Analysis of the impact of long working hours on anxiety and work-related musculoskeletal diseases among female workers in electronics manufacturing enterprises in Guangdong Province

Analysis of the impact of long working hours on anxiety and work-related musculoskeletal diseases among female workers in electronics manufacturing enterprises in Guangdong Province | 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 Analysis of the impact of long working hours on anxiety and work-related musculoskeletal diseases among female workers in electronics manufacturing enterprises in Guangdong Province Xiaoyi Li, Yushuo Liang, Huiqing Chen, Xiqing Liao, Xinyang Yu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7341990/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Female workers were the main force in electronic manufacturing enterprises. This study aims to understand the current status of long working hours among female workers in the electronic manufacturing industry in Guangdong Province, and to explore the impact of long working hours on anxiety and work-related musculoskeletal disorders (WMSDs). Methods We conducted a cross-sectional survey using convenience sampling method from July to September 2022, including a total of 193 electronic manufacturing enterprises in Guangdong Province. We distributed a total of 5,300 copies of the Individual questionnaire of the National Key Population Occupational Health Literacy Monitoring Survey , which included general demographic characteristics, and the detection rate of long working hours, anxiety and WMSDs. A total of 4,976 female workers participated in the questionnaires survey after excluding invalid questionnaires, with the effective rate of 93.9%. A binary logistic regression analysis was used to evaluate the impact of weekly working hours on anxiety and WMSDs. Results The detection rates of long working hours (working more than 40 hours a week), anxiety and WMSDs among female workers in electronics manufacturing enterprises in Guangdong Province were 67.8%, 23.0%, and 43.2%, respectively. The detection rate of WMSDs in the anxiety group was higher than that in the non-anxiety group (65.2% vs 36.7%, P < 0.001). The logistic regression model showed that long working hours were positively correlated with anxiety and WMSDs after controlling for factors such as age, education level, marital status, length of service, enterprise scale, enterprise nature and night shift (both P < 0.001). Specifically, the longer the weekly working hours, the greater the risk of developing anxiety and WMSDs, with OR (95% CI ) values of 1.067 (1.021–1.116) and 1.067 (1.028–1.108), respectively. Conclusions The female workers in electronics manufacturing enterprises in Guangdong Province generally had long working hours, which increased the risk of occurrence of anxiety and WMSDs. Electronics manufacturing enterprise Female worker Long working hours Anxiety Work-related musculoskeletal disorders Risk Background With the ongoing process of globalization and great changes in the working environment, the phenomenon of long working hours among working groups has become increasingly common, particularly in the manufacturing and internet industries [ 1 , 2 ]. Long working hours are defined as working beyond the standard limits — exceeding 8 hours per day or 40 hours per week [ 3 ]. According to the International Labour Organization, nearly 500 million workers worldwide work more than 55 hours per week [ 4 ]. As the world's second-largest economy, China's rapid economic and social development had been accompanied by increased work demands across various industries, with the weekly working hours of employees approaching 48 hours [ 5 ]. Prolonged working hours not only reduce workers' personal time, but also lead to serious physical and mental health issues, making it one of the most critical occupational risk affecting workers' well-being [ 6 , 7 ]. In recent years, WMSDs have become one of the key occupational health issues in China [ 8 , 9 ]. It affects a wide range of industries and populations, and in addition to the physiological impact on workers, it would also reduce work efficiency and quality of life, and cause economic burdens to both society and individuals [ 10 , 11 ]. A large number of epidemiological investigations showed that the occurrence of WMSDs was related to occupational, social, and individual factors [ 12 – 14 ]. Previous studies showed that long working hours were associated with adverse health outcomes such as anxiety and WMSDs [ 15 , 16 ]. Anxiety refers to the undesirable emotional state experienced by an individual in anticipation of difficulties or danger [ 17 ]. It is a clinically common, chronic, and recurrent psychiatric disorder characterized by a general sense of nervousness, fear, restlessness, and distress [ 18 ]. Our country established the Occupational Health Literacy Questionnaire of National Key Populations in 2022, which was the first time to carry out a nationwide monitoring survey on occupational health literacy of key populations [ 19 ]. One of the contents was to monitor and investigate the occupational health literacy of key populations in the country [ 20 ]. In China, manufacturing accounts for the largest proportion of the workers, with the electronics manufacturing industry being a particularly prominent sector[ 21 ]. As one of the leading industries in Guangdong Province, the electronics manufacturing industry has become an important strategic industry to promote the rapid economic development of Guangdong Province. Female workers are the main force in electronics manufacturing enterprises, and they not only take the family responsibility of giving birth to children, but also participate in production activities [ 22 ]. Due to the characteristics of the industry, female workers in the electronics manufacturing industry require to work for long hours performing repetitive, monotonous, and fast-paced operations, which result in prolonged physical fatigue. This persistent fatigue may lead injuries in the waist, neck and shoulders, and other parts, subsequently causing absenteeism, production accidents, and reduce work ability. These outcomes may lead to indirect economic losses, which was not conducive to the sustainable development of enterprises and even the social economy [ 23 , 24 ]. In accordance with the relevant requirements of the Occupational Health Literacy Questionnaire of National Key Populations , combined with the characteristics of the industrial structure of Guangdong Province, this study investigates the health risks caused by long working hours of female workers in electronic manufacturing enterprises in Guangdong Province, aims to evaluate the impact of long working hours on the anxiety and WMSDs of female workers in electronics manufacturing enterprises in Guangdong Province, and proposes key strategies for the prevention and control of the health effects caused by long working hours, and provides new ideas for promoting the physical and mental health of this occupational population. Methods Subjects This study used a convenience sampling method to conduct a cross-sectional survey between July and September 2022. It encompassed a total of 193 electronic manufacturing enterprises in Guangdong Province, with 5,300 questionnaires disseminated. Following the removal of invalid questionnaires, 4,976 female workers were selected as participants, yielding a valid response rate of 93.9%. The criteria for inclusion in the study were: i) aged over 18 years; ii) at least 6 months of work experience. Exclusion criteria comprised: i) recent significant emotional events or the presence of mental or organic diseases; ii) suffering from musculoskeletal injuries, osteoarthritis, tumors, or tuberculosis that could lead to musculoskeletal disorders. Ethics approval and consent to participate The present study was approved by the Medical Ethics Committee of the Guangdong Province Hospital for Occupational Disease Prevention and Treatment (Approval No. GDHOD MEC 2018012) and was strictly compliant with local law and the Declaration of Helsinki. All participants provided informed consent prior to administering the survey. Study Design and Population The Occupational Health Literacy Questionnaire of National Key Populations was used to investigate the sociodemographic characteristics of the research subjects, such as age, gender, education level, marital status, personal monthly income, length of service, enterprise scale, enterprise nature, night shift, weekly working hours and other occupational characteristics [20]. The classification of enterprise size was based on the Measures for the Classification of Large, Medium, Small, and Micro Enterprises in Statistics ( 2017 ), which categorizes enterprises as large- (≥1,000 employees), medium- (300 to <1,000 employees), and small- and micro-sized (40 h/week/total population)×100%. Anxiety The anxiety survey of the Occupational Health Literacy Questionnaire of National Key Populations used the generalized anxiety scale developed by Spitzer et al and translated by He XY et al [26,27]. The scale consists of seven items, and the scored was calculated based on the frequency of anxiety symptoms over the past two weeks: 0 points for "not at all", 1 point for "several days", 2 points for "more than a week", and 3 points for "almost every day". The total score was calculated as the sum of seven items, ranging from 0 to 21. A total score of 0-4 points indicated no anxiety, while 5-21 points indicated the presence of anxiety. A higher total score indicated a higher level of anxiety. Anxiety detection rate (%) = (number of participants with anxiety/total population)×100%. The Cronbach's α coefficient for the scale in this study was 0.934. Work-related Musculoskeletal Diseases The Musculoskeletal Disorders Survey Scale of the Occupational Health Literacy Questionnaire of National Key Populations was developed by Yang L et al, and was used to evaluate the WMSDs of nine body parts among the study subjects: neck, shoulder, back, elbow, waist, wrist, hip, hip, knee and ankle and foot [28]. For each body part listed above, if pain or discomfort occurred within the past year and lasted for more than 24 hours, a score of 1 was assigned; if no pain or discomfort was reported, a score of 0 was assigned. The total score was calculated as the sum of scores for nine body parts. A total score of 0 indicated no WMSDs symptoms, while a score of 1-9 indicated the presence of WMSDs symptoms. Higher total scores indicated a more severe WMSDs symptoms. WMSDs detection rate (%) = (number of participants with WMSDs at any site/total population)×100%. The Cronbach's α coefficient for the scale in this study was 0.884. Quality control The investigators, all of whom had undergone uniform training, were responsible for explaining the survey's purpose, completion instructions, and confidentiality assurances to the participants, ensuring participants' full understanding of the questionnaire and its requirements. Participants completed the questionnaire online by scanning a QR code provided by the investigators, with all items required to be answered before submission. The database was established using EpiData 3.0, and the collected questionnaires were entered into the database by two individuals for verification. Statistical analysis Data were analyzed using SPSS 21.0 software. Normally distributed measurement data were described using mean±standard deviation, while non-normally distributed data were described using median ( M ) and the 25 th and 75 th percentiles ( P 25 , P 75 ). The comparison of rates of count data was conducted using Pearson's Chi-square test or the trend Chi-square test. The impact of weekly working hours on anxiety and WMSDs was analyzed using binary logistic regression analysis. The significance level was set at α =0.05. Results Basic information A total of 4,976 participants were included in the study, with a mean age of 36.4±7.8 years. The median ( P 25 , P 75 ) of length of service was 3 (1, 8) years. Most participants were aged from 36.0 to <46.0 years (39.7%), had a junior high school education or below (59.6%), and were married (83.6%) without night shifts (75.7%). The monthly average income was primarily between 3,000-4,999 yuan (55.0%), and the length of service was mostly 0.5 to <2 years (30.9%). Regarding enterprise sized and characteristics, small- and micro-enterprises accounted for 44.5%, privately-owned enterprises accounted for 51.1%. Comparison of the detection rate of long working hours, anxiety, and WMSDs among research subjects in different characteristic groups A total of 3,375 (67.8%) study subjects had long working hours. A total of 1,146 study subjects were identified as anxiety, while 2,151 were screened as WMSDs, with a detection rate of 23.0% and 43.2%, respectively. The detection rate of long working hours among study subjects differed significantly by monthly income, enterprise size, and enterprise nature (all P <0.01). The detection rate of anxiety among the study subjects with different ages, education level, marital status, length of service, enterprise size, night shift and weekly working hours had significant difference (all P <0.05). There were significant differences in the detection rate of WMSDs among the study subjects with different length of service, enterprise size, enterprise nature, night shift and weekly working hours (all P <0.05). See Table 1. Table 1 Comparison of the detection rate of long working hours, anxiety and WMSDs among study subjects in different groups Groups Number/Proportion (%) Number of long working hours/Proportion (%) Number of anxiety/Proportion (%) Number of WMSDs/Proportion (%) Age 18-25 472（9.5） 306（64.8） 156（33.1） 200（42.4） 26-35 1,852（37.2） 1,262（68.1） 506（27.3） 781（42.2） 36-45 1,976（39.7） 1,362（68.9） 382（19.3） 884（44.7） 46-60 676（13.6） 445（65.8） 102（15.1） 286（42.3） Education level Junior high school and below 2,965（59.6） 2,002（67.5） 614（20.7） 1,263（42.6） High school/Technical secondary school 1,201（24.1） 842（70.1） 306（25.5） 521（43.4） College degree and above 810（16.3） 531（65.6） 226（27.9） 367（45.3） Marital status Married 4,158（83.6） 2,830（68.1） 886（21.3） 1,791（43.1） Unmarried/Divorced/Widowed 818（16.4） 545（66.6） 260（31.8） 360（44.0） Personal monthly income ＜¥ 3,000 776（15.6） 420（54.1） 159（20.5） 330（42.5） ¥ 3,000-¥ 4,999 2,737（55.0） 1,882（68.8） 628（22.9） 1,214（44.4） ¥ 5,000-¥ 6,999 1,197（24.1） 883（73.8） 296（24.7） 490（40.9） ≥¥ 7,000 266（5.3） 190（71.4） 63（23.7） 117（44.0） Length of service (years) 0.5-＜2.0 1,536（30.9） 1,043（67.9） 392（25.5） 631（41.1） 2.0-＜5.0 1,293（26.0） 889（68.8） 307（23.7） 568（43.9） 5.0-＜10.0 1,120（22.5） 750（67.0） 214（19.1） 472（42.1） 10.0-＜20.0 853（17.1） 581（68.1） 194（22.7） 394（46.2） 20.0-34.0 174（3.5） 112（64.4） 39（22.4） 86（49.4） Enterprise size Micro- 2,215（44.5） 1,384（62.5） 440（19.9） 864（39.0） Medium- 1,345（27.0） 977（72.6） 300（22.3） 574（42.7） Large- 1,416（28.5） 1,014（71.6） 406（28.7） 713（50.4） Enterprise nature State-owned 66（1.3） 42（63.6） 20（30.3） 51（77.3） Private sector 2,544（51.1） 1,783（70.1） 576（22.6） 1,020（40.1） Foreign capital 2,366（47.5） 1,550（65.5） 550（23.2） 1,080（45.6） Night shift No 3,767（75.7） 2,554（67.8） 816（21.7） 1,535（40.7） Yes 1,209（24.3） 821（67.9） 330（27.3） 616（51.0） Weekly working hours ≤40 1,601（32.2） — 354（22.1） 647（40.4） 41-44 857（17.2） — 179（20.9） 369（43.1） 45-48 874（17.6） — 176（20.1） 337（38.6） 49-54 619（12.4） — 155（25.0） 291（47.0） ≥55 1,025（20.6） — 282（27.5） 507（49.5） Χ 1 2 value 0.103 83.285 0.532 p 1 value 0.748 ＜0.001 0.466 Χ 2 2 value 0.141 23.283 1.812 p 2 value 0.707 ＜0.001 0.178 Χ 3 2 value 0.646 42.322 0.244 p 3 value 0.422 ＜0.001 0.621 Χ 4 2 value 62.051 3.742 0.460 p 4 value ＜0.001 0.053 0.498 Χ 5 2 value 0.378 6.596 6.284 p 5 value 0.538 0.010 0.012 Χ 6 2 value 52.547 38.350 45.541 p 6 value ＜0.001 ＜0.001 ＜0.001 Χ 7 2 value 12.296 2.248 46.990 p 7 value 0.002 0.325 ＜0.001 Χ 8 2 value 0.005 16.386 38.821 p 8 value 0.944 ＜0.001 ＜0.001 Χ 9 2 value — 11.260 20.729 p 9 value — 0.001 ＜0.001 Note: Χ 1 2 and P 1 , Χ 2 2 and P 2 , Χ 3 2 and P 3 , Χ 4 2 and P 4 , Χ 5 2 and P 5 , Χ 6 2 and P 6 , Χ 7 2 and P 7 , Χ 8 2 and P 8 , Χ 9 2 and P 9 were the statistical results of the score comparison of different age, education level, marital status, personal monthly income, length of service, enterprise scale, enterprise nature, night shift and weekly working hours. Detection rate of WMSDs among study subjects in different anxiety groups The detection rates of WMSDs among study subjects had significantly difference ( χ 2 =292.46, P <0.001) between the non-anxiety group (36.7%; 1,404/3,830) and the anxiety group (65.2%; 747/1,146). Among them, the detection rate of WMSDs for 1-2 sites in the non-anxiety group was higher than that in the anxiety group, while the detection rates of WMSDs for 3-4 sites and more than 5 sites in the anxiety group were both higher than those in the non-anxiety group. See Table 2. Table 2 Detection rate of WMSDs in study subjects with different anxiety groups Groups Number of any site/ Proportion (%) Number of 1-2 sites/ Proportion (%) Number of 3-4 sites/ Proportion (%) Number of more than 5 sites/ Proportion (%) Non-anxiety 550（27.3） 312（56.7） 137（24.9） 101（18.4） Anxiety 1,601（54.1） 606（37.9） 484（30.2） 511（31.9） Total 2,151（43.2） 918（42.7） 621（28.9） 612（28.5） Analysis of the impact of weekly working hours on both anxiety and WMSDs Binary logistic regression analyses were conducted with anxiety and WMSDs as dependent variables. Weekly working hours were the primary predictor, along with other variables that showed significant difference ( P <0.05) in univariate analysis (Table 1). Variable assignments were listed in Table 3. The results showed that after controlling for age, education level, marital status, length of service, enterprise size, enterprise nature, night shift and other factors, the weekly working hours were positively correlated with both anxiety and WMSDs (all P <0.01). Specifically, study subjects with longer weekly working hours had a higher risk of anxiety and WMSDs, with OR (95% CI ) values were 1.067 (1.021-1.116) and 1.067 (1.028-1.108), respectively. See Table 4. Table 3 Variable assignment situation Variable Assignment situation Anxiety No=0, Yes=1 WMSDs No=0, Yes=1 Age 18-25=1, 26-35=2, 36-45=3, 46-60=4 Education level Junior high school and below=1, High school/Technical secondary school=2, College degree and above=3 Marital status Married=1, Unmarried/Divorced/Widowed=2 Personal monthly income ＜¥ 3,000=1, ¥ 3,000-¥ 4,999=2, ¥ 5,000-¥ 6,999=3, ≥¥ 7,000=4 Length of service (years) 0.5-＜2.0=1, 2.0-＜5.0=2, 5.0-＜10.0=3, 10.0-＜20.0=4, 20.0-34.0=5 Enterprise scale Micro=1, Medium=2, Large=3 Night shift No=0, Yes=1 Weekly working hours ≤40=1, 41-44=2, 45-48=3, 49-54=4, ≥55=5 Table 4 Binary logistic regression analysis of the impact of weekly working hours on anxiety and WMSDs Variable β value SE value Wald χ ² value P value OR （ 95% CI ） value Anxiety 0.065 0.023 8.270 0.004 1.067（1.021~1.116） WMSDs 0.065 0.019 11.544 0.001 1.067（1.028~1.108） Discussion In recent years, the "996" (9 a.m. to 9 p.m., 6 days a week) and even "007" (without work shifts) working models have led to physical exhaustion and increasing psychological problems among workers, sparking widespread public concern about long working hours [29,30]. In this study, the detection rate of long working hours of female workers in electronics manufacturing enterprises in Guangdong Province reached 67.8%, which is comparable to the 61.1% reported by Zhu BY et al [31] in power supply enterprises and much higher than the 42.7% reported by Li SN [32] in the petrochemical industry. Although longer working hours may appear to improve productivity, it can cause the long-term negative impacts, including the loss of personal time, job burnout, depressive symptoms, WMSDs, and sleep disorders, all of which ultimately reduce work efficiency and quality [33-35]. Therefore, a series of physical and mental health risks caused by long working hours to female workers in the electronics manufacturing enterprises warrant serious attention. WMSDs are prevalent across various industries and populations with a high prevalence, representing a major occupational health challenge in China and a global concern in occupational health [36,37]. In Southeast Asia, the prevalence of WMSDs has been reported as 78.3% in Thailand, 81.3% in Indonesia, and 88.4% in Malaysia [38]. A cross-sectional study in India has indicated that the 12-month prevalence of WMSDs was 75.8% and the point prevalence was 23.3% among dentists [39]. In eastern province of Saudi Arabia, the WMSDs prevalence among teachers was 41.1% [40], and in South Korea, WMSDs accounted for 9.5% to 71.5% of the total number of occupational diseases annually, most commonly in the manufacturing industry, followed by the construction, transportation/warehousing, communication, and mining [41]. In China, nearly all occupational groups are affected by WMSDs. Studies have shown that the overall WMSDs prevalence among manufacturing workers has reached 79.7%, often involving multiple body regions instead of just a single body part [42]. Among local coal miners, the highest WMSDs prevalence among different body parts were the lower back (50.7%), followed by the neck, shoulders, knees, and elbows [43,44]. The prevalence among shipyard workers was 59.3% [45], while among ultrasound physicians, it reached 94.3%, with the right shoulder and neck being the most affected body parts [46]. The prevalence of WMSDs among employees of Internet companies was as high as 80.4% [47]. In the electronics manufacturing industry, workers are at increased risk of muscle and nerve injuries due to the nature of the work—prolonged sitting or standing, repetitive tasks, and forced postures, leading to fatigue and musculoskeletal pain [48,49]. In this study, the detection rate of WMSDs among 4,976 female workers in 193 electronics manufacturing enterprises was 43.2%, which was slightly higher than the 39.5% found in previous study in two electronics manufacturing enterprises [50], but lower than the 58.0% in the electronics manufacturing industry in a city in the Pearl River Delta reported by Feng JQ et al [51]. In addition to causing physiological risks, WMSDs could also lead to a range of negative outcomes, such as loss of work ability, reduced work efficiency, lower income level, and lower quality of life, and increase the medical burden of society, causing huge economic burdens to both society and individuals [52,53]. A large number of epidemiological investigations had shown that the development of WMSDs is related to multiple factors, including occupational factors, individual factors, and psychosocial factors [54,55]. According to a survey among workers in the automobile manufacturing industry, long working hours was a risk factor for WMSDs [56], and similar findings were reported in studies of transportation workers [57]. In this study, long working hours could increase the risk of WMSDs, and the weekly working hours were positively correlated with the risk of WMSDs. This was consistent with pervious study by our lab and by Wu YK et al in employees from internet companies [58,59]. These findings suggested that rational work scheduling and limiting working hours may serve as effective strategies to prevent and intervene WMSDs. In this study, the detection rate of anxiety among female workers in electronics manufacturing enterprises in Guangdong Province was 23.0%, which is slightly lower than the 26.8% reported by previous investigation in the Pearl River Delta region [60]. Several studies had shown that long working hours is a risk factor for anxiety [61,62]. Anxiety is an undesirable emotional experience and an unhealthy psychological state, which not only negatively impacts workers′ health, but also seriously reduces work efficiency, and imposes adverse effects on both individual well-being and society productivity [63]. The electronics manufacturing industry is a leading industry in the economic development of Guangdong Province, facing an increasing competitive pressure nowadays. Female workers in this industry were more likely to suffer from psychological disorders such as anxiety and depression, since most of them also took family responsibilities such as childbirth and parenting [64]. Demographic factors such as age, gender, body mass index, smoking, and physical exercise, as well as psychosocial factors including high mental stress, low social support, and high job satisfaction and job monotony were all played an important role in the development of WMSDs [65]. In this study, the detection rate of WMSDs involving 1-2 body parts of workers in the non-anxiety group was higher than that in the anxiety group, while the detection rate of WMSDs involving 3-4 and more than 5 body parts of workers in the anxiety group was higher than that in the non-anxiety group. These suggested that WMSDs is a common occupational health issue among electronics manufacturing workers at present. WMSDs effect on workers' anxiety might not be obvious when involving body parts were redistricted, however, once three or more body parts were involved, the physical and psychological burden may become substantial, thereby increasing the risk of anxiety. After controlling for demographic variables, hierarchical regression analysis further indicated that long working hours was a risk factor for anxiety and WMSDs among female workers. It was suggested that enterprises should pay attention to the problem of female workers' long working hours, and adopted early intervention to improve work efficiency, reduce the incidence of anxiety, and reduced the risk of WMSDs, so as to promote the sustainable healthy development of enterprises and society. Conclusion In summary, female workers in electronics manufacturing enterprises in Guangdong Province generally worked for long working hours, which would increase the risk of anxiety and WMSDs. According to the relevant provisions of the Labour Law of the People's Republic of China , employers are required to ensure employees to have at least one day off per week, and that overtime hours should not exceed one hour per day, or three hours under special circumstances, with a monthly maximum of 36 hours. Based on the results of this study, following recommendations are proposed for enterprises. i) Utilizing technological support. Implementing intelligent equipment and software tools in automate repetitive and labor-intensive processes, thereby reduce the working hours for female employees. ii) Optimizing personnel allocation. Encourage rotational leave during off-peak seasons; hiring temporary workers or reallocating internal workers during peak production periods, to reduce the frequency of prolonged work hours and protect workers′ mental health [66]. iii) Adjusting compensation structures. Establish a flexible compensation system based on workload and seasonal variations. Distinguish between basic salaries and overtime pay, and provide performance-based incentives for workers engaged in prolonged hours. iv) Enhancing training and communication. During off-peak seasons offer skill training to improve workers′ professional competence and adaptability. Also, employers should be aware of workers′ work status and emotional well-being, and respond to issues in time. As for female workers, the following suggestions are provided. i) Developing reasonable work plans. Organize work schedules according to personal capacity to reduce unnecessary overtime. ii) Strengthening professional skills. Improve personal technical skills to meet the demands of evolving industrial processes and enhance work efficiency. iii) Improving health awareness. Adopt healthy work and lifestyle to prevent anxiety and WMSDs associated with prolonged working hours. This study focused on the impact of long working hours on the anxiety and WMSDs among female workers in electronic manufacturing enterprises. However, ergonomic and psychosocial factors were not included in the current analysis. In addition, as a cross-sectional study, it cannot infer causal relationships between long working hours and anxiety/WMSDs. Future research would use a prospective cohort design to further explore the effects of adverse ergonomic factors, and provide more robust evidence for the prevention and treatment of anxiety and WMSDs. Declarations Acknowledgments The authors would like to express their sincere gratitude to all field workers and participants of this study. Authors’contributions XYL designed and wrote the manuscript. YSL and XQL analyzed and interpreted the findings. HQC, XYY, LX and JLW collected data and revised the paper. JBC and MY designed, reviewed, and revised the manuscript and approved the final manuscript as submitted. All authors have read and approved the final manuscript. Funding This study is supported by the Guangdong Medical Research Fund (grant number B2023176, A2024248, and A2025135). Availability of data and materials Original data are available on reasonable request. These were stored on password-protected computers at the Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China. Readers who wish to gain access to the data can write to the corresponding author. Ethics approval and consent to participate The study protocol was approved by the Medical Ethics Committee of Medical Ethics Committee of the Guangdong Province Hospital for Occupational Disease Prevention and Treatment (Approval No. GDHOD MEC 2018012). All participants gave written informed consent before being recruited into the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Liu XM, Wang C, Wang J, et al. Effect of long working hours and insomnia on depressive symptoms among employees of Chinese internet companies[J]. BMC Public Health,2021,21(1):1408. doi: 10.1186/s12889-021-11454-9. Li XY, Guo Y, Zhao R, et al. Association between job burnout, depressive symptoms, and insomnia among employees in electronic manufacturing industry[J]. J Environ Occup Med,2024,41(11):1205-1212. (in Chinese) Ministry of Human Resources and Social Security of the People´s Republic of China. Provisions of the State Council on the working hours of employees[EB/OL]. (1995-03-25)[2025-07-08]. https://www.mohrss.gov.cn/xxgk2020/fdzdgknr/zcfg/fg/202011/t20201103_394935.html. Pega F, Náfrádi B, Momen NC, et al. Global, regional, and national burdens of ischemic heart disease and stroke attributable to exposure to long working hours for 194 countries, 2000-2016: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury[J]. Environ Int,2021,154:106595. doi: 10.1016/j.envint.2021.106595. Wang SY. Definition and mechanism analysis of time poverty among workers in the context of long working hours[D]. Dalian: Dongbei University of Finance and Economics,2023. (in Chinese) Kim J, Kwon R, Yun H, et al. The association between long working hours, shift work, and suicidal ideation: A systematic review and meta-analyses[J]. Scand J Work Environ Health,2024,50(7):503-518. doi: 10.5271/sjweh.4182. Mansyur M, Sagitasari R, Wangge G, et al. Long working hours, poor sleep quality, and work-family conflict: determinant factors of fatigue among Indonesian tugboat crewmembers[J]. BMC Public Health,2021,21(1):1832. doi: 10.1186/s12889-021-11883-6. Liang J, Jia N, Zhang F, et al. Shoulder work-related musculoskeletal disorders and related factors of workers in 15 industries of China: a cross-sectional study[J]. BMC Musculoskelet Disord,2022,23(1):952. doi: 10.1186/s12891-022-05917-2. Yang F, Di N, Guo WW, et al. The prevalence and risk factors of work related musculoskeletal disorders among electronics manufacturing workers: a cross-sectional analytical study in China[J]. BMC Public Health,2023,23(1):10. doi: 10.1186/s12889-022-14952-6. Crizol GR, Sá KMM, Santos GM, et al. Work-related musculoskeletal disorders in dockworkers. Systematic review and meta-analysis[J]. Work,2024,79(2):585-600. doi: 10.3233/WOR-230666. Kee D. Characteristics of Work-Related Musculoskeletal Disorders in Korea[J]. Int J Environ Res Public Health,2023,20(2):1024. doi: 10.3390/ijerph20021024. Xu Q, Ling RJ, Liu YM, et al. Incidence patterns and risk factors of work-related musculoskeletal disorders in medical industry in China[J]. Chin J Dis Control Prev,2022,26(8):888-896. (in Chinese) Hussein A, Mando M, Radisauskas R. Work-Related Musculoskeletal Disorders among Dentists in the United Arab Emirates: A Cross-Sectional Study[J]. Medicina (Kaunas),2022,58(12):1744. doi: 10.3390/medicina58121744. Vieira ER, Schneider P, Guidera C, et al. Work-related musculoskeletal disorders among physical therapists: A systematic review[J]. J Back Musculoskelet Rehabil,2016,29(3):417-28. doi: 10.3233/BMR-150649. Le AB, Balogun AO, Smith TD. Long work hours, Overtime, and worker health impairment: A cross-sectional study among stone, sand, and gravel mine workers[J]. Int J Environ Res Public Health,2022,19(13):7740. doi: 10.3390/ijerph19137740. Chan SM, Au-Yeung TC, Wong H, et al. Long Working Hours, Precarious Employment and Anxiety Symptoms Among Working Chinese Population in Hong Kong[J]. Psychiatr Q,2021,92(4):1745-1757. doi: 10.1007/s11126-021-09938-3. Yang ZH. A study on the relationship and intervention of life events, anxiety and depression among medical staff[D]. Chongqing: Chongqing Normal University,2024. (in Chinese) Zhang TT. Study on anxiety and depression of elderly hospitalized patients with chronic diseases and related influencing factors in Liaocheng City[D]. Jinan: Shandong University,2024. (in Chinese) National Bureau of Statistics. Statistical survey system for monitoring and surveying the health literacy of Chinese residents and key populations[Z/OL]. (2022-03-03)[2025-07-08]. https://www.stats.gov.cn/fw/bmdcxmsp/bmzd/202302/t20230215_1907197.html. Sun YY, Sun X, Wan X, et al. Occupational health literacy level and its influencing factors among key populations in China in 2022[J]. Chin Occup Med,2023,50(3):241-247. (in Chinese) Li XY, Liang YS, Gan WZ, et al. Effect of night-shift work and anxiety on work-related musculoskeletal disorders in electronic manufacturing employees[J]. Chin Occup Med,2024,51(5):505-510. (in Chinese) Zhou SY, Chen HQ, Liu M, et al. The relationship between occupational stress and job burnout among female manufacturing workers in Guangdong, China: a cross-sectional study[J]. Sci Rep,2022,12(1):20208. doi: 10.1038/s41598-022-24491-0. Li XY, Chen HQ, Zhou SY, et al. Study on relationship among occupational stress, job burnout and depressive symptoms in female workers of labor-intensive enterprises[J]. Chin J Ind Med,2020,33(3):228-232,261. (in Chinese) Li XY, Chen HQ, Liao XQ, et al. The correlation among occupational stress, coping style and depressive symptoms in 2083 Chinese manufacturing workers[J]. Work,2025. doi:10.1177/10519815251341152. National Bureau of Statistics.Classification of large, medium, small and micro sized enterprises in statistics (2017)[EB/OL]. (2017-12-28)[2025-07-08]. https://www.stats.gov.cn/sj/tjbz/gjtjbz/202302/t20230213_1902763.html. SPITZER R L, KROENKE K, WILLIAMS J B, et al. A brief measure for assessing generalized anxiety disorder: the GAD-7[J]. Arch Intern Med,2006,166(10):1092-1097. He XY, Li CB, Qian J, et al. Reliability and validity of a generalized anxiety disorder scale in general hospital outpatients[J]. Shanghai Arch Psy,2010,22(4):200-203. (in Chinese) Yang L, HILDEBRANDT VH, Yu SF, et al. Introduction and attachment of the Musculoskeletal Disease Survey Form[J]. Ind Hlth Occup Dis,2009,35(1):25-31. (in Chinese) Kim J, Kwon R, Yun H, et al. The association between long working hours, shift work, and suicidal ideation: A systematic review and meta-analyses[J]. Scand J Work Environ Health,2024,50(7):503-518. doi: 10.5271/sjweh.4182. Ma X, Kawakami A, Inui T. Impact of long working hours on mental health status in Japan: evidence from a national representative survey[J]. Int J Environ Res Public Health,2024,21(7):842. doi: 10.3390/ijerph21070842. Zhu BY, Zhang D, Liang ZH. Effect of long working hours on work-related musculoskeletal disorders among operation and maintenance workers of power supply enterprises[J]. J Environ Occup Med,2023,40(1):55-61. (in Chinese) Li SN. Effect of long working hours on occupational stress and health in petrochemical enterprise employees[D]. Nanjing: Southeast University,2022. (in Chinese) Ochiai Y, Takahashi M, Matsuo T, et al. Characteristics of long working hours and subsequent psychological and physical responses: JNIOSH cohort study[J]. Occup Environ Med,2023,80(6):304-311. doi: 10.1136/oemed-2022-108672. Le AB, Balogun AO, Smith TD. Long work hours, overtime, and worker health impairment: a cross-sectional study among stone, sand, and gravel mine workers[J]. Int J Environ Res Public Health,2022,19(13):7740. doi: 10.3390/ijerph19137740. Leso V, Fontana L, Caturano A, et al. Impact of shift work and long working hours on worker cognitive functions: current evidence and future research needs[J]. Int J Environ Res Public Health,2021,18(12):6540. doi: 10.3390/ijerph18126540. Jensen RC. Work-related musculoskeletal problems: A look at how employers view causes[J]. Int J Environ Res Public Health,2024,21(8):1098. doi:10.3390/ijerph21081098. Tamminga SJ, Kuijer PPFM, Badarin K, et al. Towards harmonisation of case definitions for eight work-related musculoskeletal disorders - an international multi-disciplinary Delphi study[J]. BMC Musculoskelet Disord,2021,22(1):1018. doi:10.1186/s12891-021-04871-9. AkbarR KA, Try P, Viwattanakulvanid P, et al. Work-related musculoskeletal disorders among farmers in the Southeast Asia Region: a systematic review[J]. Saf Health Work,2023,14(3):243-249. Thacker H, Yasobant S, Viramgami A, et al. Prevalence and determinants of (work-related) musculoskeletal disorders among dentists- a cross sectional evaluative study[J]. Indian J Dent Res,2023,34(1):24-29. Almaghlouth MK, Alserhani NM, Aldossary FA, et al. Prevalence, patterns, and risk factors of work-related musculoskeletal diseases among teachers in the Eastern Province, Saudi Arabia: a community-based retrospective cross-sectional survey[J]. Cureus,2022,14(12):e32178. Kee D. Characteristics of work-related musculoskeletal disorders in Korea[J]. Int J Environ Res Public Health,2023,20(2):1024. Wang FJ, Jin X, Nazakat MAMAT, et al. Occurrence pattern of musculoskeletal disorders and its influencing factors among manufacturing workers[J]. J Peking Univ (Health Sci),2020,52(3):535-540. (in Chinese) Yong XT, Li FY, Ge H, et al. A cross-sectional epidemiological survey of work-related musculoskeletal disorders and analysis of its influencing factors among coal mine workers in Xinjiang[J]. Biomed Res Int,2020,31664056. Deng HJ, He DS, Li FY. Factors influencing job burnout and musculoskeletal disorders among coal miners in the Xinjiang uygur autonomous region[J]. Pain Res Manag,2021:6629807. Zhang DY, Wang ZX, Nie XQ, et al. Analysis on present status of multi-site work-related musculoskeletal disorders in workers of a shipyard and its influencing factors[J]. Chin J Ind Med,2022,35(4):307-312. (in Chinese) Zhang DY, Wang LM, Liu QF, et al. Analysis of influencing factors of shoulder work-related musculoskeletal disorders in ultrasound doctors[J]. Chin J Ind Med,2023,36(2):116-121. (in Chinese) Liu XM, Wang J, Wang C, et al. Effect of long working hours on work related musculoskeletal disorders and job burnout in internet enterprise employees[J]. Chin Occup Med,2020,47(2):135-140. (in Chinese) Ding X, Guan Z, Liu N, et al. Prevalence and risk factors of work-related musculoskeletal disorders among emerging manufacturing workers in Beijing, China[J]. Front Med (Lausanne),2023,10:1289046. doi: 10.3389/fmed.2023.1289046. Yang F, Di N, Guo WW, et al. The prevalence and risk factors of work related musculoskeletal disorders among electronics manufacturing workers: a cross-sectional analytical study in China. BMC Public Health[J]. 2023,23(1):10. doi: 10.1186/s12889-022-14952-6. Chen HQ, Li XY, Huang MQ, et al. Analysis of the correlation between work-related musculoskeletal disorders and occupational stress in electronic manufacturing workers[J]. Chin Occup Med,2024,51(1):81-84,89. (in Chinese) Feng JQ, Liu XX, Liu HZ, et al. Relationship between work-related musculoskeletal disorders and adverse ergonomic factors among workers in an electronic manufacture[J].Occup Health Emerg Rescue,2020,38(6):558-562. (in Chinese) Kee D. Characteristics of work-related musculoskeletal disorders in Korea[J]. Int J Environ Res Public Health,2023,20(2):1024. doi: 10.3390/ijerph20021024. Crizol GR, Sá KMM, Santos GM, et al. Work-related musculoskeletal disorders in dockworkers[J]. Systematic review and meta-analysis,2024,79(2):585-600. doi: 10.3233/WOR-230666. Wang K, Zeng X, Li J, et al. The prevalence and risk factors of work-related musculoskeletal disorders among nurses in China: A systematic review and meta-analysis[J]. Int J Nurs Stud,2024,157:104826. doi: 10.1016/j.ijnurstu.2024.104826. Gandolfi MG, Zamparini F, Spinelli A, et al. Musculoskeletal disorders among Italian dentists and dental hygienists[J]. Int J Environ Res Public Health,2021,18(5):2705. doi: 10.3390/ijerph18052705. Yin H, Mei Y, Zhang KK, et al. Influence of individual factors and labor organization on musculoskeletal disorders of automobile manufacturing workers[J]. J Pub Health Prev Med,2024,35(4):99-102. (in Chinese) Hu B, Wu Y, Pan Y, et al. Association of long working hours and multi-site work-related musculoskeletal disorders among transportation industry workers in Beijing, China[J]. Int Arch Occup Environ Health,2024,97(10):1063-1071. doi: 10.1007/s00420-024-02110-x. Li XY, Chen HQ, Yu XY, et al. Effect of long working hours on work-related musculoskeletal disorders and depressive symptoms among workers in electronics manufacturing[J]. Occup Health Emerg Rescue,2024,42(6):729-733. (in Chinese) Wu YK, Zhou ZH, Wu DM. Relationship between working duration and work-related musculoskeletal disorders among Internet enterprise employees[J].Prev Med,2022,34(12):1262-1266,1272. (in Chinese) Li XY, Liang YS, Gan WZ. Effect of night-shift work and anxiety on work-related musculoskeletal disorders in electronic manufacturing employees[J]. Chin Occup Med,2024,51(5):505-510. (in Chinese) Chan SM, Au-Yeung TC, Wong H, et al. Long working hours, precarious employment and anxiety symptoms among working Chinese Population in Hong Kong[J]. Psychiatr Q,2021,92(4):1745-1757. doi:10.1007/s11126-021-09938-3. Lee Y, Park H. Working hours and depressive and anxiety symptoms according to shift work and gender[J]. J Occup Environ Med,2022,64(5):e316-e321. doi:10.1097/JOM.0000000000002515. Serrano-Fernández MJ, Boada-Grau J, Boada-Cuerva M, et al. Work addiction as a predictor of anxiety and depression[J]. Work,2021,68(3):779-788. doi:10.3233/WOR-203411. Li XY, Chen HQ, Zhou SY, et al. Study on relationship among occupational stress, job burnout and depressive symptoms in female workers of labor-intensive enterprises[J]. Chin J Ind Med,2020,33(3):228-232,261. (in Chinese) He LH. Pay attention to the prevention and control of work-related musculoskeletal disorder[J]. J Environ Occup Med,2022,39(6):589-592. (in Chinese) Ochiai Y, Takahashi M, Matsuo T, et al. Characteristics of long working hours and subsequent psychological and physical responses: JNIOSH cohort study[J]. Occup Environ Med,2023,80(6):304-311. doi: 10.1136/oemed-2022-108672. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7341990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525778591,"identity":"d727763d-ce66-40e8-889f-1bca5820be40","order_by":0,"name":"Xiaoyi Li","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Li","suffix":""},{"id":525778592,"identity":"aa854ed2-08e6-45cd-8bb9-83d04dad23ed","order_by":1,"name":"Yushuo Liang","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Yushuo","middleName":"","lastName":"Liang","suffix":""},{"id":525778593,"identity":"952862c8-a141-46f2-8edb-1942aa67cd10","order_by":2,"name":"Huiqing Chen","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Huiqing","middleName":"","lastName":"Chen","suffix":""},{"id":525778594,"identity":"fcb4585d-0c18-4452-b672-adc258673ae4","order_by":3,"name":"Xiqing Liao","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Xiqing","middleName":"","lastName":"Liao","suffix":""},{"id":525778595,"identity":"b959cb33-c48f-49ff-ad6e-39d4cafa66fe","order_by":4,"name":"Xinyang Yu","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Xinyang","middleName":"","lastName":"Yu","suffix":""},{"id":525778596,"identity":"501c21f7-22ec-4935-b1e2-e16fac7d84d0","order_by":5,"name":"Lin Xu","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xu","suffix":""},{"id":525778597,"identity":"75a279ef-7f81-4047-be6c-a535cf13ce50","order_by":6,"name":"Junle Wu","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Junle","middleName":"","lastName":"Wu","suffix":""},{"id":525778598,"identity":"df538a56-ffb2-47f3-ab15-e22701dfb37f","order_by":7,"name":"Jiabin Chen","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Chen","suffix":""},{"id":525778599,"identity":"1e8eac64-5f74-435a-9593-5cd00d5749ff","order_by":8,"name":"Min Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACNv7mgw8+/pNgZmNvbHyQUFFDWAufxLFkwxlsNux8PIebDR6cOUZYixxDjpo0D1sav5xEepvkwxZmIhzGcIZNmofnsDQbQ2JbRWIDGwN/e3cCfi3MvYct50gcNmZjONh2I3GHDIPEmbMbCNhyLvHGG4PDyWyMjUAtZ9gYDCRyCWnJMZDgSThc38bM2FaQ2MZMlBYjSZ4DacxsbIxtDMRpAQXyzAYbZjYexmaJhDPHeAj6Rb4fFJUNEszy858//PijokaOv70XvxYMwEOa8lEwCkbBKBgFWAEATZNIAD3kKC0AAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-08-11 03:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7341990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7341990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93006031,"identity":"2b1d5c95-f4ab-4c10-8e67-eeeaf0d743af","added_by":"auto","created_at":"2025-10-08 06:43:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69096,"visible":true,"origin":"","legend":"","description":"","filename":"20250826FinaleditionAnalysisoftheimpactoflongworkinghoursonanxietyandworkrelatedmusculoskeletaldiseases.docx","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/9b9e02d9b747f7df214e5f9b.docx"},{"id":93005262,"identity":"0611810e-0ff8-4d8e-9ea3-f1c4cf014dcd","added_by":"auto","created_at":"2025-10-08 06:35:18","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10311,"visible":true,"origin":"","legend":"","description":"","filename":"437bdd861fa5471383d471a80b1c44ee.json","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/9eddf928fbcf43311074b5cc.json"},{"id":93005265,"identity":"1ab4ee02-60de-4ca7-8698-24d1872d28f6","added_by":"auto","created_at":"2025-10-08 06:35:18","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157374,"visible":true,"origin":"","legend":"","description":"","filename":"437bdd861fa5471383d471a80b1c44ee1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/b1839a9736d18a7a26be5778.xml"},{"id":93005266,"identity":"91546a7e-a546-47f3-8408-bf81000b2238","added_by":"auto","created_at":"2025-10-08 06:35:18","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5633,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/fb1676052fff8c91286df48a.jpeg"},{"id":93005264,"identity":"41642e40-b8f7-454e-b169-f6be821e8d7c","added_by":"auto","created_at":"2025-10-08 06:35:18","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1016,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/74013d68a84f93d15d02cabc.png"},{"id":93005268,"identity":"398b2132-a726-4945-b7e3-c1c0907b1714","added_by":"auto","created_at":"2025-10-08 06:35:18","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154887,"visible":true,"origin":"","legend":"","description":"","filename":"437bdd861fa5471383d471a80b1c44ee1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/6670eac19093b8406ce72b5b.xml"},{"id":93005267,"identity":"8c5f20e8-df96-4a10-b444-8073439114c6","added_by":"auto","created_at":"2025-10-08 06:35:18","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166620,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/d7752ac3fd5df12a2f8ec9a0.html"},{"id":108601087,"identity":"7e96ee88-e27d-4c30-a961-5645814aaf27","added_by":"auto","created_at":"2026-05-06 11:28:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":497958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7341990/v1/5b210a0d-a04d-4cc7-9019-2a3147002ba3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the impact of long working hours on anxiety and work-related musculoskeletal diseases among female workers in electronics manufacturing enterprises in Guangdong Province","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the ongoing process of globalization and great changes in the working environment, the phenomenon of long working hours among working groups has become increasingly common, particularly in the manufacturing and internet industries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Long working hours are defined as working beyond the standard limits \u0026mdash; exceeding 8 hours per day or 40 hours per week [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the International Labour Organization, nearly 500\u0026nbsp;million workers worldwide work more than 55 hours per week [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the world's second-largest economy, China's rapid economic and social development had been accompanied by increased work demands across various industries, with the weekly working hours of employees approaching 48 hours [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prolonged working hours not only reduce workers' personal time, but also lead to serious physical and mental health issues, making it one of the most critical occupational risk affecting workers' well-being [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, WMSDs have become one of the key occupational health issues in China [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It affects a wide range of industries and populations, and in addition to the physiological impact on workers, it would also reduce work efficiency and quality of life, and cause economic burdens to both society and individuals [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A large number of epidemiological investigations showed that the occurrence of WMSDs was related to occupational, social, and individual factors [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous studies showed that long working hours were associated with adverse health outcomes such as anxiety and WMSDs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Anxiety refers to the undesirable emotional state experienced by an individual in anticipation of difficulties or danger [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is a clinically common, chronic, and recurrent psychiatric disorder characterized by a general sense of nervousness, fear, restlessness, and distress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur country established \u003cem\u003ethe Occupational Health Literacy Questionnaire of National Key Populations\u003c/em\u003e in 2022, which was the first time to carry out a nationwide monitoring survey on occupational health literacy of key populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. One of the contents was to monitor and investigate the occupational health literacy of key populations in the country [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In China, manufacturing accounts for the largest proportion of the workers, with the electronics manufacturing industry being a particularly prominent sector[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As one of the leading industries in Guangdong Province, the electronics manufacturing industry has become an important strategic industry to promote the rapid economic development of Guangdong Province. Female workers are the main force in electronics manufacturing enterprises, and they not only take the family responsibility of giving birth to children, but also participate in production activities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the characteristics of the industry, female workers in the electronics manufacturing industry require to work for long hours performing repetitive, monotonous, and fast-paced operations, which result in prolonged physical fatigue. This persistent fatigue may lead injuries in the waist, neck and shoulders, and other parts, subsequently causing absenteeism, production accidents, and reduce work ability. These outcomes may lead to indirect economic losses, which was not conducive to the sustainable development of enterprises and even the social economy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn accordance with the relevant requirements of the \u003cem\u003eOccupational Health Literacy Questionnaire of National Key Populations\u003c/em\u003e, combined with the characteristics of the industrial structure of Guangdong Province, this study investigates the health risks caused by long working hours of female workers in electronic manufacturing enterprises in Guangdong Province, aims to evaluate the impact of long working hours on the anxiety and WMSDs of female workers in electronics manufacturing enterprises in Guangdong Province, and proposes key strategies for the prevention and control of the health effects caused by long working hours, and provides new ideas for promoting the physical and mental health of this occupational population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSubjects\u003c/h2\u003e\u003cp\u003eThis study used a convenience sampling method to conduct a cross-sectional survey between July and September 2022. It encompassed a total of 193 electronic manufacturing enterprises in Guangdong Province, with 5,300 questionnaires disseminated. Following the removal of invalid questionnaires, 4,976 female workers were selected as participants, yielding a valid response rate of 93.9%. The criteria for inclusion in the study were: i) aged over 18 years; ii) at least 6 months of work experience. Exclusion criteria comprised: i) recent significant emotional events or the presence of mental or organic diseases; ii) suffering from musculoskeletal injuries, osteoarthritis, tumors, or tuberculosis that could lead to musculoskeletal disorders.\u003c/p\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by the \u003cem\u003eMedical Ethics Committee of the Guangdong Province Hospital for Occupational Disease Prevention and Treatment\u003c/em\u003e (Approval No. GDHOD MEC 2018012) and was strictly compliant with local law and the Declaration of Helsinki. All participants provided informed consent prior to administering the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eOccupational Health Literacy Questionnaire of National Key Populations\u003c/em\u003e was used to investigate the sociodemographic characteristics of the research subjects, such as age, gender, education level, marital status, personal monthly income, length of service, enterprise scale, enterprise nature, night shift, weekly working hours and other occupational characteristics [20]. The classification of enterprise size was based on \u003cem\u003ethe Measures for the Classification of Large, Medium, Small, and Micro Enterprises in Statistics\u0026nbsp;\u003c/em\u003e(\u003cem\u003e2017\u003c/em\u003e), which categorizes enterprises as large- (≥1,000 employees), medium- (300 to \u0026lt;1,000 employees), and small- and micro-sized (\u0026lt;300 employees) [25]. Long working hours detection rate (%) = (number of participants working \u0026gt;40 h/week/total population)×100%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe anxiety survey of the \u003cem\u003eOccupational Health Literacy Questionnaire of National Key Populations\u003c/em\u003e used the generalized anxiety scale developed by Spitzer et al and translated by He XY et al [26,27]. The scale consists of seven items, and the scored was calculated based on the frequency of anxiety symptoms over the past two weeks: 0 points for \"not at all\", 1 point for \"several days\", 2 points for \"more than a week\", and 3 points for \"almost every day\". The total score was calculated as the sum of seven items, ranging from 0 to 21. A total score of 0-4 points indicated no anxiety, while 5-21 points indicated the presence of anxiety. A higher total score indicated a higher level of anxiety. Anxiety detection rate (%) = (number of participants with anxiety/total population)×100%. The Cronbach's α coefficient for the scale in this study was 0.934.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWork-related Musculoskeletal Diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Musculoskeletal Disorders Survey Scale\u003c/em\u003e of the \u003cem\u003eOccupational Health Literacy Questionnaire of National Key Populations\u003c/em\u003e was developed by Yang L et al, and was used to evaluate the WMSDs of nine body parts among the study subjects: neck, shoulder, back, elbow, waist, wrist, hip, hip, knee and ankle and foot [28]. For each body part listed above, if pain or discomfort occurred within the past year and lasted for more than 24 hours, a score of 1 was assigned; if no pain or discomfort was reported, a score of 0 was assigned. The total score was calculated as the sum of scores for nine body parts. A total score of 0 indicated no WMSDs symptoms, while a score of 1-9 indicated the presence of WMSDs symptoms. Higher total scores indicated a more severe WMSDs symptoms. WMSDs detection rate (%) = (number of participants with WMSDs at any site/total population)×100%. The Cronbach's \u003cem\u003eα\u003c/em\u003e coefficient for the scale in this study was 0.884.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigators, all of whom had undergone uniform training, were responsible for explaining the survey's purpose, completion instructions, and confidentiality assurances to the participants, ensuring participants' full understanding of the questionnaire and its requirements. Participants completed the questionnaire online by scanning a QR code provided by the investigators, with all items required to be answered before submission. The database was established using EpiData 3.0, and the collected questionnaires were entered into the database by two individuals for verification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS 21.0 software. Normally distributed measurement data were described using mean±standard deviation, while non-normally distributed data were described using median (\u003cem\u003eM\u003c/em\u003e) and the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e75\u003c/sub\u003e). The comparison of rates of count data was conducted using Pearson's Chi-square test or the trend Chi-square test. The impact of weekly working hours on anxiety and WMSDs was analyzed using binary logistic regression analysis. The significance level was set at \u003cem\u003eα\u003c/em\u003e=0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 4,976 participants were included in the study, with a mean age of 36.4\u0026plusmn;7.8 years. \u0026nbsp;The median (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e75\u003c/sub\u003e) of length of service was 3 (1, 8) years. Most participants were aged from 36.0 to \u0026lt;46.0 years (39.7%), had a junior high school education or below (59.6%), and were married (83.6%) without night shifts (75.7%). The monthly average income was primarily between 3,000-4,999 yuan (55.0%), and the length of service was mostly 0.5 to \u0026lt;2 years (30.9%). Regarding enterprise sized and characteristics, small- and micro-enterprises accounted for 44.5%, privately-owned enterprises accounted for 51.1%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of the detection rate of long working hours, anxiety, and WMSDs among research subjects in different characteristic groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3,375 (67.8%) study subjects had long working hours. A total of 1,146 study subjects were identified as anxiety, while 2,151 were screened as WMSDs, with a detection rate of 23.0% and 43.2%, respectively. The detection rate of long working hours among study subjects differed significantly by monthly income, enterprise size, and enterprise nature (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). The detection rate of anxiety among the study subjects with different ages, education level, marital status, length of service, enterprise size, night shift and weekly working hours had significant difference (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). There were significant differences in the detection rate of WMSDs among the study subjects with different length of service, enterprise size, enterprise nature, night shift and weekly working hours (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). See Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eComparison of the detection rate of long working hours, anxiety and WMSDs among study subjects in different groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber/Proportion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of long working hours/Proportion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of anxiety/Proportion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of WMSDs/Proportion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e18-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e472（9.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e306（64.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e156（33.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e200（42.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e26-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,852（37.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,262（68.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e506（27.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e781（42.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e36-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,976（39.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,362（68.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e382（19.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e884（44.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e46-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e676（13.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e445（65.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e102（15.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e286（42.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eJunior high school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2,965（59.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2,002（67.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e614（20.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1,263（42.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eHigh school/Technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,201（24.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e842（70.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e306（25.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e521（43.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCollege degree and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e810（16.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e531（65.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e226（27.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e367（45.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4,158（83.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2,830（68.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e886（21.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1,791（43.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eUnmarried/Divorced/Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e818（16.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e545（66.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e260（31.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e360（44.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal monthly income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e＜\u0026yen; 3,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e776（15.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e420（54.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e159（20.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e330（42.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026yen; 3,000-\u0026yen; 4,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2,737（55.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,882（68.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e628（22.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1,214（44.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026yen; 5,000-\u0026yen; 6,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,197（24.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e883（73.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e296（24.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e490（40.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026yen; 7,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e266（5.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e190（71.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e63（23.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e117（44.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of service (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e0.5-＜2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,536（30.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,043（67.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e392（25.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e631（41.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e2.0-＜5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,293（26.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e889（68.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e307（23.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e568（43.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e5.0-＜10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,120（22.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e750（67.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e214（19.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e472（42.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e10.0-＜20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e853（17.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e581（68.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e194（22.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e394（46.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e20.0-34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e174（3.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e112（64.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e39（22.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e86（49.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnterprise size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eMicro-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2,215（44.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,384（62.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e440（19.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e864（39.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eMedium-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,345（27.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e977（72.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e300（22.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e574（42.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eLarge-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,416（28.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,014（71.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e406（28.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e713（50.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnterprise nature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eState-owned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e66（1.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e42（63.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e20（30.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e51（77.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003ePrivate sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2,544（51.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,783（70.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e576（22.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1,020（40.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eForeign capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2,366（47.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1,550（65.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e550（23.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1,080（45.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNight shift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3,767（75.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2,554（67.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e816（21.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1,535（40.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,209（24.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e821（67.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e330（27.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e616（51.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeekly working hours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026le;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,601（32.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e354（22.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e647（40.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e41-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e857（17.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e179（20.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e369（43.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e45-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e874（17.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e176（20.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e337（38.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e49-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e619（12.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e155（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e291（47.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026ge;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1,025（20.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e282（27.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e507（49.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e1\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e83.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e2\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e23.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e3\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e42.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e4\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e62.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e3.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e5\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e6.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e5\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e6\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e52.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e38.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e45.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e6\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e7\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e12.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e46.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e7\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e8\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e16.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e38.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e8\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Chi;\u003c/em\u003e\u003c/strong\u003e\u003csub\u003e9\u003c/sub\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e11.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e20.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e9\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e, \u003cem\u003e\u0026Chi;\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e were the statistical results of the score comparison of different age, education level, marital status, personal monthly income, length of service, enterprise scale, enterprise nature, night shift and weekly working hours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection rate of WMSDs among study subjects in different anxiety groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detection rates of WMSDs among study subjects had significantly difference (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e=292.46, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) between the non-anxiety group (36.7%; 1,404/3,830) and the anxiety group (65.2%; 747/1,146). Among them, the detection rate of WMSDs for 1-2 sites in the non-anxiety group was higher than that in the anxiety group, while the detection rates of WMSDs for 3-4 sites and more than 5 sites in the anxiety group were both higher than those in the non-anxiety group. See Table 2.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; Detection rate of WMSDs in study subjects with different anxiety groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of any site/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of 1-2 sites/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of 3-4 sites/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of more than 5 sites/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNon-anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp; 550（27.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e312（56.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e137（24.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;101（18.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;1,601（54.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e606（37.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e484（30.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;511（31.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;2,151（43.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e918（42.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e621（28.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;612（28.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of the impact of weekly working hours on both anxiety and WMSDs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBinary logistic regression analyses were conducted with anxiety and WMSDs as dependent variables. Weekly working hours were the primary predictor, along with other variables that showed significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in univariate analysis (Table 1). Variable assignments were listed in Table 3. The results showed that after controlling for age, education level, marital status, length of service, enterprise size, enterprise nature, night shift and other factors, the weekly working hours were positively correlated with both anxiety and WMSDs (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). Specifically, study subjects with longer weekly working hours had a higher risk of anxiety and WMSDs, with \u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e) values were 1.067 (1.021-1.116) and 1.067 (1.028-1.108), respectively. See Table 4.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; Variable assignment situation\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssignment situation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eNo=0, Yes=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eWMSDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eNo=0, Yes=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003e18-25=1, 26-35=2, 36-45=3, 46-60=4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eJunior high school and below=1, High school/Technical secondary school=2, College degree and above=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eMarried=1, Unmarried/Divorced/Widowed=2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003ePersonal monthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003e＜\u0026yen; 3,000=1, \u0026yen; 3,000-\u0026yen; 4,999=2, \u0026yen; 5,000-\u0026yen; 6,999=3, \u0026ge;\u0026yen; 7,000=4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLength of service (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003e0.5-＜2.0=1, 2.0-＜5.0=2, 5.0-＜10.0=3, 10.0-＜20.0=4, 20.0-34.0=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eEnterprise scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eMicro=1, Medium=2, Large=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNight shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eNo=0, Yes=1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eWeekly working hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003e\u0026le;40=1, 41-44=2, 45-48=3, 49-54=4, \u0026ge;55=5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u0026nbsp; Binary logistic regression analysis of the impact of weekly working hours on anxiety and WMSDs\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.067（1.021~1.116）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eWMSDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.067（1.028~1.108）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, the \u0026quot;996\u0026quot; (9 a.m. to 9 p.m., 6 days a week) and even \u0026quot;007\u0026quot; (without work shifts) working models have led to physical exhaustion and increasing psychological problems among workers, sparking widespread public concern about long working hours [29,30]. In this study, the detection rate of long working hours of female workers in electronics manufacturing enterprises in Guangdong Province reached 67.8%, which is comparable to the 61.1% reported by Zhu BY et al [31] in power supply enterprises and much higher than the 42.7% reported by Li SN [32] in the petrochemical industry. Although longer working hours may appear to improve productivity, it can cause the long-term negative impacts, including the loss of personal time, job burnout, depressive symptoms, WMSDs, and sleep disorders, all of which ultimately reduce work efficiency and quality [33-35]. Therefore, a series of physical and mental health risks caused by long working hours to female workers in the electronics manufacturing enterprises warrant serious attention.\u003c/p\u003e\n\u003cp\u003eWMSDs are prevalent across various industries and populations with a high prevalence, representing a major occupational health challenge in China and a global concern in occupational health [36,37]. In Southeast Asia, the prevalence of WMSDs has been reported as 78.3% in Thailand, 81.3% in Indonesia, and 88.4% in Malaysia [38]. A cross-sectional study in India has indicated that the 12-month prevalence of WMSDs was 75.8% and the point prevalence was 23.3% among dentists [39]. In eastern province of Saudi Arabia, the WMSDs prevalence among teachers was 41.1% [40], and in South Korea, WMSDs accounted for 9.5% to 71.5% of the total number of occupational diseases annually, most commonly in the manufacturing industry, followed by the construction, transportation/warehousing, communication, and mining [41]. In China, nearly all occupational groups are affected by WMSDs. Studies have shown that the overall WMSDs prevalence among manufacturing workers has reached 79.7%, often involving multiple body regions instead of just a single body part [42]. Among local coal miners, the highest WMSDs prevalence among different body parts were the lower back (50.7%), followed by the neck, shoulders, knees, and elbows [43,44]. The prevalence among shipyard workers was 59.3% [45], while among ultrasound physicians, it reached 94.3%, with the right shoulder and neck being the most affected body parts [46]. The prevalence of WMSDs among employees of Internet companies was as high as 80.4% [47]. In the electronics manufacturing industry, workers are at increased risk of muscle and nerve injuries due to the nature of the work\u0026mdash;prolonged sitting or standing, repetitive tasks, and forced postures, leading to fatigue and musculoskeletal pain [48,49]. In this study, the detection rate of WMSDs among 4,976 female workers in 193 electronics manufacturing enterprises was 43.2%, which was slightly higher than the 39.5% found in previous study in two electronics manufacturing enterprises [50], but lower than the 58.0% in the electronics manufacturing industry in a city in the Pearl River Delta reported by Feng JQ et al [51].\u003c/p\u003e\n\u003cp\u003eIn addition to causing physiological risks, WMSDs could also lead to a range of negative outcomes, such as loss of work ability, reduced work efficiency, lower income level, and lower quality of life, and increase the medical burden of society, causing huge economic burdens to both society and individuals [52,53]. A large number of epidemiological investigations had shown that the development of WMSDs is related to multiple factors, including occupational factors, individual factors, and psychosocial factors [54,55]. According to a survey among workers in the automobile manufacturing industry, long working hours was a risk factor for WMSDs [56], and similar findings were reported in studies of transportation workers [57]. In this study, long working hours could increase the risk of WMSDs, and the weekly working hours were positively correlated with the risk of WMSDs. This was consistent with pervious study by our lab and by Wu YK et al in employees from internet companies [58,59]. These findings suggested that rational work scheduling and limiting working hours may serve as effective strategies to prevent and intervene WMSDs.\u003c/p\u003e\n\u003cp\u003eIn this study, the detection rate of anxiety among female workers in electronics manufacturing enterprises in Guangdong Province was 23.0%, which is slightly lower than the 26.8% reported by previous investigation in the Pearl River Delta region [60]. Several studies had shown that long working hours is a risk factor for anxiety [61,62]. Anxiety is an undesirable emotional experience and an unhealthy psychological state, which not only negatively impacts workers\u0026prime; health, but also seriously reduces work efficiency, and imposes adverse effects on both individual well-being and society productivity [63]. The electronics manufacturing industry is a leading industry in the economic development of Guangdong Province, facing an increasing competitive pressure nowadays. Female workers in this industry were more likely to suffer from psychological disorders such as anxiety and depression, since most of them also took family responsibilities such as childbirth and parenting [64]. Demographic factors such as age, gender, body mass index, smoking, and physical exercise, as well as psychosocial factors including high mental stress, low social support, and high job satisfaction and job monotony were all played an important role in the development of WMSDs [65]. In this study, the detection rate of WMSDs involving 1-2 body parts of workers in the non-anxiety group was higher than that in the anxiety group, while the detection rate of WMSDs involving 3-4 and more than 5 body parts of workers in the anxiety group was higher than that in the non-anxiety group. These suggested that WMSDs is a common occupational health issue among electronics manufacturing workers at present. WMSDs effect on workers\u0026apos; anxiety might not be obvious when involving body parts were redistricted, however, once three or more body parts were involved, the physical and psychological burden may become substantial, thereby increasing the risk of anxiety. After controlling for demographic variables, hierarchical regression analysis further indicated that long working hours was a risk factor for anxiety and WMSDs among female workers. It was suggested that enterprises should pay attention to the problem of female workers\u0026apos; long working hours, and adopted early intervention to improve work efficiency, reduce the incidence of anxiety, and reduced the risk of WMSDs, so as to promote the sustainable healthy development of enterprises and society.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, female workers in electronics manufacturing enterprises in Guangdong Province generally worked for long working hours, which would increase the risk of anxiety and WMSDs. According to the relevant provisions of the\u0026nbsp;\u003cem\u003eLabour Law of the People\u0026apos;s Republic of China\u003c/em\u003e, employers are required to ensure employees to have at least one day off per week, and that overtime hours should not exceed one hour per day, or three hours under special circumstances, with a monthly maximum of 36 hours. Based on the results of this study, following recommendations are proposed for enterprises. i) Utilizing technological support. Implementing intelligent equipment and software tools in automate repetitive and labor-intensive processes, thereby reduce the working hours for female employees. ii) Optimizing personnel allocation. Encourage rotational leave during off-peak seasons; hiring temporary workers or reallocating internal workers during peak production periods, to reduce the frequency of prolonged work hours and protect workers\u0026prime; mental health [66]. iii) Adjusting compensation structures. Establish a flexible compensation system based on workload and seasonal variations. Distinguish between basic salaries and overtime pay, and provide performance-based incentives for workers engaged in prolonged hours. iv) Enhancing training and communication. During off-peak seasons offer skill training to improve workers\u0026prime; professional competence and adaptability. Also, employers should be aware of workers\u0026prime; work status and emotional well-being, and respond to issues in time. As for female workers, the following suggestions are provided. i) Developing reasonable work plans. Organize work schedules according to personal capacity to reduce unnecessary overtime. ii) Strengthening professional skills. Improve personal technical skills to meet the demands of evolving industrial processes and enhance work efficiency. iii) Improving health awareness. Adopt healthy work and lifestyle to prevent anxiety and WMSDs associated with prolonged working hours.\u003c/p\u003e\n\u003cp\u003eThis study focused on the impact of long working hours on the anxiety and WMSDs among female workers in electronic manufacturing enterprises. However, ergonomic and psychosocial factors were not included in the current analysis. In addition, as a cross-sectional study, it cannot infer causal relationships between long working hours and anxiety/WMSDs. Future research would use a prospective cohort design to further explore the effects of adverse ergonomic factors, and provide more robust evidence for the prevention and treatment of anxiety and WMSDs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all field workers and participants of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXYL designed and wrote the manuscript. YSL and XQL analyzed and interpreted the findings. HQC, XYY, LX and JLW collected data and revised the paper. JBC and MY designed, reviewed, and revised the manuscript and approved the final manuscript as submitted. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the Guangdong Medical Research Fund (grant number B2023176, A2024248, and A2025135).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal data are available on reasonable request. These were stored on password-protected computers at the Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China. Readers who wish to gain access to the data can write to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the \u003cem\u003eMedical Ethics Committee of Medical Ethics Committee of the Guangdong Province Hospital for Occupational Disease Prevention and Treatment\u003c/em\u003e (Approval No. GDHOD MEC 2018012). All participants gave written informed consent before being recruited into the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu XM, Wang C, Wang J, et al. Effect of long working hours and insomnia on depressive symptoms among employees of Chinese internet companies[J]. BMC Public Health,2021,21(1):1408. doi: 10.1186/s12889-021-11454-9.\u003c/li\u003e\n\u003cli\u003eLi XY, Guo Y, Zhao R, et al. Association between job burnout, depressive symptoms, and insomnia among employees in electronic manufacturing industry[J]. J Environ Occup Med,2024,41(11):1205-1212. (in Chinese)\u003c/li\u003e\n\u003cli\u003eMinistry of Human Resources and Social Security of the People\u0026acute;s Republic of China. Provisions of the State Council on the working hours of employees[EB/OL]. (1995-03-25)[2025-07-08]. https://www.mohrss.gov.cn/xxgk2020/fdzdgknr/zcfg/fg/202011/t20201103_394935.html.\u003c/li\u003e\n\u003cli\u003ePega F, N\u0026aacute;fr\u0026aacute;di B, Momen NC, et al. Global, regional, and national burdens of ischemic heart disease and stroke attributable to exposure to long working hours for 194 countries, 2000-2016: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury[J]. Environ Int,2021,154:106595. doi: 10.1016/j.envint.2021.106595.\u003c/li\u003e\n\u003cli\u003eWang SY. Definition and mechanism analysis of time poverty among workers in the context of long working hours[D]. Dalian: Dongbei University of Finance and Economics,2023. (in Chinese)\u003c/li\u003e\n\u003cli\u003eKim J, Kwon R, Yun H, et al. The association between long working hours, shift work, and suicidal ideation: A systematic review and meta-analyses[J]. Scand J Work Environ Health,2024,50(7):503-518. doi: 10.5271/sjweh.4182.\u003c/li\u003e\n\u003cli\u003eMansyur M, Sagitasari R, Wangge G, et al. Long working hours, poor sleep quality, and work-family conflict: determinant factors of fatigue among Indonesian tugboat crewmembers[J]. BMC Public Health,2021,21(1):1832. doi: 10.1186/s12889-021-11883-6.\u003c/li\u003e\n\u003cli\u003eLiang J, Jia N, Zhang F, et al. Shoulder work-related musculoskeletal disorders and related factors of workers in 15 industries of China: a cross-sectional study[J]. BMC Musculoskelet Disord,2022,23(1):952. doi: 10.1186/s12891-022-05917-2.\u003c/li\u003e\n\u003cli\u003eYang F, Di N, Guo WW, et al. The prevalence and risk factors of work related musculoskeletal disorders among electronics manufacturing workers: a cross-sectional analytical study in China[J]. BMC Public Health,2023,23(1):10. doi: 10.1186/s12889-022-14952-6.\u003c/li\u003e\n\u003cli\u003eCrizol GR, S\u0026aacute; KMM, Santos GM, et al. Work-related musculoskeletal disorders in dockworkers. Systematic review and meta-analysis[J]. Work,2024,79(2):585-600. doi: 10.3233/WOR-230666.\u003c/li\u003e\n\u003cli\u003eKee D. Characteristics of Work-Related Musculoskeletal Disorders in Korea[J]. Int J Environ Res Public Health,2023,20(2):1024. doi: 10.3390/ijerph20021024.\u003c/li\u003e\n\u003cli\u003eXu Q, Ling RJ, Liu YM, et al. Incidence patterns and risk factors of work-related musculoskeletal disorders in medical industry in China[J]. Chin J Dis Control Prev,2022,26(8):888-896. (in Chinese)\u003c/li\u003e\n\u003cli\u003eHussein A, Mando M, Radisauskas R. Work-Related Musculoskeletal Disorders among Dentists in the United Arab Emirates: A Cross-Sectional Study[J]. Medicina (Kaunas),2022,58(12):1744. doi: 10.3390/medicina58121744.\u003c/li\u003e\n\u003cli\u003eVieira ER, Schneider P, Guidera C, et al. Work-related musculoskeletal disorders among physical therapists: A systematic review[J]. J Back Musculoskelet Rehabil,2016,29(3):417-28. doi: 10.3233/BMR-150649.\u003c/li\u003e\n\u003cli\u003eLe AB, Balogun AO, Smith TD. Long work hours, Overtime, and worker health impairment: A cross-sectional study among stone, sand, and gravel mine workers[J]. Int J Environ Res Public Health,2022,19(13):7740. doi: 10.3390/ijerph19137740.\u003c/li\u003e\n\u003cli\u003eChan SM, Au-Yeung TC, Wong H, et al. Long Working Hours, Precarious Employment and Anxiety Symptoms Among Working Chinese Population in Hong Kong[J]. Psychiatr Q,2021,92(4):1745-1757. doi: 10.1007/s11126-021-09938-3.\u003c/li\u003e\n\u003cli\u003eYang ZH. A study on the relationship and intervention of life events, anxiety and depression among medical staff[D]. Chongqing: Chongqing Normal University,2024. (in Chinese)\u003c/li\u003e\n\u003cli\u003eZhang TT. Study on anxiety and depression of elderly hospitalized patients with chronic diseases and related influencing factors in Liaocheng City[D]. Jinan: Shandong University,2024. (in Chinese)\u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics. Statistical survey system for monitoring and surveying the health literacy of Chinese residents and key populations[Z/OL]. (2022-03-03)[2025-07-08]. https://www.stats.gov.cn/fw/bmdcxmsp/bmzd/202302/t20230215_1907197.html.\u003c/li\u003e\n\u003cli\u003eSun YY, Sun X, Wan X, et al. Occupational health literacy level and its influencing factors among key populations in China in 2022[J]. Chin Occup Med,2023,50(3):241-247. (in Chinese)\u003c/li\u003e\n\u003cli\u003eLi XY, Liang YS, Gan WZ, et al. Effect of night-shift work and anxiety on work-related musculoskeletal disorders in electronic manufacturing employees[J]. Chin Occup Med,2024,51(5):505-510. (in Chinese)\u003c/li\u003e\n\u003cli\u003eZhou SY, Chen HQ, Liu M, et al. The relationship between occupational stress and job burnout among female manufacturing workers in Guangdong, China: a cross-sectional study[J]. Sci Rep,2022,12(1):20208. doi: 10.1038/s41598-022-24491-0.\u003c/li\u003e\n\u003cli\u003eLi XY, Chen HQ, Zhou SY, et al. Study on relationship among occupational stress, job burnout and depressive symptoms in female workers of labor-intensive enterprises[J]. Chin J Ind Med,2020,33(3):228-232,261. (in Chinese)\u003c/li\u003e\n\u003cli\u003eLi XY, Chen HQ, Liao XQ, et al. The correlation among occupational stress, coping style and depressive symptoms in 2083 Chinese manufacturing workers[J]. Work,2025. doi:10.1177/10519815251341152.\u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics.Classification of large, medium, small and micro sized enterprises in statistics (2017)[EB/OL]. (2017-12-28)[2025-07-08]. https://www.stats.gov.cn/sj/tjbz/gjtjbz/202302/t20230213_1902763.html.\u003c/li\u003e\n\u003cli\u003eSPITZER R L, KROENKE K, WILLIAMS J B, et al. A brief measure for assessing generalized anxiety disorder: the GAD-7[J]. Arch Intern Med,2006,166(10):1092-1097.\u003c/li\u003e\n\u003cli\u003eHe XY, Li CB, Qian J, et al. Reliability and validity of a generalized anxiety disorder scale in general hospital outpatients[J]. Shanghai Arch Psy,2010,22(4):200-203. (in Chinese)\u003c/li\u003e\n\u003cli\u003eYang L, HILDEBRANDT VH, Yu SF, et al. Introduction and attachment of the Musculoskeletal Disease Survey Form[J]. Ind Hlth Occup Dis,2009,35(1):25-31. (in Chinese)\u003c/li\u003e\n\u003cli\u003eKim J, Kwon R, Yun H, et al. The association between long working hours, shift work, and suicidal ideation: A systematic review and meta-analyses[J]. Scand J Work Environ Health,2024,50(7):503-518. doi: 10.5271/sjweh.4182.\u003c/li\u003e\n\u003cli\u003eMa X, Kawakami A, Inui T. Impact of long working hours on mental health status in Japan: evidence from a national representative survey[J]. Int J Environ Res Public Health,2024,21(7):842. doi: 10.3390/ijerph21070842.\u003c/li\u003e\n\u003cli\u003eZhu BY, Zhang D, Liang ZH. Effect of long working hours on work-related musculoskeletal disorders among operation and maintenance workers of power supply enterprises[J]. J Environ Occup Med,2023,40(1):55-61. (in Chinese)\u003c/li\u003e\n\u003cli\u003eLi SN. Effect of long working hours on occupational stress and health in petrochemical enterprise employees[D]. Nanjing: Southeast University,2022. (in Chinese)\u003c/li\u003e\n\u003cli\u003eOchiai Y, Takahashi M, Matsuo T, et al. Characteristics of long working hours and subsequent psychological and physical responses: JNIOSH cohort study[J]. Occup Environ Med,2023,80(6):304-311. doi: 10.1136/oemed-2022-108672.\u003c/li\u003e\n\u003cli\u003eLe AB, Balogun AO, Smith TD. Long work hours, overtime, and worker health impairment: a cross-sectional study among stone, sand, and gravel mine workers[J]. Int J Environ Res Public Health,2022,19(13):7740. doi: 10.3390/ijerph19137740.\u003c/li\u003e\n\u003cli\u003eLeso V, Fontana L, Caturano A, et al. Impact of shift work and long working hours on worker cognitive functions: current evidence and future research needs[J]. Int J Environ Res Public Health,2021,18(12):6540. doi: 10.3390/ijerph18126540.\u003c/li\u003e\n\u003cli\u003eJensen RC. Work-related musculoskeletal problems: A look at how employers view causes[J]. Int J Environ Res Public Health,2024,21(8):1098. doi:10.3390/ijerph21081098.\u003c/li\u003e\n\u003cli\u003eTamminga SJ, Kuijer PPFM, Badarin K, et al. Towards harmonisation of case definitions for eight work-related musculoskeletal disorders - an international multi-disciplinary Delphi study[J]. BMC Musculoskelet Disord,2021,22(1):1018. doi:10.1186/s12891-021-04871-9.\u003c/li\u003e\n\u003cli\u003eAkbarR KA, Try P, Viwattanakulvanid P, et al. Work-related musculoskeletal disorders among farmers in the Southeast Asia Region: a systematic review[J]. Saf Health Work,2023,14(3):243-249.\u003c/li\u003e\n\u003cli\u003eThacker H, Yasobant S, Viramgami A, et al. Prevalence and determinants of (work-related) musculoskeletal disorders among dentists- a cross sectional evaluative study[J]. Indian J Dent Res,2023,34(1):24-29.\u003c/li\u003e\n\u003cli\u003eAlmaghlouth MK, Alserhani NM, Aldossary FA, et al. Prevalence, patterns, and risk factors of work-related musculoskeletal diseases among teachers in the Eastern Province, Saudi Arabia: a community-based retrospective cross-sectional survey[J]. Cureus,2022,14(12):e32178.\u003c/li\u003e\n\u003cli\u003eKee D. Characteristics of work-related musculoskeletal disorders in Korea[J]. Int J Environ Res Public Health,2023,20(2):1024.\u003c/li\u003e\n\u003cli\u003eWang FJ, Jin X, Nazakat MAMAT, et al. Occurrence pattern of musculoskeletal disorders and its influencing factors among manufacturing workers[J]. J Peking Univ (Health Sci),2020,52(3):535-540. (in Chinese)\u003c/li\u003e\n\u003cli\u003eYong XT, Li FY, Ge H, et al. A cross-sectional epidemiological survey of work-related musculoskeletal disorders and analysis of its influencing factors among coal mine workers in Xinjiang[J]. Biomed Res Int,2020,31664056.\u003c/li\u003e\n\u003cli\u003eDeng HJ, He DS, Li FY. Factors influencing job burnout and musculoskeletal disorders among coal miners in the Xinjiang uygur autonomous region[J]. Pain Res Manag,2021:6629807.\u003c/li\u003e\n\u003cli\u003eZhang DY, Wang ZX, Nie XQ, et al. Analysis on present status of multi-site work-related musculoskeletal disorders in workers of a shipyard and its influencing factors[J]. Chin J Ind Med,2022,35(4):307-312. (in Chinese)\u003c/li\u003e\n\u003cli\u003eZhang DY, Wang LM, Liu QF, et al. Analysis of influencing factors of shoulder work-related musculoskeletal disorders in ultrasound doctors[J]. Chin J Ind Med,2023,36(2):116-121. (in Chinese)\u003c/li\u003e\n\u003cli\u003eLiu XM, Wang J, Wang C, et al. Effect of long working hours on work related musculoskeletal disorders and job burnout in internet enterprise employees[J]. Chin Occup Med,2020,47(2):135-140. (in Chinese)\u003c/li\u003e\n\u003cli\u003eDing X, Guan Z, Liu N, et al. Prevalence and risk factors of work-related musculoskeletal disorders among emerging manufacturing workers in Beijing, China[J]. Front Med (Lausanne),2023,10:1289046. doi: 10.3389/fmed.2023.1289046.\u003c/li\u003e\n\u003cli\u003eYang F, Di N, Guo WW, et al. The prevalence and risk factors of work related musculoskeletal disorders among electronics manufacturing workers: a cross-sectional analytical study in China. BMC Public Health[J]. 2023,23(1):10. doi: 10.1186/s12889-022-14952-6.\u003c/li\u003e\n\u003cli\u003eChen HQ, Li XY, Huang MQ, et al. Analysis of the correlation between work-related musculoskeletal disorders and occupational stress in electronic manufacturing workers[J]. Chin Occup Med,2024,51(1):81-84,89. (in Chinese)\u003c/li\u003e\n\u003cli\u003eFeng JQ, Liu XX, Liu HZ, et al. Relationship between work-related musculoskeletal disorders and adverse ergonomic factors among workers in an electronic manufacture[J].Occup Health Emerg Rescue,2020,38(6):558-562. (in Chinese)\u003c/li\u003e\n\u003cli\u003eKee D. Characteristics of work-related musculoskeletal disorders in Korea[J]. Int J Environ Res Public Health,2023,20(2):1024. doi: 10.3390/ijerph20021024.\u003c/li\u003e\n\u003cli\u003eCrizol GR, S\u0026aacute; KMM, Santos GM, et al. Work-related musculoskeletal disorders in dockworkers[J]. Systematic review and meta-analysis,2024,79(2):585-600. doi: 10.3233/WOR-230666.\u003c/li\u003e\n\u003cli\u003eWang K, Zeng X, Li J, et al. The prevalence and risk factors of work-related musculoskeletal disorders among nurses in China: A systematic review and meta-analysis[J]. Int J Nurs Stud,2024,157:104826. doi: 10.1016/j.ijnurstu.2024.104826.\u003c/li\u003e\n\u003cli\u003eGandolfi MG, Zamparini F, Spinelli A, et al. Musculoskeletal disorders among Italian dentists and dental hygienists[J]. Int J Environ Res Public Health,2021,18(5):2705. doi: 10.3390/ijerph18052705.\u003c/li\u003e\n\u003cli\u003eYin H, Mei Y, Zhang KK, et al. Influence of individual factors and labor organization on musculoskeletal disorders of automobile manufacturing workers[J]. J Pub Health Prev Med,2024,35(4):99-102. (in Chinese)\u003c/li\u003e\n\u003cli\u003eHu B, Wu Y, Pan Y, et al. Association of long working hours and multi-site work-related musculoskeletal disorders among transportation industry workers in Beijing, China[J]. Int Arch Occup Environ Health,2024,97(10):1063-1071. doi: 10.1007/s00420-024-02110-x.\u003c/li\u003e\n\u003cli\u003eLi XY, Chen HQ, Yu XY, et al. Effect of long working hours on work-related musculoskeletal disorders and depressive symptoms among workers in electronics manufacturing[J]. Occup Health Emerg Rescue,2024,42(6):729-733. (in Chinese)\u003c/li\u003e\n\u003cli\u003eWu YK, Zhou ZH, Wu DM. Relationship between working duration and work-related musculoskeletal disorders among Internet enterprise employees[J].Prev Med,2022,34(12):1262-1266,1272. (in Chinese)\u003c/li\u003e\n\u003cli\u003eLi XY, Liang YS, Gan WZ. Effect of night-shift work and anxiety on work-related musculoskeletal disorders in electronic manufacturing employees[J]. Chin Occup Med,2024,51(5):505-510. (in Chinese)\u003c/li\u003e\n\u003cli\u003eChan SM, Au-Yeung TC, Wong H, et al. Long working hours, precarious employment and anxiety symptoms among working Chinese Population in Hong Kong[J]. Psychiatr Q,2021,92(4):1745-1757. doi:10.1007/s11126-021-09938-3.\u003c/li\u003e\n\u003cli\u003eLee Y, Park H. Working hours and depressive and anxiety symptoms according to shift work and gender[J]. J Occup Environ Med,2022,64(5):e316-e321. doi:10.1097/JOM.0000000000002515.\u003c/li\u003e\n\u003cli\u003eSerrano-Fern\u0026aacute;ndez MJ, Boada-Grau J, Boada-Cuerva M, et al. Work addiction as a predictor of anxiety and depression[J]. Work,2021,68(3):779-788. doi:10.3233/WOR-203411.\u003c/li\u003e\n\u003cli\u003eLi XY, Chen HQ, Zhou SY, et al. Study on relationship among occupational stress, job burnout and depressive symptoms in female workers of labor-intensive enterprises[J]. Chin J Ind Med,2020,33(3):228-232,261. (in Chinese)\u003c/li\u003e\n\u003cli\u003eHe LH. Pay attention to the prevention and control of work-related musculoskeletal disorder[J]. J Environ Occup Med,2022,39(6):589-592. (in Chinese)\u003c/li\u003e\n\u003cli\u003eOchiai Y, Takahashi M, Matsuo T, et al. Characteristics of long working hours and subsequent psychological and physical responses: JNIOSH cohort study[J]. Occup Environ Med,2023,80(6):304-311. doi: 10.1136/oemed-2022-108672.\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":"Electronics manufacturing enterprise, Female worker, Long working hours, Anxiety, Work-related musculoskeletal disorders, Risk","lastPublishedDoi":"10.21203/rs.3.rs-7341990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7341990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFemale workers were the main force in electronic manufacturing enterprises. This study aims to understand the current status of long working hours among female workers in the electronic manufacturing industry in Guangdong Province, and to explore the impact of long working hours on anxiety and work-related musculoskeletal disorders (WMSDs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional survey using convenience sampling method from July to September 2022, including a total of 193 electronic manufacturing enterprises in Guangdong Province. We distributed a total of 5,300 copies of the \u003cem\u003eIndividual questionnaire of the National Key Population Occupational Health Literacy Monitoring Survey\u003c/em\u003e, which included general demographic characteristics, and the detection rate of long working hours, anxiety and WMSDs. A total of 4,976 female workers participated in the questionnaires survey after excluding invalid questionnaires, with the effective rate of 93.9%. A binary logistic regression analysis was used to evaluate the impact of weekly working hours on anxiety and WMSDs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe detection rates of long working hours (working more than 40 hours a week), anxiety and WMSDs among female workers in electronics manufacturing enterprises in Guangdong Province were 67.8%, 23.0%, and 43.2%, respectively. The detection rate of WMSDs in the anxiety group was higher than that in the non-anxiety group (65.2% \u003cem\u003evs\u003c/em\u003e 36.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The logistic regression model showed that long working hours were positively correlated with anxiety and WMSDs after controlling for factors such as age, education level, marital status, length of service, enterprise scale, enterprise nature and night shift (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, the longer the weekly working hours, the greater the risk of developing anxiety and WMSDs, with \u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e) values of 1.067 (1.021\u0026ndash;1.116) and 1.067 (1.028\u0026ndash;1.108), respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe female workers in electronics manufacturing enterprises in Guangdong Province generally had long working hours, which increased the risk of occurrence of anxiety and WMSDs.\u003c/p\u003e","manuscriptTitle":"Analysis of the impact of long working hours on anxiety and work-related musculoskeletal diseases among female workers in electronics manufacturing enterprises in Guangdong Province","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 06:35:13","doi":"10.21203/rs.3.rs-7341990/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":"1c3ffa3f-e00c-4fb6-9ce6-3c361121b0f3","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-06T11:17:32+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T11:26:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 06:35:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7341990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7341990","identity":"rs-7341990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}