International Journal of Industrial Ergonomics最新文献

Teatime Tales: A Deep Dive into the social sustainability of the tea garden ecosystem 《茶园故事：茶园生态系统的社会可持续性研究

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-12-10 DOI: 10.1016/j.ergon.2025.103841

Ankit Basak , Shiv Kumar Verma

Tea gardens are one of India's oldest organised sectors. However, people employed in the sector still face issues similar to those in the unorganised sector. Most studies in this domain have primarily focused on tea leaf production and operational efficiency rather than the social sustainability aspects of the tea garden ecosystem. Moreover, there is also a dearth of research that includes the perspective of females working in the domain. The current exploratory study examines the tea garden ecosystem to understand the various factors that affect workers and their working conditions, with a particular focus on gender dynamics and social sustainability. We adopted a qualitative research methodology for the study. Direct observation was conducted to study the behaviour of workers, various processes, and events in the tea garden. Videos and images were collected for a visual ethnography. Lastly, we conducted semi-structured interviews with various stakeholders in the tea garden, including workers, administrators, managers, and doctors, followed by thematic analysis to analyse the collected data. The findings reveal persistent challenges such as low wages, gender-based division of labour, lack of ergonomic support, and even the influence of gender-specific clothing on workers' health, particularly during pesticide spraying. The findings from direct observation, visual ethnography, and semi-structured interviews were then combined to provide a comprehensive view of the various issues, challenges, and working conditions faced by the tea garden workers. The research provides a foundational understanding that can inform policy, design, and future interdisciplinary studies aimed at enhancing social sustainability in similar labour-intensive sectors.

茶园是印度最古老的有组织的行业之一。然而，该行业的从业人员仍然面临着与无组织行业类似的问题。这一领域的大多数研究主要集中在茶叶生产和运营效率上，而不是茶园生态系统的社会可持续性方面。此外，也缺乏包括在该领域工作的女性视角的研究。目前的探索性研究考察了茶园生态系统，以了解影响工人及其工作条件的各种因素，特别关注性别动态和社会可持续性。我们采用了定性研究方法进行研究。通过直接观察来研究茶园工人的行为、各种过程和事件。录像和图像被收集起来作为视觉人种志。最后，我们对茶园的各种利益相关者进行了半结构化访谈，包括工人、行政人员、经理和医生，然后进行主题分析，对收集到的数据进行分析。调查结果揭示了持续存在的挑战，如低工资、基于性别的劳动分工、缺乏符合人体工程学的支持，甚至是针对性别的服装对工人健康的影响，特别是在喷洒农药时。直接观察、视觉人种学和半结构化访谈的结果结合起来，对茶园工人面临的各种问题、挑战和工作条件提供了一个全面的看法。该研究为政策、设计和未来旨在提高类似劳动密集型部门社会可持续性的跨学科研究提供了基础理解。

{"title":"Teatime Tales: A Deep Dive into the social sustainability of the tea garden ecosystem","authors":"Ankit Basak , Shiv Kumar Verma","doi":"10.1016/j.ergon.2025.103841","DOIUrl":"10.1016/j.ergon.2025.103841","url":null,"abstract":"<div><div>Tea gardens are one of India's oldest organised sectors. However, people employed in the sector still face issues similar to those in the unorganised sector. Most studies in this domain have primarily focused on tea leaf production and operational efficiency rather than the social sustainability aspects of the tea garden ecosystem. Moreover, there is also a dearth of research that includes the perspective of females working in the domain. The current exploratory study examines the tea garden ecosystem to understand the various factors that affect workers and their working conditions, with a particular focus on gender dynamics and social sustainability. We adopted a qualitative research methodology for the study. Direct observation was conducted to study the behaviour of workers, various processes, and events in the tea garden. Videos and images were collected for a visual ethnography. Lastly, we conducted semi-structured interviews with various stakeholders in the tea garden, including workers, administrators, managers, and doctors, followed by thematic analysis to analyse the collected data. The findings reveal persistent challenges such as low wages, gender-based division of labour, lack of ergonomic support, and even the influence of gender-specific clothing on workers' health, particularly during pesticide spraying. The findings from direct observation, visual ethnography, and semi-structured interviews were then combined to provide a comprehensive view of the various issues, challenges, and working conditions faced by the tea garden workers. The research provides a foundational understanding that can inform policy, design, and future interdisciplinary studies aimed at enhancing social sustainability in similar labour-intensive sectors.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103841"},"PeriodicalIF":3.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel driver monitoring and feedback system improves takeover performance in conditional automated driving 一种新的驾驶员监控和反馈系统提高了条件自动驾驶的接管性能

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-12-10 DOI: 10.1016/j.ergon.2025.103862

N. Zhang , M. Fard , S. Tohmuang , J. Xu , J.L. Davy , S.R. Robinson

As automated driving evolves, ensuring seamless human-vehicle interaction remains a critical challenge. Building upon authors’ previous study, the present study develops and investigates how a Driver Monitoring and Feedback System (DMFS) influences takeover performance, physiological responses, and user experience in conditional automated driving. Seventeen participants engaged in simulated driving sessions with two Non-Driving Related Tasks (NDRTs), namely working and resting, both with and without an active DMFS. Metrics were collected for driving performance, Heart Rate Variability (HRV) and subjective evaluations. The findings indicate that the DMFS mitigated the adverse effects of NDRTs on takeover performance by up to 47 %, particularly during resting conditions. Although the DMFS was generally perceived positively regarding effectiveness and accuracy, lower user experience scores suggest a need for a balance between functionality and user comfort. This study highlights the potential of DMFSs to enhance safety in automated driving, while also identifying challenges in maintaining driver readiness and optimising human-automation interaction. The results underscore the importance of developing adaptive, user-centric DMFS designs for future automated driving systems.

随着自动驾驶的发展，确保人车之间的无缝交互仍然是一项重大挑战。在作者先前研究的基础上，本研究开发并调查了驾驶员监控和反馈系统（DMFS）如何影响有条件自动驾驶中的接管性能、生理反应和用户体验。17名参与者参与了模拟驾驶过程，其中包括两项与驾驶无关的任务（NDRTs），即工作和休息，有或没有DMFS活动。收集了驾驶性能、心率变异性（HRV）和主观评价的指标。研究结果表明，DMFS将NDRTs对接管绩效的不利影响减轻了47%，特别是在休息条件下。虽然DMFS在有效性和准确性方面普遍被认为是积极的，但较低的用户体验分数表明需要在功能和用户舒适度之间取得平衡。本研究强调了dmfs在提高自动驾驶安全性方面的潜力，同时也确定了保持驾驶员准备就绪和优化人机交互方面的挑战。研究结果强调了为未来自动驾驶系统开发自适应、以用户为中心的DMFS设计的重要性。

{"title":"A novel driver monitoring and feedback system improves takeover performance in conditional automated driving","authors":"N. Zhang , M. Fard , S. Tohmuang , J. Xu , J.L. Davy , S.R. Robinson","doi":"10.1016/j.ergon.2025.103862","DOIUrl":"10.1016/j.ergon.2025.103862","url":null,"abstract":"<div><div>As automated driving evolves, ensuring seamless human-vehicle interaction remains a critical challenge. Building upon authors’ previous study, the present study develops and investigates how a Driver Monitoring and Feedback System (DMFS) influences takeover performance, physiological responses, and user experience in conditional automated driving. Seventeen participants engaged in simulated driving sessions with two Non-Driving Related Tasks (NDRTs), namely working and resting, both with and without an active DMFS. Metrics were collected for driving performance, Heart Rate Variability (HRV) and subjective evaluations. The findings indicate that the DMFS mitigated the adverse effects of NDRTs on takeover performance by up to 47 %, particularly during resting conditions. Although the DMFS was generally perceived positively regarding effectiveness and accuracy, lower user experience scores suggest a need for a balance between functionality and user comfort. This study highlights the potential of DMFSs to enhance safety in automated driving, while also identifying challenges in maintaining driver readiness and optimising human-automation interaction. The results underscore the importance of developing adaptive, user-centric DMFS designs for future automated driving systems.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103862"},"PeriodicalIF":3.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hand function ergonomic determinants in aging: Effects of sex, age-group, and muscle mass in Taiwanese older adults 老化中手功能的人体工程学决定因素：台湾老年人的性别、年龄和肌肉量的影响

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-12-06 DOI: 10.1016/j.ergon.2025.103852

Yi-Lang Chen , Syuan Yu , Yu-Chi Lee

As Taiwan enters a super-aged era, understanding the physiological determinants of hand function is essential for promoting independence and quality of life in older adults. This study examined the effects of sex, age group (<70 vs. ≥70 years), and testing posture on grip strength, pinch strength, and wrist range of motion (ROM), and explored their associations with segmental body composition. One hundred right-handed, community-dwelling older adults (50 males, 50 females) underwent standardized assessments. Grip strength was significantly affected by sex and age (both p < 0.001), with males outperforming females across all postures and a marked age-related decline observed in women aged ≥70. Testing posture did not significantly influence grip strength. Pinch strength differed by pinch type (p < 0.001), with lateral pinch producing the highest values; a significant sex × pinch type interaction (p < 0.001) showed the greatest male–female disparity in lateral pinch. Wrist ROM was generally preserved, although a modest sex × age interaction for radial deviation (p < 0.01) was found. Skeletal muscle mass strongly predicted grip strength (R² = 0.525–0.550), while trunk and upper-limb muscle mass differentially predicted pinch strength (R² = 0.330–0.557). These findings emphasize the need for age- and sex-specific ergonomic standards and muscle-targeted interventions in aging populations.

Relevance to industry

This study offers ergonomic insights for designing tools, interfaces, and rehabilitation programs suited to aging populations. By identifying sex-specific strength differences and muscle based hand function predictors, it supports the development of age-appropriate, task-specific interventions to maintain functional capacity and promote independence among older adults in daily life.

随着台湾进入超高龄时代，了解手部功能的生理决定因素对于促进老年人的独立性和生活质量至关重要。本研究考察了性别、年龄组（70岁vs≥70岁）和测试姿势对握力、捏力和手腕活动度（ROM）的影响，并探讨了它们与节段性身体组成的关系。100名右撇子，居住在社区的老年人（50名男性，50名女性）接受了标准化评估。握力受性别和年龄的显著影响（p < 0.001），男性握力在所有姿势上都优于女性，年龄≥70岁的女性握力明显下降。测试姿势对握力没有显著影响。夹紧强度因夹紧类型而异（p < 0.001），侧向夹紧强度最高；显著的性别与捏捏类型交互作用（p < 0.001）表明，男性与女性在侧捏方面的差异最大。腕部ROM一般保留，但发现桡骨偏差存在适度的性别与年龄相互作用（p < 0.01）。骨骼肌质量对握力有较强预测（R2 = 0.525 ~ 0.550），躯干和上肢肌肉质量对捏紧力有差异预测（R2 = 0.330 ~ 0.557）。这些发现强调了针对年龄和性别的人体工程学标准和针对老年人群的肌肉干预的必要性。这项研究为设计适合老年人的工具、界面和康复计划提供了人体工程学的见解。通过识别性别特异性的力量差异和基于肌肉的手功能预测因子，它支持开发适合年龄的、特定任务的干预措施，以维持老年人的功能能力并促进他们在日常生活中的独立性。

{"title":"Hand function ergonomic determinants in aging: Effects of sex, age-group, and muscle mass in Taiwanese older adults","authors":"Yi-Lang Chen , Syuan Yu , Yu-Chi Lee","doi":"10.1016/j.ergon.2025.103852","DOIUrl":"10.1016/j.ergon.2025.103852","url":null,"abstract":"<div><div>As Taiwan enters a super-aged era, understanding the physiological determinants of hand function is essential for promoting independence and quality of life in older adults. This study examined the effects of sex, age group (<70 vs. ≥70 years), and testing posture on grip strength, pinch strength, and wrist range of motion (ROM), and explored their associations with segmental body composition. One hundred right-handed, community-dwelling older adults (50 males, 50 females) underwent standardized assessments. Grip strength was significantly affected by sex and age (both <em>p</em> < 0.001), with males outperforming females across all postures and a marked age-related decline observed in women aged ≥70. Testing posture did not significantly influence grip strength. Pinch strength differed by pinch type (<em>p</em> < 0.001), with lateral pinch producing the highest values; a significant sex × pinch type interaction (<em>p</em> < 0.001) showed the greatest male–female disparity in lateral pinch. Wrist ROM was generally preserved, although a modest sex × age interaction for radial deviation (<em>p</em> < 0.01) was found. Skeletal muscle mass strongly predicted grip strength (R<sup>2</sup> = 0.525–0.550), while trunk and upper-limb muscle mass differentially predicted pinch strength (R<sup>2</sup> = 0.330–0.557). These findings emphasize the need for age- and sex-specific ergonomic standards and muscle-targeted interventions in aging populations.</div></div><div><h3>Relevance to industry</h3><div>This study offers ergonomic insights for designing tools, interfaces, and rehabilitation programs suited to aging populations. By identifying sex-specific strength differences and muscle based hand function predictors, it supports the development of age-appropriate, task-specific interventions to maintain functional capacity and promote independence among older adults in daily life.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103852"},"PeriodicalIF":3.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of perceived risk during partially automated driving on real road 部分自动驾驶在真实道路上感知风险的检测

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-12-04 DOI: 10.1016/j.ergon.2025.103842

Yanyi Li , Yi Wang , Xin Zhou , Wei Zhang , Jingyue Zheng

With the rapid advancement of automated driving technologies, understanding drivers' perceived risk in real-road environments is crucial for the acceptance of automated vehicles (AVs) and ensuring safety. This study proposed a deep learning framework combining convolutional neural network (CNN) and long short-term memory (LSTM) to detect the perceived risk. The framework utilized vehicle, environmental, physiological, and facial data collected from real-road experiments involving partially automated vehicles. Potential high perceived risk events were captured via a smartphone. They were further classified into low/medium/high levels through subjective evaluation. Compared with other methods, the CNN-LSTM model performs the best, achieving an accuracy of 82.8 %, an F1 score of 83.6 %, and an AUC score of 86.5 %. Key features sensitive to perceived risk changes include the steering angle, motorcycle count, skin conductance level, root mean square of electromyographic signals, and eye-related features. The model and the findings of the study may contribute to improved the design of take-over request and driving style adjustment for automated vehicles to improve safety and user acceptance.

随着自动驾驶技术的快速发展，了解驾驶员在真实道路环境中的感知风险对于接受自动驾驶汽车（AVs）和确保安全至关重要。本研究提出了一种结合卷积神经网络（CNN）和长短期记忆（LSTM）的深度学习框架来检测感知风险。该框架利用了从真实道路实验中收集的车辆、环境、生理和面部数据，这些实验涉及部分自动驾驶车辆。潜在的高感知风险事件通过智能手机被捕获。通过主观评价将其进一步分为低/中/高水平。与其他方法相比，CNN-LSTM模型表现最好，准确率为82.8%，F1得分为83.6%，AUC得分为86.5%。对感知风险变化敏感的关键特征包括方向盘角度、摩托车数量、皮肤电导水平、肌电信号均方根和眼睛相关特征。该模型和研究结果有助于改进自动驾驶汽车的接管请求设计和驾驶风格调整，以提高安全性和用户接受度。

{"title":"Detection of perceived risk during partially automated driving on real road","authors":"Yanyi Li , Yi Wang , Xin Zhou , Wei Zhang , Jingyue Zheng","doi":"10.1016/j.ergon.2025.103842","DOIUrl":"10.1016/j.ergon.2025.103842","url":null,"abstract":"<div><div>With the rapid advancement of automated driving technologies, understanding drivers' perceived risk in real-road environments is crucial for the acceptance of automated vehicles (AVs) and ensuring safety. This study proposed a deep learning framework combining convolutional neural network (CNN) and long short-term memory (LSTM) to detect the perceived risk. The framework utilized vehicle, environmental, physiological, and facial data collected from real-road experiments involving partially automated vehicles. Potential high perceived risk events were captured via a smartphone. They were further classified into low/medium/high levels through subjective evaluation. Compared with other methods, the CNN-LSTM model performs the best, achieving an accuracy of 82.8 %, an F1 score of 83.6 %, and an AUC score of 86.5 %. Key features sensitive to perceived risk changes include the steering angle, motorcycle count, skin conductance level, root mean square of electromyographic signals, and eye-related features. The model and the findings of the study may contribute to improved the design of take-over request and driving style adjustment for automated vehicles to improve safety and user acceptance.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103842"},"PeriodicalIF":3.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent prediction of ergonomics evaluation metrics in human-AI collaboration based on machine learning 基于机器学习的人机协作中人机工程学评价指标的智能预测

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-12-04 DOI: 10.1016/j.ergon.2025.103851

Jiabing Zhang, Qingxuan Jia, Siyi Li, Shiyu Zhang, Gang Chen

Ergonomic evaluations in human-AI collaboration systems are often time-consuming, labor-intensive, and prone to bias. The complexity of formulas and factors complicates automated ergonomic evaluation. To address this issue, this paper proposes a machine learning-based framework for predicting ergonomics evaluation metrics. First, a human-AI collaboration experimental method is presented for ergonomics evaluation metric data collection. During the experiment, a total of 18 human-AI collaborative experiments were conducted, comprising 12 human-AI teams and 6 all-human teams, covering various tasks like Flight, Attack, etc. Then, four regression models-linear neural network, nonlinear deep neural network, random forest regressor with data augmentation, and random forest with k-fold cross-validation-are designed to predict ergonomics evaluation metrics. Data augmentation techniques are employed to expand the dataset and enhance the model’s generalization capability. The dataset grew from 72 to 72,000 samples for neural networks and 7200 for random forests through data augmentation. The random forest model was also trained with 6-fold cross-validation, and test sets for all models were derived from the original data. Finally, the models’ accuracy and reliability in predicting these metrics are comprehensively evaluated using relative absolute error, mean relative absolute error, root mean squared error, relative root mean squared error, and the coefficient of determination

R^{2}

, ensuring their validity. The results show that the proposed machine learning framework achieves high prediction accuracy, especially the data-augmented random forest model, which outperforms other models in terms of prediction accuracy. This enables effective automated ergonomic evaluations within human-AI collaboration systems and thus guides designing fluent and efficient human-AI teams.

人类-人工智能协作系统中的人体工程学评估通常是耗时、劳动密集型的，而且容易产生偏见。公式和因素的复杂性使自动化人机工程学评估复杂化。为了解决这个问题，本文提出了一个基于机器学习的框架来预测人体工程学评估指标。首先，提出了一种人机协作实验方法，用于工效评价指标数据采集。在实验过程中，共进行了18次人- ai协同实验，包括12个人- ai团队和6个全人团队，涵盖飞行、攻击等各种任务。然后，设计了线性神经网络、非线性深度神经网络、数据增强随机森林回归器和k-fold交叉验证随机森林四种回归模型来预测人体工程学评价指标。采用数据增强技术扩展数据集，增强模型的泛化能力。通过数据增强，神经网络的数据集从72个增加到72000个，随机森林的数据集从7200个增加到7200个。随机森林模型也进行了6次交叉验证训练，所有模型的测试集均来自原始数据。最后，利用相对绝对误差、平均相对绝对误差、均方根误差、相对均方根误差和决定系数R2对模型预测这些指标的准确性和可靠性进行综合评价，确保模型的有效性。结果表明，所提出的机器学习框架具有较高的预测精度，特别是数据增强随机森林模型的预测精度优于其他模型。这可以在人机协作系统中进行有效的自动化人体工程学评估，从而指导设计流畅高效的人机协作团队。

{"title":"Intelligent prediction of ergonomics evaluation metrics in human-AI collaboration based on machine learning","authors":"Jiabing Zhang, Qingxuan Jia, Siyi Li, Shiyu Zhang, Gang Chen","doi":"10.1016/j.ergon.2025.103851","DOIUrl":"10.1016/j.ergon.2025.103851","url":null,"abstract":"<div><div>Ergonomic evaluations in human-AI collaboration systems are often time-consuming, labor-intensive, and prone to bias. The complexity of formulas and factors complicates automated ergonomic evaluation. To address this issue, this paper proposes a machine learning-based framework for predicting ergonomics evaluation metrics. First, a human-AI collaboration experimental method is presented for ergonomics evaluation metric data collection. During the experiment, a total of 18 human-AI collaborative experiments were conducted, comprising 12 human-AI teams and 6 all-human teams, covering various tasks like Flight, Attack, etc. Then, four regression models-linear neural network, nonlinear deep neural network, random forest regressor with data augmentation, and random forest with k-fold cross-validation-are designed to predict ergonomics evaluation metrics. Data augmentation techniques are employed to expand the dataset and enhance the model’s generalization capability. The dataset grew from 72 to 72,000 samples for neural networks and 7200 for random forests through data augmentation. The random forest model was also trained with 6-fold cross-validation, and test sets for all models were derived from the original data. Finally, the models’ accuracy and reliability in predicting these metrics are comprehensively evaluated using relative absolute error, mean relative absolute error, root mean squared error, relative root mean squared error, and the coefficient of determination <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, ensuring their validity. The results show that the proposed machine learning framework achieves high prediction accuracy, especially the data-augmented random forest model, which outperforms other models in terms of prediction accuracy. This enables effective automated ergonomic evaluations within human-AI collaboration systems and thus guides designing fluent and efficient human-AI teams.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"111 ","pages":"Article 103851"},"PeriodicalIF":3.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of the thermal neutral temperature of working face in deep mine 深部矿井工作面热中性温度的预测

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-11-01 DOI: 10.1016/j.ergon.2025.103830

Guoshan Wu , Fei Liu , Mengyue Wang , Wenruikun Fu , Heqing Liu , Bo You

The thermal index of the atmosphere and building environment mainly evaluated deep underground mines' hot and humid working environments. However, their predicted thermal neutral temperature often differed from the actual situation. At present, there is no straightforward method to determine the thermal neutral temperature of mine working face. The exergy analysis method was combined with the Prediction Heat Strain model to establish a human exergy model. The model was used to calculate the exergy parameter of workers with humidity of 80 %–100 %, wind speed of 0.3–4.0 m/s, moderate/severe/extremely severe labour, and the corresponding relationship between the exergy parameter thresholds was analyzed. The rationality of using the minimum exergy consumption to determine the thermal neutral temperature was verified using the subjective thermal sensation voting (TSV) obtained from the simulated mine labour experiment. The results show that when the human body exergy transfer is the largest, the exergy efficiency is the largest, and the exergy consumption is the most minor (non-high temperature environment). The thermal neutral temperature of the working face is approximately equal to the ambient temperature corresponding to the maximum exergy transfer and minimum exergy consumption. When the wind speed is above 2.5 m/s, the thermal neutral temperature can be increased, which is conducive to reducing the energy consumption of refrigeration equipment. The human exergy model can predict the thermal neutral temperature of the working face, which provides a new idea for studying the thermal comfort of workers. It provides a meaningful reference for ventilation and cooling in coal mining faces.

大气热指标和建筑环境热指标主要评价深埋地下矿井的湿热工作环境。然而，他们预测的热中性温度往往与实际情况不同。目前，还没有一种简便易行的方法来确定矿井工作面的热中性温度。将火用分析方法与预测热应变模型相结合，建立了人体火用模型。利用该模型计算了湿度为80% ~ 100%、风速为0.3 ~ 4.0 m/s、中度/重度/极重度劳动的工人的火用参数，并分析了火用参数阈值之间的对应关系。通过模拟矿井劳动实验得出的主观热感觉投票（TSV），验证了用最小火用值确定热中性温度的合理性。结果表明，当人体火用传递量最大时，火用效率最大，而火用消耗最小（非高温环境）。工作面热中性温度近似等于最大火用传递和最小火用消耗所对应的环境温度。当风速在2.5 m/s以上时，可提高热中性温度，有利于降低制冷设备的能耗。人体火用模型可以预测工作面的热中性温度，为研究工作人员的热舒适提供了新的思路。为采煤工作面通风降温提供了有意义的参考。

{"title":"Prediction of the thermal neutral temperature of working face in deep mine","authors":"Guoshan Wu , Fei Liu , Mengyue Wang , Wenruikun Fu , Heqing Liu , Bo You","doi":"10.1016/j.ergon.2025.103830","DOIUrl":"10.1016/j.ergon.2025.103830","url":null,"abstract":"<div><div>The thermal index of the atmosphere and building environment mainly evaluated deep underground mines' hot and humid working environments. However, their predicted thermal neutral temperature often differed from the actual situation. At present, there is no straightforward method to determine the thermal neutral temperature of mine working face. The exergy analysis method was combined with the Prediction Heat Strain model to establish a human exergy model. The model was used to calculate the exergy parameter of workers with humidity of 80 %–100 %, wind speed of 0.3–4.0 m/s, moderate/severe/extremely severe labour, and the corresponding relationship between the exergy parameter thresholds was analyzed. The rationality of using the minimum exergy consumption to determine the thermal neutral temperature was verified using the subjective thermal sensation voting (TSV) obtained from the simulated mine labour experiment. The results show that when the human body exergy transfer is the largest, the exergy efficiency is the largest, and the exergy consumption is the most minor (non-high temperature environment). The thermal neutral temperature of the working face is approximately equal to the ambient temperature corresponding to the maximum exergy transfer and minimum exergy consumption. When the wind speed is above 2.5 m/s, the thermal neutral temperature can be increased, which is conducive to reducing the energy consumption of refrigeration equipment. The human exergy model can predict the thermal neutral temperature of the working face, which provides a new idea for studying the thermal comfort of workers. It provides a meaningful reference for ventilation and cooling in coal mining faces.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"110 ","pages":"Article 103830"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond traditional methods: The impact of game-based learning on safety training 超越传统方法：基于游戏的学习对安全培训的影响

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-11-01 DOI: 10.1016/j.ergon.2025.103827

Sameeran G. Kanade , Jun He , Sogand Hasanzadeh , Brandon J. Pitts , Behzad Esmaeili , Mahmudur Rahman , Dharmendra K. Mishra , Vincent G. Duffy

This study evaluates the effectiveness of game-based learning in improving construction safety knowledge, focusing on the OSHA hazard identification training tool. Using a mixed-methods approach, 64 graduate students were divided into control and training groups, with half experiencing task interruptions during hazard identification tests. Participants completed pre- and post-intervention hazard identification tests, NASA-TLX questionnaires, and surveys while their visual attentional distribution was monitored using eye-tracking technology. Results showed that the training group significantly outperformed the control group in hazard identification, particularly for fall and struck-by hazards. The training group also reported lower mental demand, better performance perception, and less effort in post-intervention tests. Eye-tracking data revealed similar patterns in both groups, with decreased attention to fall hazards and increased attention to missing PPE and struck-by hazards post-intervention. However, the game-based approach was less effective in addressing missing PPE hazards. Task interruptions negatively impacted hazard identification in trained participants post-intervention, while control group performance remained unaffected. Interestingly, Qualtrics survey responses revealed a gap between the perceived and actual impact of interruptions, emphasizing the need for targeted training that raises awareness about these effects. The findings support game-based learning as a promising approach to improve safety training effectiveness, but underscore the importance of interruption management strategies in high-risk environments.

本研究以OSHA危害识别训练工具为研究对象，评估游戏式学习对提升建筑安全知识的效果。采用混合方法，64名研究生被分为对照组和训练组，其中一半在危险识别测试中经历任务中断。参与者完成了干预前和干预后的危害识别测试、NASA-TLX问卷和调查，同时使用眼动追踪技术监测他们的视觉注意力分布。结果表明，训练组在危险识别方面明显优于对照组，特别是在跌倒和撞击危险方面。在干预后的测试中，训练组还报告了更低的心理需求、更好的表现感知和更少的努力。眼动追踪数据在两组中显示出相似的模式，干预后对跌倒危险的关注减少，对缺少个人防护装备和撞击危险的关注增加。然而，基于游戏的方法在解决缺少PPE危害方面效果较差。任务中断在干预后对训练参与者的危险识别产生负面影响，而对照组的表现不受影响。有趣的是，Qualtrics的调查结果揭示了中断的感知和实际影响之间的差距，强调需要有针对性的培训来提高对这些影响的认识。研究结果支持基于游戏的学习作为一种有希望提高安全培训有效性的方法，但强调了在高风险环境中中断管理策略的重要性。

{"title":"Beyond traditional methods: The impact of game-based learning on safety training","authors":"Sameeran G. Kanade , Jun He , Sogand Hasanzadeh , Brandon J. Pitts , Behzad Esmaeili , Mahmudur Rahman , Dharmendra K. Mishra , Vincent G. Duffy","doi":"10.1016/j.ergon.2025.103827","DOIUrl":"10.1016/j.ergon.2025.103827","url":null,"abstract":"<div><div>This study evaluates the effectiveness of game-based learning in improving construction safety knowledge, focusing on the OSHA hazard identification training tool. Using a mixed-methods approach, 64 graduate students were divided into control and training groups, with half experiencing task interruptions during hazard identification tests. Participants completed pre- and post-intervention hazard identification tests, NASA-TLX questionnaires, and surveys while their visual attentional distribution was monitored using eye-tracking technology. Results showed that the training group significantly outperformed the control group in hazard identification, particularly for fall and struck-by hazards. The training group also reported lower mental demand, better performance perception, and less effort in post-intervention tests. Eye-tracking data revealed similar patterns in both groups, with decreased attention to fall hazards and increased attention to missing PPE and struck-by hazards post-intervention. However, the game-based approach was less effective in addressing missing PPE hazards. Task interruptions negatively impacted hazard identification in trained participants post-intervention, while control group performance remained unaffected. Interestingly, Qualtrics survey responses revealed a gap between the perceived and actual impact of interruptions, emphasizing the need for targeted training that raises awareness about these effects. The findings support game-based learning as a promising approach to improve safety training effectiveness, but underscore the importance of interruption management strategies in high-risk environments.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"110 ","pages":"Article 103827"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of automation stage on alerted-monitor performance and operator perception with and without concurrent task demands 有无并发任务需求时自动化阶段对报警监控性能和操作员感知的影响

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-11-01 DOI: 10.1016/j.ergon.2025.103849

Jianhua Sun , Suihuai Yu , Jianjie Chu , Xiaojiao Xie , Wenzhe Cun , Hao Fan

Alerted-monitor systems may not be perfectly reliable and are apt to generate false alarms, risking automation misuse. The possibility and severity of automation misuse can be affected by the automation stage of the alerted-monitor task and the presence of concurrent task demands, especially when there is more than one unpredictable false alarm throughout the entire task. Therefore, this study investigated the effects of the automation stage on alerted-monitor performance and operator perception with/without concurrent task demands, aiming to select the appropriate automation stage to mitigate false alarm impacts. Participants performed an alerted-monitor task under two automation stages (decision-making and action implementation) with/without a concurrent manual tracking task. Results showed that action-implementation automation consistently enhanced efficiency (e.g., reduced correct response time to false alarms, lower workload) and trust regardless of concurrent task demands, while uniquely improving accuracy in distinguishing true and false alarms only under concurrent task demands. In contrast, decision-making automation increased situational awareness exclusively in the absence of concurrent task demands. These findings suggest that action-implementation automation should be prioritized in the presence of concurrent task demands, as it mitigates false alarm effects, evidenced by its improved accuracy in distinguishing true and false alarms. In contrast, decision-making automation may be favored in contexts without concurrent task demands, as it enhances situational awareness, though its direct impact on mitigating false alarms is limited.

报警监控系统可能不是完全可靠的，而且容易产生假警报，有误用自动化的风险。自动化误用的可能性和严重程度可能受到警报监视任务的自动化阶段和并发任务需求的影响，特别是当整个任务中存在多个不可预测的假警报时。因此，本研究调查了自动化阶段对有/没有并发任务需求的报警监视器性能和操作员感知的影响，旨在选择适当的自动化阶段来减轻假警报的影响。参与者在两个自动化阶段（决策和行动实施）下执行警报监视任务，有/没有并发的手动跟踪任务。结果表明，在不考虑并发任务需求的情况下，行动执行自动化始终如一地提高了效率（例如，减少了对假警报的正确响应时间，降低了工作量）和信任，而仅在并发任务需求下，才独特地提高了区分真假警报的准确性。相比之下，决策自动化仅在没有并发任务需求的情况下增加态势感知。这些研究结果表明，在存在并发任务需求的情况下，行动实施自动化应该被优先考虑，因为它可以减轻假警报效应，这一点可以通过它在区分真假警报方面的准确性得到证明。相比之下，决策自动化在没有并发任务需求的情况下可能更受青睐，因为它增强了态势感知，尽管它对减轻假警报的直接影响有限。

{"title":"Effects of automation stage on alerted-monitor performance and operator perception with and without concurrent task demands","authors":"Jianhua Sun , Suihuai Yu , Jianjie Chu , Xiaojiao Xie , Wenzhe Cun , Hao Fan","doi":"10.1016/j.ergon.2025.103849","DOIUrl":"10.1016/j.ergon.2025.103849","url":null,"abstract":"<div><div>Alerted-monitor systems may not be perfectly reliable and are apt to generate false alarms, risking automation misuse. The possibility and severity of automation misuse can be affected by the automation stage of the alerted-monitor task and the presence of concurrent task demands, especially when there is more than one unpredictable false alarm throughout the entire task. Therefore, this study investigated the effects of the automation stage on alerted-monitor performance and operator perception with/without concurrent task demands, aiming to select the appropriate automation stage to mitigate false alarm impacts. Participants performed an alerted-monitor task under two automation stages (decision-making and action implementation) with/without a concurrent manual tracking task. Results showed that action-implementation automation consistently enhanced efficiency (e.g., reduced correct response time to false alarms, lower workload) and trust regardless of concurrent task demands, while uniquely improving accuracy in distinguishing true and false alarms only under concurrent task demands. In contrast, decision-making automation increased situational awareness exclusively in the absence of concurrent task demands. These findings suggest that action-implementation automation should be prioritized in the presence of concurrent task demands, as it mitigates false alarm effects, evidenced by its improved accuracy in distinguishing true and false alarms. In contrast, decision-making automation may be favored in contexts without concurrent task demands, as it enhances situational awareness, though its direct impact on mitigating false alarms is limited.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"110 ","pages":"Article 103849"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effects of sitting postures on seat vibrotactile interaction system for enhancing occupants’ situation awareness in high-level automated driving 高水平自动驾驶中坐姿对座椅振动触觉交互系统的影响

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-11-01 DOI: 10.1016/j.ergon.2025.103832

Chuanliang Shen, Xiaoyuan Ma, Longxu Zhang, Xiao Xiao, Yi Li, Hongyu Hu, Zhenhai Gao

This study investigated the effects of sitting postures on the enhancement of occupants’ situation awareness through seat vibrotactile interaction system in high-level automated driving scenarios during a non-driving related task. Furthermore, addressing the limitation of fixed, non-adaptive vibrotactile signal generation in existing studies, an occupant body pressure monitoring-based seat vibrotactile interaction system (OBPM-SVIS) was developed, and it was compared with the original system lacking the pressure monitoring function. The Wizard of Oz experimental approach was employed to simulate automated driving scenarios in real-road. Eighteen participants received signals indicating vehicle upcoming behaviors, including left turn, right turn, acceleration, and deceleration. The participants adopted five sitting postures, which were upright, left-leaning, right-leaning, backward-leaning, and forward-leaning. These signals were presented in both static and dynamic patterns. Effects were comprehensively evaluated based on correct response rate, reaction time, situation awareness rating technique (SART) score, rating scale mental effort (RSME) score, and user experience questionnaire (UEQ) score. Results indicated that non-upright sitting postures adversely affected the transmission of vibrotactile signals and increased the difficulty of signal recognition. Static pattern demonstrated less robustness to changes in sitting posture compared to dynamic pattern. The OBPM-SVIS effectively mitigated the adverse effects of sitting posture changes. This study provides a reference for optimizing vibrotactile interaction systems that convey information about the vehicle’s upcoming behaviors or takeover requests to occupants in high-level automated driving.

本研究通过座椅振动触觉交互系统研究了在高水平自动驾驶场景下，非驾驶相关任务中，坐姿对乘员态势感知增强的影响。针对现有研究中固定、非自适应振动触觉信号产生的局限性，研制了基于乘员身体压力监测的座椅振动触觉交互系统（OBPM-SVIS），并与不具备压力监测功能的座椅振动触觉交互系统进行了对比。采用绿野仙踪的实验方法模拟真实道路上的自动驾驶场景。18名参与者收到了指示车辆即将到来的行为的信号，包括左转、右转、加速和减速。参与者采用了五种坐姿，分别是直立、左倾、右倾、后倾和前倾。这些信号以静态和动态两种模式呈现。根据正确反应率、反应时间、态势感知评定技术（SART）评分、评定量表心理努力（RSME）评分和用户体验问卷（UEQ）评分对效果进行综合评价。结果表明，非直立坐姿对振动触觉信号的传递产生不利影响，增加了信号识别的难度。与动态模式相比，静态模式对坐姿变化的稳健性较差。OBPM-SVIS有效地减轻了坐姿改变的不利影响。该研究为优化振动触觉交互系统提供了参考，该系统可在高水平自动驾驶中向乘员传递车辆即将发生的行为或接管请求的信息。

{"title":"The effects of sitting postures on seat vibrotactile interaction system for enhancing occupants’ situation awareness in high-level automated driving","authors":"Chuanliang Shen, Xiaoyuan Ma, Longxu Zhang, Xiao Xiao, Yi Li, Hongyu Hu, Zhenhai Gao","doi":"10.1016/j.ergon.2025.103832","DOIUrl":"10.1016/j.ergon.2025.103832","url":null,"abstract":"<div><div>This study investigated the effects of sitting postures on the enhancement of occupants’ situation awareness through seat vibrotactile interaction system in high-level automated driving scenarios during a non-driving related task. Furthermore, addressing the limitation of fixed, non-adaptive vibrotactile signal generation in existing studies, an occupant body pressure monitoring-based seat vibrotactile interaction system (OBPM-SVIS) was developed, and it was compared with the original system lacking the pressure monitoring function. The Wizard of Oz experimental approach was employed to simulate automated driving scenarios in real-road. Eighteen participants received signals indicating vehicle upcoming behaviors, including left turn, right turn, acceleration, and deceleration. The participants adopted five sitting postures, which were upright, left-leaning, right-leaning, backward-leaning, and forward-leaning. These signals were presented in both static and dynamic patterns. Effects were comprehensively evaluated based on correct response rate, reaction time, situation awareness rating technique (SART) score, rating scale mental effort (RSME) score, and user experience questionnaire (UEQ) score. Results indicated that non-upright sitting postures adversely affected the transmission of vibrotactile signals and increased the difficulty of signal recognition. Static pattern demonstrated less robustness to changes in sitting posture compared to dynamic pattern. The OBPM-SVIS effectively mitigated the adverse effects of sitting posture changes. This study provides a reference for optimizing vibrotactile interaction systems that convey information about the vehicle’s upcoming behaviors or takeover requests to occupants in high-level automated driving.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"110 ","pages":"Article 103832"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heat stress and heat-related illnesses among male and female refugee agricultural workers in Lebanon 黎巴嫩男女难民农业工人的热应激和与热有关的疾病

IF 3 2区工程技术 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Ergonomics

Pub Date : 2025-11-01 DOI: 10.1016/j.ergon.2025.103836

Rima R. Habib , Lina M. Fakih , Ghida Al Nakib , Lea Saad , Fida Awada , Mira F. Kanaan , Zeinab Awad , John C. Flunker , June T. Spector , Iman Nuwayhid

Heat stress among agricultural workers has intensified as a result of climate change, with women disproportionately affected due to physiological, social, and occupational factors. This study investigates heat-related illnesses (HRI), defined by the presence of heat-related symptoms, among male and female Syrian agricultural workers in greenhouse farms of Lebanon's farming communities.

A cross-sectional analysis was carried out among 90 agricultural workers (43 male pesticide sprayers and 47 female harvesters) in 32 farms. Data were collected in summer 2024 (August–September) from Syrian workers aged between 18 and 55 years old. Heat stress was assessed using environmental measures (Wet Bulb Globe Temperature [WBGT]) and physiological indicators included heart rate and estimated core body temperature; this was complemented by field observations during the observed work period. HRI and other occupational factors were assessed using structured questionnaires. Sociodemographic, occupational, and environmental factors were compared between male and female workers. Multivariable logistic regression was used to identify HRI risk factors.

Female workers were significantly more likely to report HRI compared to males (65.9 % vs 37.2 %, p = 0.006). We attribute this difference to biological and occupational factors, particularly in the local context. Higher odds of HRI were also observed with younger age, longer work hours, more strenuous workloads, extended restroom travel times, wearing multiple layers, and working in environments where WBGT inside greenhouses exceeded 26.6 °C. This study underscores the role of task allocation within agricultural work in predicting the risk of HRI. It particularly emphasizes the health implications of gendered labor segregation in agriculture.

由于气候变化，农业工人的热应激加剧，由于生理、社会和职业因素，妇女受到的影响尤为严重。本研究调查了黎巴嫩农业社区温室农场的叙利亚男性和女性农业工人的热相关疾病（HRI），由热相关症状的存在来定义。对32个农场的90名农业工人（43名男性农药喷洒员和47名女性收割机）进行了横断面分析。数据于2024年夏季（8月至9月）从18至55岁的叙利亚工人中收集。热应激评估采用环境指标（湿球温度[WBGT]）和生理指标包括心率和估计的核心体温；在观察到的工作期间，实地观察补充了这一点。HRI和其他职业因素采用结构化问卷进行评估。对男女职工的社会人口、职业和环境因素进行比较。采用多变量logistic回归确定HRI危险因素。与男性相比，女性员工更有可能报告HRI （65.9% vs 37.2%, p = 0.006）。我们将这种差异归因于生物和职业因素，特别是在当地情况下。年龄小、工作时间长、工作量大、上厕所时间长、穿多层衣服以及在温室内WBGT超过26.6°C的环境中工作，也会增加HRI的几率。本研究强调了农业工作中任务分配在预测人力资源感染风险中的作用。它特别强调农业中性别劳动隔离对健康的影响。

{"title":"Heat stress and heat-related illnesses among male and female refugee agricultural workers in Lebanon","authors":"Rima R. Habib , Lina M. Fakih , Ghida Al Nakib , Lea Saad , Fida Awada , Mira F. Kanaan , Zeinab Awad , John C. Flunker , June T. Spector , Iman Nuwayhid","doi":"10.1016/j.ergon.2025.103836","DOIUrl":"10.1016/j.ergon.2025.103836","url":null,"abstract":"<div><div>Heat stress among agricultural workers has intensified as a result of climate change, with women disproportionately affected due to physiological, social, and occupational factors. This study investigates heat-related illnesses (HRI), defined by the presence of heat-related symptoms, among male and female Syrian agricultural workers in greenhouse farms of Lebanon's farming communities.</div><div>A cross-sectional analysis was carried out among 90 agricultural workers (43 male pesticide sprayers and 47 female harvesters) in 32 farms. Data were collected in summer 2024 (August–September) from Syrian workers aged between 18 and 55 years old. Heat stress was assessed using environmental measures (Wet Bulb Globe Temperature [WBGT]) and physiological indicators included heart rate and estimated core body temperature; this was complemented by field observations during the observed work period. HRI and other occupational factors were assessed using structured questionnaires. Sociodemographic, occupational, and environmental factors were compared between male and female workers. Multivariable logistic regression was used to identify HRI risk factors.</div><div>Female workers were significantly more likely to report HRI compared to males (65.9 % vs 37.2 %, p = 0.006). We attribute this difference to biological and occupational factors, particularly in the local context. Higher odds of HRI were also observed with younger age, longer work hours, more strenuous workloads, extended restroom travel times, wearing multiple layers, and working in environments where WBGT inside greenhouses exceeded 26.6 °C. This study underscores the role of task allocation within agricultural work in predicting the risk of HRI. It particularly emphasizes the health implications of gendered labor segregation in agriculture.</div></div>","PeriodicalId":50317,"journal":{"name":"International Journal of Industrial Ergonomics","volume":"110 ","pages":"Article 103836"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0