Exploring Health Promotion Behaviors, Occupational Burnout, and Sleep Disturbances in Traditional Industry Workers

Exploring Health Promotion Behaviors, Occupational Burnout, and Sleep Disturbances in Traditional Industry Workers | 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 Exploring Health Promotion Behaviors, Occupational Burnout, and Sleep Disturbances in Traditional Industry Workers Ying-Fen Yu, Yi-Ya Chang, Shu-Hung Chang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4761931/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Sleep disturbances can lead to poor job performance, increased work stress and absenteeism, and reduced job satisfaction and confidence among employees. Traditional industry employees experience significantly higher shift work and occupational burnout levels than other sectors. Therefore, this warrants exploring whether shift work, occupational burnout, and health promotion behavior are related to sleep disturbances among traditional industry employees. Methods A cross-sectional study with cluster sampling and structured questionnaires was conducted to collect data on the demographics, health promotion behavior, occupational burnout, and sleep disturbances of 365 employees from a traditional industry in Northern Taiwan. The study period was from 15 May 2023 to 17 June 2023. Data analysis was performed using Chi-square tests, independent sample t-tests, and logistic regression. Results The prevalence of sleep disturbances among the study subjects was 47.9%, with shift workers accounting for 63.3%. The mean score for the overall health promotion behavior scale was 2.34 ± 0.48, and the mean score for the overall occupational burnout scale was 1.96 ± 1.09. Factors associated with sleep disturbances included shift work, weekly working hours, health promotion behavior, and personal burnout. Conclusion We recommend adjusting the regularity of shift schedules and enhancing the lighting environment during night shifts to accommodate shift work, avoiding weekly working hours exceeding 40 hours, improving personal burnout levels, and encouraging the adoption of health-responsible behaviors to reduce sleep disturbances. Traditional industries health promotion behavior occupational burnout sleep disturbances Background Sleep disturbances are one of the most common complaints among patients seeking medical care, with insomnia being the most prevalent type [ 1 ]. Sleep disturbances increase employees' feelings of stress and job insecurity and reduce job satisfaction and confidence [ 2 ], leading to poor job performance [ 3 , 4 ]. Additionally, absenteeism rates rise by 16–17% [ 5 ]. A study that included 22,330 adults from 13 countries across four continents found an overall prevalence of sleep disturbances of 36.7% [ 6 ]. However, with the rapid global spread of the novel coronavirus (COVID-19) in 2019, people faced unprecedented health threats and lifestyle restrictions, causing the incidence of sleep disturbances to rise to 40.5% [ 7 ]. Therefore, the issue of sleep disturbances in current times is of significant concern. According to statistics from the Directorate General of Budget, Accounting, and Statistics, Executive Yuan, R.O.C., the population employed in the manufacturing sector in March 2024 was approximately 2.993 million, accounting for 25.84% of the total labor force [ 8 ]. Previous studies on sleep disturbances among traditional industry workers found a prevalence of 22.6% among electronic product manufacturer workers in China [ 9 ], 35.3% and 42.0% among steel factory workers in China [ 10 , 11 ], 16.7% among textile workers in China [ 12 ], 4.5% among manufacturing workers in South Korea [ 13 ], 33.2% among construction workers in India [ 14 ], and 36.5% among construction and civil engineering workers in Japan [ 3 ]. The prevalence of sleep disturbances among traditional industry workers in these countries varies significantly. However, there is currently no data on the prevalence of sleep disturbances among traditional industry workers in Taiwan. Factors influencing sleep disturbances among workplace employees can be broadly categorized into personal background factors, work patterns, occupational burnout, and health promotion behavior. Factors associated with sleep disturbances include being female, age [ 15 , 16 , 17 ], smoking habits [ 15 , 16 ], lower educational level, alcohol consumption, a higher number of chronic illnesses [ 11 , 18 , 19 ], and sleeping ≤ 6 hours per day [ 18 , 20 ]. In terms of work patterns, factors such as shift work, higher work stress [ 11 , 18 , 21 , 22 ]., excessive job involvement [ 9 ], shorter daily rest periods [ 23 , 24 ], and working more than 40 hours per week [ 19 , 20 , 25 ] are associated with a higher likelihood of sleep disturbances. Many industries implement shift work to increase productivity and reduce downtime [ 26 ]. Since traditional manufacturing industry employees often work in shifts, the probability of sleep disturbances is expected to be higher. The highly competitive pressure in global industries leads to occupational burnout [ 27 ], also known as occupational exhaustion. Its impacts include insomnia, depression, job dissatisfaction, increased absenteeism, and disability benefits [ 28 , 29 ]. Occupational burnout is considered a crisis in modern society and life [ 30 ]. Both occupational burnout and sleep disturbances are significant policy issues in many countries, with higher occupational burnout associated with a greater likelihood of sleep disturbances [ 31 , 32 ]. Xinjie [ 33 ] concluded that burnout among frozen food packaging workers increases the severity of sleep disturbances. Treating employees' sleep disturbances can enhance productivity and reduce occupational burnout [ 34 ]. Research on sleep disturbances and occupational burnout has mostly focused on physicians, teachers, police and firefighters, nurses, and tech workers [ 35 , 36 , 37 , 38 , 39 ]. Health promotion is the process of enabling people to increase control over and improve their health. Common behaviors used in health promotion include health responsibility, proper nutrition, stress management, self-actualization, exercise and leisure, and interpersonal support [ 40 ]. Adult health behaviors (AHBs) include stress management, exercise, health responsibility, life appreciation, healthy diet, and oral hygiene. This study found that these six behaviors are significantly correlated with life satisfaction, concluding that they can identify poor health behaviors in adults earlier than previous research [ 41 ]. Numerous studies have confirmed the correlation between health promotion behaviors and sleep disturbances, identifying health promotion behaviors as a major factor influencing sleep disturbances [ 42 ]. For instance, whole grain and Mediterranean diets are protective factors against sleep disturbances [ 17 , 43 ]. Conversely, insufficient physical activity [ 16 ] and sedentary behavior [ 44 ] increase the likelihood of sleep disturbances [ 42 ]. Sleep disturbances can deplete physical energy and reduce time spent on physical activities after work and those with severe sleep disturbances engage in less high-intensity physical activity [ 45 , 46 , 47 ]. Therefore, a vicious cycle may arise between sleep disturbances and unhealthy lifestyles [ 45 ], leading to increasingly severe sleep disturbances. Manufacturing is a major industry in many countries, and previous research on manufacturing employees has primarily focused on the health hazards of occupational exposure [ 48 ]. However, studies on sleep disturbances among traditional manufacturing industry employees in Taiwan are notably lacking. Moreover, research on the relationship between health promotion behaviors, occupational burnout, and sleep disturbances is even more scarce, warranting attention and further exploration. Therefore, this study aims to investigate the correlation between health promotion behaviors, occupational burnout, and sleep disturbances among employees in traditional industries. Methods Research design This is a cross-sectional research study that utilizes structured questionnaires to collect data. It aims to explore the relationships between health promotion behaviors, occupational burnout, sleep disturbances, and the related factors of sleep disturbances among employees in traditional industries. The Reporting of Observational Studies in Epidemiology (STROBE) cross-sectional study checklist [ 49 ] was used as a reporting guide. Study setting This study is part of a collaborative research project conducted by a team of nurses, aiming to assess the relationship between sleep health and work-related factors and health promotion among workplace employees. It provides insights for occupational health nurses to promote workplace health initiatives. Participants The participants included employees at a traditional industrial complex in Northern Taiwan working in the textile industry or manufacturing nitrogen compounds, chemical raw materials, fertilizers, plastic and rubber raw materials, synthetic fibers, and plastic products. Inclusion criteria were as follows: participants had to be age 18 or older, having worked in traditional industries for at least three months, and agreed to participate in the study. Pregnant individuals were excluded. Sampling and sample size Cluster sampling was employed, with the study site comprising five factories: a cotton mill, Film Plant 1, Spinning Mill 3, Spinning Mill 4, and Utility Department. The main products produced by these factories include polyester cotton, polyester chips, polyester films, solid-state polymer chips, polyester partially oriented yarns, polyester fully drawn yarns, and polyester textured yarns. The work is labor-intensive, and each factory has both shift workers and administrative staff, with a predominantly male workforce aged between 35 and 64. Due to the similar background and work nature of the five factories, all employees from Film Plant 1 were randomly selected as the study subjects. The required sample size for correlation analysis was calculated using the G*Power 3.1.0 software program for logistic regression analysis ("Z-test for Logistic regression"). Setting the odds ratio to 1.5, H0 to 0.5, significance level (α) to 0.05, and power to 0.9, the minimum sample size needed was 275. Data collection took place from 15 May 2023 to 17 June 2023, with 365 questionnaires distributed and all 365 valid questionnaires returned, yielding a response rate of 100%. Measures Data were collected using structured questionnaires, which included the following scales: Demographic attributes These included gender, age, education level, marital status, smoking, alcohol consumption, shift work, and weekly working hours, as referenced in [ 9 , 11 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 25 ]。 Health promotion behaviors This was measured using the "Adult Health Behaviors - Short Form" (AHBs) developed by Chen et al. [ 41 ]. Participants completed the questionnaire regarding their lifestyle habits over the past year using a four-point Likert scale. The scoring criteria were as follows: 1 point for "Never" (approximately 0–10% ), 2 points for "Sometimes" (approximately 11–50% ), 3 points for "Often" (approximately 51–70% ), and 4 points for "Always" (approximately 71–100% ). Higher scores indicated healthier lifestyles. The scale covered six elements: stress management, physical activity, health responsibility, life appreciation, healthy eating, and oral hygiene behaviors. The internal consistency of the AHBs was 0.85. Lower scores on the six subscales and the total score of the SF-AHB were significantly associated with lower life satisfaction. Test–retest reliability for the total AHB was r = 0.91. The validity of the AHBs was confirmed through qualitative content validation by experts in metabolism/endocrinology, cardiology, dentistry, and nursing, with an average Content Validity Index (CVI) of 0.93 [ 41 ]. Occupational burnout This study utilized the "Chinese Version of the Occupational Burnout Scale," developed by Yeh et al. [ 50 ], which references the Copenhagen Burnout Inventory (CBI) developed by Danish scholars and the Effort–Reward Imbalance Questionnaire (ERI-Q). The Cronbach's alpha for this scale was 0.86 overall (0.92 for men and 0.90 for women). The Cronbach's alpha for each subscale ranged between 0.85 and 0.87. The scale used a five-point Likert scale with the following scoring criteria: 5 points for "always" (100 points), 4 points for "often" (75 points), 3 points for "sometimes" (50 points), 2 points for "rarely" (25 points), and 1 point for "never" (0 points). Higher scores indicate a greater degree of perceived burnout [ 50 ]. Sleep disturbances The Chinese version of the Athens Insomnia Scale-eight questions (CAIS-8), jointly translated by Jiang et al. [ 51 ], was used to measure the sleep status of the research subjects in the past month by filling in the questionnaire. The subjective feelings of the subjects regarding their nightly sleep were surveyed. The questionnaire included "nighttime symptoms": time to fall asleep, waking up at night, waking up earlier than expected, total sleep time, and total sleep quality; "daytime symptoms": daytime mood, daytime physical function, daytime sleepiness, for a total of eight questions. A four-point Likert scale was used, with each question scored from 0 to 3 points, with a total score of 0 to 24 points. A total score of less than 3 points (inclusive) indicated no sleep disturbances; a total score of 4 to 5 indicates suspected sleep disturbances, and a score greater or equal to 6 indicates sleep disturbances. The dependent variables analyzed at the end of this study were "no" (score 0 to 5 points) and "yes" (score ≧ 6 points) as the cut-off points for sleep disturbances [ 52 ]. Data processing and analysis Data entry and statistical analysis were conducted using SPSS version 27.0, with an alpha value set to 0.05 (two-tailed test) as the criterion for statistical significance. Basic attributes were analyzed using percentages, frequency distributions, means, and standard deviations to understand the distribution characteristics of the measurements. Inferential statistics included the Chi-Squared Test to compare categorical variables of the study subjects with sleep disturbances (yes or no), an independent t-test to examine the differences in continuous variables of basic attributes, health promotion scale results, and occupational burnout scale results, and logistic regression to analyze the relationship between independent variables and sleep disturbances (yes or no) to identify factors related to sleep disturbances. Ethical consideration The Institutional Review Board approved this study with approval number A202305036. Participants were informed about the study through an information sheet, and data were collected using anonymous self-administered questionnaires. Participants were informed of their right to withdraw from the study at any time. Participants received small gifts after completing the questionnaire. Results Descriptive statistics of basic attributes A total of 365 subjects participated in this study. The average age was 43.86 years (SD = 11.74). The majority were not smokers (64.4%), with an average cigarette consumption of only 3.70 cigarettes per day (SD = 7.11). Most participants did not consume alcohol (72.3%). The average daily sleep duration was 6.61 hours (SD = 0.98). A significant portion of the participants were shift workers (63.3%). The majority worked ≤40 hours per week (67.4%), were non-supervisory staff (86%), and had a high school education (51.8%). Detailed information is provided in Table 1. Descriptive statistical analysis results of health promotion behavior, occupational burnout, and sleep disturbances Health promotion behavior The original scale had a maximum average score of 4 points. The overall average score on the scale was 2.34 points, with a standard deviation of 0.48. The average scores of each sub-dimension, listed from highest to lowest, were as follows: "oral hygiene behavior" with an average of 2.56 points (SD = 0.69), "life appreciation behavior" with an average of 2.46 points (SD = 0.68), "healthy eating behavior" with an average of 2.44 points (SD = 0.58), "health responsibility behavior" with an average of 2.32 points (SD = 0.78), "stress management behavior" with an average of 2.28 points (SD = 0.56), and the lowest was "exercise behavior" with an average of 2.01 points (SD = 0.75). The scores for the first five sub-dimensions fell between "occasionally" and "frequently," while exercise behavior was rated as "occasionally," indicating 2–3 days per week. Occupational burnout The original scale had a maximum average score of 4 points. The overall average score on the scale was 1.96 points, with a standard deviation of 1.09. The total scores for each sub-dimension, listed from highest to lowest, were as follows: "personal burnout" with an average of 2.30 points (SD = 0.78), "work-related Burnout" with an average of 2.16 points (SD = 0.81), and "overcommitted to work" with an average of 2.07 points (SD = 0.75). All three sub-dimensions fell between "sometimes" and "often." Sleep disturbance A total score of less than or equal to 3 indicated no sleep disturbance, accounting for 32.3% of the subjects. A total score of 4 to 5 indicates suspected sleep disturbance, accounting for 19.7%. A total score of 6 or greater indicates the presence of sleep disturbance, with 47.9% of the subjects experiencing sleep disturbances. Details are shown in Table 2. Bivariate analysis of factors affecting sleep disturbance A bivariate analysis was conducted to understand the relationship between the study subjects' basic attributes, health promotion behaviors, occupational burnout, and sleep disturbance. The dependent variable was the presence or absence of sleep disturbances, while the independent variables included basic attributes, health promotion behaviors, and occupational burnout. Categorical variables were analyzed using the Chi-Squared Test, and continuous variables were analyzed using the Independent t-test. The results are as follows: significant differences were found for shift work ( p < 0.001), shift groups ( p = 0.004), weekly working hours ( p = 0.027), stress management behavior ( p = 0.001), exercise behavior ( p < 0.001), health responsibility behavior ( p < 0.001), life appreciation behavior ( p < 0.001), healthy diet behavior ( p < 0.001), oral hygiene behavior ( p < 0.001), personal burnout ( p < 0.001), work-related burnout ( p < 0.001), and overcommitment to work ( p = 0.001). Details are shown in Table 3. Analysis of factors associated with sleep disturbances Logistic regression analysis was conducted to identify the factors influencing sleep disturbance, incorporating basic attributes, work patterns, health promotion behaviors, and occupational burnout. The analysis was divided into four models: Model 1: The relationship between basic attributes, work patterns, and sleep disturbances. Model 2: The relationship between basic attributes, health promotion behaviors, and sleep disturbances. Model 3: The relationship between basic attributes, occupational burnout, and sleep disturbances. Model 4: The relationship between basic attributes, work patterns, health promotion behaviors, occupational burnout, and sleep disturbances. For Model 1, logistic regression analysis was conducted using basic attributes and work patterns as independent variables and sleep disturbances as the dependent variable. The results are as follows: smoking quantity, odds ratio (OR) = 1.03 (95% confidence interval (CI) 1.00~1.07), p = 0.048; shift work (yes/no), OR = 2.78 (95% CI 1.69~4.54), p < 0.001; weekly working hours (41~48 hours/≤40 hours), OR = 4.11 (95% CI 1.21~14.01), p = 0.024; and weekly working hours (˃48 hours/≤40 hours), OR = 2.41 (95% CI 1.45~4.01), p = 0.001. These results indicate that less smoking, non-shift work, and weekly working hours less than or equal to 40 hours are associated with a lower likelihood of sleep disturbance. The explanatory power (R²) of this model was 0.11. See Table 4 for detailed results. Logistic regression analysis was conducted for Model 2, using basic attributes and health promotion behaviors as independent variables and sleep disturbance as the dependent variable. The results are as follows: health responsibility behavior, OR = 0.87 (95% CI 0.77~0.99), p = 0.030; life appreciation behavior, OR = 0.76 (95% CI 0.66~0.89), p < 0.001; and healthy eating behavior, OR = 0.86 (95% CI 0.76~0.97), p = 0.017. These results indicate that higher scores in health responsibility behavior, life appreciation behavior, and healthy eating behavior are associated with a lower likelihood of sleep disturbances. The explanatory power (R²) of this model was 0.21. See Table 4 for detailed results. Logistic regression analysis was conducted for Model 3, using basic attributes and occupational burnout as independent variables and sleep disturbances as the dependent variable. The results are as follows: personal burnout, OR = 1.05 (95% CI 1.03~1.08), p < 0.001, and work-related burnout, OR = 1.03 (95% CI 1.01~1.05), p = 0.010. These results indicate that lower personal and work-related burnout scores are associated with a lower likelihood of sleep disturbances. The explanatory power (R²) of this model was 0.33. See Table 4 for detailed results. For Model 4, logistic regression analysis was conducted using basic attributes, work patterns, health promotion behaviors, and occupational burnout as independent variables and sleep disturbances as the dependent variable. The results are as follows: shift work (Yes/No), OR = 3.03 (95% CI 1.64~5.56), p 48 hours/≤40 hours), OR = 1.98 (95% CI 1.03~3.81), p = 0.040; health responsibility behavior, OR = 0.85 (95% CI 0.73~1.00), p = 0.049; and personal burnout, OR = 1.06 (95% CI 1.03~1.09), p < 0.001. These results indicate a lower likelihood of sleep disturbances associated with non-shift work, higher scores in health responsibility behavior, weekly working hours of 40 or less, and lower scores in personal burnout. The explanatory power (R²) of this model was 0.49. See Table 4 for detailed results. Discussion Current status of sleep disturbances The overall rate of sleep disturbances among the study subjects was 47.9%, with 48.7% for male employees and 35% for female employees. Therefore, the prevalence was found to be higher in our study compared to other manufacturing workers in Asian countries, such as 22.6% among Chinese electronics manufacturing employees [ 9 ], 35.3% and 42% among steel plant workers [ 11 , 53 ], 16.7% among Chinese textile workers [ 12 ], 35.6% in Japanese construction and civil engineering employees [ 3 ], 33.2% among Indian construction workers [ 14 ], and 18.6% among Brazilian workers [ 54 ]. The higher rate of sleep disturbances in this study's subjects could be attributed to the majority of our participants being shift workers, with an average total daily sleep time of 6.61 hours, less than the recommended 7 hours. Additionally, most of their work involves standing or walking, which may increase burnout and contribute to a higher prevalence of sleep disturbances than workers in other countries' traditional industries. Relationship between work patterns and sleep disturbances According to the bivariate analysis, the sleep disturbance rate was 43.1% for those working ≤ 40 hours per week, 57% for those working 41–48 hours, and 61.5% for those working > 48 hours. The Model 1 results show that individuals working > 48 hours per week have 2.41 times higher odds of sleep disturbances than those working ≤ 40 hours. This aligns with findings by Halonen [ 25 ] and Lee [ 13 ], highlighting a higher probability of sleep disturbances with longer work hours. Referencing Taiwan's Hsiao [ 55 ], factors such as shift work, long hours (> 60 hours/week), heavy physical labor, and large company size (over 300 employees) are significantly related to sleep issues. This suggests that shift work and extended hours increase the risk of sleep disturbances. To reduce the rate of sleep disturbances, it is recommended to efficiently manage work hours, aiming for a standard work week of no more than 40 hours. Relationship between health promotion behaviors and sleep disturbances In Model 2, it was found that the participants’ behaviors related to "health responsibility," "appreciation of life," and "healthy eating" correlated with sleep disturbances. Drawing from the study by Weitzer [ 56 ], it was observed that practicing "appreciation of life" behaviors to maintain an optimistic outlook is a simple and effective strategy to improve sleep quality and reduce the risk of sleep disturbances. It is recommended that employees maintain a positive mindset, smile frequently, practice self-appreciation, and spend time outdoors appreciating nature, as this may lower the probability of sleep disturbances. This study aligns with findings from Cao [ 17 ] and Castro-Diehl [ 57 ], suggesting that "healthy eating behavior" is also associated with sleep disturbances. Additionally, Castro-Diehl [ 57 ] noted that a healthy diet can promote sufficient sleep duration and reduce the risk of insomnia. Mediterranean dietary patterns [ 43 ] and whole grain patterns [ 17 ] emerged as protective factors against sleep disturbances. Therefore, it is advised to adopt healthy eating habits, including consuming at least three servings of vegetables and two servings of fruits and maintaining a water intake of 1500 milliliters daily. Seeking guidance from professional nutritionists and adjusting the meal categories offered in employee cafeterias may facilitate the establishment of healthy eating behaviors among employees. "Health responsibility" aims to achieve overall well-being of body and mind, requiring individuals to adopt a proactive and responsible attitude towards their own health. This includes actively seeking knowledge and information on self-healthcare, being attentive to physiological changes and symptoms, and proactively seeking guidance and assistance from healthcare professionals when necessary. This study found that higher scores in "health responsibility" were associated with lower rates of sleep disturbances, possibly due to better management of one's physical and mental state, resulting in a lower susceptibility to sleep disturbances. While few studies have previously explored the relationship between health responsibility and sleep disturbances, future intervention-based follow-up studies could further investigate this association. While Xie [ 58 ] and Bjornsdottir [ 59 ] have shown that regular exercise can improve sleep quality and reduce insomnia severity and subjective daytime sleepiness among individuals with sleep disturbances, this study did not find a correlation between exercise behavior and sleep disturbances. This may be due to participants in this study generally not engaging in regular exercise, thus making it difficult to observe any correlation. Most participants were shift workers. Studies by Shah [ 60 ] and Shen [ 61 ] suggested that exercise can enhance cardiovascular health, leading to better sleep quality among shift workers and improving sleep disturbances caused by disrupted circadian rhythms. In the future, encouraging subjects to spend more time outdoors and actively establish a habit of regular exercise could be beneficial. This could be facilitated through technology such as smart bands, smartwatches, or workplace interventions like stair-climbing campaigns instead of using elevators, providing convenient access to exercise equipment in the workplace for stretching or physical activity during breaks. Additionally, intervention-based follow-up studies could be conducted to confirm the effectiveness of exercise in improving sleep disturbances. Relationship between occupational burnout and sleep disturbances The results of Model 3 indicate that individuals with lower scores in personal burnout and work burnout have lower rates of sleep disturbances. Baek et al. conducted a study on 18,744 Korean workers and found that higher levels of workplace burnout increased the likelihood of sleep disturbances [ 31 ]. Furthermore, significant correlations between workplace burnout and severe sleep disturbances were found among Hungarian postal workers [ 62 ]. Studies focusing on physicians [ 63 ] and nurses [ 32 ] have also shown a positive correlation between workplace burnout and sleep disturbances, with workplace burnout being a contributing factor to sleep disturbances, which aligns with the findings of this study. While many people find that they sleep better when experiencing burnout, this may not hold true for workers in labor-intensive traditional industries. Providing a comfortable work environment and allowing for short breaks (approximately 20 to 30 minutes), particularly for night shift workers, is expected to reduce personal and work burnout levels. Encouraging all employees to engage in leisure activities during breaks to relax their minds and bodies is also recommended. Additionally, hosting workshops on mental and physical health, such as aromatherapy, progressive exercise programs, and teaching and practical courses on muscle relaxation and deep breathing, is advised. These effective muscle relaxation techniques can help alleviate stress and anxiety, reduce personal burnout, and lower the likelihood of sleep disturbances, thus aiding employees in improving their burnout levels. Conclusion Overall findings indicate that shift rotation, weekly working hours, and health promotion behaviors correlate with personal burnout and sleep disturbances. Due to the significant threat sleep disturbances pose to employee health, it is recommended to adjust the shift rotation frequency for employees and develop more suitable shift schedules. For night shift workers, increasing the intensity of lighting in the work environment is advised to reduce sleep disturbances caused by circadian rhythm disruption and to assist employees in better adapting to shift work. Occupational health nurses in the workplace should assist in promoting healthy behaviors among employees, including enhancing employees' self-health awareness and regularly organizing health education seminars on sleep, exercise, diet, and other relevant topics. This helps employees understand how to maintain a healthy lifestyle and alleviate feelings of burnout. Additionally, through employee health screenings, occupational health nurses can identify high-risk cases and provide preventative, diagnostic, and health advisory services to reduce the likelihood of sleep disturbances among workers in traditional industries. Limitations Firstly, the questionnaire primarily relied on the subjective perceptions of the participants. However, incorporating objective data, such as physiological monitors (e.g., portable sleep trackers), could provide more objective data representation. Objective measurements of sleep processes may reduce research biases, enhance research credibility, and improve the impact and validity of the study. Secondly, participants making use of sleep aids should be excluded to more accurately represent the current status of sleep disturbances. Declarations Ethics approval and consent to participate This study protocol was approved by the Institutional Review Board of the Tri-Service General Hospital with protocol number A202305036. Participation in the study was voluntary, and informed consent was obtained from all participants. Consent for publication N/A Availability of data and material Data is available on request due to privacy/ethical restrictions. Competing interests The authors have declared that no competing interests exist. Funding This study was supported in part by grants TYAFGH_D_113021 from the Taoyuan Armed Forces General Hospital, Taoyuan, Taiwan. Authors' contributions YFY contributed to the study's conceptualization, methodology, data collection, data analysis, data interpretation and validation, original draft preparation, original draft revision and edit, and funding acquisition. YYC contributed to the revision and editing of the manuscript. SHC contributed to the study's conceptualization, data interpretation, validation, and revision and editing of the original draft. All authors have read and agreed to the final version of the manuscript. Acknowledgements Special thanks to nurse Zhang Jia-Zhen, the factory nurse, for her assistance References Karna, B., Sankari, A., & Tatikonda, G. (2023). Sleep Disorder. In StatPearls . StatPearls Publishing. Fietze, I., Rosenblum, L., Salanitro, M., Ibatov, A. D., Eliseeva, M. V., Penzel, T., Brand, D., & Westermayer, G. (2022). The interplay between poor sleep and work-related health. Frontiers in Public Health , 10, 866750. https://doiorg.autorpa.ndmctsgh.edu.tw/10.3389/fpubh.2022.866750 Kayaba, M., Sasai-Sakuma, T., Takaesu, Y., & Inoue, Y. (2021). The relationship between insomnia symptoms and work productivity among blue-collar and white-collar Japanese workers engaged in construction/civil engineering work: a cross-sectional study. BMC Public Health , 21(1), 1244. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1186/s12889-021-11273-y Riontino, L., & Cavallero, C. (2021). Individual differences in working memory efficiency modulate proactive interference after sleep deprivation. Psychological research , 85(2), 480–490. https://doi-org.mhdla.flysheet.com.tw:8443/10.1007/s00426-020-01292-6 Amiri, S., & Behnezhad, S. (2020). Sleep disturbances and risk of sick leave: systematic review and meta-analysis. Sleep and Biological Rhythms , 18 (4), 283-295. https://doi.org/10.1007/s41105-020-00270-0 Morin, C. M., Bjorvatn, B., Chung, F., Holzinger, B., Partinen, M., Penzel, T., Ivers, H., Wing, Y. K., Chan, N. Y., Merikanto, I., Mota-Rolim, S., Macêdo, T., De Gennaro, L., Léger, D., Dauvilliers, Y., Plazzi, G., Nadorff, M. R., Bolstad, C. J., Sieminski, M., Benedict, C., … Espie, C. A. (2021). Insomnia, anxiety, and depression during the COVID-19 pandemic: an international collaborative study. Sleep Medicine , 87 , 38–45. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1016/j.sleep.2021.07.035 Jahrami, H. A., Alhaj, O. A., Humood, A. M., Alenezi, A. F., Fekih-Romdhane, F., AlRasheed, M. M., Saif, Z. Q., Bragazzi, N. L., Pandi-Perumal, S. R., BaHammam, A. S., & Vitiello, M. V. (2022). Sleep disturbances during the COVID-19 pandemic: A systematic review, meta-analysis, and meta-regression. Sleep Medicine Reviews , 62 , 101591. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1016/j.smrv.2022.101591 Directorate General of Budget, Accounting and Statistics. (2024, April 22). Employment by industry - Table 8. Retrieved from [https://www.stat.gov.tw/News_Content.aspx?n=3703&s=233265](https://www.stat.gov.tw/News_Content.aspx?n=3703&s=233265). Fan, L. N., Yu, J. W., Yang, X. Y., & Zeng, Q. (2020). Effects of occupational stress and related factors on insomnia occurrence of workers in chip manufacturing industry. Chinese journal of industrial hygiene and occupational diseases , 38(4), 259–262. https://doi-org.mhdla.flysheet.com.tw:8443/10.3760/cma.j.cn121094-20190219-00052 Wang, L., Zhang, S., Yu, M., & Yuan, J. (2022). Association between insomnia and subclinical atherosclerosis among Chinese steelworkers: a cross-sectional survey. Arch Public Health , 80 (1), 80. https://doi.org/10.1186/s13690-022-00834-1 Li, X., Cui, S., Wu, J., Wang, L., & Yuan, J. (2020). Job category differences in the prevalence and associated factors of insomnia in steel workers in China. International journal of occupational medicine and environmental health , 33 (2), 215–233. https://doi-org.mhdla.flysheet.com.tw:8443/10.13075/ijomeh.1896.01519 Jiang, J., Wang, D., Scherffius, A., Chen, D., Ma, Z., Chen, Z., Zhang, Y., Yu, Q., & Fan, F. (2022). The Association Between Morningness-Eveningness Preference, Depression, Anxiety and Insomnia Among Chinese Textile Workers With or Without Shift Work. Front Psychiatry , 13 , 915476. https://doi.org/10.3389/fpsyt.2022.915476 Lee, D. W., Jang, T. W., Kim, H. R., & Kang, M. Y. (2021). The relationship between working hours and lifestyle behaviors: Evidence from a population-based panel study in Korea. Journal of Occupational Health , 63(1), e12280. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1002/1348-9585.12280 Suresh, S., Doraikannan, S., Jayakumar, N. D., Indiran, M. A., & Kesavan, R. (2022). Working hours impact on the sleep quality and oral health status among migrant construction workers in Chennai. J Family Med Prim Care , 11 (7), 3511-3516. https://doi.org/10.4103/jfmpc.jfmpc_2101_21 Alosta, M. R., Oweidat, I., Alsadi, M., Alsaraireh, M. M., Oleimat, B., & Othman, E. H. (2024). Predictors and disturbances of sleep quality between men and women: results from a cross-sectional study in Jordan. BMC psychiatry , 24 (1), 200. https://doi-org.mhdla.flysheet.com.tw:8443/10.1186/s12888-024-05662-x Bedford, L. E., Tang, E. H. M., Dong, W., Wong, C. K. H., Tse, E. T. Y., Ng, A. P. P., Cheung, B. M. Y., Wu, T., Yu, E. Y. T., & Lam, C. L. K. (2022). Who reports insufficient and disturbed sleep? Results from a representative population-based health survey in Hong Kong. BMJ Open , 12 (9), e058169. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2021-058169 Cao, X., Zhou, S., Yang, B., & Zhou, J. (2020). Relationship between dietary patterns and sleep disturbance among health screening population in Changsha. Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences , 45 (10), 1204–1214. https://doi-org.autorpa.ndmctsgh.edu.tw/10.11817/j.issn.1672-7347.2020.190318 Boersma, G. J., Mijnster, T., Vantyghem, P., Kerkhof, G. A., & Lancel, M. (2023). Shift work is associated with extensively disordered sleep, especially when working nights. Frontiers in psychiatry , 14 , 1233640. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpsyt.2023.1233640 Lee, J., Oh, J., Park, H., Sim, J., Lee, J., Kim, Y., & Yun, B. (2023). Exploring the relationship between work-family conflict and sleep disturbance: a study on stratification and interaction. Frontiers in psychology , 14 , 1257233. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpsyg.2023.1257233 Weston, G., Zilanawala, A., Webb, E., Carvalho, L., & McMunn, A. (2024). Work hours, weekend working, nonstandard work schedules and sleep quantity and quality: findings from the UK household longitudinal study. BMC public health , 24 (1), 309. https://doi-org.mhdla.flysheet.com.tw:8443/10.1186/s12889-024-17762-0 Hwang, Y., Jang, H., Lee, J., Jeon, S., Kim, J., Lee, S., & Kim, S. J. (2023). Sleep Reactivity and Sleep Efforts in Shift Workers. Psychiatry investigation , 20 (9), 880–887. https://doi-org.mhdla.flysheet.com.tw:8443/10.30773/pi.2023.0098 Sun, Y., Wei, M., Zhao, Q., Yang, J., Gao, J., & Dai, J. (2024). Mediating effect of sleep quality on the association between job stress and health-related productivity loss among workers in R&D enterprises in Shanghai. Frontiers in public health , 12 , 1331458. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpubh.2024.1331458 Ikeda, H., & Kubo, T. (2024). The association between work interval regularity and sleep regularity: a 2-week observational study in daytime employees. Journal of occupational health , 66 (1), uiae009. https://doi-org.mhdla.flysheet.com.tw:8443/10.1093/joccuh/uiae009 Kageyama, M., Tatsumi, A., Fujino, Y., & Watai, I. (2022).Association between social jetlag and presenteeism in Japanese industry: A cross-sectional study Sangyo eiseigaku zasshi=Journal of Occupational Health , 64 (1), 12–21. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1539/sangyoeisei.2020-049-B Halonen, J. I., Pulakka, A., Vahtera, J., Pentti, J., Laström, H., Stenholm, S., & Hanson, L. M. (2020). Commuting time to work and behavior-related health: a fixed-effect analysis. Occupational and Environmental Medicine , 77 (2), 77–83. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/oemed-2019-106173 http://europepmc.org/books/NBK560720https://www.ncbi.nlm.nih.gov/books/NBK560720 Takada M. (2018). Hygiene in Sleep: Problems of Sleeping Habits in Shift Workers Nihon eiseigaku zasshi. Japanese journal of hygiene , 73 (1), 22–26. https://doi-org.mhdla.flysheet.com.tw:8443/10.1265/jjh.73.22 Cheng, Y. W. (2011). Coping strategies for occupational burnout. New Social Policy, (14), 55-58. https://doi.org/10.29752/NST.201102.0012 Ghio, L., Patti, S., Piccinini, G., Modafferi, C., Lusetti, E., Mazzella, M., & Del Sette, M. (2021). Anxiety, Depression and Risk of Post-Traumatic Stress Disorder in Health Workers: The Relationship with Burnout during COVID-19 Pandemic in Italy. International journal of environmental research and public health , 18 (18), 9929. https://doi-org.mhdla.flysheet.com.tw:8443/10.3390/ijerph18189929 Salvagioni, D. A. J., Melanda, F. N., Mesas, A. E., González, A. D., Gabani, F. L., & Andrade, S. M. (2017). Physical, psychological and occupational consequences of job burnout: A systematic review of prospective studies. PloS One , 12 (10), e0185781. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1371/journal.pone.0185781 Lu, Y., Zhang, Z., Gao, S., Yan, H., Zhang, L., & Liu, J. (2020). The status of occupational burnout and its influence on the psychological health of factory workers and miners in wulumuqi, China. BioMed Research International , 2020 , 6890186. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1155/2020/6890186 Baek, S. U., Yoon, J. H., & Won, J. U. (2023). Association between high emotional demand at work, burnout symptoms, and sleep disturbance among Korean workers: a cross-sectional mediation analysis. Scientific reports, 13(1), 16688. https://doi-org.mhdla.flysheet.com.tw:8443/10.1038/s41598-023-43451-w Membrive-Jiménez, M. J., Gómez-Urquiza, J. L., Suleiman-Martos, N., Velando-Soriano, A., Ariza, T., De la Fuente-Solana, E. I., & Cañadas-De la Fuente, G. A. (2022). Relation between burnout and sleep pblems in nurses: A systematic review with meta-analysis. Healthcare (Basel, Switzerland) , 10 (5), 954. https://doiorg.autorpa.ndmctsgh.edu.tw/10.3390/healthcare10050954 Xinjie, D., Runlian, H., Ahmed, O., Cho, E., & Chung, S. (2023). Mediating Role of Viral Anxiety and Insomnia in Relationship Between Work-Related Stress and Depression Among Cold Chain Workers During COVID-19 Pandemic. Journal of Korean medical science , 38(43), e338. https://doi-org.mhdla.flysheet.com.tw:8443/10.3346/jkms.2023.38.e338 Vega-Escaño, J., Porcel-Gálvez, A. M., Diego-Cordero, R., Romero-Sánchez, J. M., Romero-Saldaña, M., & Barrientos-Trigo, S. (2020). Insomnia Interventions in the Workplace: A Systematic Review and Meta-Analysis. International journal of environmental research and public health , 17 (17), 6401. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3390/ijerph17176401 Li, J. S. (2018). Health quality survey of high-tech workers in Taiwan. (Master's thesis, Chung Yuan Christian University, Department of Environmental Engineering). https://hdl.handle.net/11296/pt385a Lee, L. J., Wehrlen, L., Ding, Y., & Ross, A. (2022). Professional quality of life, sleep disturbance and health among nurses: A mediation analysis. Nursing Open , 9 (6), 2771–2780. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1002/nop2.978 Lin, Y. L., Chen, C. H., Chu, W. M., Hu, S. Y., Liou, Y. S., Yang, Y. C., & Tsan, Y. T. (2019). Modifiable risk factors related to burnout levels in the medical workplace in Taiwan: cross-sectional study. BMJ Open , 9 (11), e032779. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2019-032779 Peterson, S. A., Wolkow, A. P., Lockley, S. W., O'Brien, C. S., Qadri, S., Sullivan, J. P., Czeisler, C. A., Rajaratnam, S. M. W., & Barger, L. K. (2019). Associations between shift work characteristics, shift work schedules, sleep and burnout in North American police officers: a cross-sectional study. BMJ open , 9 (11), e030302. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2019-030302 Wolkow, A. P., Barger, L. K., O'Brien, C. S., Sullivan, J. P., Qadri, S., Lockley, S. W., Czeisler, C. A., & Rajaratnam, S. M. W. (2019). Associations between sleep disturbances, mental health outcomes and burnout in firefighters, and the mediating role of sleep during overnight work: A cross-sectional study. Journal of Sleep Research , 28 (6), e12869.https://doi-org.autorpa.ndmctsgh.edu.tw/10.1111/jsr.12869 Walker, S. N., Sechrist, K. R., & Pender, N. J. (1987). The Health-Promoting Lifestyle Profile: development and psychometric characteristics. Nursing Research , 36 (2), 76–81. Kuo, Y. W., Chang, S. H., Huang, C. H., & Chen, M. Y. (2022). A parsimonious and comprehensive screening tool for adult health behaviors. Public Health Mursing (Boston, Mass.) , 39 (5), 965–972. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1111/phn.13085 Choi, D. S., & Kim, S. H. (2022). Factors Affecting Occupational Health of Shift Nurses: Focusing on Job Stress, Health Promotion Behavior, Resilience, and Sleep Disturbance. Safety and health at work , 13(1), 3–8. https://doi-org.mhdla.flysheet.com.tw:8443/10.1016/j.shaw.2021.09.001 Godos, J., Ferri, R., Lanza, G., Caraci, F., Vistorte, A. O. R., Yelamos Torres, V., Grosso, G., & Castellano, S. (2024). Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence. Nutrients , 16 (2). https://doi.org/10.3390/nu16020282 Pengpid, S., & Peltzer, K. (2019). Sedentary behavior and 12 Sleep problem indicators among middle-aged and elderly adults in South Africa. International journal of environmental research and public health , 16 (8), 1422. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3390/ijerph16081422 Bláfoss, R., Sundstrup, E., Jakobsen, M. D., Bay, H., Garde, A. H., & Andersen, L. L. (2019). Are insomnia type sleep problems associated with a less physically active lifestyle? A cross-sectional study among 7,700 adults from the general working population. Frontiers in Ppublic Health , 7 , 117. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3389/fpubh.2019.00117 Bottolfs, M., Støa, E. M., Reinboth, M. S., Svendsen, M. V., Schmidt, S. K., Oellingrath, I. M., & Bratland-Sanda, S. (2020). Resilience and lifestyle-related factors as predictors for health-related quality of life among early adolescents: a cross-sectional study. The Journal of International Medical Research , 48 (2), 300060520903656.https://doi-org.autorpa.ndmctsgh.edu.tw/10.1177/0300060520903656. Kinoshita, K., Ozato, N., Yamaguchi, T., Sudo, M., Yamashiro, Y., Mori, K., Katsuragi, Y., Yasukawa, T., Murashita, K., Nakaji, S., & Ihara, K. (2022). Association between Objectively Measured Sedentary Behavior and Sleep Quality in Japanese Adults: A Population-Based Cross-Sectional Study. International journal of environmental research and public health , 19 (5), 3145. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3390/ijerph19053145 Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: recent research and its implications for psychiatry. World Psychiatry : Official journal of the World Psychiatric Association (WPA) , 15 (2), 103–111. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1002/wps.20311 von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2014). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. International Journal of Surgery, 12 (12), 1495-1499. https://doi.org/https://doi.org/10.1016/j.ijsu.2014.07.013 Yeh, W. Y., Cheng, Y. W., Chen, M. R., & Chiu, W. S. (2008). Development and validation of an occupational burnout inventory. Taiwan Journal of Public Health, 27(5), 349-364. https://doi.org/10.6288/tjph2008-27-05-01 Jiang, H. L., Chen, S. C., Bai, Q. H., Lu, X. H., Li, M. B., Lai, S. H., & Zhou, B. S. (2009). A validation study of the chinese version of the Athens Insomnia Scale. Taiwanese Journal of Psychiatry , 23(1), 43-52. https://doi.org/10.29478/tjp.200903.0005 Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2003). The diagnostic validity of the Athens Insomnia Scale. Journal of Psychosomatic Research , 55 (3), 263–267. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1016/s0022-3999(02)00604-9 Wang, L., Zhang, S., Yu, M., & Yuan, J. (2022). Association between insomnia and subclinical atherosclerosis among Chinese steelworkers: a cross-sectional survey. Archives of public health = Archives belges de sante publique , 80 (1), 80. https://doi-org.mhdla.flysheet.com.tw:8443/10.1186/s13690-022-00834-1 Lima, M. G., Barros, M. B. A., Malta, D. C., Medina, L. P. B., & Szwarcwald, C. L. (2022). Association of self-reported sleep problems with morbidities and multimorbidities according to sex: National Health Survey 2019. Associação do autorrelato de problemas no sono com morbidades e multimorbidades segundo sexo: Pesquisa Nacional de Saúde 2019. Epidemiologia e Servicos de Saude : Revista do Sistema Unico de Saude do Brasil , 31 (spe 1), e2021386. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1590/SS2237-9622202200007.especial Hsiao, C.-W. (2010). Investigation of work stress, shiftwork, fatigue and health status among manufacturing workers [Master’s thesis, Chang Jung Christian University]. https://hdl.handle.net/11296/cm686t Weitzer, J., Papantoniou, K., Lázaro-Sebastià, C., Seidel, S., Klösch, G., & Schernhammer, E. (2021). The contribution of dispositional optimism to understanding insomnia symptomatology: Findings from a cross-sectional population study in Austria. Journal of Sleep Research , 30 (1), e13132. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1111/jsr.13132 Castro-Diehl, C., Wood, A. C., Redline, S., Reid, M., Johnson, D. A., Maras, J. E., Jacobs, D. R., Jr, Shea, S., Crawford, A., & St-Onge, M. P. (2018). Mediterranean diet pattern and sleep duration and insomnia symptoms in the Multi-Ethnic Study of Atherosclerosis. Sleep , 41 (11), zsy158. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1093/sleep/zsy158 Xie, Y., Liu, S., Chen, X. J., Yu, H. H., Yang, Y., & Wang, W. (2021). Effects of Exercise on Sleep Quality and Insomnia in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Frontiers in psychiatry , 12, 664499. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpsyt.2021.664499 Bjornsdottir, E., Thorarinsdottir, E. H., Lindberg, E., Benediktsdottir, B., Franklin, K., Jarvis, D., Demoly, P., Perret, J. L., Garcia Aymerich, J., Dorado-Arenas, S., Heinrich, J., Torén, K., Garcia Larsen, V., Jögi, R., Gislason, T., & Janson, C. (2024). Association between physical activity over a 10-year period and current insomnia symptoms, sleep duration and daytime sleepiness: a European population-based study. BMJ open , 14 (3), e067197. https://doi-org.mhdla.flysheet.com.tw:8443/10.1136/bmjopen-2022-067197 Shah, N. M., Bennett, C., Hassan, H., & Kaltsakas, G. (2023). Sleep disorders and exercise: a mini-review. Journal of thoracic disease, 15(10), 5863–5872. https://doi-org.mhdla.flysheet.com.tw:8443/10.21037/jtd-23-17 Shen, B., Ma, C., Wu, G., Liu, H., Chen, L., & Yang, G. (2023). Effects of exercise on circadian rhythms in humans. Frontiers in pharmacology , 14 , 1282357. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fphar.2023.1282357 Kovács, M., Muity, G., Szapáry, Á., Nemeskéri, Z., Váradi, I., Kapus, K., Tibold, A., Zalayné, N. M., Horvath, L., & Fehér, G. (2023). The prevalence and risk factors of burnout and its association with mental issues and quality of life among hungarian postal workers: a cross-sectional study. BMC Public Health , 23 (1), 75. https://doi.org/10.1186/s12889-023-15002-5 Medisauskaite, A., & Kamau, C. (2019). Does occupational distress raise the risk of alcohol use, binge-eating, ill health and sleep problems among medical doctors? A UK cross-sectional study. BMJ Open , 9 (5), e027362. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2018-027362 Tables Table 1 Descriptive statistics of basic attributes (N = 365) Characteristics n (%) M SD 1. Gender Male Female 345 20 94.5 5.50 2. Age (years) 43.86 11.74 4. Marital Status Single Married Divorced Widowed 130 219 14 2 35.6 60.0 3.80 0.50 5. Smoking Habits None Quit Smoking Current Smoker 235 34 96 64.4 9.30 26.3 6. Number of Cigarettes Smoked (per/day) 3.70 7.11 7. Drinking Habits None Quit Drinking Occasionally (1–2 days/week) Frequently (3–5 days/week) Always (6–7 days/week) 264 7 83 7 4 72.3 1.90 22.7 1.90 1.10 8. Shift Work No Yes 134 231 36.7 63.3 11. Managerial position No Yes 314 51 86.0 14.0 12. Education level Primary, Secondary Senior High (including vocational) Associate degree Bachelor’s degree Master’s degree (including post-masters) 2 189 64 93 17 0.50 51.8 17.5 25.5 4.70 Note M = mean; SD = standard deviation Table 2 Descriptive statistics of health promotion behaviors, occupational burnout, and sleep disturbances (N = 365) Characteristics n (%) M SD Health promotion behaviors 2.34 0.48 1. Stress management behaviors 2.28 0.56 2. Physical activity behaviors 2.01 0.75 3. Health responsibility behaviors 2.32 0.78 4. Life appreciation behaviors 2.46 0.68 5. Healthy eating behaviors 2.44 0.58 6. Oral hygiene behaviors 2.56 0.69 Occupational burnout 1.96 1.09 1. Personal burnout 2.30 0.78 2. Work-related burnout 2.16 0.81 3. Overcommitted to work 2.07 0.75 Sleep condition 1. No sleep disturbance 118 32.3 2. Suspected sleep disturbance 72 19.7 3. Sleep disturbance 175 47.9 Table 3 Analysis of Differences in basic attributes, health promotion behaviors, occupational burnout, and sleep disturbances (N = 365) Characteristics n Sleep disturbances Yes n (%) No n (%) χ²/t p 1. Gender Male Female 345 20 168 (48.7) 7 (35.0) 177 (51.3) 13 (65.0) 1.42 .23 2. Age 365 43.3 ± 11.5 44.3 ± 12.0 0.17 .42 3. Number of Cigarettes Smoked 365 4.4 ± 7.7 3.09 ± 6.4 11.53 .09 4. Drinking Habits No Yes 129 (47.6) 46 (48.9) 142 (52.4) 48 (51.1) 0.05 .91 5. Shift Work No Yes 134 231 48 (35.8) 127 (55.0) 86 (64.2) 104 (45.0) 12.47 <.001 6. Weekly Working (hour) ≦40 41~48 ˃48 246 106 13 106 (43.1) 61 (57.5) 8 (61.5) 140 (56.9) 45 (42.5) 5 (38.5) 7.20 .027 7. Managerial position No Yes 314 51 152 (48.4) 23 (45.1) 162 (51.6) 28 (54.9) 0.19 .66 8. Health promotion behaviors Stress management behaviors 365 2.18 ± 0.46 2.37 ± 0.63 3.31 .001 Physical activity behaviors 365 1.87 ± 0.62 2.14 ± 0.84 3.55 <.001 Health responsibility behaviors 365 2.09 ± 0.67 2.53 ± 0.82 5.68 <.001 Life appreciation behaviors 365 2.22 ± 0.60 2.69 ± 0.68 6.96 <.001 Healthy eating behaviors 365 2.26 ± 0.54 2.61 ± 0.58 5.60 <.001 Oral hygiene behaviors 365 2.43 ± 0.64 2.70 ± 0.72 3.82 <.001 9. Occupational burnout Personal burnout 365 2.69 ± 0.78 1.95 ± 0.59 –10.10 <.001 Work-related burnout 365 2.53 ± 0.84 1.83 ± 0.62 –8.94 <.001 Overcommitted to work 365 2.20 ± 0.78 1.95 ± 0.71 –3.24 .001 Table 4 Logistic regression analysis of basic attributes, health promotion behaviors, occupational burnout, and sleep disturbances （ N = 365 ） Characteristics Model 1 Odds ratio (95% CI) Model 2 Odds ratio (95% CI) Model 3 Odds ratio (95% CI) Model 4 Odds ratio (95% CI) Basic attributes Gender (Male/Female # ) Age Number of Cigarettes Smoked Drinking Habits (No/Yes # ） Work patterns Shift Work（Yes/No # ） Weekly Working (hours) 41~48/≦40 # ˃48/≦40 # 0.93 (0.34~2.56) 0.98 (0.98~1.02) 1.03 (1.00~1.07) * 0.96 (0.58~1.60) 2.78 (1.69~4.54) *** 4.11 (1.21~14.01) * 2.41 (1.45~4.01) ** 1.12 (0.39~3.19) 0.98 (0.96~1.00) 1.00 (0.96~1.03) 0.85 (0.50~1.44) 0.34 (0.09~1.29) 1.00 (0.98~1.03) 1.00 (0.96~2.01) 0.81 (0.42~1.58) 0.51 (0.15~1.75) 1.00 (0.98~1.03) 1.01 (0.97~1.05) 0.81 (0.43~1.54) 3.03 (1.64~5.56) *** 1.30 (0.24~6.97) 1.98 (1.03~3.81) * Health promotion behaviors Stress management Physical activity Health responsibility Life appreciation Healthy eating Oral hygiene 1.00 (0.91~1.11) 1.01 (0.92~1.10) 0.87 (0.77~0.99) * 0.76 (0.66~0.89) *** 0.86 (0.76~0.97) * 1.04 (0.93~1.15) 0.91 (0.80~1.04) 0.99 (0.89~1.11) 0.85 (0.73~1.00) * 0.87 (0.72~1.04) 0.88 (0.76~1.02) 1.00 (0.86~1.14) Occupational burnout Personal burnout Work-related burnout Overcommitted to work 1.05 (1.03~1.08) *** 1.03 (1.01~1.05) ** 0.98 (0.98~1.01) 1.06 (1.03~1.09) *** 1.02 (0.99~1.04) 1.00 (0.99~1.02) R 2 0.11 0.21 0.33 0.49 Note 1. * p <0.05 ** p <0.01 *** p <0.001，2. # reference group 3. CI= Confidence Interval Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviews received at journal 18 Aug, 2024 Reviewers agreed at journal 25 Jul, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 22 Jul, 2024 Submission checks completed at journal 19 Jul, 2024 First submitted to journal 18 Jul, 2024 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-4761931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339749561,"identity":"df17243b-9e5b-48da-882f-3d7a822cb51d","order_by":0,"name":"Ying-Fen Yu","email":"","orcid":"","institution":"Taoyuan Armed Forces General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying-Fen","middleName":"","lastName":"Yu","suffix":""},{"id":339749562,"identity":"76c481a6-6466-467f-a870-68f6597d4f8f","order_by":1,"name":"Yi-Ya Chang","email":"","orcid":"","institution":"Chang Gung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi-Ya","middleName":"","lastName":"Chang","suffix":""},{"id":339749563,"identity":"0c4c0e15-018f-4ba2-9545-f6acfd5d99ca","order_by":2,"name":"Shu-Hung Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDACCQY2ECXHwMDYeAAuykOEFmOglgaIFjaIFglCWhIbgARxWuRnNz978HNHbfra9sNAW2pqE+fPb2B88LaNoc7gAHYtBneOmRv2njmeu+1MIlDLseOJG44xMBvObWOQwKlFIodNgrftWO62A0AtjA3HEjewMbBJ8wK1mOHQIj8jh03yb9uxdLPzDyFa5rcxsP/Gp4XhRg7IzJoEsxtgW2oSG44xsDHj02JwI81MWrbtgOG2G0BbEo4dMN5wLLFZcs45Ccn9OB2W/EzybVudvNn59IcPPtTUyc5vPnzww5syG37JBhwOg4DDECoBzGAEqcUdk1BQh8EYBaNgFIyCUQAHAPKhZAqo4KcnAAAAAElFTkSuQmCC","orcid":"","institution":"Chang Gung University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Shu-Hung","middleName":"","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2024-07-18 10:39:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4761931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4761931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62422296,"identity":"037d22cc-b862-43a9-a8c8-e6d5390a8376","added_by":"auto","created_at":"2024-08-14 04:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1059212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4761931/v1/4566eacb-f2bb-47b9-8ff9-9a4baed8c0a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Health Promotion Behaviors, Occupational Burnout, and Sleep Disturbances in Traditional Industry Workers","fulltext":[{"header":"Background","content":"\u003cp\u003eSleep disturbances are one of the most common complaints among patients seeking medical care, with insomnia being the most prevalent type [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Sleep disturbances increase employees' feelings of stress and job insecurity and reduce job satisfaction and confidence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], leading to poor job performance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, absenteeism rates rise by 16\u0026ndash;17% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A study that included 22,330 adults from 13 countries across four continents found an overall prevalence of sleep disturbances of 36.7% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, with the rapid global spread of the novel coronavirus (COVID-19) in 2019, people faced unprecedented health threats and lifestyle restrictions, causing the incidence of sleep disturbances to rise to 40.5% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the issue of sleep disturbances in current times is of significant concern.\u003c/p\u003e \u003cp\u003eAccording to statistics from the Directorate General of Budget, Accounting, and Statistics, Executive Yuan, R.O.C., the population employed in the manufacturing sector in March 2024 was approximately 2.993\u0026nbsp;million, accounting for 25.84% of the total labor force [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies on sleep disturbances among traditional industry workers found a prevalence of 22.6% among electronic product manufacturer workers in China [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], 35.3% and 42.0% among steel factory workers in China [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], 16.7% among textile workers in China [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], 4.5% among manufacturing workers in South Korea [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], 33.2% among construction workers in India [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and 36.5% among construction and civil engineering workers in Japan [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The prevalence of sleep disturbances among traditional industry workers in these countries varies significantly. However, there is currently no data on the prevalence of sleep disturbances among traditional industry workers in Taiwan.\u003c/p\u003e \u003cp\u003eFactors influencing sleep disturbances among workplace employees can be broadly categorized into personal background factors, work patterns, occupational burnout, and health promotion behavior. Factors associated with sleep disturbances include being female, age [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], smoking habits [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], lower educational level, alcohol consumption, a higher number of chronic illnesses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and sleeping\u0026thinsp;\u0026le;\u0026thinsp;6 hours per day [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of work patterns, factors such as shift work, higher work stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]., excessive job involvement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], shorter daily rest periods [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and working more than 40 hours per week [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] are associated with a higher likelihood of sleep disturbances. Many industries implement shift work to increase productivity and reduce downtime [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Since traditional manufacturing industry employees often work in shifts, the probability of sleep disturbances is expected to be higher.\u003c/p\u003e \u003cp\u003eThe highly competitive pressure in global industries leads to occupational burnout [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], also known as occupational exhaustion. Its impacts include insomnia, depression, job dissatisfaction, increased absenteeism, and disability benefits [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Occupational burnout is considered a crisis in modern society and life [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Both occupational burnout and sleep disturbances are significant policy issues in many countries, with higher occupational burnout associated with a greater likelihood of sleep disturbances [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Xinjie [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] concluded that burnout among frozen food packaging workers increases the severity of sleep disturbances. Treating employees' sleep disturbances can enhance productivity and reduce occupational burnout [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Research on sleep disturbances and occupational burnout has mostly focused on physicians, teachers, police and firefighters, nurses, and tech workers [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealth promotion is the process of enabling people to increase control over and improve their health. Common behaviors used in health promotion include health responsibility, proper nutrition, stress management, self-actualization, exercise and leisure, and interpersonal support [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Adult health behaviors (AHBs) include stress management, exercise, health responsibility, life appreciation, healthy diet, and oral hygiene. This study found that these six behaviors are significantly correlated with life satisfaction, concluding that they can identify poor health behaviors in adults earlier than previous research [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Numerous studies have confirmed the correlation between health promotion behaviors and sleep disturbances, identifying health promotion behaviors as a major factor influencing sleep disturbances [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For instance, whole grain and Mediterranean diets are protective factors against sleep disturbances [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Conversely, insufficient physical activity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and sedentary behavior [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] increase the likelihood of sleep disturbances [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Sleep disturbances can deplete physical energy and reduce time spent on physical activities after work and those with severe sleep disturbances engage in less high-intensity physical activity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, a vicious cycle may arise between sleep disturbances and unhealthy lifestyles [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], leading to increasingly severe sleep disturbances.\u003c/p\u003e \u003cp\u003eManufacturing is a major industry in many countries, and previous research on manufacturing employees has primarily focused on the health hazards of occupational exposure [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, studies on sleep disturbances among traditional manufacturing industry employees in Taiwan are notably lacking. Moreover, research on the relationship between health promotion behaviors, occupational burnout, and sleep disturbances is even more scarce, warranting attention and further exploration. Therefore, this study aims to investigate the correlation between health promotion behaviors, occupational burnout, and sleep disturbances among employees in traditional industries.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eThis is a cross-sectional research study that utilizes structured questionnaires to collect data. It aims to explore the relationships between health promotion behaviors, occupational burnout, sleep disturbances, and the related factors of sleep disturbances among employees in traditional industries. The Reporting of Observational Studies in Epidemiology (STROBE) cross-sectional study checklist [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] was used as a reporting guide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThis study is part of a collaborative research project conducted by a team of nurses, aiming to assess the relationship between sleep health and work-related factors and health promotion among workplace employees. It provides insights for occupational health nurses to promote workplace health initiatives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe participants included employees at a traditional industrial complex in Northern Taiwan working in the textile industry or manufacturing nitrogen compounds, chemical raw materials, fertilizers, plastic and rubber raw materials, synthetic fibers, and plastic products. Inclusion criteria were as follows: participants had to be age 18 or older, having worked in traditional industries for at least three months, and agreed to participate in the study. Pregnant individuals were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSampling and sample size\u003c/h2\u003e \u003cp\u003eCluster sampling was employed, with the study site comprising five factories: a cotton mill, Film Plant 1, Spinning Mill 3, Spinning Mill 4, and Utility Department. The main products produced by these factories include polyester cotton, polyester chips, polyester films, solid-state polymer chips, polyester partially oriented yarns, polyester fully drawn yarns, and polyester textured yarns. The work is labor-intensive, and each factory has both shift workers and administrative staff, with a predominantly male workforce aged between 35 and 64. Due to the similar background and work nature of the five factories, all employees from Film Plant 1 were randomly selected as the study subjects.\u003c/p\u003e \u003cp\u003eThe required sample size for correlation analysis was calculated using the G*Power 3.1.0 software program for logistic regression analysis (\"Z-test for Logistic regression\"). Setting the odds ratio to 1.5, H0 to 0.5, significance level (α) to 0.05, and power to 0.9, the minimum sample size needed was 275. Data collection took place from 15 May 2023 to 17 June 2023, with 365 questionnaires distributed and all 365 valid questionnaires returned, yielding a response rate of 100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eData were collected using structured questionnaires, which included the following scales:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic attributes\u003c/h2\u003e \u003cp\u003eThese included gender, age, education level, marital status, smoking, alcohol consumption, shift work, and weekly working hours, as referenced in [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]。\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHealth promotion behaviors\u003c/h2\u003e \u003cp\u003eThis was measured using the \"Adult Health Behaviors - Short Form\" (AHBs) developed by Chen et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Participants completed the questionnaire regarding their lifestyle habits over the past year using a four-point Likert scale. The scoring criteria were as follows: 1 point for \"Never\" (approximately 0\u0026ndash;10% ), 2 points for \"Sometimes\" (approximately 11\u0026ndash;50% ), 3 points for \"Often\" (approximately 51\u0026ndash;70% ), and 4 points for \"Always\" (approximately 71\u0026ndash;100% ). Higher scores indicated healthier lifestyles. The scale covered six elements: stress management, physical activity, health responsibility, life appreciation, healthy eating, and oral hygiene behaviors. The internal consistency of the AHBs was 0.85. Lower scores on the six subscales and the total score of the SF-AHB were significantly associated with lower life satisfaction. Test\u0026ndash;retest reliability for the total AHB was r\u0026thinsp;=\u0026thinsp;0.91. The validity of the AHBs was confirmed through qualitative content validation by experts in metabolism/endocrinology, cardiology, dentistry, and nursing, with an average Content Validity Index (CVI) of 0.93 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eOccupational burnout\u003c/h2\u003e \u003cp\u003eThis study utilized the \"Chinese Version of the Occupational Burnout Scale,\" developed by Yeh et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which references the Copenhagen Burnout Inventory (CBI) developed by Danish scholars and the Effort\u0026ndash;Reward Imbalance Questionnaire (ERI-Q). The Cronbach's alpha for this scale was 0.86 overall (0.92 for men and 0.90 for women). The Cronbach's alpha for each subscale ranged between 0.85 and 0.87. The scale used a five-point Likert scale with the following scoring criteria: 5 points for \"always\" (100 points), 4 points for \"often\" (75 points), 3 points for \"sometimes\" (50 points), 2 points for \"rarely\" (25 points), and 1 point for \"never\" (0 points). Higher scores indicate a greater degree of perceived burnout [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSleep disturbances\u003c/h2\u003e \u003cp\u003eThe Chinese version of the Athens Insomnia Scale-eight questions (CAIS-8), jointly translated by Jiang et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], was used to measure the sleep status of the research subjects in the past month by filling in the questionnaire. The subjective feelings of the subjects regarding their nightly sleep were surveyed. The questionnaire included \"nighttime symptoms\": time to fall asleep, waking up at night, waking up earlier than expected, total sleep time, and total sleep quality; \"daytime symptoms\": daytime mood, daytime physical function, daytime sleepiness, for a total of eight questions. A four-point Likert scale was used, with each question scored from 0 to 3 points, with a total score of 0 to 24 points. A total score of less than 3 points (inclusive) indicated no sleep disturbances; a total score of 4 to 5 indicates suspected sleep disturbances, and a score greater or equal to 6 indicates sleep disturbances. The dependent variables analyzed at the end of this study were \"no\" (score 0 to 5 points) and \"yes\" (score\u0026thinsp;≧\u0026thinsp;6 points) as the cut-off points for sleep disturbances [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003cp\u003eData entry and statistical analysis were conducted using SPSS version 27.0, with an alpha value set to 0.05 (two-tailed test) as the criterion for statistical significance. Basic attributes were analyzed using percentages, frequency distributions, means, and standard deviations to understand the distribution characteristics of the measurements. Inferential statistics included the Chi-Squared Test to compare categorical variables of the study subjects with sleep disturbances (yes or no), an independent t-test to examine the differences in continuous variables of basic attributes, health promotion scale results, and occupational burnout scale results, and logistic regression to analyze the relationship between independent variables and sleep disturbances (yes or no) to identify factors related to sleep disturbances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003e The Institutional Review Board approved this study with approval number A202305036. Participants were informed about the study through an information sheet, and data were collected using anonymous self-administered questionnaires. Participants were informed of their right to withdraw from the study at any time. Participants received small gifts after completing the questionnaire.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive statistics of basic attributes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 365 subjects participated in this study. The average age was 43.86 years (SD = 11.74). The majority were not smokers (64.4%), with an average cigarette consumption of only 3.70 cigarettes per day (SD = 7.11). Most participants did not consume alcohol (72.3%). The average daily sleep duration was 6.61 hours (SD = 0.98). A significant portion of the participants were shift workers (63.3%). The majority worked \u0026le;40 hours per week (67.4%), were non-supervisory staff (86%), and had a high school education (51.8%). Detailed information is provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive statistical analysis results of health promotion behavior, occupational burnout, and sleep disturbances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth promotion behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original scale had a maximum average score of 4 points. The overall average score on the scale was 2.34 points, with a standard deviation of 0.48. The average scores of each sub-dimension, listed from highest to lowest, were as follows: \u0026quot;oral hygiene behavior\u0026quot; with an average of 2.56 points (SD = 0.69), \u0026quot;life appreciation behavior\u0026quot; with an average of 2.46 points (SD = 0.68), \u0026quot;healthy eating behavior\u0026quot; with an average of 2.44 points (SD = 0.58), \u0026quot;health responsibility behavior\u0026quot; with an average of 2.32 points (SD = 0.78), \u0026quot;stress management behavior\u0026quot; with an average of 2.28 points (SD = 0.56), and the lowest was \u0026quot;exercise behavior\u0026quot; with an average of 2.01 points (SD = 0.75). The scores for the first five sub-dimensions fell between \u0026quot;occasionally\u0026quot; and \u0026quot;frequently,\u0026quot; while exercise behavior was rated as \u0026quot;occasionally,\u0026quot; indicating 2\u0026ndash;3 days per week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOccupational burnout\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original scale had a maximum average score of 4 points. The overall average score on the scale was 1.96 points, with a standard deviation of 1.09. The total scores for each sub-dimension, listed from highest to lowest, were as follows: \u0026quot;personal burnout\u0026quot; with an average of 2.30 points (SD = 0.78), \u0026quot;work-related Burnout\u0026quot; with an average of 2.16 points (SD = 0.81), and \u0026quot;overcommitted to work\u0026quot; with an average of 2.07 points (SD = 0.75). All three sub-dimensions fell between \u0026quot;sometimes\u0026quot; and \u0026quot;often.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSleep disturbance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total score of less than or equal to 3 indicated no sleep disturbance, accounting for 32.3% of the subjects. A total score of 4 to 5 indicates suspected sleep disturbance, accounting for 19.7%. A total score of 6 or greater indicates the presence of sleep disturbance, with 47.9% of the subjects experiencing sleep disturbances. Details are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate analysis of factors affecting sleep disturbance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA bivariate analysis was conducted to understand the relationship between the study subjects\u0026apos; basic attributes, health promotion behaviors, occupational burnout, and sleep disturbance. The dependent variable was the presence or absence of sleep disturbances, while the independent variables included basic attributes, health promotion behaviors, and occupational burnout. Categorical variables were analyzed using the Chi-Squared Test, and continuous variables were analyzed using the Independent t-test. The results are as follows: significant differences were found for shift work (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), shift groups (\u003cem\u003ep\u003c/em\u003e = 0.004), weekly working hours (\u003cem\u003ep\u003c/em\u003e = 0.027), stress management behavior (\u003cem\u003ep\u003c/em\u003e = 0.001), exercise behavior (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), health responsibility behavior (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), life appreciation behavior (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), healthy diet behavior (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), oral hygiene behavior (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), personal burnout (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), work-related burnout (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and overcommitment to work (\u003cem\u003ep\u003c/em\u003e = 0.001). Details are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of factors associated with sleep disturbances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis was conducted to identify the factors influencing sleep disturbance, incorporating basic attributes, work patterns, health promotion behaviors, and occupational burnout. The analysis was divided into four models: Model 1: The relationship between basic attributes, work patterns, and sleep disturbances. Model 2: The relationship between basic attributes, health promotion behaviors, and sleep disturbances. Model 3: The relationship between basic attributes, occupational burnout, and sleep disturbances. Model 4: The relationship between basic attributes, work patterns, health promotion behaviors, occupational burnout, and sleep disturbances.\u003c/p\u003e\n\u003cp\u003eFor Model 1,\u0026nbsp;logistic regression analysis was conducted using basic attributes and work patterns as independent variables and sleep disturbances as the dependent variable. The results are as follows:\u003c/p\u003e\n\u003cp\u003esmoking quantity, odds ratio (OR) = 1.03 (95% confidence interval (CI) 1.00~1.07), \u003cem\u003ep\u003c/em\u003e = 0.048; shift work (yes/no), OR = 2.78 (95% CI 1.69~4.54), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; weekly working hours (41~48 hours/\u0026le;40 hours), OR = 4.11 (95% CI 1.21~14.01), \u003cem\u003ep\u003c/em\u003e = 0.024; and weekly working hours (˃48 hours/\u0026le;40 hours), OR = 2.41 (95% CI 1.45~4.01), \u003cem\u003ep\u003c/em\u003e = 0.001. These results indicate that less smoking, non-shift work, and weekly working hours less than or equal to 40 hours are associated with a lower likelihood of sleep disturbance. The explanatory power (R\u0026sup2;) of this model was 0.11. See Table 4 for detailed results.\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis was conducted for Model 2, using basic attributes and health promotion behaviors as independent variables and sleep disturbance as the dependent variable. The results are as follows: health responsibility behavior, OR = 0.87 (95% CI 0.77~0.99), \u003cem\u003ep\u003c/em\u003e = 0.030; life appreciation behavior, OR = 0.76 (95% CI 0.66~0.89), p \u0026lt; 0.001; and healthy eating behavior, OR = 0.86 (95% CI 0.76~0.97), \u003cem\u003ep\u003c/em\u003e = 0.017. These results indicate that higher scores in health responsibility behavior, life appreciation behavior, and healthy eating behavior are associated with a lower likelihood of sleep disturbances. The explanatory power (R\u0026sup2;) of this model was 0.21. See Table 4 for detailed results.\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis was conducted for Model 3, using basic attributes and occupational burnout as independent variables and sleep disturbances as the dependent variable. The results are as follows: personal burnout, OR = 1.05 (95% CI 1.03~1.08), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, and work-related burnout, OR = 1.03 (95% CI 1.01~1.05), \u003cem\u003ep\u003c/em\u003e = 0.010. These results indicate that lower personal and work-related burnout scores are associated with a lower likelihood of sleep disturbances. The explanatory power (R\u0026sup2;) of this model was 0.33. See Table 4 for detailed results.\u003c/p\u003e\n\u003cp\u003eFor Model 4, logistic regression analysis was conducted using basic attributes, work patterns, health promotion behaviors, and occupational burnout as independent variables and sleep disturbances as the dependent variable. The results are as follows: shift work (Yes/No), OR = 3.03 (95% CI 1.64~5.56), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; weekly working hours (\u0026gt;48 hours/\u0026le;40 hours), OR = 1.98 (95% CI 1.03~3.81), \u003cem\u003ep\u003c/em\u003e = 0.040; health responsibility behavior, OR = 0.85 (95% CI 0.73~1.00), \u003cem\u003ep\u003c/em\u003e = 0.049; and personal burnout, OR = 1.06 (95% CI 1.03~1.09), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. These results indicate a lower likelihood of sleep disturbances associated with non-shift work, higher scores in health responsibility behavior, weekly working hours of 40 or less, and lower scores in personal burnout. The explanatory power (R\u0026sup2;) of this model was 0.49. See Table 4 for detailed results.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003eCurrent status of sleep disturbances\u003c/h2\u003e\n \u003cp\u003eThe overall rate of sleep disturbances among the study subjects was 47.9%, with 48.7% for male employees and 35% for female employees. Therefore, the prevalence was found to be higher in our study compared to other manufacturing workers in Asian countries, such as 22.6% among Chinese electronics manufacturing employees [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e], 35.3% and 42% among steel plant workers [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], 16.7% among Chinese textile workers [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], 35.6% in Japanese construction and civil engineering employees [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e], 33.2% among Indian construction workers [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], and 18.6% among Brazilian workers [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. The higher rate of sleep disturbances in this study\u0026apos;s subjects could be attributed to the majority of our participants being shift workers, with an average total daily sleep time of 6.61 hours, less than the recommended 7 hours. Additionally, most of their work involves standing or walking, which may increase burnout and contribute to a higher prevalence of sleep disturbances than workers in other countries\u0026apos; traditional industries.\u003c/p\u003e\n \u003ch2\u003eRelationship between work patterns and sleep disturbances\u003c/h2\u003e\n \u003cp\u003eAccording to the bivariate analysis, the sleep disturbance rate was 43.1% for those working\u0026thinsp;\u0026le;\u0026thinsp;40 hours per week, 57% for those working 41\u0026ndash;48 hours, and 61.5% for those working\u0026thinsp;\u0026gt;\u0026thinsp;48 hours. The Model 1 results show that individuals working\u0026thinsp;\u0026gt;\u0026thinsp;48 hours per week have 2.41 times higher odds of sleep disturbances than those working\u0026thinsp;\u0026le;\u0026thinsp;40 hours. This aligns with findings by Halonen [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] and Lee [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], highlighting a higher probability of sleep disturbances with longer work hours. Referencing Taiwan\u0026apos;s Hsiao [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e], factors such as shift work, long hours (\u0026gt;\u0026thinsp;60 hours/week), heavy physical labor, and large company size (over 300 employees) are significantly related to sleep issues. This suggests that shift work and extended hours increase the risk of sleep disturbances. To reduce the rate of sleep disturbances, it is recommended to efficiently manage work hours, aiming for a standard work week of no more than 40 hours.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eRelationship between health promotion behaviors and sleep disturbances\u003c/h2\u003e\n \u003cp\u003eIn Model 2, it was found that the participants\u0026rsquo; behaviors related to \u0026quot;health responsibility,\u0026quot; \u0026quot;appreciation of life,\u0026quot; and \u0026quot;healthy eating\u0026quot; correlated with sleep disturbances. Drawing from the study by Weitzer [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e], it was observed that practicing \u0026quot;appreciation of life\u0026quot; behaviors to maintain an optimistic outlook is a simple and effective strategy to improve sleep quality and reduce the risk of sleep disturbances. It is recommended that employees maintain a positive mindset, smile frequently, practice self-appreciation, and spend time outdoors appreciating nature, as this may lower the probability of sleep disturbances.\u003c/p\u003e\n \u003cp\u003eThis study aligns with findings from Cao [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] and Castro-Diehl [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e], suggesting that \u0026quot;healthy eating behavior\u0026quot; is also associated with sleep disturbances. Additionally, Castro-Diehl [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e] noted that a healthy diet can promote sufficient sleep duration and reduce the risk of insomnia. Mediterranean dietary patterns [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] and whole grain patterns [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] emerged as protective factors against sleep disturbances. Therefore, it is advised to adopt healthy eating habits, including consuming at least three servings of vegetables and two servings of fruits and maintaining a water intake of 1500 milliliters daily. Seeking guidance from professional nutritionists and adjusting the meal categories offered in employee cafeterias may facilitate the establishment of healthy eating behaviors among employees.\u003c/p\u003e\n \u003cp\u003e\u0026quot;Health responsibility\u0026quot; aims to achieve overall well-being of body and mind, requiring individuals to adopt a proactive and responsible attitude towards their own health. This includes actively seeking knowledge and information on self-healthcare, being attentive to physiological changes and symptoms, and proactively seeking guidance and assistance from healthcare professionals when necessary. This study found that higher scores in \u0026quot;health responsibility\u0026quot; were associated with lower rates of sleep disturbances, possibly due to better management of one\u0026apos;s physical and mental state, resulting in a lower susceptibility to sleep disturbances. While few studies have previously explored the relationship between health responsibility and sleep disturbances, future intervention-based follow-up studies could further investigate this association. While Xie [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e] and Bjornsdottir [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e] have shown that regular exercise can improve sleep quality and reduce insomnia severity and subjective daytime sleepiness among individuals with sleep disturbances, this study did not find a correlation between exercise behavior and sleep disturbances. This may be due to participants in this study generally not engaging in regular exercise, thus making it difficult to observe any correlation. Most participants were shift workers. Studies by Shah [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e] and Shen [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e] suggested that exercise can enhance cardiovascular health, leading to better sleep quality among shift workers and improving sleep disturbances caused by disrupted circadian rhythms. In the future, encouraging subjects to spend more time outdoors and actively establish a habit of regular exercise could be beneficial. This could be facilitated through technology such as smart bands, smartwatches, or workplace interventions like stair-climbing campaigns instead of using elevators, providing convenient access to exercise equipment in the workplace for stretching or physical activity during breaks. Additionally, intervention-based follow-up studies could be conducted to confirm the effectiveness of exercise in improving sleep disturbances.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eRelationship between occupational burnout and sleep disturbances\u003c/h2\u003e\n \u003cp\u003eThe results of Model 3 indicate that individuals with lower scores in personal burnout and work burnout have lower rates of sleep disturbances. Baek et al. conducted a study on 18,744 Korean workers and found that higher levels of workplace burnout increased the likelihood of sleep disturbances [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, significant correlations between workplace burnout and severe sleep disturbances were found among Hungarian postal workers [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. Studies focusing on physicians [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] and nurses [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] have also shown a positive correlation between workplace burnout and sleep disturbances, with workplace burnout being a contributing factor to sleep disturbances, which aligns with the findings of this study. While many people find that they sleep better when experiencing burnout, this may not hold true for workers in labor-intensive traditional industries. Providing a comfortable work environment and allowing for short breaks (approximately 20 to 30 minutes), particularly for night shift workers, is expected to reduce personal and work burnout levels. Encouraging all employees to engage in leisure activities during breaks to relax their minds and bodies is also recommended. Additionally, hosting workshops on mental and physical health, such as aromatherapy, progressive exercise programs, and teaching and practical courses on muscle relaxation and deep breathing, is advised. These effective muscle relaxation techniques can help alleviate stress and anxiety, reduce personal burnout, and lower the likelihood of sleep disturbances, thus aiding employees in improving their burnout levels.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall findings indicate that shift rotation, weekly working hours, and health promotion behaviors correlate with personal burnout and sleep disturbances. Due to the significant threat sleep disturbances pose to employee health, it is recommended to adjust the shift rotation frequency for employees and develop more suitable shift schedules. For night shift workers, increasing the intensity of lighting in the work environment is advised to reduce sleep disturbances caused by circadian rhythm disruption and to assist employees in better adapting to shift work.\u003c/p\u003e \u003cp\u003eOccupational health nurses in the workplace should assist in promoting healthy behaviors among employees, including enhancing employees' self-health awareness and regularly organizing health education seminars on sleep, exercise, diet, and other relevant topics. This helps employees understand how to maintain a healthy lifestyle and alleviate feelings of burnout. Additionally, through employee health screenings, occupational health nurses can identify high-risk cases and provide preventative, diagnostic, and health advisory services to reduce the likelihood of sleep disturbances among workers in traditional industries.\u003c/p\u003e "},{"header":"Limitations","content":"\u003cp\u003eFirstly, the questionnaire primarily relied on the subjective perceptions of the participants. However, incorporating objective data, such as physiological monitors (e.g., portable sleep trackers), could provide more objective data representation. Objective measurements of sleep processes may reduce research biases, enhance research credibility, and improve the impact and validity of the study. Secondly, participants making use of sleep aids should be excluded to more accurately represent the current status of sleep disturbances.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the Institutional Review Board of the Tri-Service General Hospital with protocol number A202305036. Participation in the study was voluntary, and informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available on request due to privacy/ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by grants TYAFGH_D_113021 from the Taoyuan Armed Forces General Hospital, Taoyuan, Taiwan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYFY contributed to the study\u0026apos;s conceptualization, methodology, data collection, data analysis, data interpretation and validation, original draft preparation, original draft revision and edit, and funding acquisition. YYC contributed to the revision and editing of the manuscript. SHC contributed to the study\u0026apos;s conceptualization, data interpretation, validation, and revision and editing of the original draft. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial thanks to nurse Zhang Jia-Zhen, the factory nurse, for her assistance\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKarna, B., Sankari, A., \u0026amp; Tatikonda, G. (2023). Sleep Disorder. In \u003cem\u003eStatPearls\u003c/em\u003e. StatPearls Publishing.\u003c/li\u003e\n\u003cli\u003eFietze, I., Rosenblum, L., Salanitro, M., Ibatov, A. D., Eliseeva, M. V., Penzel, T., Brand, D., \u0026amp; Westermayer, G. (2022). The interplay between poor sleep and work-related health. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, 10, 866750. https://doiorg.autorpa.ndmctsgh.edu.tw/10.3389/fpubh.2022.866750\u003c/li\u003e\n\u003cli\u003eKayaba, M., Sasai-Sakuma, T., Takaesu, Y., \u0026amp; Inoue, Y. (2021). The relationship between insomnia symptoms and work productivity among blue-collar and white-collar Japanese workers engaged in construction/civil engineering work: a cross-sectional study. \u003cem\u003eBMC Public Health\u003c/em\u003e, 21(1), 1244. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1186/s12889-021-11273-y\u003c/li\u003e\n\u003cli\u003eRiontino, L., \u0026amp; Cavallero, C. (2021). Individual differences in working memory efficiency modulate proactive interference after sleep deprivation. \u003cem\u003ePsychological research\u003c/em\u003e, 85(2), 480\u0026ndash;490. https://doi-org.mhdla.flysheet.com.tw:8443/10.1007/s00426-020-01292-6\u003c/li\u003e\n\u003cli\u003eAmiri, S., \u0026amp; Behnezhad, S. (2020). Sleep disturbances and risk of sick leave: systematic review and meta-analysis. \u003cem\u003eSleep and Biological Rhythms\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 283-295. https://doi.org/10.1007/s41105-020-00270-0\u003c/li\u003e\n\u003cli\u003eMorin, C. M., Bjorvatn, B., Chung, F., Holzinger, B., Partinen, M., Penzel, T., Ivers, H., Wing, Y. K., Chan, N. Y., Merikanto, I., Mota-Rolim, S., Mac\u0026ecirc;do, T., De Gennaro, L., L\u0026eacute;ger, D., Dauvilliers, Y., Plazzi, G., Nadorff, M. R., Bolstad, C. J., Sieminski, M., Benedict, C., \u0026hellip; Espie, C. A. (2021). Insomnia, anxiety, and depression during the COVID-19 pandemic: an international collaborative study. \u003cem\u003eSleep Medicine\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e, 38\u0026ndash;45. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1016/j.sleep.2021.07.035 \u003c/li\u003e\n\u003cli\u003eJahrami, H. A., Alhaj, O. A., Humood, A. M., Alenezi, A. F., Fekih-Romdhane, F., AlRasheed, M. M., Saif, Z. Q., Bragazzi, N. L., Pandi-Perumal, S. R., BaHammam, A. S., \u0026amp; Vitiello, M. V. (2022). Sleep disturbances during the COVID-19 pandemic: A systematic review, meta-analysis, and meta-regression. \u003cem\u003eSleep Medicine Reviews\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e, 101591. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1016/j.smrv.2022.101591\u003c/li\u003e\n\u003cli\u003eDirectorate General of Budget, Accounting and Statistics. (2024, April 22). \u003cem\u003eEmployment by industry\u003c/em\u003e - Table 8. Retrieved from [https://www.stat.gov.tw/News_Content.aspx?n=3703\u0026amp;s=233265](https://www.stat.gov.tw/News_Content.aspx?n=3703\u0026amp;s=233265).\u003c/li\u003e\n\u003cli\u003eFan, L. N., Yu, J. W., Yang, X. Y., \u0026amp; Zeng, Q. (2020). Effects of occupational stress and related factors on insomnia occurrence of workers in chip manufacturing industry. \u003cem\u003eChinese journal of industrial hygiene and occupational diseases\u003c/em\u003e, 38(4), 259\u0026ndash;262. https://doi-org.mhdla.flysheet.com.tw:8443/10.3760/cma.j.cn121094-20190219-00052\u003c/li\u003e\n\u003cli\u003eWang, L., Zhang, S., Yu, M., \u0026amp; Yuan, J. (2022). Association between insomnia and subclinical atherosclerosis among Chinese steelworkers: a cross-sectional survey. \u003cem\u003eArch Public Health\u003c/em\u003e,\u003cem\u003e 80\u003c/em\u003e(1), 80. https://doi.org/10.1186/s13690-022-00834-1 \u003c/li\u003e\n\u003cli\u003eLi, X., Cui, S., Wu, J., Wang, L., \u0026amp; Yuan, J. (2020). Job category differences in the prevalence and associated factors of insomnia in steel workers in China. \u003cem\u003eInternational journal of occupational medicine and environmental health\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 215\u0026ndash;233. https://doi-org.mhdla.flysheet.com.tw:8443/10.13075/ijomeh.1896.01519\u003c/li\u003e\n\u003cli\u003eJiang, J., Wang, D., Scherffius, A., Chen, D., Ma, Z., Chen, Z., Zhang, Y., Yu, Q., \u0026amp; Fan, F. (2022). The Association Between Morningness-Eveningness Preference, Depression, Anxiety and Insomnia Among Chinese Textile Workers With or Without Shift Work. \u003cem\u003eFront Psychiatry\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e, 915476. https://doi.org/10.3389/fpsyt.2022.915476\u003c/li\u003e\n\u003cli\u003eLee, D. W., Jang, T. W., Kim, H. R., \u0026amp; Kang, M. Y. (2021). The relationship between working hours and lifestyle behaviors: Evidence from a population-based panel study in Korea. \u003cem\u003eJournal of Occupational Health\u003c/em\u003e,\u003cem\u003e 63(1),\u003c/em\u003e e12280. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1002/1348-9585.12280\u003c/li\u003e\n\u003cli\u003eSuresh, S., Doraikannan, S., Jayakumar, N. D., Indiran, M. A., \u0026amp; Kesavan, R. (2022). Working hours impact on the sleep quality and oral health status among migrant construction workers in Chennai. \u003cem\u003eJ Family Med Prim Care\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(7), 3511-3516. https://doi.org/10.4103/jfmpc.jfmpc_2101_21 \u003c/li\u003e\n\u003cli\u003eAlosta, M. R., Oweidat, I., Alsadi, M., Alsaraireh, M. M., Oleimat, B., \u0026amp; Othman, E. H. (2024). Predictors and disturbances of sleep quality between men and women: results from a cross-sectional study in Jordan. \u003cem\u003eBMC psychiatry\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 200. https://doi-org.mhdla.flysheet.com.tw:8443/10.1186/s12888-024-05662-x\u003c/li\u003e\n\u003cli\u003eBedford, L. E., Tang, E. H. M., Dong, W., Wong, C. K. H., Tse, E. T. Y., Ng, A. P. P., Cheung, B. M. Y., Wu, T., Yu, E. Y. T., \u0026amp; Lam, C. L. K. (2022). Who reports insufficient and disturbed sleep? Results from a representative population-based health survey in Hong Kong. \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(9), e058169. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2021-058169 \u003c/li\u003e\n\u003cli\u003eCao, X., Zhou, S., Yang, B., \u0026amp; Zhou, J. (2020). Relationship between dietary patterns and sleep disturbance among health screening population in Changsha. \u003cem\u003eZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(10), 1204\u0026ndash;1214. https://doi-org.autorpa.ndmctsgh.edu.tw/10.11817/j.issn.1672-7347.2020.190318\u003c/li\u003e\n\u003cli\u003eBoersma, G. J., Mijnster, T., Vantyghem, P., Kerkhof, G. A., \u0026amp; Lancel, M. (2023). Shift work is associated with extensively disordered sleep, especially when working nights. \u003cem\u003eFrontiers in psychiatry\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1233640. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpsyt.2023.1233640\u003c/li\u003e\n\u003cli\u003eLee, J., Oh, J., Park, H., Sim, J., Lee, J., Kim, Y., \u0026amp; Yun, B. (2023). Exploring the relationship between work-family conflict and sleep disturbance: a study on stratification and interaction. \u003cem\u003eFrontiers in psychology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1257233. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpsyg.2023.1257233\u003c/li\u003e\n\u003cli\u003eWeston, G., Zilanawala, A., Webb, E., Carvalho, L., \u0026amp; McMunn, A. (2024). Work hours, weekend working, nonstandard work schedules and sleep quantity and quality: findings from the UK household longitudinal study. \u003cem\u003eBMC public health\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 309. https://doi-org.mhdla.flysheet.com.tw:8443/10.1186/s12889-024-17762-0\u003c/li\u003e\n\u003cli\u003eHwang, Y., Jang, H., Lee, J., Jeon, S., Kim, J., Lee, S., \u0026amp; Kim, S. J. (2023). Sleep Reactivity and Sleep Efforts in Shift Workers. \u003cem\u003ePsychiatry investigation\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(9), 880\u0026ndash;887. https://doi-org.mhdla.flysheet.com.tw:8443/10.30773/pi.2023.0098\u003c/li\u003e\n\u003cli\u003eSun, Y., Wei, M., Zhao, Q., Yang, J., Gao, J., \u0026amp; Dai, J. (2024). Mediating effect of sleep quality on the association between job stress and health-related productivity loss among workers in R\u0026amp;D enterprises in Shanghai. \u003cem\u003eFrontiers in public health\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 1331458. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpubh.2024.1331458\u003c/li\u003e\n\u003cli\u003eIkeda, H., \u0026amp; Kubo, T. (2024). The association between work interval regularity and sleep regularity: a 2-week observational study in daytime employees. \u003cem\u003eJournal of occupational health\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(1), uiae009. https://doi-org.mhdla.flysheet.com.tw:8443/10.1093/joccuh/uiae009\u003c/li\u003e\n\u003cli\u003eKageyama, M., Tatsumi, A., Fujino, Y., \u0026amp; Watai, I. (2022).Association between social jetlag and presenteeism in Japanese industry: A cross-sectional study \u003cem\u003eSangyo eiseigaku zasshi=Journal of Occupational Health\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(1), 12\u0026ndash;21. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1539/sangyoeisei.2020-049-B\u003c/li\u003e\n\u003cli\u003eHalonen, J. I., Pulakka, A., Vahtera, J., Pentti, J., Lastr\u0026ouml;m, H., Stenholm, S., \u0026amp; Hanson, L. M. (2020). Commuting time to work and behavior-related health: a fixed-effect analysis. \u003cem\u003eOccupational and Environmental Medicine\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(2), 77\u0026ndash;83. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/oemed-2019-106173 http://europepmc.org/books/NBK560720https://www.ncbi.nlm.nih.gov/books/NBK560720 \u003c/li\u003e\n\u003cli\u003eTakada M. (2018). Hygiene in Sleep: Problems of Sleeping Habits in Shift Workers \u003cem\u003eNihon eiseigaku zasshi. Japanese journal of hygiene\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(1), 22\u0026ndash;26. https://doi-org.mhdla.flysheet.com.tw:8443/10.1265/jjh.73.22\u003c/li\u003e\n\u003cli\u003eCheng, Y. W. (2011). Coping strategies for occupational burnout. New Social Policy, (14), 55-58. https://doi.org/10.29752/NST.201102.0012\u003c/li\u003e\n\u003cli\u003eGhio, L., Patti, S., Piccinini, G., Modafferi, C., Lusetti, E., Mazzella, M., \u0026amp; Del Sette, M. (2021). Anxiety, Depression and Risk of Post-Traumatic Stress Disorder in Health Workers: The Relationship with Burnout during COVID-19 Pandemic in Italy. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(18), 9929. https://doi-org.mhdla.flysheet.com.tw:8443/10.3390/ijerph18189929\u003c/li\u003e\n\u003cli\u003eSalvagioni, D. A. J., Melanda, F. N., Mesas, A. E., Gonz\u0026aacute;lez, A. D., Gabani, F. L., \u0026amp; Andrade, S. M. (2017). Physical, psychological and occupational consequences of job burnout: A systematic review of prospective studies. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10), e0185781. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1371/journal.pone.0185781 \u003c/li\u003e\n\u003cli\u003eLu, Y., Zhang, Z., Gao, S., Yan, H., Zhang, L., \u0026amp; Liu, J. (2020). The status of occupational burnout and its influence on the psychological health of factory workers and miners in wulumuqi, China. \u003cem\u003eBioMed Research International\u003c/em\u003e, \u003cem\u003e2020\u003c/em\u003e, 6890186. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1155/2020/6890186 \u003c/li\u003e\n\u003cli\u003eBaek, S. U., Yoon, J. H., \u0026amp; Won, J. U. (2023). Association between high emotional demand at work, burnout symptoms, and sleep disturbance among Korean workers: a cross-sectional mediation analysis. Scientific reports, 13(1), 16688. https://doi-org.mhdla.flysheet.com.tw:8443/10.1038/s41598-023-43451-w\u003c/li\u003e\n\u003cli\u003eMembrive-Jim\u0026eacute;nez, M. J., G\u0026oacute;mez-Urquiza, J. L., Suleiman-Martos, N., Velando-Soriano, A., Ariza, T., De la Fuente-Solana, E. I., \u0026amp; Ca\u0026ntilde;adas-De la Fuente, G. A. (2022). Relation between burnout and sleep pblems in nurses: A systematic review with meta-analysis. \u003cem\u003eHealthcare (Basel, Switzerland)\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5), 954. https://doiorg.autorpa.ndmctsgh.edu.tw/10.3390/healthcare10050954\u003c/li\u003e\n\u003cli\u003eXinjie, D., Runlian, H., Ahmed, O., Cho, E., \u0026amp; Chung, S. (2023). Mediating Role of Viral Anxiety and Insomnia in Relationship Between Work-Related Stress and Depression Among Cold Chain Workers During COVID-19 Pandemic. \u003cem\u003eJournal of Korean medical science\u003c/em\u003e, 38(43), e338. https://doi-org.mhdla.flysheet.com.tw:8443/10.3346/jkms.2023.38.e338\u003c/li\u003e\n\u003cli\u003eVega-Esca\u0026ntilde;o, J., Porcel-G\u0026aacute;lvez, A. M., Diego-Cordero, R., Romero-S\u0026aacute;nchez, J. M., Romero-Salda\u0026ntilde;a, M., \u0026amp; Barrientos-Trigo, S. (2020). Insomnia Interventions in the Workplace: A Systematic Review and Meta-Analysis. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(17), 6401. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3390/ijerph17176401\u003c/li\u003e\n\u003cli\u003eLi, J. S. (2018). Health quality survey of high-tech workers in Taiwan. (Master\u0026apos;s thesis, Chung Yuan Christian University, Department of Environmental Engineering). https://hdl.handle.net/11296/pt385a\u003c/li\u003e\n\u003cli\u003eLee, L. J., Wehrlen, L., Ding, Y., \u0026amp; Ross, A. (2022). Professional quality of life, sleep disturbance and health among nurses: A mediation analysis. \u003cem\u003eNursing Open\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(6), 2771\u0026ndash;2780. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1002/nop2.978\u003c/li\u003e\n\u003cli\u003eLin, Y. L., Chen, C. H., Chu, W. M., Hu, S. Y., Liou, Y. S., Yang, Y. C., \u0026amp; Tsan, Y. T. (2019). Modifiable risk factors related to burnout levels in the medical workplace in Taiwan: cross-sectional study. \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(11), e032779. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2019-032779\u003c/li\u003e\n\u003cli\u003ePeterson, S. A., Wolkow, A. P., Lockley, S. W., O\u0026apos;Brien, C. S., Qadri, S., Sullivan, J. P., Czeisler, C. A., Rajaratnam, S. M. W., \u0026amp; Barger, L. K. (2019). Associations between shift work characteristics, shift work schedules, sleep and burnout in North American police officers: a cross-sectional study. \u003cem\u003eBMJ open\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(11), e030302. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2019-030302\u003c/li\u003e\n\u003cli\u003eWolkow, A. P., Barger, L. K., O\u0026apos;Brien, C. S., Sullivan, J. P., Qadri, S., Lockley, S. W., Czeisler, C. A., \u0026amp; Rajaratnam, S. M. W. (2019). Associations between sleep disturbances, mental health outcomes and burnout in firefighters, and the mediating role of sleep during overnight work: A cross-sectional study. \u003cem\u003eJournal of Sleep Research\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(6), e12869.https://doi-org.autorpa.ndmctsgh.edu.tw/10.1111/jsr.12869 \u003c/li\u003e\n\u003cli\u003eWalker, S. N., Sechrist, K. R., \u0026amp; Pender, N. J. (1987). The Health-Promoting Lifestyle Profile: development and psychometric characteristics. \u003cem\u003eNursing Research\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(2), 76\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eKuo, Y. W., Chang, S. H., Huang, C. H., \u0026amp; Chen, M. Y. (2022). A parsimonious and comprehensive screening tool for adult health behaviors. \u003cem\u003ePublic Health Mursing (Boston, Mass.)\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(5), 965\u0026ndash;972. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1111/phn.13085\u003c/li\u003e\n\u003cli\u003eChoi, D. S., \u0026amp; Kim, S. H. (2022). Factors Affecting Occupational Health of Shift Nurses: Focusing on Job Stress, Health Promotion Behavior, Resilience, and Sleep Disturbance. \u003cem\u003eSafety and health at work\u003c/em\u003e, 13(1), 3\u0026ndash;8. https://doi-org.mhdla.flysheet.com.tw:8443/10.1016/j.shaw.2021.09.001\u003c/li\u003e\n\u003cli\u003eGodos, J., Ferri, R., Lanza, G., Caraci, F., Vistorte, A. O. R., Yelamos Torres, V., Grosso, G., \u0026amp; Castellano, S. (2024). Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence. \u003cem\u003eNutrients\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(2). https://doi.org/10.3390/nu16020282 \u003c/li\u003e\n\u003cli\u003ePengpid, S., \u0026amp; Peltzer, K. (2019). Sedentary behavior and 12 Sleep problem indicators among middle-aged and elderly adults in South Africa. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(8), 1422. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3390/ijerph16081422\u003c/li\u003e\n\u003cli\u003eBl\u0026aacute;foss, R., Sundstrup, E., Jakobsen, M. D., Bay, H., Garde, A. H., \u0026amp; Andersen, L. L. (2019). Are insomnia type sleep problems associated with a less physically active lifestyle? A cross-sectional study among 7,700 adults from the general working population. \u003cem\u003eFrontiers in Ppublic Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 117. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3389/fpubh.2019.00117\u003c/li\u003e\n\u003cli\u003eBottolfs, M., St\u0026oslash;a, E. M., Reinboth, M. S., Svendsen, M. V., Schmidt, S. K., Oellingrath, I. M., \u0026amp; Bratland-Sanda, S. (2020). Resilience and lifestyle-related factors as predictors for health-related quality of life among early adolescents: a cross-sectional study. \u003cem\u003eThe Journal of International Medical Research\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(2), 300060520903656.https://doi-org.autorpa.ndmctsgh.edu.tw/10.1177/0300060520903656. \u003c/li\u003e\n\u003cli\u003eKinoshita, K., Ozato, N., Yamaguchi, T., Sudo, M., Yamashiro, Y., Mori, K., Katsuragi, Y., Yasukawa, T., Murashita, K., Nakaji, S., \u0026amp; Ihara, K. (2022). Association between Objectively Measured Sedentary Behavior and Sleep Quality in Japanese Adults: A Population-Based Cross-Sectional Study. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), 3145. https://doi-org.autorpa.ndmctsgh.edu.tw/10.3390/ijerph19053145\u003c/li\u003e\n\u003cli\u003eMaslach, C., \u0026amp; Leiter, M. P. (2016). Understanding the burnout experience: recent research and its implications for psychiatry. \u003cem\u003eWorld Psychiatry : Official journal of the World Psychiatric Association (WPA)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 103\u0026ndash;111. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1002/wps.20311\u003c/li\u003e\n\u003cli\u003evon Elm, E., Altman, D. G., Egger, M., Pocock, S. J., G\u0026oslash;tzsche, P. C., \u0026amp; Vandenbroucke, J. P. (2014). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. \u003cem\u003eInternational Journal of Surgery, 12\u003c/em\u003e(12), 1495-1499. https://doi.org/https://doi.org/10.1016/j.ijsu.2014.07.013\u003c/li\u003e\n\u003cli\u003eYeh, W. Y., Cheng, Y. W., Chen, M. R., \u0026amp; Chiu, W. S. (2008). Development and validation of an occupational burnout inventory. Taiwan Journal of Public Health, 27(5), 349-364. https://doi.org/10.6288/tjph2008-27-05-01\u003c/li\u003e\n\u003cli\u003eJiang, H. L., Chen, S. C., Bai, Q. H., Lu, X. H., Li, M. B., Lai, S. H., \u0026amp; Zhou, B. S. (2009). A validation study of the chinese version of the Athens Insomnia Scale. \u003cem\u003eTaiwanese Journal of Psychiatry\u003c/em\u003e, 23(1), 43-52. https://doi.org/10.29478/tjp.200903.0005\u003c/li\u003e\n\u003cli\u003eSoldatos, C. R., Dikeos, D. G., \u0026amp; Paparrigopoulos, T. J. (2003). The diagnostic validity of the Athens Insomnia Scale. \u003cem\u003eJournal of Psychosomatic Research\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(3), 263\u0026ndash;267. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1016/s0022-3999(02)00604-9 \u003c/li\u003e\n\u003cli\u003eWang, L., Zhang, S., Yu, M., \u0026amp; Yuan, J. (2022). Association between insomnia and subclinical atherosclerosis among Chinese steelworkers: a cross-sectional survey. \u003cem\u003eArchives of public health = Archives belges de sante publique\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(1), 80. https://doi-org.mhdla.flysheet.com.tw:8443/10.1186/s13690-022-00834-1\u003c/li\u003e\n\u003cli\u003eLima, M. G., Barros, M. B. A., Malta, D. C., Medina, L. P. B., \u0026amp; Szwarcwald, C. L. (2022). Association of self-reported sleep problems with morbidities and multimorbidities according to sex: National Health Survey 2019. Associa\u0026ccedil;\u0026atilde;o do autorrelato de problemas no sono com morbidades e multimorbidades segundo sexo: Pesquisa Nacional de Sa\u0026uacute;de 2019. \u003cem\u003eEpidemiologia e Servicos de Saude : Revista do Sistema Unico de Saude do Brasil\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(spe 1), e2021386. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1590/SS2237-9622202200007.especial\u003c/li\u003e\n\u003cli\u003eHsiao, C.-W. (2010). Investigation of work stress, shiftwork, fatigue and health status among manufacturing workers [Master\u0026rsquo;s thesis, Chang Jung Christian University]. https://hdl.handle.net/11296/cm686t\u003c/li\u003e\n\u003cli\u003eWeitzer, J., Papantoniou, K., L\u0026aacute;zaro-Sebasti\u0026agrave;, C., Seidel, S., Kl\u0026ouml;sch, G., \u0026amp; Schernhammer, E. (2021). The contribution of dispositional optimism to understanding insomnia symptomatology: Findings from a cross-sectional population study in Austria. \u003cem\u003eJournal of Sleep Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), e13132. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1111/jsr.13132 \u003c/li\u003e\n\u003cli\u003eCastro-Diehl, C., Wood, A. C., Redline, S., Reid, M., Johnson, D. A., Maras, J. E., Jacobs, D. R., Jr, Shea, S., Crawford, A., \u0026amp; St-Onge, M. P. (2018). Mediterranean diet pattern and sleep duration and insomnia symptoms in the Multi-Ethnic Study of Atherosclerosis. \u003cem\u003eSleep\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(11), zsy158. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1093/sleep/zsy158\u003c/li\u003e\n\u003cli\u003eXie, Y., Liu, S., Chen, X. J., Yu, H. H., Yang, Y., \u0026amp; Wang, W. (2021). Effects of Exercise on Sleep Quality and Insomnia in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.\u003cem\u003e Frontiers in psychiatry\u003c/em\u003e, 12, 664499. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fpsyt.2021.664499\u003c/li\u003e\n\u003cli\u003eBjornsdottir, E., Thorarinsdottir, E. H., Lindberg, E., Benediktsdottir, B., Franklin, K., Jarvis, D., Demoly, P., Perret, J. L., Garcia Aymerich, J., Dorado-Arenas, S., Heinrich, J., Tor\u0026eacute;n, K., Garcia Larsen, V., J\u0026ouml;gi, R., Gislason, T., \u0026amp; Janson, C. (2024). Association between physical activity over a 10-year period and current insomnia symptoms, sleep duration and daytime sleepiness: a European population-based study. \u003cem\u003eBMJ open\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), e067197. https://doi-org.mhdla.flysheet.com.tw:8443/10.1136/bmjopen-2022-067197\u003c/li\u003e\n\u003cli\u003eShah, N. M., Bennett, C., Hassan, H., \u0026amp; Kaltsakas, G. (2023). Sleep disorders and exercise: a mini-review. Journal of thoracic disease, 15(10), 5863\u0026ndash;5872. https://doi-org.mhdla.flysheet.com.tw:8443/10.21037/jtd-23-17\u003c/li\u003e\n\u003cli\u003eShen, B., Ma, C., Wu, G., Liu, H., Chen, L., \u0026amp; Yang, G. (2023). Effects of exercise on circadian rhythms in humans. \u003cem\u003eFrontiers in pharmacology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1282357. https://doi-org.mhdla.flysheet.com.tw:8443/10.3389/fphar.2023.1282357\u003c/li\u003e\n\u003cli\u003eKov\u0026aacute;cs, M., Muity, G., Szap\u0026aacute;ry, \u0026Aacute;., Nemesk\u0026eacute;ri, Z., V\u0026aacute;radi, I., Kapus, K., Tibold, A., Zalayn\u0026eacute;, N. M., Horvath, L., \u0026amp; Feh\u0026eacute;r, G. (2023). The prevalence and risk factors of burnout and its association with mental issues and quality of life among hungarian postal workers: a cross-sectional study. \u003cem\u003eBMC Public Health\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(1), 75. https://doi.org/10.1186/s12889-023-15002-5 \u003c/li\u003e\n\u003cli\u003eMedisauskaite, A., \u0026amp; Kamau, C. (2019). Does occupational distress raise the risk of alcohol use, binge-eating, ill health and sleep problems among medical doctors? A UK cross-sectional study. \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), e027362. https://doi-org.autorpa.ndmctsgh.edu.tw/10.1136/bmjopen-2018-027362 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cstrong\u003eDescriptive statistics of basic attributes (N = 365)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e1.\u0026nbsp;Gender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e2.\u0026nbsp;Age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e43.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e4.\u0026nbsp;Marital Status\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e5.\u0026nbsp;Smoking Habits\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003eQuit Smoking\u003c/p\u003e\n \u003cp\u003eCurrent Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003cp\u003e9.30\u003c/p\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e6.\u0026nbsp;Number of Cigarettes Smoked (per/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e7.\u0026nbsp;Drinking Habits\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003eQuit Drinking\u003c/p\u003e\n \u003cp\u003eOccasionally (1\u0026ndash;2 days/week)\u003c/p\u003e\n \u003cp\u003eFrequently (3\u0026ndash;5 days/week)\u003c/p\u003e\n \u003cp\u003eAlways (6\u0026ndash;7 days/week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72.3\u003c/p\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e8.\u0026nbsp;Shift Work\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e11. Managerial position \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86.0\u003c/p\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.244897959183675%\" valign=\"top\"\u003e\n \u003cp\u003e12.\u0026nbsp;Education level\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Primary, Secondary\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Senior High (including vocational)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Associate degree\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Bachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Master\u0026rsquo;s degree (including post-masters)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003cp\u003e4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote M\u0026thinsp;=\u0026thinsp;mean; SD\u0026thinsp;=\u0026thinsp;standard deviation\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Descriptive statistics of health promotion behaviors, occupational burnout, and sleep disturbances (N = 365)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003eHealth promotion behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e1. Stress management behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e2. Physical activity behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e3. Health responsibility behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e4. Life appreciation behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e5. Healthy eating behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e6. Oral hygiene behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003eOccupational burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e1. Personal burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e2. Work-related burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e3. Overcommitted to work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003eSleep condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e1. No sleep disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e2. Suspected sleep disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.91752577319588%\" valign=\"top\"\u003e\n \u003cp\u003e3. Sleep disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e47.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Analysis of Differences in basic attributes, health promotion behaviors, occupational burnout, and sleep disturbances (N = 365)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep disturbances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes \u003cem\u003en\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.30769230769231%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.307692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026chi;\u0026sup2;/t\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e1. Gender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e168 (48.7)\u003c/p\u003e\n \u003cp\u003e7 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e177 (51.3)\u003c/p\u003e\n \u003cp\u003e13 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e2. Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e43.3 \u0026plusmn; 11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e44.3 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e3. Number of Cigarettes Smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e4.4 \u0026plusmn; 7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.09 \u0026plusmn; 6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e11.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e4. Drinking Habits\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e129 (47.6)\u003c/p\u003e\n \u003cp\u003e46 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e142 (52.4)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e5. Shift Work\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48 (35.8)\u003c/p\u003e\n \u003cp\u003e127 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86 (64.2)\u003c/p\u003e\n \u003cp\u003e104 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e12.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e6. Weekly Working (hour) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e≦40\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 41~48\u003c/p\u003e\n \u003cp\u003e˃48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e106 (43.1)\u003c/p\u003e\n \u003cp\u003e61 (57.5)\u003c/p\u003e\n \u003cp\u003e8 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e140 (56.9)\u003c/p\u003e\n \u003cp\u003e45 (42.5)\u003c/p\u003e\n \u003cp\u003e5 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e7. Managerial position\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e152 (48.4)\u003c/p\u003e\n \u003cp\u003e23 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e162 (51.6)\u003c/p\u003e\n \u003cp\u003e28 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e8. Health promotion behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003eStress management behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.18 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.37 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003ePhysical activity behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e1.87 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.14 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003eHealth responsibility behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.09 \u0026plusmn; 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.53 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003eLife appreciation behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.22 \u0026plusmn; 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.69 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003eHealthy eating behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.26 \u0026plusmn; 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.61 \u0026plusmn; 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.23711340206186%\" valign=\"top\"\u003e\n \u003cp\u003eOral hygiene behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.43 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" valign=\"top\"\u003e\n \u003cp\u003e2.70 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003e9. Occupational burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003ePersonal burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.69 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003eWork-related burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.53 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.83 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\" valign=\"top\"\u003e\n \u003cp\u003eOvercommitted to work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e2.20 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Logistic regression analysis of basic attributes, health promotion behaviors, occupational burnout, and sleep disturbances\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eN = 365\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.161616161616163%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Odds ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003eModel 3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003eModel 4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic attributes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGender (Male/Female\u003csup\u003e#\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eNumber of Cigarettes Smoked\u003c/p\u003e\n \u003cp\u003eDrinking Habits\u003c/p\u003e\n \u003cp\u003e(No/Yes\u003csup\u003e#\u003c/sup\u003e）\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWork patterns\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eShift Work（Yes/No\u003csup\u003e#\u003c/sup\u003e）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Weekly Working\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(hours)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 41~48/≦40\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; ˃48/≦40\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.93 (0.34~2.56)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98 (0.98~1.02)\u003c/p\u003e\n \u003cp\u003e1.03 (1.00~1.07)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.96 (0.58~1.60)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.78 (1.69~4.54)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.11 (1.21~14.01)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e2.41 (1.45~4.01)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.12 (0.39~3.19)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98 (0.96~1.00)\u003c/p\u003e\n \u003cp\u003e1.00 (0.96~1.03)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.85 (0.50~1.44)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.34 (0.09~1.29)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00 (0.98~1.03)\u003c/p\u003e\n \u003cp\u003e1.00 (0.96~2.01)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.81 (0.42~1.58)\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.51 (0.15~1.75)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00 (0.98~1.03)\u003c/p\u003e\n \u003cp\u003e1.01 (0.97~1.05)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.81 (0.43~1.54)\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.03 (1.64~5.56)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.30 (0.24~6.97)\u003c/p\u003e\n \u003cp\u003e1.98 (1.03~3.81)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth promotion behaviors \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStress management\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePhysical activity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHealth responsibility\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLife appreciation\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHealthy eating\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOral hygiene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00 (0.91~1.11)\u003c/p\u003e\n \u003cp\u003e1.01 (0.92~1.10)\u003c/p\u003e\n \u003cp\u003e0.87 (0.77~0.99)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.76 (0.66~0.89)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.86 (0.76~0.97)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e1.04 (0.93~1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.91 (0.80~1.04)\u003c/p\u003e\n \u003cp\u003e0.99 (0.89~1.11)\u003c/p\u003e\n \u003cp\u003e0.85 (0.73~1.00)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.87 (0.72~1.04)\u003c/p\u003e\n \u003cp\u003e0.88 (0.76~1.02)\u003c/p\u003e\n \u003cp\u003e1.00 (0.86~1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational burnout\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePersonal burnout\u003c/p\u003e\n \u003cp\u003eWork-related burnout\u003c/p\u003e\n \u003cp\u003eOvercommitted to work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.05 (1.03~1.08)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e1.03 (1.01~1.05)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.98 (0.98~1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.06 (1.03~1.09)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e1.02 (0.99~1.04)\u003c/p\u003e\n \u003cp\u003e1.00 (0.99~1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote 1.\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 \u0026nbsp;\u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01 \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001，2.\u003csup\u003e#\u003c/sup\u003e reference group 3. CI= Confidence Interval\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-medicine-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmet","sideBox":"Learn more about [Journal of Occupational Medicine and Toxicology](http://occup-med.biomedcentral.com/)","snPcode":"12995","submissionUrl":"https://submission.nature.com/new-submission/12995/3","title":"Journal of Occupational Medicine and Toxicology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Traditional industries, health promotion behavior, occupational burnout, sleep disturbances","lastPublishedDoi":"10.21203/rs.3.rs-4761931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4761931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSleep disturbances can lead to poor job performance, increased work stress and absenteeism, and reduced job satisfaction and confidence among employees. Traditional industry employees experience significantly higher shift work and occupational burnout levels than other sectors. Therefore, this warrants exploring whether shift work, occupational burnout, and health promotion behavior are related to sleep disturbances among traditional industry employees.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study with cluster sampling and structured questionnaires was conducted to collect data on the demographics, health promotion behavior, occupational burnout, and sleep disturbances of 365 employees from a traditional industry in Northern Taiwan. The study period was from 15 May 2023 to 17 June 2023. Data analysis was performed using Chi-square tests, independent sample t-tests, and logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of sleep disturbances among the study subjects was 47.9%, with shift workers accounting for 63.3%. The mean score for the overall health promotion behavior scale was 2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48, and the mean score for the overall occupational burnout scale was 1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09. Factors associated with sleep disturbances included shift work, weekly working hours, health promotion behavior, and personal burnout.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe recommend adjusting the regularity of shift schedules and enhancing the lighting environment during night shifts to accommodate shift work, avoiding weekly working hours exceeding 40 hours, improving personal burnout levels, and encouraging the adoption of health-responsible behaviors to reduce sleep disturbances.\u003c/p\u003e","manuscriptTitle":"Exploring Health Promotion Behaviors, Occupational Burnout, and Sleep Disturbances in Traditional Industry Workers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-14 03:58:31","doi":"10.21203/rs.3.rs-4761931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-09-06T17:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181736951718657680388165440277295186801","date":"2024-08-27T07:51:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-18T21:41:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129855379574880304063837717415231893211","date":"2024-07-25T13:34:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-22T08:42:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-22T07:53:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-19T11:13:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Occupational Medicine and Toxicology","date":"2024-07-18T10:38:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-occupational-medicine-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmet","sideBox":"Learn more about [Journal of Occupational Medicine and Toxicology](http://occup-med.biomedcentral.com/)","snPcode":"12995","submissionUrl":"https://submission.nature.com/new-submission/12995/3","title":"Journal of Occupational Medicine and Toxicology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62f4592d-b1dc-4539-8b8c-4d8caa94c42c","owner":[],"postedDate":"August 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-14T03:58:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-14 03:58:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4761931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4761931","identity":"rs-4761931","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}