Mohammad Sadegh Sohrabi, Hassan Khotanlou, Rashid Heidarimoghadam, Iraj Mohammadfam, Mohammad Babamiri, Ali Reza Soltanian
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The data included the information of 311 office workers (aged 32.04±5.34). Method neighborhood component analysis (NCA) was used to measure the effect of factors affecting WMSDs, and then support vector machines (SVMs) and decision tree algorithms were utilized to classify the decrease or increase of disorders.</p><p><strong>Results: </strong>Three classified models were designed according to the follow-up times of the field study, with accuracies of 86.5%, 80.3%, and 69%, respectively. These models could estimate most influencer factors with acceptable sensitivity. The main factors included age, body mass index, interventions, QWL, some subscales, and several psychological factors. Models predicted that relative absenteeism and presenteeism were not related to the outputs.</p><p><strong>Conclusion: </strong>In this study, the focus was on disorders in the neck, and the obtained models revealed that individual and management interventions can be the main factors in reducing WMSDs in the neck. Modeling with ML methods can create a new understanding of the relationships between variables affecting WMSDs.</p>","PeriodicalId":17164,"journal":{"name":"Journal of research in health sciences","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380738/pdf/","citationCount":"0","resultStr":"{\"title\":\"Modeling the Impact of Ergonomic Interventions and Occupational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office Workers with Machine Learning Methods.\",\"authors\":\"Mohammad Sadegh Sohrabi, Hassan Khotanlou, Rashid Heidarimoghadam, Iraj Mohammadfam, Mohammad Babamiri, Ali Reza Soltanian\",\"doi\":\"10.34172/jrhs.2024.158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Modeling with methods based on machine learning (ML) and artificial intelligence can help understand the complex relationships between ergonomic risk factors and employee health. 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引用次数: 0
摘要
背景:利用基于机器学习(ML)和人工智能的方法进行建模,有助于了解人体工程学风险因素与员工健康之间的复杂关系。本研究旨在使用 ML 方法估算个体因素、人体工程学干预措施、工作生活质量(QWL)和生产率对办公室工作人员颈部工作相关肌肉骨骼疾病(WMSDs)的影响。研究设计:准随机对照试验:为了衡量干预措施的影响,对一项准随机对照试验的数据采用 ML 方法进行了建模。数据包括 311 名上班族(年龄为 32.04±5.34)的信息。采用邻近成分分析法(NCA)来衡量影响WMSDs的因素的影响,然后利用支持向量机(SVM)和决策树算法来对疾病的减少或增加进行分类:根据实地研究的随访时间设计了三种分类模型,准确率分别为 86.5%、80.3% 和 69%。这些模型可以估算出大多数影响因素,灵敏度尚可。主要因素包括年龄、体重指数、干预措施、QWL、某些分量表以及一些心理因素。模型预测相对缺勤率和缺勤率与产出无关:本研究的重点是颈部疾病,所得模型显示,个人和管理干预是减少颈部 WMSDs 的主要因素。使用 ML 方法建模可以对影响 WMSDs 的变量之间的关系产生新的认识。
Modeling the Impact of Ergonomic Interventions and Occupational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office Workers with Machine Learning Methods.
Background: Modeling with methods based on machine learning (ML) and artificial intelligence can help understand the complex relationships between ergonomic risk factors and employee health. The aim of this study was to use ML methods to estimate the effect of individual factors, ergonomic interventions, quality of work life (QWL), and productivity on work-related musculoskeletal disorders (WMSDs) in the neck area of office workers. Study Design: A quasi-randomized control trial.
Methods: To measure the impact of interventions, modeling with the ML method was performed on the data of a quasi-randomized control trial. The data included the information of 311 office workers (aged 32.04±5.34). Method neighborhood component analysis (NCA) was used to measure the effect of factors affecting WMSDs, and then support vector machines (SVMs) and decision tree algorithms were utilized to classify the decrease or increase of disorders.
Results: Three classified models were designed according to the follow-up times of the field study, with accuracies of 86.5%, 80.3%, and 69%, respectively. These models could estimate most influencer factors with acceptable sensitivity. The main factors included age, body mass index, interventions, QWL, some subscales, and several psychological factors. Models predicted that relative absenteeism and presenteeism were not related to the outputs.
Conclusion: In this study, the focus was on disorders in the neck, and the obtained models revealed that individual and management interventions can be the main factors in reducing WMSDs in the neck. Modeling with ML methods can create a new understanding of the relationships between variables affecting WMSDs.
期刊介绍:
The Journal of Research in Health Sciences (JRHS) is the official journal of the School of Public Health; Hamadan University of Medical Sciences, which is published quarterly. Since 2017, JRHS is published electronically. JRHS is a peer-reviewed, scientific publication which is produced quarterly and is a multidisciplinary journal in the field of public health, publishing contributions from Epidemiology, Biostatistics, Public Health, Occupational Health, Environmental Health, Health Education, and Preventive and Social Medicine. We do not publish clinical trials, nursing studies, animal studies, qualitative studies, nutritional studies, health insurance, and hospital management. In addition, we do not publish the results of laboratory and chemical studies in the field of ergonomics, occupational health, and environmental health