Assessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM)

Assessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM) | 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 Assessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM) David Kusmawan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4165778/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Occupational fatigue among oil and gas workers can have perilous consequences related to safety, health, economy, and wellbeing aspect. This makes it necessary to discover major factors related to fatigue and implement appropriate prevention programs and education. Therefore, this study aimed to investigate the relationship between mental workload, sleep quality, and occupational fatigue in oil and gas office workers in Jambi Province. Design and methods: Mental workload, sleep quality, and occupational fatigue were measured using the NASA-Total Load Index (TLX), the Pittsburgh Sleep Quality Index (PSQI), and the Indonesian Questionnaire Measuring Feelings of Work Fatigue (KAUPK2), respectively. A Partial Least Square-Structural Equation Modeling (PLS-SEM) approach was used to determine the association between mental workload, sleep quality, and occupational fatigue. Results: Out of the 117 oil and gas workers in Jambi Province who participated in this study, 58.6% were male, 54.3% had Senior High School or less, 85.3% were not smoking, and 88.8% were married. The mean with a standard deviation of body height, weight, and mass index were 165.35 ± 5.46 cm, 64.65 ± 6.89 kg, and 23.64 ± 2.23, respectively. Respondents had working experience from 0.17 to 34 years with a mean of 16.23 and a standard deviation of 8.93 years. The PLS-SEM model illustrated that the direct effect of mental workload on occupational fatigue was not significant (path coefficient: 0.179; p <0.036). Meanwhile, the mental workload had a significant effect on sleep quality (path coefficient: -0.405; p 0.000), which significantly affected fatigue (path coefficient: -0.035; p = 0.709). This indicated that the effect of workload on fatigue was fully mediated by sleep quality. Conclusions: The effect of sleep quality was very significant to overcome the fatigue level of an employee when the mental workload increases. This study revealed that occupational fatigue may be reduced by implementing mental workload coping strategies, regularly measuring, and a sleep hygiene program among oil and gas workers. Occupational Medicine Epidemiology Occupational fatigue mental workload PLS-SEM oil and gas workers Figures Figure 1 Introduction Occupational fatigue is a common impingement in the working population, with acute fatigue being a primary concern in most occupational environments. The definition of fatigue and its measurements vary because of the background science variety. Occupational fatigue is a change in the psychophysiological control mechanism that regulates task behavior, resulting from preceding mental or physical efforts. Generally, fatigue is defined as a physiological state where a person is inadequate to perform a task or has decreased ability at a desired level of performance due to reduced mental or/and physical strength, sleep loss, circadian phase, and workload 1, 2, 3 . Several factors that contribute to occupational fatigue include insufficient free time and rest, high work demands, low social support, non-supervisory, female gender, lower age, lack of exercise, inability to stop thinking about work during leisure time, snoring, poor sleep quality, sleep deprivation, emotional predisposition, muscular and mental exertion, workload characteristics, overtime and long work hours, social environment at workplace, distress, incomplete recovery, and stressful working condition in COVID-19 pandemic 2,3,4 . Sleep deprivation and workload is the foremost and prevalent cause of fatigue in the workplace. Previous studies discovered that occupational fatigue, mental workload, and sleep hygiene had perilous consequences for workers 5, 6, 7, 8 . Furthermore, fatigue can have a potentially adverse effect on metabolic and cardiovascular health, risk of cancers, and mental health 9 , mood changes, job stress, cognitive degradation 10, 11 , sleep disturbance 12, 13, 14 , the incidence of MSDs 15, 16, 17 , and absenteeism from work 18 , as well as a safety concern, behavior, performance, and climate 19,20,21,22 . It can also affect organizational productivity, ability to perform, decreased human performance level 23 , risk of injury rates, probability rate of the incident, quality of life, wellbeing, accident 24, 25, 12, 6 , and at-risk behavior causing death among oil and gas workers. Mental workload is another paramount predictor that affects fatigue. Generally, the workload has long been considered an important and influential factor in the performance of workers in a complex system. The workload can be physical or mental and both are always interrelated, making them inseparable when a person performs a specific task 26 . Working more than 40 h per week and long work hours have been recognized as potential causes of fatigue 3 . This is because long time working hours can also decrease required sleep time. Käthner et al., (2014) showed the impact of mental workload on fatigue during prolonged usage of a P300 brain–computer interface 27 , indicating workload negatively affected sleep. Multidimensional measures such as NASA-Total Load Index (NASA-TLX) can provide a better measure of workload. Based on the background provided above, the effects of workload on fatigue are classified into two ways, namely direct positive (hypothesis 1) and indirect effects that are mediated by sleep quality (hypothesis 2). Material and methods Study Design and Respondents This cross-sectional study was conducted between August and November 2022 at two national oil and gas companies located in Muara Jambi and Jambi City, Jambi Province, Indonesia. A total of 116 respondents were selected by convenience sampling, consisting of those oil and gas workers who agreed to participate. This study was approved by the Research Ethics Commission Faculty of Public Health UNAND University (Ethic Number: 19/UN16.12/KEP-FKM/2022). The informed consent form was signed by respondents before data collection and their confidentiality and anonymity were guaranteed. Occupational Fatigue The KAUPK2 questionnaire, consisting of 17 questions related to fatigue complaints, was prepared to evaluate the feelings of occupational fatigue in chronically fatigued Indonesian workers. Sleep Quality The Pittsburgh Sleep Quality Index (PSQI) questionnaire contained 9 items, with 5 items consisting of 10 sub-items and 7 subscales including subjective sleep quality, sleep duration, sleep latency, sleep disturbance, habitual sleep efficiency, daytime dysfunction, and use of sleeping medication. Each dimension was weighted equally on a scale from 0 to 3 and the scores for all 7 dimensions were summed to produce a total score between 0 and 21, where a higher score indicated worse sleep quality. Mental Workload The NASA-TLX questionnaire was developed by NASA Research Center 28 to measure the total mental workload of oil and gas workers. The NASA-TLX consisted of 6 items scale, which was used to evaluate different aspects of workload, Mental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Effort (EF), Performance (PE), and Frustration Level (FR). For measuring workload two steps were completed. In the first step, respondents made a total of 15 pairwise comparisons among all 6 dimensions of the instruments to determine which dimensions of the NASA-TLX were more prominent in performing their daily task. The weighted average of scores associated with various dimensions would be considered as the total score on workload. This instrument was valid and widely used for measuring mental workload 29, 30, 31 . Data Analysis Descriptive statistics were used to summarize demographic data. The study hypotheses were investigated using Partial Least Square-Structural Equation Modelling (PLS-SEM) approach. Assessing the measurement reflective model was named outer model analysis which consisted of several steps. These included examining the indicator loadings and assessing composite reliability, as well as the convergent and discriminant validity. For indicator loadings, items with values lower than 0.6 were excluded. Cronbach’s alpha values higher than 0,6 were considered as indicative of acceptable internal consistency reliability. AVE values ≥ 0.05 were also concerned as indicative of acceptable convergent validity and the discriminant validity testing was obtained from the HTMT values < 0.85. The inner model analysis was assessed using several indicators namely the coefficient of determination (R2) and Predictive Relevance (Q2). R2 values of 0.75, 0.50, and 0.25 were considered substantial, moderate, and weak, respectively, while values larger than zero were meaningful. To assess the direct effect of mental workload and sleep quality on occupational fatigue, the value of probability from the bootstrap t-test was examined 32 . Results Characteristic of Respondents Demographic information of the 116 respondents was presented in Table 1 . The results showed that respondents were aged from 18 to 56 years, with a mean and standard deviation of 42.6 and 9.32 years, respectively. Furthermore, 58.6% of respondents were male, 54.3% had Senior High School or less, 85.3% were not smoking, and 88.8% were married. The mean with the standard deviation of body height, weight, and mass index were 165.35 ± 5.46 cm, 64.65 ± 6.89 kg, and 23.64 ± 2.23, respectively. Respondents had working experience from 0.17 to 34 years with a mean of 16.23 and a standard deviation of 8.93 years. Table 1 Demographic characteristics (n = 116). Variables Total (n) 1. Gender (n (%)) Male 68 (58.6) Female 48 (41.4) 2. Age (year) Mean (± SD) 42,6 (9.32) Range 18–56 3. Education (n (%)) Vocational degree or higher 53 (45.7) Senior High School or less 63 (54.3) 4. Smoking (n (%)) Yes 17 (14.7) No 99 (85.3) 5. Marital status (n (%)) Married 103 (88.8) Single 13 (11.2) 6. Body height (cm) Mean (± SD) 165.35 Range 149–176 7. Body weight (kg) Mean (± SD) 64.65 Range 45–92 8. Body Mass Index Mean (± SD) 23.64 Range 18.83–31.10 9. Working experience (year) Mean (± SD) 16.23 (8.93) Range 0.17–34 Table 2 presented several steps in examining the reflective measurement model (outer model analysis) and the results showed an acceptable level of indicator loadings, reliability, and validity. To examine the indicator loadings, the results were presented in Table 2 , with all items having a significant factor loading with a coefficient higher than 0.6 on the associated construct, which was all acceptable. This indicated that the construct explained more than 50% of the indicator’s variance, thereby providing acceptable item reliability. For the internal consistency reliability for the mental workload, sleep quality, and occupational fatigue, the values of composite reliability were higher than 0.6. All constructs had an acceptable level of reliability, which were 0.801, 0.782, and 0.923, while Cronbach’s Alpha was 0.504, 0.584, and 0.910, respectively. The AVE values of constructs in Table 3 were 0.657, 0.546, and 0.502, respectively, suggesting that all constructs had an acceptable level of convergent validity. The discriminant validity testing was from a cross-loading column, respectively. Table 2 demonstrated that the loading factor values of each construct were higher than others. This showed that all constructs had an acceptable level of discriminant validity. Figure 1 presented the significance of each path in the hypothetical model. Based on the results, the mental workload had a direct and positive effect on sleep quality (path coefficient: 0.179; p < 0.036), indicating that as the mental workload increased, sleep quality also improved. Sleep quality had a direct and negative effect on occupational fatigue (path coefficient: -0.405; p 0.000), suggesting that occupational fatigue decreased with increasing sleep quality. However, the direct effect of workload on fatigue was negative but not significant (path coefficient: -0.035; p = 0.709). This indicated that the effect of workload on fatigue was mediated by sleep quality because all segments of the indirect path were significant (p < 0.05). Discussion This study was conducted to confirm and explain hypothesized association between mental workload, sleep quality, and occupational fatigue using the PLS-SEM approach. The two hypotheses included ( 1 ) workload had a direct effect on fatigue and ( 2 ) the effect of mental workload on fatigue was mediated by sleep quality. The first hypothesis was rejected, indicating that mental workload had no direct effect on occupational fatigue. However, the second hypothesis was accepted, indicating that the effect of mental workload on occupational fatigue was mediated by sleep quality. These results were in line with previous investigations 33, 34 . The mental workload was measured using the NASA-TLX which had been recognized as a valid and reliable instrument for assessing mental workload. The mental workload had no significant direct effect on occupational fatigue at 95% CI. The total effect was still not significant, indicating that workers had a fairly high-level workload, their tendency was to argue they had a fairly good quality of sleep and did not feel too tired. However, a previous report 35 stated that mental workload was a risk factor for occupational fatigue. This discrepancy might be due to the nature of the work carried out by hospital service personnel as compared to oil and gas workers. Many studies showed that medical workers had high physical, psychological stress, and mental workload. This was because working in a clinical setting as a stressful environment imposed a high level of physical and mental demand 36, 37 . The results showed that workload had a significant indirect effect on fatigue with sleep quality interventions. When someone experienced a fairly high level of mental workload, their tendency was to think that they had a fairly good quality of sleep and did not feel too tired. In this case, the effect of sleep quality was very significant. Healthy sleep involved adequate duration, timing, efficiency, and a level of satisfaction, leaving people feeling alert and functional in the day 38 . In this study, sleep quality had a significant direct effect on fatigue at 95% CI. It was postulated that when workers had good sleep quality, they were less likely to feel tired. Therefore, good sleep hygiene was found to be important for the productivity of workers 39 . Sleep is vital for survival and optimal day-to-day cognitive functioning, and insufficient sleep is due to sleep deprivation, sleep restriction, and/or sleep disorder. Insufficient and sleep disorders are highly prevalent in the population and oil and gas workplace and associated with significant morbidity, mortality, physiological problems, sleep disorder, cardiometabolic stress, and cognitive impairment which have been identified as underlying factors in increased risk for accidents, obesity, type 2 diabetes, and coronary health disease 40, 41, 42 . Furthermore, sleep quality problems are often associated with shift duty and the night shift 8 . This study found that workload had a significant direct effect on sleep quality at 95% CI. When the workload was high, the quality of their sleep tended to be good. However, a previous study 43 discovered that high mental workload correlated with all parameters of low sleep quality in a sample of students. This may be influenced by individual characteristics and types of work. Furthermore, the effect of mental workload on occupational fatigue was mediated by sleep quality. The urgency, management, and strategies for optimizing sleep opportunities among oil and gas workers had also been investigated and discussed 5, 41 . Limitation This study had several limitations due to its cross-sectional nature from which causation cannot be inferred. The data were collected from a limited number of workers in the oil and gas company in Jambi Province, which can influence the generalizability of the results. Furthermore, other factors may affect the association found in this study, including work stress, shift work and family role conflict, personal traits, and heat stress exposure due to the hot ambient temperature of Jambi City. Conclusion This study showed that sleep quality was a complete mediator of the effect of mental workload on occupational fatigue. Therefore, implementing mental workload coping strategies, sleep hygiene programs, and education to improve knowledge among oil and gas workers can reduce fatigue. Geolocation Information This study was conducted in Jambi Province, Indonesia. Abbreviations SQ Sleep Quality KAUPK2 Indonesian Questionnaire Measuring Feelings of Work Fatigue MD Mental Demand PD Physical Demand TD Temporal Demand OP Performance EF Effort FR Frustration Level NASA-TLX National Aeronautics and Space Administration – Task Load Index PSQI Pittsburgh Sleep Quality Index. Declarations Acknowledgments The authors are grateful to the Directorate of Research and Community Engagement Universitas Jambi, Indonesia, for the funding provided (grant number 247/UN21.11/PT.01.05/SPK/2022), and also all workers who have participated in this study. Conflict of Interest The authors declared that there is no potential conflict of interest. References van Dijk FJ, Swaen GM. Fatigue at work. Occupational and Environmental Medicine. 2003 Jun 1;60(suppl 1): i1-2. Åkerstedt T, Knutsson A, Westerholm P, Theorell T, Alfredsson L, Kecklund G. Mental fatigue, work and sleep. 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Sleep quality in shift workers of offshore petroleum industries. Journal of Sleep Sciences. 2018;3(1-2):36-40 Kecklund G, Axelsson J. Health consequences of shift work and insufficient sleep. Bmj. 2016 Nov 1;355]. Jansen EC, Peterson KE, O’Brien L, Hershner S, Boolani A. Associations between mental workload and sleep quality in a sample of young adults recruited from a US college town. Behavioral sleep medicine. 2020 Jul 3;18(4):513-22. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4165778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283806532,"identity":"7a049d68-c4c5-469f-9d07-f538c8c5afe0","order_by":0,"name":"David Kusmawan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYDCCA0DEw5AA49pAhICAB0oT1JJGnBYGJC2H0aSwAL7jZwwPvGFIS5w/7fAziZ87ziduZzz8gOFHDYMMHw4tkmdyDA7OYchJ3HA7zUyy98ztxJ0NxwwYe44x8Eji0GJwIC3hMA9DReIG6QSzG7xttxM3HDhgwMDbwMBjgEvL+WcQLfNnp3+7+bftHFDL8Q+Mf/FpuZF8AKglJ7Hhdo7Zbd62A0AtZwyY8dkieePxgYNzDNKMN9zOKf8t25ZsDNRScFjmmAROv/CdT2z+8KYiWRbosM2Gb9vsZDfcOL7x4ZsaG3tcIQZ1HjJH4gAoRiTwqUcH/A2kqB4Fo2AUjIIRAAAFS23bIyYX8gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3649-4965","institution":"UNJA","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Kusmawan","suffix":""}],"badges":[],"createdAt":"2024-03-25 22:21:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4165778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4165778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53553723,"identity":"eb3c8bd6-8afb-47bf-bde8-3fcc704b6e63","added_by":"auto","created_at":"2024-03-27 11:57:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84011,"visible":true,"origin":"","legend":"\u003cp\u003eFinal Model of The Occupational Fatigue Among Oil and Gas Workers\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4165778/v1/809e13ca80ce56b7bdd91b5f.png"},{"id":53554423,"identity":"ed67f5fa-08e0-40b5-a2d0-c8b8be42e74a","added_by":"auto","created_at":"2024-03-27 12:05:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":376194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4165778/v1/3448383b-0e9e-4341-a18e-d7621981f09f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOccupational fatigue is a common impingement in the working population, with acute fatigue being a primary concern in most occupational environments. The definition of fatigue and its measurements vary because of the background science variety. Occupational fatigue is a change in the psychophysiological control mechanism that regulates task behavior, resulting from preceding mental or physical efforts. Generally, fatigue is defined as a physiological state where a person is inadequate to perform a task or has decreased ability at a desired level of performance due to reduced mental or/and physical strength, sleep loss, circadian phase, and workload \u003csup\u003e1, 2, 3\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeveral factors that contribute to occupational fatigue include insufficient free time and rest, high work demands, low social support, non-supervisory, female gender, lower age, lack of exercise, inability to stop thinking about work during leisure time, snoring, poor sleep quality, sleep deprivation, emotional predisposition,\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emuscular and mental exertion, workload characteristics, overtime and long work hours, social environment at workplace, distress, incomplete recovery, and stressful working condition in COVID-19 pandemic \u003csup\u003e2,3,4\u003c/sup\u003e. Sleep deprivation and workload is the foremost and prevalent cause of fatigue in the workplace.\u003c/p\u003e \u003cp\u003ePrevious studies discovered that occupational fatigue, mental workload, and sleep hygiene had perilous consequences for workers \u003csup\u003e5, 6, 7, 8\u003c/sup\u003e. Furthermore, fatigue can have a potentially adverse effect on metabolic and cardiovascular health, risk of cancers, and mental health \u003csup\u003e9\u003c/sup\u003e, mood changes, job stress, cognitive degradation \u003csup\u003e10, 11\u003c/sup\u003e, sleep disturbance \u003csup\u003e12, 13, 14\u003c/sup\u003e, the incidence of MSDs \u003csup\u003e15, 16, 17\u003c/sup\u003e, and absenteeism from work \u003csup\u003e18\u003c/sup\u003e, as well as a safety concern, behavior, performance, and climate \u003csup\u003e19,20,21,22\u003c/sup\u003e. It can also affect organizational productivity, ability to perform, decreased human performance level \u003csup\u003e23\u003c/sup\u003e, risk of injury rates, probability rate of the incident, quality of life, wellbeing, accident \u003csup\u003e24, 25, 12, 6\u003c/sup\u003e, and at-risk behavior causing death among oil and gas workers. Mental workload is another paramount predictor that affects fatigue. Generally, the workload has long been considered an important and influential factor in the performance of workers in a complex system. The workload can be physical or mental and both are always interrelated, making them inseparable when a person performs a specific task \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWorking more than 40 h per week and long work hours have been recognized as potential causes of fatigue\u003csup\u003e3\u003c/sup\u003e. This is because long time working hours can also decrease required sleep time. K\u0026auml;thner et al., (2014) showed the impact of mental workload on fatigue during prolonged usage of a P300 brain\u0026ndash;computer interface \u003csup\u003e27\u003c/sup\u003e, indicating workload negatively affected sleep. Multidimensional measures such as NASA-Total Load Index (NASA-TLX) can provide a better measure of workload. Based on the background provided above, the effects of workload on fatigue are classified into two ways, namely direct positive (hypothesis 1) and indirect effects that are mediated by sleep quality (hypothesis 2).\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Respondents\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted between August and November 2022 at two national oil and gas companies located in Muara Jambi and Jambi City, Jambi Province, Indonesia. A total of 116 respondents were selected by convenience sampling, consisting of those oil and gas workers who agreed to participate. This study was approved by the Research Ethics Commission Faculty of Public Health UNAND University (Ethic Number: 19/UN16.12/KEP-FKM/2022). The informed consent form was signed by respondents before data collection and their confidentiality and anonymity were guaranteed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOccupational Fatigue\u003c/h2\u003e \u003cp\u003eThe KAUPK2 questionnaire, consisting of 17 questions related to fatigue complaints, was prepared to\u003c/p\u003e \u003cp\u003eevaluate the feelings of occupational fatigue in chronically fatigued Indonesian workers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSleep Quality\u003c/h2\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) questionnaire contained 9 items, with 5 items consisting of 10 sub-items and 7 subscales including subjective sleep quality, sleep duration, sleep latency, sleep disturbance, habitual sleep efficiency, daytime dysfunction, and use of sleeping medication. Each dimension was weighted equally on a scale from 0 to 3 and the scores for all 7 dimensions were summed to produce a total score between 0 and 21, where a higher score indicated worse sleep quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMental Workload\u003c/h2\u003e \u003cp\u003eThe NASA-TLX questionnaire was developed by NASA Research Center \u003csup\u003e28\u003c/sup\u003e to measure the total mental workload of oil and gas workers. The NASA-TLX consisted of 6 items scale, which was used to evaluate different aspects of workload, Mental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Effort (EF), Performance (PE), and Frustration Level (FR). For measuring workload two steps were completed. In the first step, respondents made a total of 15 pairwise comparisons among all 6 dimensions of the instruments to determine which dimensions of the NASA-TLX were more prominent in performing their daily task. The weighted average of scores associated with various dimensions would be considered as the total score on workload. This instrument was valid and widely used for measuring mental workload \u003csup\u003e29, 30, 31\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize demographic data. The study hypotheses were investigated using Partial Least Square-Structural Equation Modelling (PLS-SEM) approach. Assessing the measurement reflective model was named outer model analysis which consisted of several steps. These included examining the indicator loadings and assessing composite reliability, as well as the convergent and discriminant validity. For indicator loadings, items with values lower than 0.6 were excluded. Cronbach\u0026rsquo;s alpha values higher than 0,6 were considered as indicative of acceptable internal consistency reliability. AVE values\u0026thinsp;\u0026ge;\u0026thinsp;0.05 were also concerned as indicative of acceptable convergent validity and the discriminant validity testing was obtained from the HTMT values\u0026thinsp;\u0026lt;\u0026thinsp;0.85. The inner model analysis was assessed using several indicators namely the coefficient of determination (R2) and Predictive Relevance (Q2). R2 values of 0.75, 0.50, and 0.25 were considered substantial, moderate, and weak, respectively, while values larger than zero were meaningful. To assess the direct effect of mental workload and sleep quality on occupational fatigue, the value of probability from the bootstrap t-test was examined \u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristic of Respondents\u003c/h2\u003e\n \u003cp\u003eDemographic information of the 116 respondents was presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The results showed that respondents were aged from 18 to 56 years, with a mean and standard deviation of 42.6 and 9.32 years, respectively. Furthermore, 58.6% of respondents were male, 54.3% had Senior High School or less, 85.3% were not smoking, and 88.8% were married. The mean with the standard deviation of body height, weight, and mass index were 165.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46 cm, 64.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.89 kg, and 23.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23, respectively. Respondents had working experience from 0.17 to 34 years with a mean of 16.23 and a standard deviation of 8.93 years.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic characteristics (n\u0026thinsp;=\u0026thinsp;116).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Gender (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Age (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42,6 (9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Education (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVocational degree or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior High School or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Smoking (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (85.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Marital status (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Body height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u0026ndash;176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. Body weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8. Body Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.83\u0026ndash;31.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9. Working experience (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.23 (8.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presented several steps in examining the reflective measurement model (outer model analysis) and the results showed an acceptable level of indicator loadings, reliability, and validity. To examine the indicator loadings, the results were presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, with all items having a significant factor loading with a coefficient higher than 0.6 on the associated construct, which was all acceptable. This indicated that the construct explained more than 50% of the indicator\u0026rsquo;s variance, thereby providing acceptable item reliability. For the internal consistency reliability for the\u003c/p\u003e\n \u003cp\u003emental workload, sleep quality, and occupational fatigue, the values of composite reliability were higher than 0.6. All constructs had an acceptable level of reliability, which were 0.801, 0.782, and 0.923, while Cronbach\u0026rsquo;s Alpha was 0.504, 0.584, and 0.910, respectively. The AVE values of constructs in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e were 0.657, 0.546, and 0.502, respectively, suggesting that all constructs had an acceptable level of convergent validity. The discriminant validity testing was from a cross-loading column, respectively. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated that the loading factor values of each construct were higher than others. This showed that all constructs had an acceptable level of discriminant validity.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presented the significance of each path in the hypothetical model. Based on the results, the mental workload had a direct and positive effect on sleep quality (path coefficient: 0.179; p\u0026thinsp;\u0026lt;\u0026thinsp;0.036), indicating that as the mental workload increased, sleep quality also improved. Sleep quality had a direct and negative effect on occupational fatigue (path coefficient: -0.405; p 0.000), suggesting that occupational fatigue decreased with increasing sleep quality. However, the direct effect of workload on fatigue was negative but not significant (path coefficient: -0.035; p\u0026thinsp;=\u0026thinsp;0.709). This indicated that the effect of workload on fatigue was mediated by sleep quality because all segments of the indirect path were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was conducted to confirm and explain hypothesized association between mental workload, sleep quality, and occupational fatigue using the PLS-SEM approach. The two hypotheses included (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) workload had a direct effect on fatigue and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the effect of mental workload on fatigue was mediated by sleep quality. The first hypothesis was rejected, indicating that mental workload had no direct effect on occupational fatigue. However, the second hypothesis was accepted, indicating that the effect of mental workload on occupational fatigue was mediated by sleep quality. These results were in line with previous investigations \u003csup\u003e33, 34\u003c/sup\u003e. The mental workload was measured using the NASA-TLX which had been recognized as a valid and reliable instrument for assessing mental workload.\u003c/p\u003e \u003cp\u003eThe mental workload had no significant direct effect on occupational fatigue at 95% CI. The total effect was still not significant, indicating that workers had a fairly high-level workload, their tendency was to argue they had a fairly good quality of sleep and did not feel too tired. However, a previous report \u003csup\u003e35\u003c/sup\u003e stated that mental workload was a risk factor for occupational fatigue. This discrepancy might be due to the nature of the work carried out by hospital service personnel as compared to oil and gas workers. Many studies showed that medical workers had high physical, psychological stress, and mental workload. This was because working in a clinical setting as a stressful environment imposed a high level of physical and mental demand \u003csup\u003e36, 37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results showed that workload had a significant indirect effect on fatigue with sleep quality interventions. When someone experienced a fairly high level of mental workload, their tendency was to think that they had a fairly good quality of sleep and did not feel too tired. In this case, the effect of sleep quality was very significant. Healthy sleep involved adequate duration, timing, efficiency, and a level of satisfaction, leaving people feeling alert and functional in the day \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, sleep quality had a significant direct effect on fatigue at 95% CI. It was postulated that when workers had good sleep quality, they were less likely to feel tired. Therefore, good sleep hygiene was found to be important for the productivity of workers \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSleep is vital for survival and optimal day-to-day cognitive functioning, and insufficient sleep is due to sleep deprivation, sleep restriction, and/or sleep disorder. Insufficient and sleep disorders are highly prevalent in the population and oil and gas workplace and associated with significant morbidity, mortality, physiological problems, sleep disorder, cardiometabolic stress, and cognitive impairment which have been identified as underlying factors in increased risk for accidents, obesity, type 2 diabetes, and coronary health disease \u003csup\u003e40, 41, 42\u003c/sup\u003e. Furthermore, sleep quality problems are often associated with shift duty and the night shift \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study found that workload had a significant direct effect on sleep quality at 95% CI. When the workload was high, the quality of their sleep tended to be good. However, a previous study \u003csup\u003e43\u003c/sup\u003e discovered that high mental workload correlated with all parameters of low sleep quality in a sample of students. This may be influenced by individual characteristics and types of work. Furthermore, the effect of mental workload on occupational fatigue was mediated by sleep quality. The urgency, management, and strategies for optimizing sleep opportunities among oil and gas workers had also been investigated and discussed \u003csup\u003e5, 41\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThis study had several limitations due to its cross-sectional nature from which causation cannot be inferred. The data were collected from a limited number of workers in the oil and gas company in Jambi Province, which can influence the generalizability of the results. Furthermore, other factors may affect the association found in this study, including work stress, shift work and family role conflict, personal traits, and heat stress exposure due to the hot ambient temperature of Jambi City.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study showed that sleep quality was a complete mediator of the effect of mental workload on occupational fatigue. Therefore, implementing mental workload coping strategies, sleep hygiene programs, and education to improve knowledge among oil and gas workers can reduce fatigue.\u003c/p\u003e\n\u003ch3\u003eGeolocation Information\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in Jambi Province, Indonesia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSleep Quality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKAUPK2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndonesian Questionnaire Measuring Feelings of Work Fatigue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMental Demand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical Demand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTemporal Demand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerformance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFrustration Level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASA-TLX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNational Aeronautics and Space Administration \u0026ndash; Task Load Index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eThe authors are grateful to the Directorate of Research and Community Engagement Universitas Jambi, Indonesia, for the funding provided (grant number 247/UN21.11/PT.01.05/SPK/2022), and also all workers who have participated in this study.\u003c/p\u003e\n\u003ch3\u003eConflict of Interest\u003c/h3\u003e\n\u003cp\u003eThe authors declared that there is no potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003evan Dijk FJ, Swaen GM. Fatigue at work. Occupational and Environmental Medicine. 2003 Jun 1;60(suppl 1): i1-2.\u003c/li\u003e\n\u003cli\u003e\u0026Aring;kerstedt T, Knutsson A, Westerholm P, Theorell T, Alfredsson L, Kecklund G. Mental fatigue, work and sleep. Journal of psychosomatic Research. 2004 Nov 1;57(5):427-33.\u003c/li\u003e\n\u003cli\u003eTechera U, Hallowell M, Stambaugh N, Littlejohn R. Causes and consequences of occupational fatigue. Journal of occupational and environmental medicine. 2016 Oct 1;58(10):961-73. \u003c/li\u003e\n\u003cli\u003eSagherian K, Steege LM, Cobb SJ, Cho H. Insomnia, fatigue and psychosocial well‐being during COVID‐19 pandemic: A cross‐sectional survey of hospital nursing staff in the United States. Journal of clinical nursing. 2020 Nov 20.\u003c/li\u003e\n\u003cli\u003eCaldwell JA, Caldwell JL, Thompson LA, Lieberman HR. Fatigue and its management in the workplace. 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Sleep Health. 2020 Jun 1;6(3):330-7.\u003c/li\u003e\n\u003cli\u003eTechera U, Hallowell M, Littlejohn R, Rajendran S. Measuring and Predicting Fatigue in Construction Workers: An Empirical Field Study. Measuring and Managing Construction Worker Fatigue. 2017 Apr 1; 1001:96.\u003c/li\u003e\n\u003cli\u003eKokoroko E, Sanda MA. Effect of workload on job stress of Ghanaian OPD nurses: The role of coworker support. Safety and health at work. 2019 Sep 1;10(3):341-6.\u003c/li\u003e\n\u003cli\u003eJames SM, Honn KA, Gaddameedhi S, Van Dongen H. Shift work: disrupted circadian rhythms and sleep implications for health and well-being. Current sleep medicine reports. 2017 Jun;3(2):104-12.\u003c/li\u003e\n\u003cli\u003eHyun MK. How fatigue level is related to sleep disturbances: A large cross-sectional community study. European Journal of Integrative Medicine. 2022 Jan 1;49: 102097.\u003c/li\u003e\n\u003cli\u003eSatterfield BC, Van Dongen HP. 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Investigation of the role of mental workload, fatigue, and sleep quality in the development of musculoskeletal disorders. Journal of Occupational Hygiene Engineering Volume. 2019 Jan 1;5(4):1-7.\u003c/li\u003e\n\u003cli\u003eSagherian K, Geiger-Brown J, Rogers VE, Ludeman E. Fatigue and risk of sickness absence in the working population. Scandinavian Journal of Work, Environment \u0026amp; Health. 2019 Jan 1;45(4):333-45.\u003c/li\u003e\n\u003cli\u003eGu J, Guo F. How fatigue affects the safety behavior intentions of construction workers an empirical study in Hunan, China. Safety science. 2022 May 1;149: 105684.\u003c/li\u003e\n\u003cli\u003eFang D, Jiang Z, Zhang M, Wang H. An experimental method to study the effect of fatigue on construction workers\u0026rsquo; safety performance. Safety science. 2015 Mar 1;73: 80-91\u003c/li\u003e\n\u003cli\u003eHystad SW, Nielsen MB, Eid J. The impact of sleep quality, fatigue and safety climate on the perceptions of accident risk among seafarers. European review of applied psychology. 2017 Sep 1;67(5):259-67.\u003c/li\u003e\n\u003cli\u003eChenarboo FJ, Hekmatshoar R, Fallahi M. The influence of physical and mental workload on the safe behavior of employees in the automobile industry. Heliyon. 2022 Oct 1;8(10): e11034.\u0026agrave;\u003c/li\u003e\n\u003cli\u003eDahlan A, Widanarko B. Impact of Occupational Fatigue on Human Performance among Oil and Gas Workers in Indonesia. Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal). 2022 Feb;17(1):54-9.\u003c/li\u003e\n\u003cli\u003eHinze A, K\u0026ouml;nig JL, Bowen J. Worker-fatigue contributing to workplace incidents in New Zealand Forestry. Journal of safety research. 2021 Dec 1;79: 304-20.\u003c/li\u003e\n\u003cli\u003eBazazan A, Dianat I, Mombeini Z, Aynehchi A, Jafarabadi MA. Fatigue as a mediator of the relationship between quality of life and mental health problems in hospital nurses. Accident Analysis \u0026amp; Prevention. 2019 May 1;126: 31-6.\u003c/li\u003e\n\u003cli\u003eLean Y, Shan F. Brief review on physiological and biochemical evaluations of human mental workload. Human Factors and Ergonomics in Manufacturing \u0026amp; Service Industries. 2012 May;22(3):177-87.\u003c/li\u003e\n\u003cli\u003eK\u0026auml;thner I, Wriessnegger SC, M\u0026uuml;ller-Putz GR, K\u0026uuml;bler A, Halder S. Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain\u0026ndash;computer interface. Biological psychology. 2014 Oct 1;102: 118-29.\u003c/li\u003e\n\u003cli\u003eHart SG, Staveland LE. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psychology 1988 Jan 1 (Vol. 52, pp. 139-183). North-Holland.\u003c/li\u003e\n\u003cli\u003eLund S, Yan M, D\u0026apos;Angelo J, Wang T, Hallbeck MS, Heller S, Zielinski M. NASA-TLX assessment of workload in resident physicians and faculty surgeons covering trauma, surgical intensive care unit, and emergency general surgery services. The American Journal of Surgery. 2021 Dec 1;222(6):1158-62. (Sarah Lund)\u003c/li\u003e\n\u003cli\u003eTubbs-Cooley HL, Mara CA, Carle AC, Gurses AP. The NASA Task Load Index as a measure of overall workload among neonatal, pediatric and adult intensive care nurses. Intensive and Critical Care Nursing. 2018 Jun 1;46: 64-9.\u003c/li\u003e\n\u003cli\u003eMouz\u0026eacute;-Amady M, Raufaste E, Prade H, Meyer JP. Fuzzy-TLX: using fuzzy integrals for evaluating human mental workload with NASA-Task Load indeX in laboratory and field studies. Ergonomics. 2013 May 1;56(5):752-63.\u003c/li\u003e\n\u003cli\u003eHair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. European business review. 2019 Jan 14;31(1):2-4.\u003c/li\u003e\n\u003cli\u003eGhasemi F, Samavat P, Soleimani F. The links among workload, sleep quality, and fatigue in nurses: a structural equation modeling approach. Fatigue: biomedicine, health \u0026amp; behavior. 2019 Jul 3;7(3):141-52.\u003c/li\u003e\n\u003cli\u003eAhmadi M, Choobineh A, Mousavizadeh A, Daneshmandi H. Physical and psychological workloads and their association with occupational fatigue among hospital service personnel. BMC Health Services Research. 2022 Dec;22(1):1-8.\u003c/li\u003e\n\u003cli\u003eHidayanti RC, Sumaryono S. The Role of Sleep Quality as Mediator of Relationship between Workload and Work Fatigue in Mining Workers. Jurnal Psikologi. 2021;48(1):62-79.\u003c/li\u003e\n\u003cli\u003eZhou AY, Panagioti M, Hann M, Agius R, Van Tongeren M, Esmail A, Bower P. Contributors to stress and burnout in junior doctors during the COVID-19 pandemic. Safety and Health at Work. 2022 Jan;13: S294.\u003c/li\u003e\n\u003cli\u003eTaheri MR, Khorvash F, Hasan Zadeh A. Assessment of mental workload and relationship with needle stick injuries among Isfahan Alzahra hospital nurses. medical journal of mashhad university of medical sciences. 2016;58(10):70-577.\u003c/li\u003e\n\u003cli\u003eBuysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014; 37:9\u0026ndash;17\u003c/li\u003e\n\u003cli\u003eIshibashi Y, Shimura A. Association between work productivity and sleep health: a cross-sectional study in Japan. Sleep health. 2020 Jun 1;6(3):270-6.\u003c/li\u003e\n\u003cli\u003eGrandner MA. Sleep, health, and society. Sleep medicine clinics. 2017 Mar 1;12(1):1-22.\u003c/li\u003e\n\u003cli\u003eSadeghniiat-Haghighi K, Aminian O, Najafi A, Rahimi-Golkhandan A, Zahabi A. Sleep quality in shift workers of offshore petroleum industries. Journal of Sleep Sciences. 2018;3(1-2):36-40\u003c/li\u003e\n\u003cli\u003eKecklund G, Axelsson J. Health consequences of shift work and insufficient sleep. Bmj. 2016 Nov 1;355].\u003c/li\u003e\n\u003cli\u003eJansen EC, Peterson KE, O\u0026rsquo;Brien L, Hershner S, Boolani A. Associations between mental workload and sleep quality in a sample of young adults recruited from a US college town. Behavioral sleep medicine. 2020 Jul 3;18(4):513-22.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Occupational fatigue, mental workload, PLS-SEM, oil and gas workers","lastPublishedDoi":"10.21203/rs.3.rs-4165778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4165778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eOccupational fatigue among oil and gas workers can have perilous consequences related to safety, health, economy, and wellbeing aspect. This makes it necessary to discover major factors related to fatigue and implement appropriate prevention programs and education. Therefore, this study aimed to investigate the relationship between mental workload, sleep quality, and occupational fatigue in oil and gas office workers in Jambi Province.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign and methods: \u003c/strong\u003eMental workload, sleep quality, and occupational fatigue were measured using the NASA-Total Load Index (TLX), the Pittsburgh Sleep Quality Index (PSQI), and the Indonesian Questionnaire Measuring Feelings of Work Fatigue (KAUPK2), respectively. A Partial Least Square-Structural Equation Modeling (PLS-SEM) approach was used to determine the association between mental workload, sleep quality, and occupational fatigue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOut of the 117 oil and gas workers in Jambi Province who participated in this study, 58.6% were male, 54.3% had Senior High School or less, 85.3% were not smoking, and 88.8% were married. The mean with a standard deviation of body height, weight, and mass index were 165.35 ± 5.46 cm, 64.65 ± 6.89 kg, and 23.64 ± 2.23, respectively. Respondents had working experience from 0.17 to 34 years with a mean of 16.23 and a standard deviation of 8.93 years. The PLS-SEM model illustrated that the direct effect of mental workload on occupational fatigue was not significant (path coefficient: 0.179; p \u0026lt;0.036). Meanwhile, the mental workload had a significant effect on sleep quality (path coefficient: -0.405; p 0.000), which significantly affected fatigue (path coefficient: -0.035; p = 0.709). This indicated that the effect of workload on fatigue was fully mediated by sleep quality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe effect of sleep quality was very significant to overcome the fatigue level of an employee when the mental workload increases. This study revealed that occupational fatigue may be reduced by implementing mental workload coping strategies, regularly measuring, and a sleep hygiene program among oil and gas workers.\u003c/p\u003e","manuscriptTitle":"Assessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 11:57:33","doi":"10.21203/rs.3.rs-4165778/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92dc672d-a79a-4aeb-a02c-5807110ebd29","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29875240,"name":"Occupational Medicine"},{"id":29875241,"name":"Epidemiology"}],"tags":[],"updatedAt":"2024-03-27T11:57:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 11:57:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4165778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4165778","identity":"rs-4165778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}