Document Type: Original Articles



AbstractBackground: In this cross-sectional study, 501 employees of petrochemical companies were selected by simple sampling method.Methods: Data were collected using Swedish Occupational Fatigue Inventory (SOFI-20), Occupational Fatigue/Exhaustion Recovery (OFER-15), and General Health Questionnaire (GHQ-28). To identify the factors associated with fatigue and general health, we used ordinary least squares regression (OLS) and SUR and the results were compared. The analysis showed that satisfaction, mental disorder and sleepiness were the important factors associated with fatigue among these workers. However, the SUR estimator provided higher precision of the estimates than the OLS estimator as the parameters obtained by SUR are characterized by lower standard errors. As the models are intended to predict the fatigue risk factors, we particularly focused on the SUR method because it assesses the precision of the model in predicting fatigue determination. SUR estimators performed consistently better than the OLS estimators since SUR takes the correlation between error terms into account. Results: The findings showed that the study population were young and almost had a low job tenure.  The correlation test showed that there was a significant relationship between fatigue and general health with job satisfaction (p=0.05), sleep disorder (p=0.01) and mental disorder (p=0.001). Finally, the analysis showed that fatigue as the result of work was affected by some organizational and individual risk factors, among which "general health status" in general fatigue and "job satisfaction and mental disorders" in mental, physical, shift work, chronic and acute fatigue had the most effect.Conclusion: The prevalence of fatigue among the study population was assessed high. Thus, elimination and reduction of casual risk factors are necessary to reduce the prevalence of fatigue at work environmental.


  1. References
  2. Hossienzadeh,K.,A.(2012).Validity and Reliability of the Farsi version of the Individual Strength Questionnaire Checklist in the Iranian Working Population. Armaghane-danesh 18(4):295-304.
  3. Ahsberg E, Gamberale F, Kjellberg A. Perceived quality of fatigue during different occupational tasks Development of a questionnaire. International Journal of Industrial Ergonomics 1997; 20:121-135.
  4. Skinner, N. & Dorrian, J. (February 2012). ‘A work-life perspective on sleep and fatigue – it’s not just the shift workers who are at risk’. Paper presented at the 26th AIRAANZ Conference Re-organising Work’, Gold Coast, Queensland.
  5. Kristal-Boneh, E.,P.Froom,et al.(1996)Fatigue Among Israeli Industrial Employees.Journal of Occupational &Environmental Medicine 38(11):1145-1150.
  6. Zwarts MJ, Bleijenberg G, Engelen BGM. Clinical neurophysiology of fatigue. Clinical Neurophysiology 2008;119:2-10.
  7. Fang, J., W . Kunaviktikul, et al.(2008). Factors influencing fatigue in chinese nurses. Nursing and health sciences 10:291-299.
  8. Akerstedt, T., A. Knutssonb, et al.(2004).Mental fatigue , work and sleep . Journal of psychosomatic Reaserch.
  9. Parhizi, S., L. M. Steege , et al. (2013).Mining the relationships between phychosocial factors and fatigue dimensions among registered nurses. International Journal of Ergonomics.
  10. Ahsberg E. Perceived fatigue related to work. University of Stockholm, Department of Psychology, National Institute for Working Life Department for Work and Health, ISBN 91–7153–830–5.
  11. Javadpour , F.(2013).Investigation of Fatigue dimensions and some aspects of their health impacts on petrochemical employees , Thesis for the degree of master of sciences. Shiraz university of Medical Sciences School of Health.
  12. Franssen, P.M. L., U. Bultmann, et al.(2003). The association between chronic diseases and fatigue in the working population .Journal of Psychosomatic Research.
  13. Akerstedt, T., A. Knutssonb, et al .(2004). Mental fatigue, Work and Sleep. Journal Of Psychosomatic Research 57:427-433.
  14. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests foraggregation bias. Journal of the American Statistical Association.
  15. Judge, G.G, Hill, R.C., Griffiths, W.E., Lutkepohl. H., &Lee, T.C. (1988). Introduction to the teory and practice of econometrics. 2 edition. Wiley, New York.
  16. Yahya,W.B.,Adebayo,S.B.jolayemi,E.T.Oyejola,B.Sanni.(2008)Effects of orthogonality efficiency of seemingly unrelated regression models.
  17. Ahsberg, E. (2000).Dimensions of fatigue in different working populations . Scandinavian journal of psychology
  18. Ahsberg, E., F. Gamberale (1998). Perceived fatigue after mental work: an expremental evaluation of a fatigue inventory .Ergonomics 43(2):252-268.
  19. Ahsberg, E. and F. Gamberale(1998). Perceived fatigue during physical work : an experimental evaluation of a fatigue inventort . International Journal of Industrial Ergonomics 21:117-131.
  20. Javadpour F, Keshavarzi S.(2015). Validity and reliability of the Swedish Occupational Fatigue Inventory (SOFI-20) among Iranian working population.Journal of Ergonomics 3(1):50-57.
  21. Winwood, p. c., A. H. Winefield, et al.(2005).Development and Validation of a Scale to Measure Work-Related Fatigue and Recovery: The Occupational Fatigue Exhaustion/Recovery Scale (OFER). JOEM 45(6):594-606.
  22. Javadpour F, Keshavarzi S.(2014). Validity and reliability of Occupational Fatigue/Exhaustion Recovery scale (OFER-15) among Iranian working population. Iran Occupational Health 11(6):75-82.
  23. Mohammad Beigi, a., n. Mohammad Salehi, et al.(2009). Depression symptoms prevalence , general health status and its risk factors in dormitory students of Arak universities 2008. Arak Medical UniversityJournal 12(3):116-123.
  24. Jahani Hashemi, H. and K. Noroozi(2004). Mental health in student in Qazvin University of Medical Science . Payesh 2(3):145-152.
  25. Keshavarzi, S. Ayatollahi, M, et al. (2013). Quality of Life Child bearing age women and associated factors: an application of seemingly unrelated regression models. Springer Science 22:1255-1263.
  26. Timm, N.H. (2002). Applied multivariate analysis. New York: Springer.
  27. Tian, Y., Sun, Y. (2014). Some Overall Properties Of Seemingly Unrelated Regression Models. Asta Adv Stat Anal. 98:103-120.
  28. Zellner, A. (1963). Estimators for seemingly Unrelated Regressions : Some Exact Finite Sample Results, J. Am. Assoc., 58,977-992;Corrigendum(1972),67,255.
  29. Beasley, T. M. (2008). Seemingly unrelated regression (SUR) models as a solution to path analytic models with correlated errors. Multiple Linear Regression Viewpoints, 34(1), 1–7.
  30. Vasco A. P. Cadavez. and Arne. Henningsen. The Use of Seemingly Unrelated Regression (SUR) to Predict the Carcass Composition of Lambs. Institute of Food and Resource Economics University of Copenhagen.