Volume 25, Issue 3 (Iranian South Medical Journal 2022)
Iran South Med J 2022, 25(3): 250-260 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mououdi M A, Sammak Amani A, Ghezelbash K, Ghahari M, Kebriyaee Nasab T. Musculoskeletal Disorders (MSDS) in the Administrative Staff of the National Iranian Gas Transmission Company-District 9 (NIGTC-D9). Iran South Med J 2022; 25 (3) :250-260
URL: http://ismj.bpums.ac.ir/article-1-1629-en.html
URL: http://ismj.bpums.ac.ir/article-1-1629-en.html
Mohammad Amin Mououdi1 
, Arezoo Sammak Amani * 2, Kievan Ghezelbash3 , Mehdi Ghahari4 , Tahmeineh Kebriyaee Nasab5
, Arezoo Sammak Amani * 2, Kievan Ghezelbash3 , Mehdi Ghahari4 , Tahmeineh Kebriyaee Nasab5
1- Department of Occupational Health Engineering, School of Health, Mazandaran University of Medical Sciences
2- Department of Occupational Health Engineering, School of Health, Mazandaran University of Medical Sciences ,arezoo.sam76@yahoo.com
3- Department of Computer Engineering-Artificial Intelligence, Rouzbahan Educational Institute
4- Department of Occupational Health Engineering, School of Health, Yazd Shahid Sadoughi University of Medi-cal Sciences
5- Department of HSE, Abadan School of Petroleum (Shahid Tondgouyan), Petroleum University of Technology
2- Department of Occupational Health Engineering, School of Health, Mazandaran University of Medical Sciences ,
3- Department of Computer Engineering-Artificial Intelligence, Rouzbahan Educational Institute
4- Department of Occupational Health Engineering, School of Health, Yazd Shahid Sadoughi University of Medi-cal Sciences
5- Department of HSE, Abadan School of Petroleum (Shahid Tondgouyan), Petroleum University of Technology
Abstract: (1476 Views)
Background: MSDS have harmful effects on productivity and public health worldwide. These disorders are more common in the upper limbs, including the neck and lumbar, among administrative staff and computer users. The aim of this study was to assess musculoskeletal disorders.
Materials and Methods: This cross-sectional study was performed in 2020. All of the 256 administrative staff of the NIGTC-D9 were recruited in the study. The CMDQ was distributed among the candidates and the completed questionnaires were then reviewed. The information obtained from the questionnaires were entered in SPSS ver. 25 and the correlation and relationship between the demographic variables and the prevalence of discomfort in different organs of the body were determined. The data was also examined and evaluated by artificial intelligence (machine learning).
Results: The distribution of sex between the subjects was 229 male and 27 female. The mean age and body mass index were 38.3±6.15 and 27.2±4.73, respectively. The highest score obtained in the questionnaire was 1023. The highest burden of discomfort was in the upper limbs and lumbar (11.85%) and the lowest in the right forearm (1.33%). A score above 367 was determined as a critical point by AI (machine learning).
Conclusion: The main areas affected by MSDS were the neck, followed by the lumbar region and the upper back. Considering the presence of MSDS in the examined candidates, control and preventive measures such as providing ‘back school’ training programs, promoting awareness among workers about appropriate work postures and designing ergonomic workstations (sitting/standing and standing) seems necessary.
Materials and Methods: This cross-sectional study was performed in 2020. All of the 256 administrative staff of the NIGTC-D9 were recruited in the study. The CMDQ was distributed among the candidates and the completed questionnaires were then reviewed. The information obtained from the questionnaires were entered in SPSS ver. 25 and the correlation and relationship between the demographic variables and the prevalence of discomfort in different organs of the body were determined. The data was also examined and evaluated by artificial intelligence (machine learning).
Results: The distribution of sex between the subjects was 229 male and 27 female. The mean age and body mass index were 38.3±6.15 and 27.2±4.73, respectively. The highest score obtained in the questionnaire was 1023. The highest burden of discomfort was in the upper limbs and lumbar (11.85%) and the lowest in the right forearm (1.33%). A score above 367 was determined as a critical point by AI (machine learning).
Conclusion: The main areas affected by MSDS were the neck, followed by the lumbar region and the upper back. Considering the presence of MSDS in the examined candidates, control and preventive measures such as providing ‘back school’ training programs, promoting awareness among workers about appropriate work postures and designing ergonomic workstations (sitting/standing and standing) seems necessary.
Type of Study: Original |
Subject:
General
Received: 2022/08/16 | Accepted: 2022/07/30 | Published: 2022/09/22
Received: 2022/08/16 | Accepted: 2022/07/30 | Published: 2022/09/22
References
1. Afifehzadeh-Kashani H, Choobineh A, Bakand S, et al. Validity and Reliability Farsi Version Cornell Musculoskeletal Discomfort Questionnaire (CMDQ). Iran Occup Health 2011; 7(4): 10-0. (Persian) [Article]
2. Showraki N, Fakhraei F, Saadatmand N, et al. Effects of Teaching Ergonomic Principles on Working Status in Dental Students. Iran South Med J 2019; 22(2): 130-140. (Persian) [Article]
3. Sohrabi M, Faridizad AM, Farasati F. Comparing Results of Musculoskeletal Disorders Evaluation in Computer Users with CMDQ, RULA and ROSA Methods. J Ilam Uni Med Sci 2015; 23(4): 53-62. (Persian) [Article]
4. Mohammadian Mastan M, Motamedzade M, Faradmal J. Investigating the Correlations of results of three methods OCRA Index, Strain Index, ACGIH HAL to evaluate the risk of upper extremity musculoskeletal disorders. Iran J Ergon 2013; 1(2): 63-71. (Persian) [Article]
5. Mououdi MA, Shokrolahi I, Shahpuri R, et al. Ergonomically Adjustable Laptop Desk Designed Based on Anthropometric Characteristics of 20-30Year-Old Students of Mazandaran University of Medical Sciences. Iran J Ergon 2017; 5(2): 55-60. (Persian) [Article]
6. David GC. Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders. Occup Med (Lond) 2005; 55(3): 190-9. [DOI]
7. Motamedzadeh M, Golmohamadi R, Soultanian A, et al. The prevalence of musculoskeletal disorders as examined by the Hand Activity Level and Threshold Limit Value (HAL-TLV) method and the Human Body Map and the implementation of an ergonomic intervention at a tea factory. J Occup Hyg Eng 2015; 2(2): 62-71. (Persian) [Article]
8. Azad P, Choobineh A, Ghaffari F. Musculoskeletal Disorders Risk Assessment in Patient Transfers among Nurses in a hospital in Tehran Using MAPO Technique and provide control measures. J Occup Hyg Eng 2017; 3(4): 38-45. (Persian) [Article]
9. Poole D, Mackworth A, Goebel R. Computational Intelligence. New York: Oxford University Press, 1998, 1-576.
10. Nilsson NJ. Artificial intelligence: a new synthe Burlington, Massachusetts: Morgan Kaufmann, 1998, 1-514.
11. Legg S, Hutter M. A collection of definitions of intelligence. FAIA 2007; 157: 17-24. [Article]
12. Russell S, Norvig P. Artificial intelligence: a modern approach. 2nd ed. New Jersey: Prentice Hall, 2002, 1-1112.
13. Everitt BS, Landau S, Leese M, et al. Cluster analysis. 5th ed. Hobok : John Wiley, 2011, 1-352.
14. Mirmohammadi SJ, Mehrparvar AH, Soleimani H, et al. Musculoskeletal disorders among video display terminal (VDT) workers comparing with other office workers. Iran Occup Health 2010; 7(2): 11-4. (Persian) [Article]
15. Khakkar S, FarhangDehghan S, HajiEsmaeilHajar F, et al. Prevalence of musculoskeletal disorders among the office staff of a communication service company and its relationship to work load and occupational fatigue. J Prevent Med 2018; 5(1): 1-11. (Persian) [Article]
16. Nadri H, Nadri A, Khanjani N, et al. Evaluating the Factors Effective on Musculoskeletal Disorders among the Employees of one of Qazvin’s Governmental Offices. Health Dev J 2013; 2(2): 106-116. (Persian) [Article]
17. Gorgi Z, Assadollahi Z, Ghaffarian A, et al. The Prevalence of Musculoskeletal Disorders in the Employees of Office Systems at Rafsanjan University of Medical Sciences in 2012. J Rafsanjan Uni Med Sci 2014; 12(12): 991-1002. (Persian) [Article]
18. Akbari J, Kazemi M, Mazareie A, et al. The Ergonomic assessment of exposure to risk factors that cause musculoskeletal disorders in Office workers by using ROSA. J Ilam Uni Med Sci 2017; 25(2): 8-17. (Persian) [Article]
Send email to the article author
| Rights and Permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
