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Impact of working hours on depressive symptoms in working population: The mediating role of internet use

Journal of Environmental and Occupational Medicine.2025;42(11):1342-1349. doi:10.11836/JEOM25097

Background In the context of China's rapid economic growth and social acceleration, long working hours are emerging as a key factor affecting well-being and contributing to depression. Objective To examine the longitudinal interaction between working hours and depressive symptoms, and evaluate potential mediating role of internet use in the relationship between working hours and depressive symptoms. Method This study utilized two waves of panel data from the China Family Panel Studies (CFPS) collected between 2016 and 2018. It constructed a longitudinal sample of 4900 individuals aged 18-70 years who were employed with known working hours across various occupations. Based on survey data and the Center for Epidemiologic Studies Depression Scale (CES-D), we measured and compared working hours, internet use, and depressive symptoms. Pearson correlation analysis was employed to examine the associations among key variables. Through cross-lagged panel models and a semi-longitudinal mediation model, we examined the bidirectional interactive mechanisms between working hours and depressive symptoms, with a particular focus on revealing the mediating pathways of internet use and any group heterogeneity. Result The study sample comprised 4900 participants, aged 18 to 70 years. Of these, 43.8% were male, and 77.35% were married. In 2016, the average working hours were (48.55 ± 22.17) h, and over half of the participants used the internet (59.84%). The average score on the depressive symptoms scale was (5.11 ± 3.78). The cross-lagged panel models revealed a dynamic interplay between working hours, internet use, and depressive symptoms. Specifically, working hours, internet use, and depressive symptoms in 2016 all positively predicted their respective counterparts in 2018. Furthermore, working hours in 2016 positively predicted internet use in 2018 (β=0.045, P < 0.001) and depressive symptoms in 2018 (β=0.027, P < 0.05), indicating that longer working hours were associated with an increased likelihood of internet use and worsened depressive symptoms. The half-longitudinal mediation models demonstrated a significant mediating effect of internet use in the relationship between working hours and depressive symptoms (effect size=−0.0013, P < 0.05), suggesting that internet use may buffer the adverse effects of long working hours on depressive symptoms. The subgroup analyses of the half-longitudinal mediation model revealed significant mediation effects of internet use within the female and married female subgroups, as well as significant mediating pathways within the older adult and employment stability subgroups. The Panel Logit regression results indicated that internet use (high or low) was associated with a lower risk of depressive symptoms compared to non-internet use, positioning internet use as a protective factor against depressive symptoms symptoms among younger adults, those with stable employment, and married women. Conclusion This study confirms that prolonged working hours are detrimental to psychological health. Internet use mediates the relationship between working hours and depressive symptoms, effectively mitigating the psychological harm of long working hours. This mediating effect is particularly pronounced for women, especially married women.

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Prevalence of musculoskeletal disorders among bus drivers in a first-tier city and associated influencing factors

Linfeng LUO ; Huining WANG ; Huijie ZHANG ; Baolong LIU ; Tenglong YAN ; Jue LI

Journal of Environmental and Occupational Medicine.2025;42(11):1350-1357. doi:10.11836/JEOM25160

Background During urbanization, the passenger load on urban public transport systems continues to increase, exposing bus drivers to a high risk of musculoskeletal disorders (MSDs). This occupational health issue may also potentially compromise public transport safety. Objective To investigate the prevalence of MSDs among bus drivers in a first-tier city and to explore associated influencing factors. Methods A self-administered questionnaire survey was conducted from December 2024 to March 2025 among 1300 bus drivers in a bus company. Data were collected on general demographic information, occupational activities, and MSDs indicators. A total of 1268 valid questionnaires were returned, yielding a response rate of 97.5%. Descriptive statistics were used to analyze the distribution characteristics of MSDs. Pearson χ² tests were employed to examine the association between MSDs prevalence and individual/occupational characteristics. Logistic regression was performed to identify the influencing factors of MSDs. Results The survey revealed that the prevalence of MSDs among the bus drivers was 28.5%, with the highest prevalence in the 41–50 years age group (30.5%). The conditions primarily affected the cervical spine (60.5%) and lumbar spine (61.9%), with some cases involving multiple sites. The logistic regression model indicated that the following factors were associated with a higher risk of MSDs: frequent smoking (OR=1.477), poor or moderate self-reported health status (OR=4.422), 11–20 years of service (OR=1.749), daily cumulative driving time ≥361 min (OR=1.616), frequent whole-body fatigue during driving (OR=2.077), occasional whole-body fatigue during driving (OR=1.873), feeling tense during driving (OR=1.518), fatigue after work (OR=2.485), work-related stress (OR=1.882), and prolonged smartphone use with head-down posture (OR=1.671). Conversely, occasional exercise (OR=0.713)/frequent exercise (OR=0.569), driving intervals ≥21 min per round (OR=0.645), and perceiving the driving seat comfortable (OR=0.429) were associated with a lower risk of MSDs. Conclusion Physical exercise, smoking, years of service, daily cumulative driving time, driving intervals per round, prolonged smartphone use with head-down posture, self-reported health status, and psychological factors such as work-related stress are influencing factors for MSDs among bus drivers. Drivers should increase physical exercise and change unhealthy living habits. Employers should optimize working conditions, arrange reasonable driving schedules, and foster a positive work environment to reduce MSDs prevalence.

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Interaction between shift work and occupational stress on occupational burnout among employees in power companies

Peifang LIU ; Bo SHEN ; Guofeng LI ; Jing LIAO ; Yifei LIU ; Sitong FANG ; Yu JIANG

Journal of Environmental and Occupational Medicine.2025;42(12):1415-1421. doi:10.11836/JEOM25279

Background The power industry is characterized by typical shift work systems, with 24-hour uninterrupted work demands, high intensity and high standard job characteristics, as well as emergent task pressure, which exposes employees to the long-term dual pressure of shift work and occupational stress and may lead to occupational burnout. It not only endangers the physical and mental health of employees, but also threaten the safe and stable operation of the power system. Objective To explore the impact of shift work and occupational stress, as well as their potential interaction, on occupational burnout among employees in power enterprises. Methods From November 2024 to April 2025, cluster sampling was used to select 1378 workers from two power companies in Fuzhou. A self-designed questionnaire, the Effort-Reward Imbalance model, and the Maslach Burnout Inventory were used to investigate the participants’ general information，shift work patterns, occupational stress, and occupational burnout. Statistical analysis was conducted using SPSS 19.0 software. Chi-square test was employed for intergroup comparison. Logistic regression was used to examine the associations between shift work, occupational stress, and job burnout. The multiplicative interaction and additive interaction between shift work and occupational stress on occupational burnout were evaluated. Results A total of 1378 questionnaires were distributed, and 1113 were valid questionnaires, with an effective rate of 80.77%. Among the participants, 37.92% were engaged in shift work, the positive rate of occupational stress was 29.83%, and the positive rate of occupational burnout was 55.80%. The logistic regression analysis showed that after adjusting for confounding factors, the risks of occupational burnout in the non-shift x occupational stress group and the shift x occupational stress group were 1.59 times (95%CI: 1.12, 2.27; P=0.01) and 4.07 times (95%CI: 2.56, 6.45; P<0.01) higher than that in the non-shift x non-occupational stress group. A multiplicative interaction (shift work and occupational stress b=0.75, P=0.01, OR=2.12, 95%CI: 1.18, 3.80) and an additive interaction (relative excess risk ratio=2.24, 95%CI: 0.54, 3.94; attributable fraction=0.55, 95%CI: 0.32, 0.78; interaction index=3.71, 95%CI: 1.47, 9.40) between shift work and occupational stress on job burnout were identified. Conclusion Shift work and occupational stress are risk factors for occupational burnout among employees in power enterprises, and there is a synergistic interaction between these two factors in terms of their impact on occupational burnout.

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Effects of extreme temperature exposure on other infectious diarrhea morbidity risk in Taizhou, Zhejiang

Mingming GU ; Qiang CHENG ; Hongbiao LIANG ; Xueqing ZHANG ; Feixiang XU

Journal of Environmental and Occupational Medicine.2025;42(12):1422-1428. doi:10.11836/JEOM25259

Background Other infectious diarrhea (OID) refers to infectious diarrhea other than cholera, dysentery, and typhoid/paratyphoid fever. In the global climate change context, frequent extreme temperature events pose a significant threat to population health, yet the association between extreme temperatures and OID remains unclear. Objective To understand the impact of extreme temperature events on the incidence of OID in Taizhou region, and provide a scientific basis for formulating targeted public health intervention strategies. Methods Case data of OID from 2016 to 2024 and meteorological and pollutant data of Taizhou, Zhejiang during the same period were collected. The case data were derived from National Notifiable Infectious Diseases Reporting System, while the meteorological data and pollutant data were obtained from National Meteorological Science Data Center and Taizhou Ecology and Environment Bureau, respectively. An extreme heat event is defined as a period of two or more consecutive days in the warm season during which the daily mean temperature exceeds the 90th percentile value for that season. Similarly, an extreme cold event is defined as a period of two or more consecutive days in the cold season during which the daily mean temperature falls below the 10th percentile value. A distributed lag nonlinear model based on Poisson regression was used to assess the impact of extreme temperatures on OID incidence. Furthermore, subgroup analyses were conducted by gender and age. Results From 2016 to 2024, a total of 108 670 OID cases were reported in Taizhou, with higher incidence in males (61 253 cases) than females (47 417 cases). In terms of single-day lag effect, exposure to extreme cold presented an inverted U-shaped risk pattern: a significant risk effect began to emerge on the 4th day after exposure, continued to rise, and reached a peak on the 7th day (RR=1.044, 95%CI: 1.029, 1.058), whereas extreme heat had no significant impact. In terms of cumulative lag effect, exposure to extreme cold presented a J-shaped cumulative effect curve: short-term exposure (0-2 days) showed no risk effect, but as time extended (0-9 days), a risk effect emerged and reached the maximum risk value during the 0-12 d cumulative period (RR=1.243, 95%CI: 1.057, 1.461). In contrast, extreme heat had no cumulative lag effect. The subgroup analysis revealed that under extreme cold exposure, the single-day lag effects for both males and females exhibited an inverted U-shaped curve, with the risk peaking on day 7 for males (RR=1.045, 95%CI: 1.026, 1.065) and day 8 for females (RR=1.041, 95%CI: 1.019, 1.064). The risk pattern in the 1-5 years age group was similar to that of the overall population but with a higher effect size (RR=1.063, 95%CI: 1.041, 1.085). In terms of the cumulative lag effects, the cumulative risk curves for both males and females under extreme cold exposure showed a J-shaped pattern with varied risk profiles. Significant variations in risk patterns were observed across different age groups. No significant cumulative lag effects were observed for extreme heat exposure in any subgroup. Conclusion Extreme cold can increase the incidence of OID in Taizhou, and the risk effects are heterogeneous among different groups. No risk effect is observed for extreme heat.

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Role of mitochondrial biogenesis in rat model of coal workers' pneumoconiosis based on PGC-1α-NRF1-TFAM signaling pathway

Mei ZHANG ; Xiaoqiang HAN ; Lulu LIU ; Yan WANG ; Xin MA ; Yu XIONG ; Huifang YANG ; Na ZHANG

Journal of Environmental and Occupational Medicine.2025;42(12):1429-1437. doi:10.11836/JEOM25253

Background Mitochondrial biogenesis is pivotal in coal workers' pneumoconiosis fibrosis, yet the role of the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α)-nuclear respiratory factor 1 (NRF1)-mitochondrial transcription factor A (TFAM) pathway inmitochondrial biogenesis remains elusive, warranting further investigation. Objective To elucidate the role of the PGC-1α-NRF1-TFAM pathway in mitochondrial biogenesis in a rat coal workers' pneumoconiosis model through in vivo and in vitro experiments. Methods (1)n vivo: twelve SPF male SD rats (200-220 g) were randomized into a control group and a coal dust group (n=6 per group). After acclimatization, the coal dust group received 1 mL 50 mg·mL⁻¹ coal dust suspension via intratracheal instillation; the controls received saline. Lung tissues were harvested after two months for histopathology [HE, Masson, and transmission electron microscopy (TEM) ], protein and mRNA analysis, and mitochondrial DNA (mtDNA) quantification by quantitative real-time polymerase chain reaction (qPCR). (2) In vitro: rat lung type II epithelial cells (RLE-6TN) cells were exposed to coal dust (50, 100, 200, and 400 mg·L⁻¹, 24 h). CCK-8 assay determined optimal doses. Ultrastructural changes were analyzed by TEM. Cells were transfected with OE-PGC-1α (PGC-1α overexpression) or shRNA-PGC-1α plasmids (PGC-1α knockdown), and the transfection efficiency was determined by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). The expression levels of alpah-smooth muscle actin (α-SMA), citrate synthase (CS), PGC-1α, NRF1, TFAM, and fibronectin (Fn) proteins and their corresponding mRNA were detected using Western blot and RT-qPCR, respectively. The relative content of mtDNA was determined by qPCR. Results In vivo: the control group lung samples exhibited soft, pink parenchyma, while the coal dust-exposed lungs showed blackened surfaces with soft texture. The histopathological evaluation revealed intact alveolar walls in the controls versus structural destruction, micro-nodules, and fibrotic areas in the coal dust group. After Masson staining, coal dust deposits were found surrounded by blue collagen fibers in the exposed lungs, but absent in the controls. The coal dust group displayed significant upregulation of fibrotic marker α-SMA and downregulation of mitochondrial biogenesis markers (CS, PGC-1α, NRF1, TFAM) and mtDNA compared to the controls (P<0.05). In vitro: coal dust exposure reduced cell density and induced morphological alterations. TEM revealed evenly distributed normal mitochondria in controls versus mitochondrial swelling, disrupted cristae, and reduced numbers in exposed cells. The mitochondrial biogenesis markers were elevated in the coal dust + OE-PGC-1α group compared to the coal dust + OE-NC group (P<0.05); in contrast, they were decreased in the coal dust + shRNA-PGC-1α group compared to the coal dust + shRNA-NC group (P<0.05). Compared to the control group, the expression levels of the fibrosis marker α-SMA mRNA and protein were increased in the coal dust group (P<0.05). Overexpression of PGC-1α reduced α-SMA expression, while downregulation of PGC-1α increased its expression (P<0.05). Conclusion Coal dust exposure induces mitochondrial dysfunction and pulmonary fibrosis in vivo and in vitro via the PGC-1α-NRF1-TFAM pathway dysregulation. Targeting this pathway may mitigate coal dust-induced fibrosis by restoring mitochondrial biogenesis.

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miR-411-3p attenuates silica-induced pulmonary fibrosis in mice by suppressing alveolar type II epithelial-mesenchymal transition via targeting SMURF2 regulation

Siyi WANG ; Jiakun DU ; Siyuan SHAN ; Bingbing LI ; Xinyu WANG ; Zhongqiu WEI ; Hong XU ; Xuemin GAO

Journal of Environmental and Occupational Medicine.2025;42(12):1438-1445. doi:10.11836/JEOM25218

Background Pneumoconiosis is the most serious occupational disease in China, among which silicosis accounts for more than 50%. microRNA (miRNA) plays an important role in the occurrence process of silicosis fibrosis, but the mechanism of it has not been fully clarified yet. Objective To explore the molecular mechanism by which miR-411-3p modulates the ubiquitination degradation of SMAD specific E3 ubiquitin protein ligase (SMURF) 2/Smad7, thereby suppressing epithelial-mesenchymal transition (EMT) in mouse alveolar type II epithelial cells and counteracting silica-induced pulmonary fibrosis. Methods Twenty-four 8-week-old SPF male C57BL/6J mice were randomly divided into four groups: Control group, silica group, silica +miR-411-3p agomir-NC group, and silica +miR-411-3p agomir group, with 6 mice in each group. Silicosis model was prepared by a one-time bronchial infusion of silicon dioxide (SiO₂) (200 mg·mL^-1, 50 μL). In vitro MLE-12 cells were divided into (1) control group and SiO₂ group, (2) SiO₂+negative control siRNA (siRNA-NC) group and SiO₂+Smurf2 gene silencing (si-Smurf2) group, (3) SiO₂+solvent (DMSO) group and SiO₂+protease inhibitor (MG132) group, (4) mutant sequence plasmid (Mut)+miR-411-3p mimic control (miR-NC) group, Mut+miR-411-3p mimic group, wild sequence plasmid (Wt)+miR-NC group, and Wt+miR-411-3p mimic group, (5) SiO₂+miR-NC group and SiO₂+miR-411-3p mimic group. The pathological morphology and collagen deposition of lung tissue were observed after staining. Detection of miR-411-3p and proteins was conducted by real-time fluorescent quantitative PCR and Western blot. The binding of SMURF2 to Smad7 protein and Smad7 to ubiquitin (Ub) were detected by co-immunoprecipitation (Co-IP) method. Dual-luciferase reporter gene assay was adopted to verify the regulatory effect of miR-411-3p on Smurf2. Results In the SiO₂-induced MLE-12 cells, compared to the control group, the SiO₂-treated group showed significantly upregulated expressions of N-cadherin (N-Cad), collagen I (CoL I), SMURF2, transforming growth factor-β1 (TGF-β1), and phosphorylated Smad2/3 (p-Smad2/3). In contrast, the expressions of E-cadherin (E-Cad), Smad7, and miR-411-3p were significantly downregulated (P<0.05). The dual-luciferase reporter gene assay revealed a regulatory effect of miR-411-3p on Smurf2 (P<0.05). Meanwhile, in the MLE-12 cells induced by SiO₂, the miR-411-3p mimic down-regulated the protein expressions of SMURF2, N-Cad, CoL I, TGF-β1, and p-Smad2/3, while up-regulated the protein expressions of E-Cad and Smad7 (P<0.05). The silenced Smurf2 gene inhibited the expressions of N-Cad, CoL I, and p-Smad2/3 proteins, while promoted the expressions of E-Cad and Smad7 proteins in the MLE-12 cells (P<0.05). The Co-IP results showed that the binding of SMURF2 to Smad7 was enhanced, and the ubiquitin binding ability of Smad7 was enhanced in the SiO₂ group. In the lung tissue of mice, the results of pathological observation with hematoxylin-eosin (HE) and sirius red (VG) staining showed that compared with the agomir-NC, the lesion was relieved in the lung tissue of the miR-411-3p agomir group. Meanwhile, the expressions of SMURF2, N-Cad, CoL I, TGF-β1, and p-Smad2/3 were significantly down-regulated, while the expressions of E-Cad and Smad7 were significantly up-regulated (P<0.05). Conclusion MiR-411-3p alleviates the EMT of alveolar type II epithelial cells and antagonizes silicosis fibrosis progression in mice by inhibiting SMURF2-mediated ubiquitination and degradation of Smad7.

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Boosting prediction of occupational stress among manufacturing employees by reconstructing cumulative fatigue features with Bayesian sparse autoencoder

Tao SONG ; Yuting ZHOU ; Xinyi LU ; Xinkai WEI ; Qingxin MENG ; Jianlin LOU ; Hongchang ZHOU ; Jin WANG ; Shuang LI

Journal of Environmental and Occupational Medicine.2025;42(12):1446-1455. doi:10.11836/JEOM25270

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

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Relationship between work-family conflict, depressive mood, and eating behavior of occupational populations aged 18-60 years

Haixia ZHAO ; Zhifang LIU ; Chunlan FU ; Meng ZHANG ; Wei LUO

Journal of Environmental and Occupational Medicine.2025;42(12):1456-1464. doi:10.11836/JEOM25069

Background Psychological disturbances such as work-family conflict and depressive mood are prevalent among occupational groups and are closely related to eating behavior. Therefore, investigating the influencing factors of eating behavior is of great significance for promoting the health behaviors of occupational populations. Objective To clarify the current situation of eating behavior among the occupational populations aged 18 to 60 years in China, and to explore the relationship between work-family conflict, depressive mood, and eating behavior, and to test the mediating role of depressive mood in the relationship. Methods The study used a data set containing occupational populations aged 18 to 60 years extracted from the 2021 Psychology and Behavior Investigation of Chinese Residents. The Work-Family Conflict Scale, the Chinese version of the Sakata Eating Behavior Scale Short Form, and the Patient Health Questionnaire-Depression Scale were used. Potential influencing factors of eating behavior of the occupational populations were evaluated by multiple linear regression. Structural equation modeling was used to analyze the relationships between work-family conflict, depressive mood, and eating behavior, and the Bootstrap method was used to test the mediating effect of depressive mood on the relationship of work-family conflict and eating behavior. Results Among the occupational populations, the proportion of reporting high work-family conflict was 48.4%, and the proportion of reporting mild depression and above was 48.7%. The total score of eating behavior was (16.16±4.64), and the proportion of high abnormal eating behavior tendency was 39.1%. There were significant differences in eating behavior score among different age, educational level, marital status, number of offspring, occupation, smoking, and drinking groups (P<0.05). The partial correlation analysis showed that work-family conflict and depressive mood were positively correlated with abnormal eating behavior (r=0.367, 0.386, P<0.001); work-family conflict was positively correlated with depressive mood (r=0.466, P<0.001). The results of the multiple linear regression showed that depressive mood, work-family conflict, age, smoking, drinking, and education level were associated with eating behavior (P<0.05). The structural equation modeling indicated that work-family conflict positively associated with depressive mood (b=0.529, P<0.001), depressive mood positively associated with abnormal eating behavior (b=0.292, P<0.001), and work-family conflict positively associated with abnormal eating behavior (b=0.270, P<0.001). Depressive mood played a partial mediating role in the relationship between work-family conflict and eating behavior, and the effect value was 0.154 (95%CI: 0.132, 0.179) that accounted for 36.32% of the total effect. Conclusion Work-family conflict could directly affect the eating behavior among occupational populations, and also indirectly affect eating behavior through a mediating effect of depressive mood. Therefore, optimizing the allocation of tasks between work and family, providing psychological support in need, alleviating work-family conflict and depressive mood may improve the eating behavior and mental health of working populations.

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Relationship between work load and sleep quality of employees at an airport in Hunan Province: Mediating role of job burnout

Tinghua YAN ; Yunfan ZHENG ; Ting LIU ; Ding LI ; Yunfeng NIE ; Shi CHEN

Journal of Environmental and Occupational Medicine.2025;42(12):1465-1471. doi:10.11836/JEOM25189

Background With the unprecedented development of aviation business, the work load and job burnout of airport employees are becoming more and more serious, and the impact on sleep quality needs to be addressed. Objective To explore the current situation of work load and sleep quality of airport employees, and whether job burnout plays a role in the relationship between the two variables. Methods During September 2023, a cross-sectional survey was conducted among employees of an airport in Hunan Province, and 1503 employees were surveyed. A basic information questionnaire, a work load measurement questionnaire, Maslach Burnout Inventory General Survey, and Pittsburgh Sleep Quality Index were used in the survey. Logistic regression was used to analyze the influencing factors of sleep quality. SPSS 25.0 PROCESS v4.1 plug-in was used to analyze the mediating effect of job burnout. Results A total of 1503 questionnaires were distributed in this study, and 1424 valid questionnaires were recovered, with a recovery rate of 94.7%. The rate of heavy work load was 50.0% (712/1424), the rate of job burnout was 74.6% (1062/1424), and the rate of sleep disorders was 58.7% (836/1424). The results of logistic regression analysis showed that individuals with job burnout had a 1.734 times higher risk of reporting sleep disorders compared to those without job burnout (95% CI: 1.306, 2.302); compared to those with light work load, individuals with heavy work load have a higher risk of sleep disorders (OR=3.422, 95%CI: 2.639, 4.437). The results of mediating effect test indicated a work load-burnout-sleep quality pathway (P <0.001), with a mediation effect value of 0.1052 (95%CI: 0.019, 0.067), accounting for 18.02% of the total effect. Conclusion The rate of job burnout and the rate of reporting poor sleep quality are high among airport employees, and job burnout plays a mediating role in the relationship between work load and sleep quality. Adopting measures to reduce work load and reduce burnout can improve the sleep quality of airport employees.

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Latent profile analysis of occupational burnout and its influencing factors among biosafety laboratory workers

Baojun LI ; Lei DING ; Jing YU ; Mengjie XIA ; Zhencheng LIU ; Qingyue YANG ; Yaoqin LU

Journal of Environmental and Occupational Medicine.2025;42(12):1472-1479. doi:10.11836/JEOM25232

Background Staff in biosafety laboratories (BSL) are more likely to experience occupational burnout and other psychological issues due to their unique working environment and high job demands. However, current research in this field tends to focus on overall analyses, overlooking the internal differences within this group. Objective To explore latent profiles of occupational burnout among BSL workers and their influencing factors, providing a reference for targeted burnout interventions. Methods In 2022, cluster random sampling was used to select 8023 BSL workers in Xinjiang. Data were collected via a questionnaire survey, including a self-designed general information questionnaire, the Maslach Burnout Inventory-General Survey (MBI-GS), and the Effort-Reward Imbalance (ERI) scale, to analyze demographic characteristics, occupational burnout, and job stress status. After data cleaning, latent profile analysis (LPA) was applied to identify burnout profiles, followed by K-means clustering for verification. Logistic regression was used to analyze influencing factors with burnout profiles as the dependent variable. Results After excluding invalid questionnaires, 7924 valid questionnaires were retained, with an recovery rate of 98.77%. The LPA results indicated that occupational burnout among BSL staff was categorized into three latent profiles: a low burnout profile (2208 individuals, 27.86%), a moderate burnout profile (4636 individuals, 58.51%), and a high burnout profile (1080 individuals, 13.63%). A similar grouping was derived from K-means clustering analysis (28.71%, 53.37%, and 17.92%), and a Sankey diagram demonstrated significant overlap between the two classification results. Statistically significant differences were observed across the three latent profiles in all three dimensions of burnout: emotional exhaustion, cynicism, and reduced personal accomplishment. The final logistic regression model revealed that the influencing factors exhibited characteristics of partial overlap and partial differentiation. Among the common influencing factors, the shared protective factors included: age ≥50 years (OR_{moderate burnout}=0.42, OR_{high burnout}=0.45), monthly income of 4000-6000 RMB (OR_{moderate burnout}=0.83, OR_{high burnout}=0.71), good sleep quality (OR_{moderate burnout}=0.70, OR_{high burnout}=0.13) and very good sleep quality (OR_{moderate burnout}=0.51, OR_{high burnout}=0.12), and engaging in physical exercise ≥5 times per week (OR_{moderate burnout}=0.74, OR_{high burnout}=0.48). The protective effects were more pronounced for the high burnout profile. The common risk factors included: 21–30 years of service (OR_{moderate burnout}=1.38, OR_{high burnout}=3.00), ≥31 years of service (OR_{moderate burnout}=1.47, OR_{high burnout}=3.39), working in "other positions" (OR_{moderate burnout}=1.46, OR_{high burnout}=1.56), and occupational stress (ERI>1) (OR_{moderate burnout}=2.62, OR_{high burnout}=3.25), with the risk effects being more significant for the high burnout profile. Regarding differentiated influencing factors, protective factors exclusively associated with the moderate burnout profile included having an associate degree (OR=0.70) and engaging in physical exercise 3-4 times per week (OR=0.74). For the high burnout profile, exclusive protective factors included age 40-50 years (OR=0.56), while exclusive risk factors included having a senior professional title (OR=1.85), holding a managerial role (OR=1.78), a high number of night shifts per month (OR=2.66), and a very high number of night shifts per month (OR=3.93). Conclusion BSL workers exhibit three distinct latent burnout profiles. Occupational burnout requires greater attention, and targeted prevention and intervention strategies should be developed based on worker subtypes.

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