A questionnaire survey and an event-related potential (ERP) experiment were used to reveal the impact of safety attitudes on risk perception. The results revealed that during hazard identification, the N130 amplitude of subjects with negative safety attitude was significantly higher, which implied that subjects with negative safety attitude were more likely to feel confused. During risk analysis, subjects with positive safety attitude were more inclined to overestimate the probability and damage degree of risks; subjects with positive safety attitudes displayed higher P150 and late positive potential amplitudes, which indicated that subjects with positive safety attitudes devoted more attention to risks in the early stage of risk analysis and had a more intense affective response in the later period. The risk judgment ability of subjects with positive safety attitude was affected by time pressure, and they exhibited higher risk judgment accuracy only under no time pressure.

OBJECTIVE: Investigate the impact of daily occupational walking steps on the progression of papillary thyroid cancer (PTC), a topic hitherto underresearched.
METHODS: The authors analyzed the data from 800 individuals with PTC across stages 0-IV. Participants were evenly divided into 2 distinct occupational groups: office workers and construction workers (N = 400 each). Data included comprehensive records of daily walking steps, demographic information, and clinical indicators. Pearson\'s correlation coefficients or analysis of variance (ANOVA) were employed to assess the linkage between daily walking steps and PTC risk and stage, as well as associated biochemical markers.
RESULTS: The analysis revealed a significant inverse relationship between daily walking steps and PTC risk. A higher frequency of daily steps was associated with reduced chances of PTC onset and a lower diagnostic stage of the disease. This protective effect of physical activity was particularly pronounced in the construc- tion worker cohort. Subsequent evaluations showed that construction workers who consistently logged higher daily steps had markedly lower levels of thyroid-stimulating hormone (TSH), free triiodothyronine, free thyroxine, thyroid peroxidase antibody, thyroglobulin antibody, and thy- roglobulin (Tg). Notably, daily walking steps exhibited a strong inverse correlation with body mass index (BMI), age, PTC volumes, and levels of TSH and Tg across both occupational groups (ρ < -0.37). The increase in daily steps was associated with the reduction in PTC stages (p < 0.001).
CONCLUSIONS: The research underscores the potential benefits of increased daily walking steps, suggesting that they may play a protective role in reducing PTC risk and moderating its progression. Int J Occup Med Environ Health. 2024;37(1):58-71.

Effective hazard recognition and decision-making are crucial factors in ensuring workplace safety in the construction industry. Workers\' cognition closely relates to that hazard-handling behavior. Functional near-infrared spectroscopy (fNIRS) is a neurotechique tool that can evaluate the concentration vibration of oxygenated hemoglobin [HbO2] and deoxygenated hemoglobin [HbR] to reflect the cognition process. It is essential to monitor workers\' brain activity by fNIRS to analyze their cognitive status and reveal the mechanism in hazard recognition and decision-making process, providing guidance for capability evaluation and management enhancement. This review offers a systematic assessment of fNIRS, encompassing the basic theory, experiment analysis, data analysis, and discussion. A literature search and content analysis are conducted to identify the application of fNIRS in construction safety research, the limitations of selected studies, and the prospects of fNIRS in future research. This article serves as a guide for researchers keen on harnessing fNIRS to bolster construction safety standards and forwards insightful recommendations for subsequent studies.

The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers\' unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers\' behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers\' behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers\' unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers\' behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers\' behavior monitoring and management.

Construction worker fatigue is an important factor leading to unsafe behavior, a major cause of construction accidents. Uncovering the impact mechanism of fatigue on workers\' unsafe behavior can prevent construction accidents. However, it is difficult to effectively measure workers\' fatigue onsite and analyze the impact of worker fatigue on their unsafe behavior.
This research analyzes the relationship between the physical and mental fatigue of construction workers and their unsafe behavior via physiological measurement based on a simulated experiment on handling tasks.
It is found that: (a) both physical fatigue and mental fatigue have negative effects on workers\' cognitive ability and motion ability, and the negative effects are more serious under the combination of the two types of fatigue; (b) mental fatigue can easily change workers\' risk propensity, making them more willing to face risks, and in a state of the two types of fatigue, they are more likely to make choices with less pay and higher risk; (c) the number of signal identification errors is positively correlated with LF (low frequency)/HF (high frequency), and negatively correlated with the standard deviation of normal-to-normal intervals (SDNN), while the number of footstep control errors is negatively correlated with the time elapsed between two successive R waves (RR interval) and skin temperature (SKT).
These findings can enrich construction safety management theory from a perspective of quantified fatigue and facilitate safety management practices on construction sites, thus contributing to the body of knowledge and practices of construction safety management.

Work-related musculoskeletal disorders (WMSDs) are recognized as a leading cause of nonfatal injuries in construction, but no review of existing studies has systematically analyzed and visualized the trends of WMSDs among construction workers. The current science mapping-based review summarized research published between 2000 and 2021 related to WMSDs among construction workers through co-word, co-author, and citation analysis.
A total of 63 bibliographic records retrieved from the Scopus database were analyzed.
The results identified influential authors with high impacts in this research domain. Moreover, the results indicated that MSDs, ergonomics, and construction not only had the highest occurrence of been studied, but also the highest impact in terms of total link strength. In addition, the most significant contributions to research relating to WMSDs among construction workers have originated primarily from the United States, Hong Kong, and Canada. Furthermore, a follow-up in-depth qualitative discussion was conducted to focus on summarizing mainstream research topics, identifying existing research gaps, and proposing directions for future studies.
This review provides an in-depth understanding of related research on WMSDs among construction workers and proposes the emerging trends in this research field.

To develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers.
Seven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge builders\' lung health. The potential predictors subsets of the risk of LRD were screened by the least absolute shrinkage and selection operator regression and univariate analysis, and determined by using multivariate logistic regression analysis, then were used for developing a prediction nomogram for the risk of LRD. C-index, calibration curve, receiver operating characteristic curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to evaluation the identification, calibration, predictive ability and clinical effectiveness of the nomogram.
Five hundred and twenty-six construction workers were allocated to training group and 226 to validation group. The predictors included in the nomogram were symptoms, years of dust exposure, work in shifts and labor intensity. Our model showed good discrimination ability, with a bootstrap-corrected C index of 0.931 (95% CI = 0.906-0.956), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were (95% CI = 0.906-0.956) and 0.945 (95% CI = 0.891-0.999) in the training and validation groups, respectively. The results of DCA and CICA indicated that the nomogram may have clinical usefulness.
We established and validated a novel nomogram that can provide individual prediction of LRD for construction workers. This practical prediction model may help occupational physicians in decision making and design of occupational health examination.

Construction has been regarded as one of the most stressful industries, and the COVID-19 pandemic has deteriorated this situation. This research developed and tested a model of the impact of COVID-19 pandemic perception on job stress of construction workers. Both problem-focused and emotion-focused coping were considered as mediators. Empirical data were collected using a detailed questionnaire from the Chinese construction industry. The results showed that pandemic perception was significantly related to psychological and physical stress. Emotion-focused coping was mainly triggered by pandemic fear and job insecurity, while problem-focused coping was mainly triggered by organizational pandemic response. Furthermore, the effects of pandemic fear and organizational pandemic response on job stress were mediated by problem-focused coping. Finally, the theoretical and practical significance, research limitations, and future research directions of this study are discussed.

The construction industry is one of the most dangerous industries with grave situation owing to high accident rate and mortality rate, which accompanied with a series of security management issues that need to be tackled urgently. The unsafe behavior of construction workers is a critical reason for the high incidence of safety accidents. Affective Events Theory suggests that individual emotional states interfere with individual decisions and behaviors, which means the individual emotional states can significantly influence construction workers\' unsafe behaviors. As the complexity of the construction site environment and the lack of attention to construction workers\' emotions by managers, serious potential emotional problems were planted, resulting in the inability of construction workers to effectively recognize safety hazards, thus leading to safety accidents. Consequently, the study designs a behavioral experiment with E-prime software based on social cognitive neuroscience theories. Forty construction workers\' galvanic skin response signals were collected by a wearable device (HKR-11C+), and the galvanic skin response data were classified into different emotional states with support vector machine (SVM) algorithm. Variance analysis, correlation analysis and regression analysis were used to analyze the influence of emotional states on construction workers\' recognition ability of safety hazards. The research findings indicate that the SVM algorithm could effectively classify galvanic skin response data. The construct ion workers\' the reaction time to safety hazards and emotional valence were negatively correlated, while the accuracy of safety hazards recognition and the perception level of safety hazard separately had an inverted \"U\" type relationship with emotional valence. For construction workers with more than 20 years of working experience, work experience could effectively reduce the influence of emotional fluctuations on the accuracy of safety hazards identification. This study contributes to the application of physiological measurement techniques in construction safety management and shed a light on improving the theoretical system of safety management.

Construction is one of the most dangerous industries because of its open working environment and risky construction conditions. In the process of construction, risk events cause great losses for owners and workers. Most of the risk events are closely related to unsafe behaviors of workers. Therefore, it is of great significance for contractors to establish management measures, e.g., incentive and punishment mechanism, to induce workers to reduce unsafe behaviors. This paper aims to take the incentive and punishment mechanism into consideration and develop an evolutionary game model to improve the effectiveness of safety management. The evolutionary stability strategies which can help reduce unsafe behaviors are obtained and analyzed. Results show that there are 12 equilibrium strategies under the condition of different parameters. Specifically, the incentive and punishment mechanism has played an important role for the evolution direction. A balanced incentive and punishment mechanism for the investment and positive stimulus for workers can effectively promote both sides to take positive behaviors, and then realize good evolutionary stable situations. In addition, the initial perceptions of both sides have a decisive impact on the evolution direction. Strengthening communication with the mutual trust between both sides can improve safety performance of both sides. This study is valuable for contractors to design appropriate incentive and punishment measures and establish relevant strategies to promote safe behaviors of construction workers.