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Shift and on-call workers in the electricity sector have a high risk of experiencing fatigue, which impacts cognitive function, productivity, health and safety. This fatigue is influenced by work-related risk factors (on-call system, work shift, work period, workload) and non-work-related risk factors (age, sleep, nutritional status, side job, married status, disease history). The purpose of this study was to analyze the risk factors for fatigue in shift and on-call workers. The study used a cross sectional design with mixed methods. Quantitative data were obtained from 98 respondents using OFER, PSQI, NASA-TLX questionnaires, height and weight measurements, and supported by qualitative data through open interviews. The results of this study showed that workload was significantly associated with acute fatigue (p = 0.027; OR = 2.703) and chronic fatigue (p = 0.034; OR = 2.618). In addition, sleep quantity (p = 0.035; OR = 3.906) and married status (p = 0.003; OR = 4.354) had significant associations with acute fatigue. The conclusion of this study emphasizes the importance of implementing fatigue management and increasing self-awareness in managing fatigue.
Distress is a form of negative stress caused by adverse events that can lead to a decline in work performance. The electricity sector involves high complexity and risk in its business processes, making workers in this sector more vulnerable to experiencing distress, as indicated by previous studies and preliminary observations. This study aims to describe and analyze the relationship between risk factors and the level of distress among workers at PT X in the electricity sector. A mixed-method approach with a cross-sectional design was employed. Quantitative data were obtained through questionnaires and analyzed using the chi-square test, while qualitative data were collected through interviews and analyzed thematically. The risk factors examined include individual factors (age, length of employment, marital status), work-related factors (workload and work pace, organizational role, career development, interpersonal relationships, home-work interface), non-work-related factors (domestic-family demands), and buffering factors (social support). The results of the descriptive analysis indicated that 70.6% of workers experienced moderate stress, 27.2% experienced severe stress, and 2.2% experienced mild stress. Inferential analysis showed that only the variable career development (p = 0.021) had a significant relationship with the level of distress, with an odds ratio (OR) value of 2.457, indicating that workers with negative perceptions of career development were 2.457 times more likely to experience distress.
Penelitian ini bertujuan untuk menganalisis kelelahan dan faktor risiko kelelahan (fatigue)
pada pekerja konstruksi di Proyek A PT XYZ tahun 2025. Faktor terkait pekerjaan yang
diteliti mencakup waktu kerja, beban kerja, shift kerja dan lingkungan kerja. Sedangkan,
faktor tidak terkait pekerjaan yang diteliti mencakup usia, status gizi, kualitas tidur,
kuantitas tidur, kebiasaan merokok, konsumsi kafein, dan commuting time (waktu
perjalanan). Penelitian ini menggunakan pendekatan kuantitatif dengan desain studi cross
sectional dan dilaksanakan pada Februari – Juni 2025 di Proyek A PT XYZ.
Pengumpulan data dilakukan melalui kuesioner yang mencakup identitas responden,
Fatigue Assessment Scale for Construction Workers (FASCW), NASA Task Load Index
(NASA TLX) dan Pittsburgh Sleep Quality Index (PSQI). Jumlah responden dalam
penelitian ini adalah sebanyak 78 orang. Hasil penelitian menunjukkan bahwa 36
responden (46,2%) mengalami kelelahan, sedangkan 42 responden (53,8%) tidak
mengalami kelelahan. Berdasarkan analisis inferensial menggunakan uji Chi-Square,
diketahui bahwa terdapat hubungan yang signifikan antara shift kerja (p-value = 0,024;
OR = 0,227) dan usia (p-value = 0,024; OR = 3,000) dengan kelelahan pada pekerja
konstruksi di Proyek A PT XYZ.
This study aims to analyze fatigue and the risk factors associated with fatigue among construction workers at Project A, PT XYZ, in 2025. Work-related factors examined include working hours, workload, work shifts, and work environment. Meanwhile, non work-related factors analyzed include age, nutritional status, sleep quality, sleep quantity, smoking habits, caffeine consumption, and commuting time. This research employs a quantitative approach with a cross-sectional study design, conducted from February to June 2025 at Project A, PT XYZ. Data collection was carried out using a questionnaire covering respondent identity, the Fatigue Assessment Scale for Construction Workers (FASCW), NASA Task Load Index (NASA TLX), and Pittsburgh Sleep Quality Index (PSQI). The total number of respondents in this study was 78. The results showed that 36 respondents (46.2%) experienced fatigue, while 42 respondents (53.8%) did not. Based on inferential analysis using the Chi-Square test, a significant relationship was found between work shifts (p-value = 0.024; OR = 0.227) and age (p-value = 0.024; OR = 3.000) and fatigue among construction workers at Project A of PT XYZ.
Kata Kunci: Kelelahan; Fatigue Assessment Scale (FAS); Shift Kerja; Tidur; Fitbit
Operator haul truck Haul truck operator is one of the high-risk occupations in experiencing fatigue caused by the implementation of shift work, sleep quantity and quality disturbance, other related factors. The objective of this study was to analyze the relationship between shift work, quantity and quality of sleep, and other factors associated with fatigue on the haul truck operator. A cross-sectional study was conducted in this study using questionnaires of Fatigue Assessment Scale (FAS), measurement of stress using cocorometer, and measurement of sleep quantity and quality using fitbit among 196 male respondents who work as haul truck operator. The result of this study shown there is a significant correlation between the quantity of sleep (OR = 3,222, p = 0,028) and fatigue, also between the quality of sleep (OR = 2,800, p = 0.025) and fatigue. However, shift work has no significant correlation with fatigue. Other factors, including mental workload (OR = 2,296, p = 0,027), work environment (OR = 2,400, p = 0,014), monotonous work (OR = 3,371, p = 0,002), age (OR = 2,708, p = 0,005), and sleep hygiene (OR = 3,840, p = 0,001) also have significant correlation with operator fatigue in PT X.
Keywords: Fatigue; Fatigue Assessment Scale (FAS); Shift Work, Sleep; Fitbit; Haul truck operator
Shift workers are part of critical workers at the RSG GAS Installation. The shift work system and complex work demands in nuclear installations cause shift workers to be prone to work fatigue. Work fatigue has contributed to decreased work performance, decreased concentration, decreased in procedure compliance, and reduced worker alertness. The impact of work fatigue must be able to be identified and controlled, especially in nuclear installations that have a large potential hazard. This study aims to evaluate the level of work fatigue of shift workers and analyze the risk factors that contribute to the work fatigue of shift workers at RSG-GAS in 2022. This research is a quantitative descriptive study with a cross-sectional design. The instruments used in this research are questionnaires, measuring tools for the environment, and interviews. The method of analysis in this research is descriptive and inferential analysis with a correlation test. The results showed that all shift workers experienced fatigue with mild fatigue level of 31,25%, moderate fatigue of 64,58%, and severe fatigue of 4,17%. The risk factors for work fatigue (total score) are nutritional status as measured by body mass index, sleep quality, workload and task design. The risk factors for general fatigue are body mass index, sleep quality, sleep quantity, caffeine consumption, workload, and task design. The risk factors for physical fatigue are age, sleep quality, sleep quantity, years of service, job role, workload, task design, and negative air pressure. The risk factor for reduced activity is job satisfaction. The risk factors for reduced motivation are sleep quality, sleep quantity, task design, and night shift. The risk factors for mental fatigue are sleep quality, workload, task design, lighting, and negative air pressure. Control recommendations involve management and shift worker by developing a work fatigue management program according to the conditions at the Nuclear Installation.
This thesis discusses the factors of fatigue and recovery, level of fatigue and recovery, the potential human performance degradation due to acute and chronic fatigue. This research was conducted at the Migas X company which is an oil and gas company with a complex and critical process that causes fatigue to workers and potentially cause work accidents. The design of this study is a cross sectional and the data conducted by online questionnaire which filled out by 1650 respondents obtained by random sampling in April till May 2021. The results showed that there were six risk faktors that significantly affected acute fatigue, nine risk faktors that significantly affected chronic fatigue, two faktors that significantly exhaust fatigue recovery, and there is a significant relationship between an increase in chronic or acute fatigue and a decrease in Human Performance
X construction project workers have relatively high work hazards and risks, particularly while working under the COVID-19 pandemic situation. As construction projects must go on, the workers likely have a higher risk of the COVID-19 exposure. When some workers directly or indirectly are exposed to the COVID-19, their jobs are taken over by co-workers. This case results in fatigue for construction workers. Work fatigue is one of the causes of occupational accidents as the fatigue reduces their focus, decision-making abilities, muscle strength, communication skills, productivity, alertness, physical and psychological performance and work motivation. This study aimed to determine the impact of the COVID-19 pandemic on fatigue in PT. X workers. This study applied an observational analytic method with a cross-sectional study design. Samples were taken from the total population of foundry workers in construction projects as many as 100 workers. *Multidimensional Fatigue Inventory Questionnaire*. Data analysis with a quantitative approach used univariate and bivariate analysis. Based on the results of the Pearson correlation analysis with a significant level of p < 0.05, variables that had a relationship with fatigue variable were age (p = 0.048), sleep time (p = 0.040), comorbid (p=0.004) and the COVID-19 pandemic (p=0.001)
