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Original Research

Open Access

Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine

  • Haewon Byeon1,2,*,

1Department of Digital Anti-Aging Healthcare (BK21), Inje University, 50834 Gimhae, Republic of Korea

2Department of AI-Software, INJE Medical Big Data Research Center, Inje University, 50834 Gimhae, Republic of Korea

DOI: 10.22514/jomh.2024.199 Vol.20,Issue 12,December 2024 pp.41-49

Submitted: 11 July 2024 Accepted: 06 August 2024

Published: 30 December 2024

*Corresponding Author(s): Haewon Byeon E-mail: byeon@inje.ac.kr

Abstract

This study aims to investigate the prevalence and risk factors of musculoskeletal disorders (MSDs) among South Korean male office workers and to introduce a robust predictive model using the Robust and Sparse Twin Support Vector Machine (RSTSVM). A cross-sectional survey was conducted among male office workers in South Korea to assess the prevalence of MSDs and identify associated risk factors. Data on ergonomic and psychosocial factors were collected and analyzed. The RSTSVM model was developed and compared with traditional machine learning models, including Support Vector Machine (SVM) and Gradient Boosting Machine (GBM), to predict the risk of MSDs. The analysis revealed a high prevalence of MSDs among the surveyed office workers, attributed to factors such as prolonged sitting, repetitive hand/arm movements, standing posture and carrying heavy objects. Prolonged static postures were significantly linked to lower back pain and other musculoskeletal issues. Poor workstation ergonomics and psychosocial stressors, such as high job demands and low job control, were also identified as significant predictors of MSDs. The RSTSVM model demonstrated superior performance in predicting MSDs, with an Area under the Receiver Operating Characteristic Curve (AUC-ROC) value of 0.84, effectively managing high-dimensional data and maintaining robustness against outliers and noise. Furthermore, the RSTSVM model provided enhanced interpretability, making it easier to identify and understand key risk factors compared to traditional models. The study underscores the critical need for multifaceted intervention strategies to address the ergonomic and psychosocial risk factors associated with MSDs among office workers. Future research should focus on longitudinal studies to establish causal relationships and evaluate the effectiveness of various interventions across different occupational groups.


Keywords

Musculoskeletal disorders; Psychosocial factors; Office workers; RSTSVM model


Cite and Share

Haewon Byeon. Predicting occupational musculoskeletal disorders in South Korean male office workers using a robust and sparse twin support vector machine. Journal of Men's Health. 2024. 20(12);41-49.

References

[1] Korea Occupational Safety and Health Agency. Prevalence of among South Korean workers. Journal of Occupational Health. 2022; 64: 123–130.

[2] Korea Occupational Safety and Health Agency. Health survey of agricultural workers in South Korea. Journal of Agricultural Safety and Health. 2021; 27: 56–64.

[3] Ministry of Employment and Labor, South Korea. Updated policies on occupational diseases in South Korea. Journal of Occupational Medicine. 2022; 58: 210–220.

[4] International Labour Organization. Updated facts on safe work practices. 2021. Available at: https://www.ilo.org/topics/safety-and-health-work (Accessed: 15 June 2024).

[5] Hannu T, Kritzinger J. MORBI ARTIFICUM. Acta medico-historica Adriatica: AMHA. 2021; 19: 195–220.

[6] World Health Organization. Global health expenditure database. 2021. Available at: https://apps who int/nha/database (Accessed: 15 June 2024).

[7] World Health Organization. Protecting workers’ health in the 21st century. 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/occupational-health--health-workers (Accessed: 15 June 2024).

[8] National Institute for Occupational Safety and Health (NIOSH). Current research on occupational musculoskeletal disorders. 2021. Available at: https://www.cdc.gov/niosh/ergonomics/index.html/ (Accessed: 15 June 2024).

[9] Korea Statistical Information Service (KOSIS). Updated national health statistics. 2021. Available at: https://kosis.kr/index/index.do/ (Accessed: 15 June 2024).

[10] Ministry of Health and Welfare, South Korea. Comprehensive chronic disease management strategies. 2021. Available at: http://www mohw go kr/eng/ (Accessed: 15 June 2024).

[11] European Foundation for the Improvement of Living and Working Conditions. Sixth European working conditions survey. 2020. Available at: https://www.eurofound.europa.eu/en/surveys/european-working-conditions-surveys-ewcs (Accessed: 15 June 2024).

[12] Vapnik V. The nature of statistical learning theory. 3rd edn. Springer-Verlag: New York. 2021.

[13] Mekonnen TH. Work-related factors associated with low back pain among nurse professionals in east and west Wollega Zones, Western Ethiopia, 2017: a cross-sectional study. Pain and Therapy. 2019; 8: 239–247.

[14] Eubank BHF, Lackey SW, Slomp M, Werle JR, Kuntze C, Sheps DM. Consensus for a primary care clinical decision-making tool for assessing, diagnosing, and managing shoulder pain in Alberta, Canada. BMC Family Practice. 2021; 22: 201.

[15] Tanveer M, Rajani T, Rastogi R, Shao YH, Ganaie MA. Comprehensive review on twin support vector machines. Annals of Operations Research. 2024; 339: 1223–1268.

[16] Wang S, Taha AF, Abokifa AA. How effective is model predictive control in real-time water quality regulation? State-space modeling and scalable control. Water Resources Research. 2021; 57: e2020WR027771.

[17] Bongers PM, Ijmker S, van den Heuvel S, Blatter BM. Epidemiology of work related neck and upper limb problems: psychosocial and personal risk factors (part I) and effective interventions from a bio behavioural perspective (part II). Journal of Occupational Rehabilitation. 2006; 16: 279–302.

[18] Huysmans MA, IJmker S, Blatter BM, Knol DL, van Mechelen W, Bongers PM, et al. The relative contribution of work exposure, leisure time exposure, and individual characteristics in the onset of arm-wrist-hand and neck-shoulder symptoms among office workers. International Archives of Occupational and Environmental Health. 2012; 85: 651–666.

[19] Hartvigsen J, Leboeuf-Yde C, Lings S, Corder EH. Is sitting-while-at-work associated with low back pain? A systematic, critical literature review. Scandinavian Journal of Public Health. 2000; 28: 230–239.

[20] Bongers PM, de Winter CR, Kompier MA, Hildebrandt VH. Psychosocial factors at work and musculoskeletal disease. Scandinavian Journal of Work, Environment & Health. 1993; 19: 297–312.

[21] Kazeminasab S, Nejadghaderi SA, Amiri P, Pourfathi H, Araj-Khodaei M, Sullman MJ, et al. Neck pain: global epidemiology, trends and risk factors. BMC Musculoskeletal Disorders. 2022; 23: 26.

[22] Basakci Calik B, Yagci N, Oztop M, Caglar D. Effects of risk factors related to computer use on musculoskeletal pain in office workers. International Journal of Occupational Safety and Ergonomics. 2022; 28: 269–274.

[23] Bonlioli R, Caraballo-Arias Y, Salmen-Navarro A. Epidemiology of work-related musculoskeletal disorders. Current Opinion in Epidemiology and Public Health. 2022; 1: 18–24.

[24] Bao S, Howard N, Lin JH. Are work-related musculoskeletal disorders claims related to risk factors in workplaces of the manufacturing industry? Annals of Work Exposures and Health. 2020; 64: 152–164.

[25] Kulkarni M, Yadav T. Prevalence of cumulative trauma disorder of wrist joint in auto mechanical workers. Indian Journal of Public Health Research & Development. 2020; 11: 619–623.

[26] van der Molen HF, Nieuwenhuijsen K, Frings-Dresen MH, de Groene G. Work-related psychosocial risk factors for stress-related mental disorders: an updated systematic review and meta-analysis. BMJ Open. 2020; 10: e034849.

[27] El Bannany M, Khedr AM, Sreedharan M, Kanakkayil S. Financial distress prediction based on multi-layer perceptron with parameter optimization. IAENG International Journal of Computer Science. 2021; 48: 1–12.

[28] Argus M, Pääsuke M. Musculoskeletal disorders and functional characteristics of the neck and shoulder: comparison between office workers using a laptop or desktop computer. Work. 2023; 75: 1289–1299.

[29] Khan SH, Mohan TRC, Abed AAAA, KB S, Bhumik A. Posture related musculoskeletal disorders (MSDs) among computer users in higher education sectors of Malaysia. Malaysian Journal of Medicine & Health Sciences. 2020; 16: 71–78.

[30] Jain R, Meena ML, Rana KB. Risk factors of musculoskeletal symptoms among mobile device users during work from home. International Journal of Occupational Safety and Ergonomics. 2022; 28: 2262–2268.

[31] Tang KHD. A review of psychosocial models for the development of musculoskeletal disorders and common psychosocial instruments. Archives of Current Research International. 2020; 20: 9–19.

[32] Besharati A, Daneshmandi H, Zareh K, Fakherpour A, Zoaktafi M. Work-related musculoskeletal problems and associated factors among office workers. International Journal of Occupational Safety and Ergonomics. 2020; 26: 632–638.

[33] Afroz S, Haque MI. Ergonomics in the workplace for a better quality of work life. Ergonomics for Improved Productivity (pp. 503–511). Springer: Singapore. 2021.

[34] Choobineh A, Shakerian M, Faraji M, Modaresifar H, Kiani J, Hatami M, et al. A multilayered ergonomic intervention program on reducing musculoskeletal disorders in an industrial complex: a dynamic participatory approach. International Journal of Industrial Ergonomics. 2021; 86: 103221.


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