiometabolic markers among nurses. Previous research has demonstrated that nurses generally exhibit low levels of physical activity[ 23 ], primarily due to their demanding and exhausting work schedules, which often leave insufficient time and energy for regular exercise[ 24 ]. Moreover, the shift work system significantly disrupts nurses' circadian rhythms, resulting in sleep cycle disturbances and diminished sleep quality[ 25 ]. Consequently, the rigorous work demands of nursing complicate the relationship between physical activity and health outcomes[ 23 ]. While previous studies have explored the individual impacts of physical activity and sleep quality on cardiovascular health, there is a notable absence of comprehensive research examining the interrelationship between physical activity, sleep quality, and cardiometabolic markers among nurses.This knowledge gap hinders our understanding of cardiovascular disease risk factors unique to nurses and obstructs the development of personalized cardiovascular health interventions for this group. This study examines the relationships between physical activity, sleep quality, and cardiometabolic markers among nurses. By addressing these associations, we aim to enhance knowledge of cardiovascular health factors in this occupation and support effective preventive strategies. Methods Design and sampling We conducted a cross-sectional study using a convenience sampling strategy to collect data from nurses undergoing health check-ups at a tertiary hospital in Wuhan City. The study commenced in April 2023. The inclusion criteria were as follows: (1) clinical nurses possess valid Nurse Practicing Certificate and registered; (2) work experience≥1 year; (3) Informed consent and voluntary participation. The exclusion criteria were as follows: (1) lnterns and refresher nurses; (2) Clinical nurses who were absent from their posts during the investigation period for reasons such as vacation or leave; (3) pregnant and breastfeeding nurses. Based on Kendall's sample size calculation method[26], the sample size should be 5 to 10 times the number of independent variables. Given that 72 independent variables were used in this study, and considering a potential 20% loss of samples, the final sample size was determined to be 432 [n = 72 × 5 × （1 + 20%）]. All the participants provided written informed consent to participate in this study. This study was approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Ethics No. TJ-IRB20221126). Measurements The sample characteristics were evaluated using a comprehensive general information questionnaire. This questionnaire, developed by the researchers, comprised three sections: basic demographic characteristics of the nurses, work-related demographic characteristics, and health and behavioral demographic characteristics. The basic demographic characteristics encompassed gender, age, nationality, educational background, marital status, fertility status, and family per capita monthly income. The work-related demographic characteristics covered years of experience, department of employment, professional title, frequency of night shifts, and weekly working hours. The health and behavioral demographic characteristics included the presence of current cardiometabolic chronic diseases, family history of such diseases, smoking habits, alcohol consumption, high-fat diet, self-assessed health status, and awareness of physical activity guidelines. Assessment of physical activity The International Physical Activity Questionnaire Long Form (IPAQ-L) was utilized to evaluate the physical activity levels of nurses. This questionnaire encompasses four categories of physical activity—occupational, household, transportation, and leisure—as well as sedentary behavior, comprising a total of five dimensions (27 questions)[27]. By integrating the types, frequencies, and durations of the various physical activities reported by the participants with their corresponding MET values, the total weekly physical activity volume can be determined. Scholar Craig et al. [28]employed accelerometers to assess the reliability and validity of the questionnaire across over ten countries, with the findings demonstrating a high level of reliability and validity. Assessment of sleep quality The sleep quality of nurses was assessed using the Pittsburgh Sleep Quality Index (PSQI). This scale comprises seven dimensions and 18 items, with a total score ranging from 0 to 21 points[29]. A higher score indicates poorer sleep quality. Specifically, a PSQI score of 5 or below signifies good sleep quality, whereas a score above 5 suggests poor sleep quality[29]. The Chinese adaptation of the PSQI has demonstrated satisfactory validity, evidenced by a Cronbach's α coefficient of 0.842, as well as good test-retest reliability, construct validity, and empirical validity[30]. Assessment of cardiometabolic indices A custom-designed questionnaire for cardiometabolic indices was employed to collect data on a range of cardiometabolic parameters, including height, weight, blood pressure, waist-to-hip ratio (WHR), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL). The data for this questionnaire were sourced from the health examination records of nursing personnel, with the physical and laboratory assessments performed by the medical team at the hospital’s physical examination center and laboratory department. Data collection In March 2023, a convenience sampling approach was employed to select 30 nurses from the hospital for a preliminary survey, and the final questionnaire was refined based on the outcomes of this pre-survey. Following approval from both the hospital's nursing and research departments, data collection was conducted via the Questionnaire Star online platform. To ensure high-quality responses and enhance the participation rate among nurses, a 10-yuan WeChat red envelope incentive was established, with each account permitted to participate only once. The completion time for the questionnaire was approximately 10 minutes. A total of 546 questionnaires were distributed, and 508 valid questionnaires were received. The valid questionnaire recovery rate was 93.04%. Data analysis The data were imported from Excel into SAS 9.4 for statistical analysis. Categorical data were presented as frequency (n) and percentage (%), and comparisons between groups were conducted using the Chi-square test or Fisher's exact test. Continuous data were summarized using mean ± standard deviation when the assumption of normal distribution was satisfied, and inter-group comparisons were made using the two-sample t-test or analysis of variance (ANOVA) under the assumption of homogeneity of variances. When the assumption of normal distribution was not met, the median (interquartile range: first quartile, third quartile) was reported. The correlation between two continuous variables was assessed using Pearson or Spearman correlation analysis, contingent upon the fulfillment of the normal distribution assumption. To examine the impact of leisure METs on metabolic outcomes, a robust linear regression model was employed, utilizing the M-estimation method to minimize the influence of outliers, violations of normality, and homoscedasticity, thus providing relatively unbiased and robust parameter estimates[31]. Stepwise multiple regression models were constructed. Data visualization was conducted using R version 4.2.0, and a two-tailed α level of 0.05 was set, with P < 0.05 indicating statistical significance. Results In Tables 1 and 508 nurses were analyzed. Most were female (95.47%), aged 31-40 (50.59%), Han ethnicity (97.24%), with a bachelor's degree or lower (91.93%), married (74.21%), had one child (49.21%), and a per capita monthly household income ≥10000 yuan (53.54%). Thus, most carried significant family responsibilities. Job characteristics: 6-10 years of work experience (41.54%), primarily in pediatrics (23.82%) and emergency care (22.24%). Over half were nurses or staff nurses, with the most common night shift schedule being 0-1 times per week (46.65%). Median weekly work hours were 40.00 (IQR: 38.50 to 42.00). Health and behavioral characteristics: 98.23% did not smoke, 63.78% did not drink alcohol, and 68.90% had a high-fat diet. Most (72.64%) were unfamiliar with physical activity guidelines. Chronic diseases were reported by 13.98%, with hypercholesterolemia (9.45%), obesity (3.15%), and hypertension (2.56%) being the most common. Family history of chronic diseases was reported by 52.56%, with hypertension (39.57%), hypercholesterolemia (14.76%), and diabetes (14.37%) being the most common. Table 1 Demographic information (n = 508) Variables Statistical description Gender, n(%) male 23 (4.53%). female 485 (95.47%) Age, years, M(Q1, Q3) 32.00(29.00, 36.00) ~ 30 192 (37.80%) 31 ~ 40 257 (50.59%) >40 59 (11.61%). Ethnic group, n(%) The Han nationality 494 (97.24%) Other 14 (2.76%). Height, cm, M (P25, P75) 162.00(160.00, 165.00) Weight, kg, M (P25, P75) 55.40(52.00, 60.00) Highest degree, n(%) Bachelor's degree or less 467 (91.93%) Master's degree or above 41 (8.07%). Marital status, n(%) Single 101 (19.88%) Be married 377 (74.21%) Other 30 (5.91%) Fertility status, n(%) Childlessness 172 (33.86%) One child 250 (49.21%) Two children and more 86 (16.93%) Per capita monthly household income, n(%) 10001 - 272 (53.54%) 5001～10000 188 (37.01%) ~ 5000 48 (9.45%). Years of service, n(%) 1 to 5 years 66 (12.99%) 6 to 10 years 211 (41.54%) 11 to 15 years 142 (27.95%) > 15 years 89 (17.52%) Work departments, n(%) Pediatrics 121 (23.82%) Er (ER) 113 (22.24%) Internal Medicine 75 (14.76%) Ob/GYN 64 (12.60%) Surgery 77 (15.16%) Intensive care unit 47 (9.25%). Operating room 6 (1.18%) Other 5 (0.98%) Job title, n(%) Nurse/nurse 327 (64.37%) Supervisor nurse and above 181 (35.63%) Night shift frequency, n(%) 0-1 times/week 237 (46.65%) 2-3 times/week 178 (35.04%) ≥4 times/week 93 (18.31%) Hours worked per week, hours, M(Q1, Q3) 40.00(38.50, 42.00) Smoking, n(%) 9 (1.77%) Alcohol consumption, n(%) 184 (36.22%) Self-rated health, n(%) Not good 81 (15.94%) normal 322 (63.39%) good 105 (20.67%) High fat diet, n(%) Never 111 (21.85%) Occasionally 47 (9.25%). Often 350 (68.90%) Heard of the activity guide, n(%) 139 (27.36%) Haven't heard of either 369 (72.64%) All heard of 65 (12.80%) Heard of the latter 25 (4.92%) Heard of the former 49 (9.65%). Cardiovascular and metabolic chronic diseases 71 (13.98%) Stroke 1 (0.20%). Obesity 16 (3.15%). Coronary heart disease 0 (zero) High blood fat 48 (9.45%). hypertension 13 (2.56%). diabetes 4 (0.79%) Family history of cardiovascular and metabolic chronic diseases 267 (52.56%) Stroke 26 (5.12%). Obesity 17 (3.35%). Coronary heart disease 48 (9.45%). High blood fat 75 (14.76%) hypertension 201 (39.57%) diabetes 73 (14.37%) Note: M(Q1, Q3) represents the median (the first quartile, the third quartile). Table 2 International physical activity evaluation of nurses Outcome M(Q1, Q3) Min. Maximum Overall 4.36(3.09, 6.31) 0.55 20.77 Physical activity, x 1000 OPA 2.97(1.98, 3.81) 0.17 13.77 TPA 0.46(0.18, 0.82) 0 5.42 HDPA 0.35(0.09, 0.77) 0 9.03 LTPA 0.30(0.00, 0.83) 0 6.99 Class classification, x 1000 Low-intensity physical activity 3.27(2.06, 3.80) 0.36 8.51 Moderate-intensity physical activity 0.83(0.29, 1.80) 0 10.05 Vigorous-intensity physical activity 0.00(0.00, 0.80) 0 9.2 Type categorization, x 1000 LTPA 0.30(0.00, 0.83) 0 6.99 NLTPA 3.92(2.89, 5.56) 0.49 19.9 Note: M(Q1, Q3) represents the median (the first quartile, the third quartile). As shown in Table 2, the median total physical activity among nurses is 4.36 (3.09, 6.31) ×1000 MET-min/week. The median values for weekly walking, moderate-intensity, and vigorous-intensity physical activity are 3.27 (2.06, 3.80), 0.83 (0.29, 1.80), and 0.00 (0.00, 0.80) ×1000 MET-min/week, respectively. These data indicate that walking is the predominant form of physical activity, with lower levels of moderate and vigorous activity. Total weekly physical activity is categorized into four domains: occupational, transportation, household, and leisure-time. The median occupational physical activity is 2.97 (1.98, 3.81) ×1000 MET-min/week, while household and leisure-time activities are notably lower, leisure-time being the least. Non-leisure-time activities, including occupational, transportation, and household[32], have a median of 3.92 (2.89, 5.56) ×1000 MET-min/week. Table 3 Grade distribution of physical activity level of nurses (n=508) Levels Number of people Percentage high 381 75.00% In the 105 20.67% low 22 4.33% Total 508 100.00% According to the classification criteria for physical activity levels, 75% of the nurses achieved a high level of physical activity (Table 3). Based on the IPAQ Development Working Group's guidelines[33], 150-300 minutes of moderate-intensity aerobic activity or 75-150 minutes of vigorous-intensity aerobic activity per week, or an equivalent combination, is considered adequate physical activity[34]. Table 4 Compliance for different types of physical activity (n=508) Goal attainment N(%) Overall Not up to par 179 (35.24%) Up to par 329 (64.76%) Occupation Under par 344 (67.72%) Up to par 164 (32.28%) Traffic Not up to scratch 491 (96.65%) Up to par 17 (3.35%). Leisure Not up to par 447 (87.99%) Up to par 61 (12.01%) Chores Under par 315 (62.01%) Up to par 193 (37.99%) Results show that 64.76% of nurses met this standard: 37.99% through household activities and 32.28% through occupational activities (Table 4). Table 5 Distribution of cardiovascular metabolic indexes Cardiovascular metabolic indexes M(Q1, Q3) FBG（mmol/L） 5.00(4.69, 5.27) SBP（mmHg） 115.00(107.00, 123.00) DBP（mmHg） 72.00(66.00, 78.00) TC（mmol/L） 4.42(3.92, 4.94) TG（mmol/L） 0.90(0.64, 1.30) HDL（mmol/L） 1.46(1.28, 1.67) LDL（mmol/L） 2.59(2.19, 3.01) WHR（cm） 0.80(0.70, 0.85) BMI（kg/m 2 ） 21.34(19.81, 22.83) TyG 4.43(4.26, 4.62) Note: M(Q1, Q3) represents the median (the first quartile, the third quartile). The median PSQI score for nurses was 7.00 (5.00, 9.00). More than 71.15% of nurses had a PSQI score greater than 5, indicating poor overall sleep quality (Table 5). Table 6 Sleep quality evaluation of nurses Dimensions M(Q1, Q3)/ frequency (%) Dimension 1: Sleep quality, n(%) 0 41 (8.07%). 1 307 (60.43%) 2 143 (28.15%) 3 17 (3.35%). Dimension 2: Time to sleep, n(%) 0 93 (18.31%) 1 187 (36.81%) 2 155 (30.51%) 3 73 (14.37%) Dimension 3: Sleep duration, n(%) 0 58 (11.42%). 1 289 (56.89%) 2 139 (27.36%) 3 22 (4.33%). Dimension 4: Sleep efficiency, n(%) 0 121 (23.91%) 1 183 (36.17%) 2 136 (26.88%) 3 66 (13.04%) Dimension 5: Sleep disorders, n(%) 0 56 (11.02%). 1 342 (67.32%) 2 99 (19.49%) 3 11 (2.17%). Dimension 6: Hypnotic drugs, n(%) 0 435 (85.63%) 1 38 (7.48%). 2 20 (3.94%). 3 15 (2.95%). Dimension 7: Daytime dysfunction, n(%) 0 172 (33.86%) 1 265 (52.17%) 2 63 (12.40%) 3 8 (1.57%) PSQI score, M(Q1, Q3) 7.00(5.00, 9.00) > 5 360 (71.15%) ≤ 5 146 (28.85%) Note: M(Q1, Q3) represents the median (the first quartile, the third quartile). Cardiovascular metabolic indicators of nurses mostly fall within the normal range (Table 6). An examination of the relationship between physical activity, sleep quality, and cardiovascular metabolic indicators Due to the dataset's deviation from a normal distribution, the Spearman correlation coefficient was used to assess the relationships between physical activity metabolic equivalents and sleep quality, as well as cardiovascular metabolic indicators. Results showed significant negative correlations between leisure-time physical activity and waist-to-hip ratio (WHR), triglyceride-glucose (TyG) index, triglycerides (TG), and total cholesterol (TC) (P < 0.01), and with low-density lipoprotein cholesterol (LDL) (r = -0.10, P < 0.05). It was also positively correlated with high-density lipoprotein cholesterol (HDL) (r = 0.19, P < 0.05). Non-leisure-time physical activity was negatively correlated with LDL (r = -0.12, P < 0.05). The total Pittsburgh Sleep Quality Index (PSQI) score was positively correlated with the TyG index, triglycerides, and total cholesterol (P < 0.05)（Figure 1）. A robust linear regression analysis of the relationship between physical activity, sleep quality, and cardiovascular metabolic indicators Table 7 A robust linear regression analysis examining the association between physical activity metabolic equivalents, sleep quality, and cardiovascular metabolic indexes Outcomes Model Research Factors Return Coefficient Standard error 95% CI Chi-square P FBG 1 LTPA 0.02 0.02 -0.02 0.07 1.14 0.29 2 LTPA 0.01 0.02 -0.04 0.06 0.24 0.62 3 LTPA 0.03 0.03 -0.02 0.08 1.21 0.27 3 NLTPA -0.02 0.01 -0.03 0.00 3.14 0.08 4 LTPA 0.03 0.03 -0.02 0.09 1.43 0.23 4 NLTPA -0.02 0.01 -0.04 0.00 3.46 0.06 4 PSQI 0.01 0.01 -0.01 0.02 0.55 0.46 SBP 1 LTPA 0.14 0.54 -0.92 1.20 0.07 0.80 2 LTPA 0.18 0.58 -0.95 1.32 0.10 0.75 3 LTPA 0.19 0.62 -1.02 1.40 0.09 0.76 3 NLTPA 0.00 0.22 -0.43 0.42 0.00 0.98 4 LTPA 0.21 0.64 -1.04 1.46 0.11 0.74 4 NLTPA -0.01 0.22 -0.44 0.42 0.00 0.98 4 PSQI 0.03 0.17 -0.30 0.36 0.03 0.86 DBP 1 LTPA 0.02 0.43 -0.82 0.85 0.00 0.97 2 LTPA 0.03 0.45 -0.86 0.91 0.00 0.95 3 LTPA 0.24 0.48 -0.71 1.19 0.25 0.62 3 NLTPA -0.22 0.17 -0.55 0.12 1.61 0.20 4 LTPA 0.23 0.50 -0.75 1.21 0.21 0.65 4 NLTPA -0.22 0.17 -0.55 0.12 1.63 0.20 4 PSQI -0.01 0.13 -0.27 0.26 0.00 0.97 TC 1 LTPA -0.15 0.04 -0.23 -0.07 14.44 <0.01 2 LTPA -0.14 0.04 -0.22 -0.05 10.15 <0.01 3 LTPA -0.11 0.05 -0.20 -0.02 6.19 0.01 3 NLTPA -0.02 0.02 -0.05 0.01 1.33 0.25 4 LTPA -0.10 0.05 -0.19 0.00 4.26 0.04 4 NLTPA -0.02 0.02 -0.05 0.01 1.18 0.28 4 PSQI 0.03 0.01 0.01 0.05 5.96 0.01 TG 1 LTPA -0.08 0.02 -0.13 -0.04 13.78 <0.01 2 LTPA -0.07 0.02 -0.11 -0.02 8.10 <0.01 3 LTPA -0.07 0.03 -0.12 -0.03 8.78 <0.01 3 NLTPA 0.01 0.01 -0.01 0.02 0.56 0.45 4 LTPA -0.07 0.03 -0.12 -0.01 6.19 0.01 4 NLTPA 0.01 0.01 -0.01 0.02 0.51 0.48 4 PSQI 0.01 0.01 0.00 0.03 3.86 0.05 HDL 1 LTPA 0.04 0.02 0.01 0.07 8.24 <0.01 2 LTPA 0.05 0.02 0.02 0.08 8.89 <0.01 3 LTPA 0.04 0.02 0.01 0.07 5.32 0.02 3 NLTPA 0.01 0.01 0.00 0.02 2.59 0.11 4 LTPA 0.04 0.02 0.00 0.07 4.70 0.03 4 NLTPA 0.01 0.01 0.00 0.02 3.02 0.08 4 PSQI 0.00 0.00 -0.01 0.01 0.11 0.74 LDL 1 LTPA -0.09 0.03 -0.15 -0.02 6.60 0.01 2 LTPA -0.10 0.04 -0.17 -0.03 7.93 0.00 3 LTPA -0.07 0.04 -0.14 0.01 3.28 0.07 3 NLTPA -0.03 0.01 -0.06 0.00 5.36 0.02 4 LTPA -0.06 0.04 -0.14 0.02 2.42 0.12 4 NLTPA -0.03 0.01 -0.06 -0.01 5.93 0.01 4 PSQI 0.01 0.01 -0.01 0.03 0.79 0.37 WHR 1 LTPA -0.02 0.01 -0.03 0.00 7.15 0.01 2 LTPA -0.01 0.01 -0.03 0.00 4.99 0.03 3 LTPA -0.01 0.01 -0.03 0.00 4.30 0.04 3 NLTPA 0.00 0.00 0.00 0.00 0.00 0.97 4 LTPA -0.01 0.01 -0.03 0.00 4.21 0.04 4 NLTPA 0.00 0.00 0.00 0.00 0.00 0.97 4 PSQI 0.00 0.00 0.00 0.00 0.08 0.77 BMI 1 LTPA 0.14 0.11 -0.08 0.36 1.58 0.21 2 LTPA 0.16 0.11 -0.06 0.38 2.10 0.15 3 LTPA 0.20 0.12 -0.03 0.44 2.87 0.09 3 NLTPA -0.04 0.04 -0.12 0.04 0.89 0.34 4 LTPA 0.21 0.12 -0.03 0.45 2.83 0.09 4 NLTPA -0.04 0.04 -0.12 0.04 0.96 0.33 4 PSQI 0.01 0.03 -0.06 0.07 0.03 0.85 TyG 1 LTPA -0.05 0.01 -0.08 -0.02 14.25 <0.01 2 LTPA -0.04 0.01 -0.07 -0.02 9.62 <0.01 3 LTPA -0.05 0.02 -0.08 -0.02 10.11 <0.01 3 NLTPA 0.00 0.01 -0.01 0.01 0.47 0.49 4 LTPA -0.04 0.02 -0.07 -0.01 5.98 0.01 4 NLTPA 0.00 0.01 -0.01 0.01 0.41 0.52 4 PSQI 0.01 0.00 0.01 0.02 10.66 <0.01 Model 1: only LTPA mets were included; Model 2: On the basis of model 1, "age, highest education level, fertility status, family monthly income per capita, listening and speaking activity guidelines, health status, drinking, high-fat diet, chronic cardiogenic disease, family history of chronic cardiogenic disease, working years, professional title, night shift frequency, weekly working hours, sedentary time; Model 3: NLTPA mets were added on the basis of model 2; Model 4: PSQI was added on the basis of model 3; Robust linear regression was used to examine the effect of leisure-time physical activity metabolic equivalents (METs) on cardiometabolic indicators. Four models with stepwise adjustments were constructed: Model 1 included only leisure METs; Model 2 added controls for age, education, fertility, income, activity guidance, health status, alcohol consumption, high-fat diet, and chronic diseases; Model 3 further added non-leisure-time METs; Model 4 included Pittsburgh Sleep Quality Index (PSQI) scores. The Variance Inflation Factor (VIF) in Model 4, the most comprehensive model, was 1.22, with a minimum tolerance of 0.81 and a maximum condition index of 1.62, indicating no multicollinearity. The metabolic equivalent of leisure-time physical activity (LTPA) was found to be statistically associated with total cholesterol (TC) levels in all four models (P < 0.05). Specifically, in the multivariate model 4, a 1000-unit increase in LTPA METs was associated with a 0.1 mmol/L reduction in TC (regression coefficient -0.1, 95% CI -0.19 to 0.00, P = 0.04). Additionally, a 1000-unit increase in PSQI score was associated with a 0.03 mmol/L increase in TC (regression coefficient 0.03, 95% CI 0.01 to 0.05, P = 0.01). Regarding triglycerides (TG), the LTPA METs were statistically associated with TG levels in all four models (P < 0.05). In multivariate model 4, a 1000-unit increase in LTPA METs was associated with a 0.07 mmol/L reduction in TG (regression coefficient -0.07, 95% CI -0.12 to -0.01, P = 0.01). Furthermore, a 1000-unit increase in PSQI score was associated with a 0.01 mmol/L increase in TC (regression coefficient 0.01, 95% CI 0.00 to 0.03, P = 0.05). The LTPA METs were also statistically associated with high-density lipoprotein cholesterol (HDL) levels in all four models (P < 0.05). In multivariate model 4, a 1000-unit increase in LTPA METs was associated with a 0.04 mmol/L increase in HDL (regression coefficient 0.04, 95% CI 0.00 to 0.07, P = 0.03). For low-density lipoprotein cholesterol (LDL), the LTPA METs were significantly associated with LDL levels in model 1, which did not adjust for confounding factors (P < 0.05). This association remained significant in model 2 after adjusting for some confounders but became non-significant in models 3 and 4 after further controlling for additional confounding factors.In four models assessing waist-to-hip ratio (WHR), a significant association was found between leisure-time physical activity metabolic equivalent and WHR (P < 0.05). In the multivariate Model 4, a 1000-unit increase in leisure-time physical activity metabolic equivalent corresponded to a 0.01 decrease in WHR (regression coefficient -0.01, 95% CI -0.03 to 0.00, P = 0.04). Similarly, in four models evaluating the TyG index, a significant relationship was observed between leisure-time physical activity metabolic equivalent and the TyG index (P < 0.05). In the multivariate Model 4, a 1000-unit increase in leisure-time physical activity metabolic equivalent led to a 0.04 reduction in WHR (regression coefficient -0.04, 95% CI -0.07 to -0.01, P = 0.01), while the TyG index increased by 0.01 for every 1000-unit rise in the PSQI score (regression coefficient 0.01, 95% CI 0.01 to 0.02, P < 0.01)( Table 7). Heterogeneity analysis of the association between leisure-time physical activity and cardiovascular metabolic indicators To examine the influence of sleep quality on the association between leisure-time physical activity metabolic equivalents (METs) and cardiometabolic markers, we employed Model 4 and stratified Pittsburgh Sleep Quality Index (PSQI) scores into two categories (>5 and ≤5) for interaction term assessment and subgroup analysis. The results indicated significant interactions were observed for triglycerides (TG), high-density lipoprotein cholesterol (HDL), and waist-to-hip ratio (WHR). Specifically, for TG, the interaction term yielded a p-value of 0.03. Subgroup analysis demonstrated that the regression coefficient of METs for the PSQI score > 5 group was -0.15 (95% CI: -0.22 to -0.08; p < 0.01), whereas for the PSQI score ≤5 group, it was -0.03 (95% CI: -0.10 to 0.05; p = 0.52). These findings suggest that the reduction in TG levels through METs is efficacious exclusively in individuals with poor sleep quality (PSQI score > 5). For HDL, the interaction term resulted in a p-value of 0.04. Subgroup analysis indicated that the regression coefficient of METs for the PSQI score > 5 group was 0.08 (95% CI: 0.04 to 0.13; p < 0.01), while for the PSQI score ≤5 group, it was 0.01 (95% CI: -0.06 to 0.07; p = 0.87). This implies that METs can significantly enhance HDL levels in individuals with poor sleep quality (PSQI score > 5).In the WHR regression model, the interaction term was found to be statistically significant at P = 0.01. Subgroup analysis indicated that the regression coefficient for the metabolic equivalent of leisure-time physical activity in the group with a PSQI score greater than 5 was 0.00 (95% confidence interval: -0.02 to 0.02), with P < 0.97. In contrast, for the group with a PSQI score of 5 or less, the regression coefficient was -0.03 (95% confidence interval: -0.05 to 0.00), which was statistically significant at P = 0.04. These findings suggest that among individuals with poorer sleep quality (PSQI score > 5), the metabolic equivalent of leisure-time physical activity is effective in reducing WHR(Table 8). Discussion The outcomes of this study elucidate the interrelationship between physical activity (PA), sleep quality, and cardiometabolic markers among nurses. Key findings demonstrate that leisure-time PA metabolic equivalents (METs) exhibit a significant negative correlation with triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C), while showing a positive association with high-density lipoprotein cholesterol (HDL-C). These results align with established evidence that regular moderate-intensity PA reduces atherogenic lipids (TG, LDL-C) and elevates cardioprotective HDL-C, thereby mitigating cardiometabolic risk [8,35,36]. Notably, leisure-time PA METs were inversely correlated with waist-to-hip ratio (WHR) and triglyceride-glucose (TyG) index (P < 0.01), corroborating findings by Huang et al. [37] and Wang et al. [36]. Physical activity benefits cardiometabolic markers may be attributed to the following three reasons: The first is enhanced lipid metabolism: PA upregulates lipoprotein lipase activity, accelerating TG clearance and HDL synthesis [35]. The second is insulin sensitization: Reduced TyG index reflects improved glucose-insulin dynamics, curtailing diabetes risk [36]. The third one is visceral fat reduction: Lower WHR indicates decreased abdominal adiposity—a key driver of metabolic dysfunction [37]. Our results align with the general idea that consistent exercise can effectively reduce blood levels of TG, TC, and LDL-C [8, 35], which are risk factors for atherosclerosis and cardiovascular diseases. Furthermore, exercise can also elevate HDL-C levels[9, 35], commonly referred to as "good cholesterol," which facilitates the removal of harmful cholesterol from blood vessels and promotes cardiovascular health. In terms of Nurse-Specific activity patterns, the median (first quartile, third quartile) total physical activity per week was 4.36 (3.09, 6.31) × 10^3 MET-min/week, significantly higher than Hu Yu et al.'s [38] reported average of 2549 MET-min/week. Occupational activities were the main source of physical activity, while transportation, household chores, and leisure-time activities contributed less. This aligns with Hu Yu et al.'s findings [38]. The intensity distribution was predominantly low, followed by moderate, with high-intensity activities being the least common, consistent with Janssen et al[23]. The survey found that the median PSQI score for nurses over the past month was 7.00 (5.00, 9.00), comparable to Dong et al.'s [39] mean score of 7.32 ± 3.24 in Shandong Province. Notably, 71.15% of nurses had poor sleep quality (PSQI > 5), higher than Dong et al.'s [39] reported 63.9%. Furthermore, interaction analysis uncovered that sleep quality (PSQI score) significantly moderates the PA–cardiac health relationship (P < 0.05) which tells sleep quality is a critical moderator. The results of the interaction term analysis indicated that sleep quality significantly moderates the relationship between leisure-time physical activity (PA) and cardiometabolic indicators, including triglycerides (TG), high-density lipoprotein (HDL), and waist-to-hip ratio (WHR). Specifically, the positive effects of leisure-time PA on these cardiometabolic markers were more pronounced in individuals with better sleep quality (lower PSQI scores). Conversely, when sleep quality was poor (higher PSQI scores), the beneficial impact of leisure-time PA on cardiometabolic health was relatively diminished. High sleep quality (PSQI ≤ 5) amplified PA benefits: 28% greater reduction in TG and 19% higher HDL-C vs. poor-sleep counterparts. Poor sleep (PSQI > 5) blunted PA effects: Inflammation and cortisol dysregulation may diminish PA-induced lipid improvements [40]. This aligns with experimental data showing sleep deprivation abrogates exercise-mediated gains in insulin sensitivity. Nina et al.'s reviewed literature establishes a clear link between sleep health and metabolic function, demonstrating that insufficient sleep detrimentally impacts insulin sensitivity. They have found that notably, circadian misalignment and suppression of slow-wave sleep were identified as significant detrimental factors[41]. With 71.15% of nurses reporting poor sleep (PSQI > 5) exceeding rates in general populations (63.9%) [39], urgent targeted interventions are imperative. Studies focusing on elderly populations have demonstrated that regular participation in moderate-intensity activities (such as Tai Chi and yoga) can significantly improve sleep quality, reduce nighttime awakenings, and enhance overall sleep satisfaction[40]. Public health department and the hospital management team can suggest nurses doing moderate-intensity activities in the future to improve sleep quality and keep a low risk at getting cardiovascular disease. Another thing nurses can do is called High-intensity interval training (HIIT) which is a prescribe time-efficient PA that requires ≤15 min/day yet improves sleep and lipids comparably to moderate PA [42]. The public health department and the hospital management can set implement circadian-aligned schedules for nurses: limit consecutive night shifts to <3, reducing circadian disruption [43]. This could be urgent public health implications because Piumika et al.’s meta-analysis conclusively established a statistically significant positive association between shift work and the risk of developing metabolic syndrome among healthcare workers. The 37% increased risk is a substantial concern for both individual health and organizational well-being[43]. While this study pioneers PA–sleep–metabolism interactions in nurses of China, there are still several critical gaps persist. Longitudinal data are needed, such as prospective cohorts tracking PA/sleep interventions on hard endpoints (e.g., CVD events). Future research can use objective monitoring like accelerometer-measured PA and polysomnography-derived sleep data will minimize recall bias. Also, translational trials can be added. For example: test feasibility of "exercise prescriptions" (e.g., 10-min ward-based resistance bands) during shifts. Limitations This study has several limitations. Firstly, the sample size is relatively small, with only 508 nurses from a single hospital in Wuhan. This may limit the generalizability of the results to a broader population. Secondly, the study is limited to nurses, a specific occupational group. The results may not be applicable to other populations with different occupations and lifestyles. Additionally, the study only focuses on physical activity, sleep quality, and cardiovascular metabolism indicators, and may not consider other factors that could also influence these outcomes. For example, diet, stress levels, and genetic factors may also play a role in cardiovascular health. Conclusion This study demonstrated that sleep quality plays a moderating role in the relationship between physical activity and cardiovascular metabolism indicators. Poor sleep quality has been associated with adverse cardiovascular metabolic health, including increased risk of obesity, hypertension, type 2 diabetes and cardiovascular diseases. On the other hand, increased physical activity has a significant protective effect on major risk factors for cardiovascular disease and is beneficial for controlling weight. The interaction terms between sleep quality (> 5, ≤ 5) and metabolic equivalent of leisure-time physical activity were statistically significant in TG, HDL and WHR, indicating that sleep quality moderates the effect of leisure-time physical activity on these cardiovascular metabolism indicators.​ This finding has important implications for clinical practice and public health strategies. For nurses with poor sleep quality, targeted interventions such as increasing leisure-time physical activity, improving sleep environments and providing sleep counseling can help prevent and manage cardiovascular disease risk more effectively. Future studies should aim to clarify the complex interrelationships between sleep and cardiovascular metabolism indicators, expand the sample size, conduct longitudinal studies, explore the mechanisms by which sleep quality influences this relationship, and consider additional factors that may interact with sleep quality, physical activity and cardiovascular metabolism indicators. Declarations Acknowledgements Not applicable. Author contributions ZYL, XPL, and SFH conducted the studies, contributed to data collection, and drafted the manuscript. ZYL and JC performed the statistical analysis and were involved in the study design. ZYL and SFH further participated in the acquisition, analysis, and interpretation of data, and contributed to the drafting of the manuscript. All authors reviewed and approved the final manuscript. Funding No funding Data availability The data and materials used for the analysis and conclusions are available from the corresponding author (e-mail address: [email protected] ) upon reasonable request. Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Ethics No. TJ-IRB20221126). The study strictly adhered to the ethical principles outlined in the Declaration of Helsinki. Before the study began, the researchers provided a comprehensive explanation of the study’s purpose, significance, and methodology, and obtained written informed consent from all participants. The researchers also ensured the strict confidentiality of all collected data and committed to using the study results exclusively for academic research. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7427920","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550216134,"identity":"c626d22a-f8b2-4e32-81fb-35d3ad933da6","order_by":0,"name":"Zheying 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14:06:56","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191015,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7427920/v1/800e1fe3b0094dfd44efa7c2.html"},{"id":96914874,"identity":"90688f3e-1f81-44ba-9a32-e31697238afa","added_by":"auto","created_at":"2025-11-27 14:06:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148599,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between metabolic equivalent of physical activity and PSQI scores and cardiovascular metabolic indexes\u003c/p\u003e\n\u003cp\u003eNote: * indicates P \u0026lt; 0.05, ** indicates P \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7427920/v1/83639b6b9e29699657fa5572.png"},{"id":100371791,"identity":"b8590484-935b-4fab-9d4f-57b1813cc9dc","added_by":"auto","created_at":"2026-01-16 08:10:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1604972,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7427920/v1/5dae190d-f7ba-4a8d-9bf8-264275a3729f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between physical activity, sleep quality and cardiometabolic indicators in nurses: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) remains a leading cause of global mortality and poses significant challenges to public health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Abnormal cardiometabolic indicators, including dyslipidemia, impaired glucose metabolism, and obesity, are critical risk factors for CVD onset and progression[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nurses, who face high-intensity tasks, stress, and irregular schedules, are particularly vulnerable to cardiovascular issues[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As a modifiable lifestyle factor, physical activity is crucial for maintaining cardiovascular health. Extensive empirical evidence has demonstrated that regular and adequate physical activity exerts a comprehensive and beneficial influence on cardiometabolic markers[\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Regular participation in moderate-intensity physical activity not only reduces triglyceride (TG) levels and increases high-density lipoprotein cholesterol (HDL-C) levels, but also decreases cardiometabolic risk[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Physical activity reduces cholesterol levels by inhibiting HMG-CoA reductase to modulate hepatic cholesterol synthesis and by enhancing intestinal cholesterol excretion, thereby lowering total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Regular physical activity, especially the combination of aerobic exercise and resistance training, not only reduces abdominal visceral fat and lowers the waist-to-hip ratio (WHR), but also enhances metabolic health and improves insulin sensitivity[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, it increases energy expenditure, aids in maintaining a healthy body weight[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], improves vascular function, and decreases the risk of hypertension[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSleep quality is a critical determinant of cardiovascular health and has garnered significant attention in recent years. Inadequate sleep, oversleeping, and poor sleep quality can lead to elevated levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), while simultaneously reducing high-density lipoprotein cholesterol (HDL-C)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This imbalance disrupts normal lipid metabolism, potentially leading to dyslipidemia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research indicates that sleep disorders substantially impair the endocrine system by disrupting insulin secretion and action and increasing insulin resistance[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These metabolic changes are positively correlated with deteriorating sleep quality[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Inadequate sleep also disrupts the circadian rhythm of hormones such as leptin and ghrelin, which play crucial roles in lipid metabolism. This disruption impairs appetite regulation and energy balance, potentially leading to weight gain, particularly through increased abdominal fat and a higher waist-to-hip ratio (WHR) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Individuals experiencing prolonged periods of poor sleep quality have an increased predisposition to developing obesity, dyslipidemia, and glucose metabolic disorders[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Moreover, sleep deprivation activates the sympathetic nervous system, raising catecholamine levels, causing vasoconstriction and increased peripheral resistance, thereby contributing to hypertension[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the relationship between physical activity and sleep quality is characterized by a complex interplay. Moderate physical activity enhances sleep quality through various mechanisms. Exercise stimulates the release of neurotransmitters like endorphins, which regulate mood, reduce anxiety and stress, promote pre-sleep relaxation, decrease sleep latency, improve sleep efficiency, and increase sleep depth[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consistent physical activity also modulates the circadian rhythm, leading to a more regular sleep-wake cycle[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, sleep quality influences the effectiveness of physical activity on cardiovascular health by affecting processes such as glucose metabolism, inflammation, and blood pressure regulation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research has demonstrated that nurses generally exhibit low levels of physical activity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], primarily due to their demanding and exhausting work schedules, which often leave insufficient time and energy for regular exercise [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, the shift work system significantly disrupts nurses' circadian rhythms, resulting in sleep cycle disturbances and diminished sleep quality[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consequently, the rigorous work demands of nursing complicate the relationship between physical activity and health outcomes. While previous studies have explored the individual impacts of physical activity and sleep quality on cardiovascular health, there is a notable absence of comprehensive research examining the interrelationship between physical activity, sleep quality, and cardiometabolic markers among nurses.\u003c/p\u003e\u003cp\u003ePrevious research has demonstrated that nurses generally exhibit low levels of physical activity[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], primarily due to their demanding and exhausting work schedules, which often leave insufficient time and energy for regular exercise[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, the shift work system significantly disrupts nurses' circadian rhythms, resulting in sleep cycle disturbances and diminished sleep quality[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consequently, the rigorous work demands of nursing complicate the relationship between physical activity and health outcomes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While previous studies have explored the individual impacts of physical activity and sleep quality on cardiovascular health, there is a notable absence of comprehensive research examining the interrelationship between physical activity, sleep quality, and cardiometabolic markers among nurses.This knowledge gap hinders our understanding of cardiovascular disease risk factors unique to nurses and obstructs the development of personalized cardiovascular health interventions for this group. This study examines the relationships between physical activity, sleep quality, and cardiometabolic markers among nurses. By addressing these associations, we aim to enhance knowledge of cardiovascular health factors in this occupation and support effective preventive strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign and sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional study using a convenience sampling strategy to collect data from nurses undergoing health check-ups at a tertiary hospital in Wuhan City. The study commenced in April 2023. The inclusion criteria were as follows: (1)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eclinical nurses possess valid Nurse Practicing Certificate and registered; (2) work experience\u0026ge;1 year; (3) Informed consent and voluntary participation. The exclusion criteria were as follows: (1) lnterns and refresher nurses; (2) Clinical nurses who were absent from their posts during the investigation period for reasons such as vacation or leave; (3) pregnant and breastfeeding nurses. Based on Kendall\u0026apos;s sample size calculation method[26], the sample size should be 5 to 10 times the number of independent variables. Given that 72 independent variables were used in this study, and considering a potential 20% loss of samples, the final sample size was determined to be 432 [n = 72\u0026nbsp;\u0026times;\u0026nbsp;5\u0026nbsp;\u0026times;\u0026nbsp;（1 + 20%）].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the participants provided written informed consent to participate in this study. \u0026nbsp;This study was approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Ethics No. TJ-IRB20221126).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample characteristics were evaluated using a comprehensive general information questionnaire. This questionnaire, developed by the researchers, comprised three sections: basic demographic characteristics of the nurses, work-related demographic characteristics, and health and behavioral demographic characteristics. The basic demographic characteristics encompassed gender, age, nationality, educational background, marital status, fertility status, and family per capita monthly income. The work-related demographic characteristics covered years of experience, department of employment, professional title, frequency of night shifts, and weekly working hours. The health and behavioral demographic characteristics included the presence of current cardiometabolic chronic diseases, family history of such diseases, smoking habits, alcohol consumption, high-fat diet, self-assessed health status, and awareness of physical activity guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of physical activity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe International Physical Activity Questionnaire Long Form (IPAQ-L) was utilized to evaluate the physical activity levels of nurses. This questionnaire encompasses four categories of physical activity\u0026mdash;occupational, household, transportation, and leisure\u0026mdash;as well as sedentary behavior, comprising a total of five dimensions (27 questions)[27]. By integrating the types, frequencies, and durations of the various physical activities reported by the participants with their corresponding MET values, the total weekly physical activity volume can be determined. Scholar Craig et al. [28]employed accelerometers to assess the reliability and validity of the questionnaire across over ten countries, with the findings demonstrating a high level of reliability and validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of sleep quality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sleep quality of nurses was assessed using the Pittsburgh Sleep Quality Index (PSQI). This scale comprises seven dimensions and 18 items, with a total score ranging from 0 to 21 points[29]. A higher score indicates poorer sleep quality. Specifically, a PSQI score of 5 or below signifies good sleep quality, whereas a score above 5 suggests poor sleep quality[29]. The Chinese adaptation of the PSQI has demonstrated satisfactory validity, evidenced by a Cronbach\u0026apos;s\u0026nbsp;\u0026alpha;\u0026nbsp;coefficient of 0.842, as well as good test-retest reliability, construct validity, and empirical validity[30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of cardiometabolic indices\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA custom-designed questionnaire for cardiometabolic indices was employed to collect data on a range of cardiometabolic parameters, including height, weight, blood pressure, waist-to-hip ratio (WHR), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL). The data for this questionnaire were sourced from the health examination records of nursing personnel, with the physical and laboratory assessments performed by the medical team at the hospital\u0026rsquo;s physical examination center and laboratory department.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn March 2023, a convenience sampling approach was employed to select 30 nurses from the hospital for a preliminary survey, and the final questionnaire was refined based on the outcomes of this pre-survey. Following approval from both the hospital\u0026apos;s nursing and research departments, data collection was conducted via the Questionnaire Star online platform. To ensure high-quality responses and enhance the participation rate among nurses, a 10-yuan WeChat red envelope incentive was established, with each account permitted to participate only once. The completion time for the questionnaire was approximately 10 minutes. A total of 546 questionnaires were distributed, and 508 valid questionnaires were received. The valid questionnaire recovery rate was 93.04%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were imported from Excel into SAS 9.4 for statistical analysis. Categorical data were presented as frequency (n) and percentage (%), and comparisons between groups were conducted using the Chi-square test or Fisher\u0026apos;s exact test. Continuous data were summarized using mean \u0026plusmn; standard deviation when the assumption of normal distribution was satisfied, and inter-group comparisons were made using the two-sample t-test or analysis of variance (ANOVA) under the assumption of homogeneity of variances. When the assumption of normal distribution was not met, the median (interquartile range: first quartile, third quartile) was reported. The correlation between two continuous variables was assessed using Pearson or Spearman correlation analysis, contingent upon the fulfillment of the normal distribution assumption. To examine the impact of leisure METs on metabolic outcomes, a robust linear regression model was employed, utilizing the M-estimation method to minimize the influence of outliers, violations of normality, and homoscedasticity, thus providing relatively unbiased and robust parameter estimates[31]. Stepwise multiple regression models were constructed. Data visualization was conducted using R version 4.2.0, and a two-tailed \u0026alpha; level of 0.05 was set, with P \u0026lt; 0.05 indicating statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn Tables 1 and 508 nurses were analyzed. Most were female (95.47%), aged 31-40 (50.59%), Han ethnicity (97.24%), with a bachelor\u0026apos;s degree or lower (91.93%), married (74.21%), had one child (49.21%), and a per capita monthly household income\u0026nbsp;\u0026ge;10000 yuan (53.54%). Thus, most carried significant family responsibilities. Job characteristics: 6-10 years of work experience (41.54%), primarily in pediatrics (23.82%) and emergency care (22.24%). Over half were nurses or staff nurses, with the most common night shift schedule being 0-1 times per week (46.65%). Median weekly work hours were 40.00 (IQR: 38.50 to 42.00). Health and behavioral characteristics: 98.23% did not smoke, 63.78% did not drink alcohol, and 68.90% had a high-fat diet. Most (72.64%) were unfamiliar with physical activity guidelines. Chronic diseases were reported by 13.98%, with hypercholesterolemia (9.45%), obesity (3.15%), and hypertension (2.56%) being the most common. Family history of chronic diseases was reported by 52.56%, with hypertension (39.57%), hypercholesterolemia (14.76%), and diabetes (14.37%) being the most common.\u003c/p\u003e\n\u003cp\u003eTable 1 Demographic information (n = 508)\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 398px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 218px;\"\u003e\n \u003cp\u003eStatistical description\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e23 (4.53%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e485 (95.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eAge, years, M(Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e32.00(29.00, 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; ~ 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e192 (37.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 31 ~ 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e257 (50.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e59 (11.61%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eEthnic group, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; The Han nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e494 (97.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e14 (2.76%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHeight, cm, M (P25, P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e162.00(160.00, 165.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eWeight, kg, M (P25, P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e55.40(52.00, 60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHighest degree, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Bachelor\u0026apos;s degree or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e467 (91.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Master\u0026apos;s degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e41 (8.07%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eMarital status, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e101 (19.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Be married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e377 (74.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e30 (5.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eFertility status, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Childlessness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e172 (33.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; One child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e250 (49.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Two children and more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e86 (16.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003ePer capita monthly household income, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 10001 -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e272 (53.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 5001～10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e188 (37.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; ~ 5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e48 (9.45%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eYears of service, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1 to 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e66 (12.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 6 to 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e211 (41.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 11 to 15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e142 (27.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt; 15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e89 (17.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eWork departments, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Pediatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e121 (23.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Er (ER)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e113 (22.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Internal Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e75 (14.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Ob/GYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e64 (12.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e77 (15.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Intensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e47 (9.25%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Operating room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e6 (1.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e5 (0.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eJob title, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Nurse/nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e327 (64.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Supervisor nurse and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e181 (35.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eNight shift frequency, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0-1 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e237 (46.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2-3 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e178 (35.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;4 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e93 (18.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHours worked per week, hours, M(Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e40.00(38.50, 42.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eSmoking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e9 (1.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eAlcohol consumption, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e184 (36.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eSelf-rated health, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Not good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e81 (15.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e322 (63.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e105 (20.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHigh fat diet, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e111 (21.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Occasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e47 (9.25%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Often\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e350 (68.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHeard of the activity guide, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e139 (27.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Haven\u0026apos;t heard of either\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e369 (72.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; All heard of\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e65 (12.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Heard of the latter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e25 (4.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Heard of the former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e49 (9.65%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eCardiovascular and metabolic chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e71 (13.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e1 (0.20%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e16 (3.15%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e0 (zero)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHigh blood fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e48 (9.45%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e13 (2.56%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e4 (0.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eFamily history of cardiovascular and metabolic chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e267 (52.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e26 (5.12%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e17 (3.35%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e48 (9.45%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003eHigh blood fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e75 (14.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e201 (39.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 398px;\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e73 (14.37%)\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\u003eNote: M(Q1, Q3) represents the median (the first quartile, the third quartile).\u003c/p\u003e\n\u003cp\u003eTable 2 International physical activity evaluation of nurses\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003eM(Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4.36(3.09, 6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e20.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 226px;\"\u003e\n \u003cp\u003ePhysical activity, x 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; OPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.97(1.98, 3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e13.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; TPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.46(0.18, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; HDPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.35(0.09, 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; LTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.30(0.00, 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eClass classification, x 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low-intensity physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3.27(2.06, 3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; Moderate-intensity physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.83(0.29, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; Vigorous-intensity physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.00(0.00, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eType categorization, x 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; LTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.30(0.00, 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; NLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3.92(2.89, 5.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e19.9\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\u003eNote: M(Q1, Q3) represents the median (the first quartile, the third quartile).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, the median total physical activity among nurses is 4.36 (3.09, 6.31) \u0026times;1000 MET-min/week. The median values for weekly walking, moderate-intensity, and vigorous-intensity physical activity are 3.27 (2.06, 3.80), 0.83 (0.29, 1.80), and 0.00 (0.00, 0.80) \u0026times;1000 MET-min/week, respectively. These data indicate that walking is the predominant form of physical activity, with lower levels of moderate and vigorous activity. Total weekly physical activity is categorized into four domains: occupational, transportation, household, and leisure-time. The median occupational physical activity is 2.97 (1.98, 3.81) \u0026times;1000 MET-min/week, while household and leisure-time activities are notably lower, leisure-time being the least. Non-leisure-time activities, including occupational, transportation, and household[32], have a median of 3.92 (2.89, 5.56) \u0026times;1000 MET-min/week.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3 Grade distribution of physical activity level of nurses (n=508)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eNumber of people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e75.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eIn the\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e20.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e4.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e100.00%\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\u003eAccording to the classification criteria for physical activity levels, 75% of the nurses achieved a high level of physical activity (Table 3). Based on the IPAQ Development Working Group\u0026apos;s guidelines[33], 150-300 minutes of moderate-intensity aerobic activity or 75-150 minutes of vigorous-intensity aerobic activity per week, or an equivalent combination, is considered adequate physical activity[34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4 Compliance for different types of physical activity (n=508)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"464\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eGoal attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eN(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eNot up to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e179 (35.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUp to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e329 (64.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUnder par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e344 (67.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUp to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e164 (32.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eTraffic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eNot up to scratch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e491 (96.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUp to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e17 (3.35%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eLeisure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eNot up to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e447 (87.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUp to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e61 (12.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eChores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUnder par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e315 (62.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003eUp to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e193 (37.99%)\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\u003eResults show that 64.76% of nurses met this standard: 37.99% through household activities and 32.28% through occupational activities (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 5 Distribution of cardiovascular metabolic indexes\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eCardiovascular metabolic indexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 235px;\"\u003e\n \u003cp\u003eM(Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eFBG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e5.00(4.69, 5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eSBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e115.00(107.00, 123.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eDBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e72.00(66.00, 78.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eTC（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e4.42(3.92, 4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eTG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e0.90(0.64, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eHDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e1.46(1.28, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eLDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e2.59(2.19, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eWHR（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e0.80(0.70, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eBMI（kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e21.34(19.81, 22.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e4.43(4.26, 4.62)\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\u003eNote: M(Q1, Q3) represents the median (the first quartile, the third quartile).\u003c/p\u003e\n\u003cp\u003eThe median PSQI score for nurses was 7.00 (5.00, 9.00). More than 71.15% of nurses had a PSQI score greater than 5, indicating poor overall sleep quality (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 6 Sleep quality evaluation of nurses\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eM(Q1, Q3)/ frequency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 1: Sleep quality, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (8.07%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e307 (60.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143 (28.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (3.35%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 2: Time to sleep, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (18.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e187 (36.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e155 (30.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73 (14.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 3: Sleep duration, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (11.42%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e289 (56.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139 (27.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (4.33%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 4: Sleep efficiency, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121 (23.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183 (36.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e136 (26.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66 (13.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 5: Sleep disorders, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (11.02%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e342 (67.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (19.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (2.17%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 6: Hypnotic drugs, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e435 (85.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (7.48%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (3.94%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (2.95%).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimension 7: Daytime dysfunction, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e172 (33.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e265 (52.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63 (12.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (1.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSQI score, M(Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00(5.00, 9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e360 (71.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026le; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146 (28.85%)\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\u003eNote: M(Q1, Q3) represents the median (the first quartile, the third quartile).\u003c/p\u003e\n\u003cp\u003eCardiovascular metabolic indicators of nurses mostly fall within the normal range (Table 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn examination of the relationship between physical activity, sleep quality, and cardiovascular metabolic indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the dataset\u0026apos;s deviation from a normal distribution, the Spearman correlation coefficient was used to assess the relationships between physical activity metabolic equivalents and sleep quality, as well as cardiovascular metabolic indicators. Results showed significant negative correlations between leisure-time physical activity and waist-to-hip ratio (WHR), triglyceride-glucose (TyG) index, triglycerides (TG), and total cholesterol (TC) (P \u0026lt; 0.01), and with low-density lipoprotein cholesterol (LDL) (r = -0.10, P \u0026lt; 0.05). It was also positively correlated with high-density lipoprotein cholesterol (HDL) (r = 0.19, P \u0026lt; 0.05). Non-leisure-time physical activity was negatively correlated with LDL (r = -0.12, P \u0026lt; 0.05). The total Pittsburgh Sleep Quality Index (PSQI) score was positively correlated with the TyG index, triglycerides, and total cholesterol (P \u0026lt; 0.05)（Figure 1）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA robust linear regression analysis of the relationship between physical activity, sleep quality, and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecardiovascular metabolic indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 A robust linear regression analysis examining the association between physical activity metabolic equivalents, sleep quality, and cardiovascular metabolic indexes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" summary=\"Page Layout\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eResearch\u003c/p\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eReturn\u003c/p\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eChi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: only LTPA mets were included;\u003c/p\u003e\n\u003cp\u003eModel 2: On the basis of model 1, \u0026quot;age, highest education level, fertility status, family monthly income per capita, listening and speaking activity guidelines, health status, drinking, high-fat diet, chronic cardiogenic disease, family history of chronic cardiogenic disease, working years, professional title, night shift frequency, weekly working hours, sedentary time;\u003c/p\u003e\n\u003cp\u003eModel 3: NLTPA mets were added on the basis of model 2;\u003c/p\u003e\n\u003cp\u003eModel 4: PSQI was added on the basis of model 3;\u003c/p\u003e\n\u003cp\u003eRobust linear regression was used to examine the effect of leisure-time physical activity metabolic equivalents (METs) on cardiometabolic indicators. Four models with stepwise adjustments were constructed: Model 1 included only leisure METs; Model 2 added controls for age, education, fertility, income, activity guidance, health status, alcohol consumption, high-fat diet, and chronic diseases; Model 3 further added non-leisure-time METs; Model 4 included Pittsburgh Sleep Quality Index (PSQI) scores. The Variance Inflation Factor (VIF) in Model 4, the most comprehensive model, was 1.22, with a minimum tolerance of 0.81 and a maximum condition index of 1.62, indicating no multicollinearity.\u003c/p\u003e\n\u003cp\u003eThe metabolic equivalent of leisure-time physical activity (LTPA) was found to be statistically associated with total cholesterol (TC) levels in all four models (P \u0026lt; 0.05). Specifically, in the multivariate model 4, a 1000-unit increase in LTPA METs was associated with a 0.1 mmol/L reduction in TC (regression coefficient -0.1, 95% CI -0.19 to 0.00, P = 0.04). Additionally, a 1000-unit increase in PSQI score was associated with a 0.03 mmol/L increase in TC (regression coefficient 0.03, 95% CI 0.01 to 0.05, P = 0.01). Regarding triglycerides (TG), the LTPA METs were statistically associated with TG levels in all four models (P \u0026lt; 0.05). In multivariate model 4, a 1000-unit increase in LTPA METs was associated with a 0.07 mmol/L reduction in TG (regression coefficient -0.07, 95% CI -0.12 to -0.01, P = 0.01). Furthermore, a 1000-unit increase in PSQI score was associated with a 0.01 mmol/L increase in TC (regression coefficient 0.01, 95% CI 0.00 to 0.03, P = 0.05). The LTPA METs were also statistically associated with high-density lipoprotein cholesterol (HDL) levels in all four models (P \u0026lt; 0.05). In multivariate model 4, a 1000-unit increase in LTPA METs was associated with a 0.04 mmol/L increase in HDL (regression coefficient 0.04, 95% CI 0.00 to 0.07, P = 0.03). For low-density lipoprotein cholesterol (LDL), the LTPA METs were significantly associated with LDL levels in model 1, which did not adjust for confounding factors (P \u0026lt; 0.05). This association remained significant in model 2 after adjusting for some confounders but became non-significant in models 3 and 4 after further controlling for additional confounding factors.In four models assessing waist-to-hip ratio (WHR), a significant association was found between leisure-time physical activity metabolic equivalent and WHR (P \u0026lt; 0.05). In the multivariate Model 4, a 1000-unit increase in leisure-time physical activity metabolic equivalent corresponded to a 0.01 decrease in WHR (regression coefficient -0.01, 95% CI -0.03 to 0.00, P = 0.04). Similarly, in four models evaluating the TyG index, a significant relationship was observed between leisure-time physical activity metabolic equivalent and the TyG index (P \u0026lt; 0.05). In the multivariate Model 4, a 1000-unit increase in leisure-time physical activity metabolic equivalent led to a 0.04 reduction in WHR (regression coefficient -0.04, 95% CI -0.07 to -0.01, P = 0.01), while the TyG index increased by 0.01 for every 1000-unit rise in the PSQI score (regression coefficient 0.01, 95% CI 0.01 to 0.02, P \u0026lt; 0.01)( Table 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity analysis of the association between leisure-time physical activity and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecardiovascular metabolic indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1764094570.png\" width=\"747\" height=\"491\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the influence of sleep quality on the association between leisure-time physical activity metabolic equivalents (METs) and cardiometabolic markers, we employed Model 4 and stratified Pittsburgh Sleep Quality Index (PSQI) scores into two categories (\u0026gt;5 and \u0026le;5) for interaction term assessment and subgroup analysis. The results indicated significant interactions were observed for triglycerides (TG), high-density lipoprotein cholesterol (HDL), and waist-to-hip ratio (WHR). Specifically, for TG, the interaction term yielded a p-value of 0.03. Subgroup analysis demonstrated that the regression coefficient of METs for the PSQI score \u0026gt; 5 group was -0.15 (95% CI: -0.22 to -0.08; p \u0026lt; 0.01), whereas for the PSQI score \u0026le;5 group, it was -0.03 (95% CI: -0.10 to 0.05; p = 0.52). These findings suggest that the reduction in TG levels through METs is efficacious exclusively in individuals with poor sleep quality (PSQI score \u0026gt; 5). For HDL, the interaction term resulted in a p-value of 0.04. Subgroup analysis indicated that the regression coefficient of METs for the PSQI score \u0026gt; 5 group was 0.08 (95% CI: 0.04 to 0.13; p \u0026lt; 0.01), while for the PSQI score \u0026le;5 group, it was 0.01 (95% CI: -0.06 to 0.07; p = 0.87). This implies that METs can significantly enhance HDL levels in individuals with poor sleep quality (PSQI score \u0026gt; 5).In the WHR regression model, the interaction term was found to be statistically significant at P = 0.01. Subgroup analysis indicated that the regression coefficient for the metabolic equivalent of leisure-time physical activity in the group with a PSQI score greater than 5 was 0.00 (95% confidence interval: -0.02 to 0.02), with P \u0026lt; 0.97. In contrast, for the group with a PSQI score of 5 or less, the regression coefficient was -0.03 (95% confidence interval: -0.05 to 0.00), which was statistically significant at P = 0.04. These findings suggest that among individuals with poorer sleep quality (PSQI score \u0026gt; 5), the metabolic equivalent of leisure-time physical activity is effective in reducing WHR(Table 8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe outcomes of this study elucidate the interrelationship between physical activity (PA), sleep quality, and cardiometabolic markers among nurses. Key findings demonstrate that leisure-time PA metabolic equivalents (METs) exhibit a significant negative correlation with triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C), while showing a positive association with high-density lipoprotein cholesterol (HDL-C). These results align with established evidence that regular moderate-intensity PA reduces atherogenic lipids (TG, LDL-C) and elevates cardioprotective HDL-C, thereby mitigating cardiometabolic risk [8,35,36]. Notably, leisure-time PA METs were inversely correlated with waist-to-hip ratio (WHR) and triglyceride-glucose (TyG) index (P \u0026lt; 0.01), corroborating findings by Huang et al. [37] and Wang et al. [36].\u003c/p\u003e\n\u003cp\u003ePhysical activity benefits cardiometabolic markers may be attributed to the following three reasons: The first is enhanced lipid metabolism: PA upregulates lipoprotein lipase activity, accelerating TG clearance and HDL synthesis [35]. The second is insulin sensitization: Reduced TyG index reflects improved glucose-insulin dynamics, curtailing diabetes risk [36]. The third one is visceral fat reduction: Lower WHR indicates decreased abdominal adiposity\u0026mdash;a key driver of metabolic dysfunction [37].\u003c/p\u003e\n\u003cp\u003eOur results align with the general idea that consistent exercise can effectively reduce blood levels of TG, TC, and LDL-C [8, 35], which are risk factors for atherosclerosis and cardiovascular diseases. Furthermore, exercise can also elevate HDL-C levels[9, 35], commonly referred to as \u0026quot;good cholesterol,\u0026quot; which facilitates the removal of harmful cholesterol from blood vessels and promotes cardiovascular health.\u003c/p\u003e\n\u003cp\u003eIn terms of Nurse-Specific activity patterns, the median (first quartile, third quartile) total physical activity per week was 4.36 (3.09, 6.31)\u0026nbsp;\u0026times;\u0026nbsp;10^3 MET-min/week, significantly higher than Hu Yu et al.\u0026apos;s [38] reported average of 2549 MET-min/week. Occupational activities were the main source of physical activity, while transportation, household chores, and leisure-time activities contributed less. This aligns with Hu Yu et al.\u0026apos;s findings [38]. The intensity distribution was predominantly low, followed by moderate, with high-intensity activities being the least common, consistent with Janssen et al[23]. The survey found that the median PSQI score for nurses over the past month was 7.00 (5.00, 9.00), comparable to Dong et al.\u0026apos;s [39] mean score of 7.32\u0026nbsp;\u0026plusmn;\u0026nbsp;3.24 in Shandong Province. Notably, 71.15% of nurses had poor sleep quality (PSQI \u0026gt; 5), higher than Dong et al.\u0026apos;s\u0026nbsp;[39]\u0026nbsp;reported 63.9%.\u003c/p\u003e\n\u003cp\u003eFurthermore, interaction analysis uncovered that sleep quality (PSQI score) significantly moderates the PA\u0026ndash;cardiac health relationship (P \u0026lt; 0.05) which tells sleep quality is a critical moderator. The results of the interaction term analysis indicated that sleep quality significantly moderates the relationship between leisure-time physical activity (PA) and cardiometabolic indicators, including triglycerides (TG), high-density lipoprotein (HDL), and waist-to-hip ratio (WHR). Specifically, the positive effects of leisure-time PA on these cardiometabolic markers were more pronounced in individuals with better sleep quality (lower PSQI scores). Conversely, when sleep quality was poor (higher PSQI scores), the beneficial impact of leisure-time PA on cardiometabolic health was relatively diminished. High sleep quality (PSQI\u0026nbsp;\u0026le;\u0026nbsp;5) amplified PA benefits: 28% greater reduction in TG and 19% higher HDL-C vs. poor-sleep counterparts. Poor sleep (PSQI \u0026gt; 5) blunted PA effects: Inflammation and cortisol dysregulation may diminish PA-induced lipid improvements [40]. This aligns with experimental data showing sleep deprivation abrogates exercise-mediated gains in insulin sensitivity. Nina et al.\u0026apos;s reviewed literature establishes a clear link between sleep health and metabolic function, demonstrating that insufficient sleep detrimentally impacts insulin sensitivity. They have found that notably, circadian misalignment and suppression of slow-wave sleep were identified as significant detrimental factors[41].\u003c/p\u003e\n\u003cp\u003eWith 71.15% of nurses reporting poor sleep (PSQI \u0026gt; 5) exceeding rates in general populations (63.9%) [39], urgent targeted interventions are imperative. Studies focusing on elderly populations have demonstrated that regular participation in moderate-intensity activities (such as Tai Chi and yoga) can significantly improve sleep quality, reduce nighttime awakenings, and enhance overall sleep satisfaction[40]. Public health department and the hospital management team can suggest nurses doing moderate-intensity activities in the future to improve sleep quality and keep a low risk at getting cardiovascular disease. Another thing nurses can do is called High-intensity interval training (HIIT) which is a prescribe time-efficient PA that requires\u0026nbsp;\u0026le;15 min/day yet improves sleep and lipids comparably to moderate PA [42]. The public health department and the hospital management can set implement circadian-aligned schedules for nurses: limit consecutive night shifts to \u0026lt;3, reducing circadian disruption [43]. This could be urgent public health implications because Piumika et al.\u0026rsquo;s meta-analysis conclusively established a statistically significant positive association between shift work and the risk of developing metabolic syndrome among healthcare workers. The 37% increased risk is a substantial concern for both individual health and organizational well-being[43].\u003c/p\u003e\n\u003cp\u003eWhile this study pioneers PA\u0026ndash;sleep\u0026ndash;metabolism interactions in nurses of China, there are still several critical gaps persist. Longitudinal data are needed, such as prospective cohorts tracking PA/sleep interventions on hard endpoints (e.g., CVD events). Future research can use objective monitoring like accelerometer-measured PA and polysomnography-derived sleep data will minimize recall bias. Also, translational trials can be added. For example: test feasibility of \u0026quot;exercise prescriptions\u0026quot; (e.g., 10-min ward-based resistance bands) during shifts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Firstly, the sample size is relatively small, with only 508 nurses from a single hospital in Wuhan. This may limit the generalizability of the results to a broader population. Secondly, the study is limited to nurses, a specific occupational group. The results may not be applicable to other populations with different occupations and lifestyles. Additionally, the study only focuses on physical activity, sleep quality, and cardiovascular metabolism indicators, and may not consider other factors that could also influence these outcomes. For example, diet, stress levels, and genetic factors may also play a role in cardiovascular health.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that sleep quality plays a moderating role in the relationship between physical activity and cardiovascular metabolism indicators. Poor sleep quality has been associated with adverse cardiovascular metabolic health, including increased risk of obesity, hypertension, type 2 diabetes and cardiovascular diseases. On the other hand, increased physical activity has a significant protective effect on major risk factors for cardiovascular disease and is beneficial for controlling weight. The interaction terms between sleep quality (\u0026gt;\u0026thinsp;5, \u0026le;\u0026thinsp;5) and metabolic equivalent of leisure-time physical activity were statistically significant in TG, HDL and WHR, indicating that sleep quality moderates the effect of leisure-time physical activity on these cardiovascular metabolism indicators.​\u003c/p\u003e\u003cp\u003eThis finding has important implications for clinical practice and public health strategies. For nurses with poor sleep quality, targeted interventions such as increasing leisure-time physical activity, improving sleep environments and providing sleep counseling can help prevent and manage cardiovascular disease risk more effectively. Future studies should aim to clarify the complex interrelationships between sleep and cardiovascular metabolism indicators, expand the sample size, conduct longitudinal studies, explore the mechanisms by which sleep quality influences this relationship, and consider additional factors that may interact with sleep quality, physical activity and cardiovascular metabolism indicators.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZYL, XPL, and SFH conducted the studies, contributed to data collection, and drafted the manuscript. ZYL and JC performed the statistical analysis and were involved in the study design. ZYL and SFH further participated in the acquisition, analysis, and interpretation of data, and contributed to the drafting of the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials used for the analysis and conclusions are available from the corresponding author (e-mail address: [email protected]) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Ethics No. TJ-IRB20221126). The study strictly adhered to the ethical principles outlined in the Declaration of Helsinki. Before the study began, the researchers provided a comprehensive explanation of the study\u0026rsquo;s purpose, significance, and methodology, and obtained written informed consent from all participants. The researchers also ensured the strict confidentiality of all collected data and committed to using the study results exclusively for academic research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: Update from the GBD 2019 study. J Am Coll Cardiol. 2020;76:2982\u0026ndash;3021.\u003c/li\u003e\n \u003cli\u003eMa Liyuan, Wu Yazhe, Chen Weiwei. Key Points of \u0026quot;China Cardiovascular Disease Report 2018\u0026quot;. Chinese Journal of Hypertension. 2019; 27: 712-6.\u003c/li\u003e\n \u003cli\u003eFan Xiaozhen, Wang Suping. A Longitudinal Investigation on the Health Status of Nursing Staff in a Comprehensive Hospital. Nursing Research. 2010; 24: 2087 - 8.\u003c/li\u003e\n \u003cli\u003eBurns K, Gross B, Zanin M. Cardiovascular risk study: A comparison between northeast ohio cardiovascular nurses and the nation. J Community Health Nurs. 2010;27:187\u0026ndash;96.\u003c/li\u003e\n \u003cli\u003eFair JM, Gulanick M, Braun LT. Cardiovascular risk factors and lifestyle habits among preventive cardiovascular nurses. 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Elevated ghrelin predicts food intake during experimental sleep restriction. Obesity (Silver Spring). 2016;24:132\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eAssociation of sleep duration and quality with blood lipids: A systematic review and meta-analysis of prospective studies - PubMed. https://pubmed.ncbi.nlm.nih.gov/29247105/. Accessed 21 Dec 2024.\u003c/li\u003e\n \u003cli\u003eBrian\u0026ccedil;on-Marjollet A, Weiszenstein M, Henri M, Thomas A, Godin-Ribuot D, Polak J. The impact of sleep disorders on glucose metabolism: Endocrine and molecular mechanisms. Diabetol Metab Syndr. 2015;7:25.\u003c/li\u003e\n \u003cli\u003eLiu S, Wang X, Zheng Q, Gao L, Sun Q. Sleep deprivation and central appetite regulation. Nutrients. 2022;14:5196.\u003c/li\u003e\n \u003cli\u003eChaput J-P, McHill AW, Cox RC, Broussard JL, Dutil C, da Costa BGG, et al. The role of insufficient sleep and circadian misalignment in obesity. 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Master\u0026apos;s Thesis. Huainan Normal University; 2017.\u003c/li\u003e\n \u003cli\u003eDong H, Zhang Q, Sun Z, Sang F, Xu Y. Sleep disturbances among Chinese clinical nurses in general hospitals and its influencing factors. BMC Psychiatry. 2017;17:241.\u003c/li\u003e\n \u003cli\u003eSolis-Navarro L, Masot O, Torres-Castro R, Otto-Yanez M, Fernandez-Jane C, Sola-Madurell M, et al. Effects on sleep quality of physical exercise programs in older adults: A systematic review and meta-analysis. Clocks Sleep. 2023;5:152\u0026ndash;66.\u003c/li\u003e\n \u003cli\u003eSondrup N, Termannsen AD, Eriksen JN, Hjorth MF, F\u0026aelig;rch K, Klingenberg L,et al. Effects of sleep manipulation on markers of insulin sensitivity: A systematic review and meta-analysis of randomized controlled trials. Sleep Med Rev. 2022;62:101594.\u003c/li\u003e\n \u003cli\u003eReed JL, Terada T, Vidal-Almela S, Tulloch HE, Mistura M, Birnie DH, et al. 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Obes Rev. 2022;23(10):e13489.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nurses, Sleep quality, Physical activity, Leisure-time physical activity","lastPublishedDoi":"10.21203/rs.3.rs-7427920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7427920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aims to explore the associations between physical activity, sleep quality and cardiovascular metabolism indicators among nurses of a top-tier Grade III Class A Chinese hospital.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eConducting convenience sampling method to recruit nurses(N\u0026thinsp;=\u0026thinsp;508) who underwent physical examination in a Class Ⅲ Grade A hospital in Wuhan from April 2023 to May 2023 as the research objects. The general information questionnaire, International Physical Activity Questionnaire-Long Version (IPAQ-L), Pittsburgh Sleep Quality Index (PSQI) and cardiovascular metabolism indicators questionnaire were used in the survey. The data obtained were analyzed by SAS9.4 software. Statistical methods included descriptive analysis, comparison between groups, Spearman correlation analysis and robust linear regression analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe interaction terms between sleep quality (\u0026gt;\u0026thinsp;5, \u0026le;\u0026thinsp;5) and metabolic equivalent of leisure-time physical activity were statistically significant in TG, HDL and WHR (P \u0026lt;0.05), indicating that sleep quality played a moderating role in the effect of metabolic equivalent of leisure-time physical activity on TG, HDL and WHR.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe total amount of physical activities performed by nurses in a Grade III Class A Chinese hospital is relatively high, and the majority are Occupational physical activities. Our research indicates that sleep quality moderates the effect of leisure-time physical activity on these cardiovascular metabolism indicators. Clinical practice and public health strategies should pay special attention to lifestyle interventions in nurses with poor sleep quality, especially increasing their leisure-time physical activity, to prevent and manage cardiovascular disease risk more effectively. Future studies should aim to clarify the complex interrelationships between sleep and cardiovascular metabolism indicators.\u003c/p\u003e","manuscriptTitle":"Relationship between physical activity, sleep quality and cardiometabolic indicators in nurses: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 18:24:34","doi":"10.21203/rs.3.rs-7427920/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb098afb-bb2a-47a8-8ea1-8399d75021e5","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T15:24:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 18:24:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7427920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7427920","identity":"rs-7427920","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}