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 intervent...
| Publicado en: | Journal of Research in Health Sciences Vol. 224; no. 3; pp. 56 - 63 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Hamadan University of Medical Sciences, School of Public Health
Summer2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179549174&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179549174 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22287795 903Q jtl: Journal of Research in Health Sciences issn: 22287795 maglogo: N pubinfo: dt: Summer2024 vid: 224 iid: 3 pid: 54266 pub: Hamadan University of Medical Sciences, School of Public Health artinfo: ui: 179549174 179549174 179549174 10.34172/jrhs.2024.158 179549174 ppf: 56 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Modeling the Impact of Ergonomic Interventions and Occupational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office Workers with Machine Learning Methods. aug: au: Sohrabi, Mohammad Sadegh Khotanlou, Hassan Heidarimoghadam, Rashid Mohammadfam, Iraj Babamiri, Mohammad Soltanian, Ali Reza affil: Center of Excellence for Occupational Health, Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan, Iran sug: subj: White Collar Workers Machine Learning Artificial Intelligence Musculoskeletal Diseases Risk Factors Occupational Diseases Risk Factors Neck Pathology Risk Assessment Ergonomics Quality of Working Life Productivity Human Funding Source Quasi-Experimental Studies Randomized Controlled Trials Descriptive Statistics Data Analysis Software Male Female Adult Iran Analysis of Variance Algorithms Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|