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Can the Revised NIOSH Lifting Equation Be Improved by Incorporating Personal Characteristics?

  • Menekse Salar Barim
  • Richard F. Sesek
  • M. Fehmi Capanoglu
  • Sean Gallagher
  • Mark C. SchallJr.
  • Gerard A. Davis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 820)

Abstract

The impact of manual material handling such as lifting, lowering, pushing and pulling have been extensively studied. Many models using these external demands to predict injury have been proposed and employed by safety and health professionals. However, ergonomic models incorporating personal characteristics into a comprehensive model are lacking. This study explores the utility of adding personal characteristics such as the estimated L5/S1 Intervertebral Disc (IVD) cross sectional area, height, age, gender and Body Mass Index (BMI) to the Revised NIOSH Lifting Equation (RNLE) with the goal to improve injury prediction. A dataset with known RNLE Cumulative Lifting Indices (CLIs) and related health outcomes was used to evaluate the impact of personal characteristics on RNLE performance. The dataset included 29 cases and 101 controls selected from a cohort of 1,022 subjects performing 667 jobs. RNLE performance was significantly improved by incorporation of personal characteristics. Adding gender and intervertebral disc size multipliers to the RNLE raised the odds ratio for a CLI of 3.0 from 6.71 (CI: 2.2–20.9, PPV: 0.60, NPV: 0.82) to 24.75 (CI: 2.8–215.4, PPV: 0.86, NPV: 0.80). The most promising RNLE change involved incorporation of the multiplier based on the estimated IVD cross-sectional area (CSA). This multiplier was developed by normalizing against the IVD CSA for a 50th percentile woman. This multiplier could assume values greater than one (for subjects with larger IVD CSA than a 50th percentile woman). Thus, CLI could both decrease and increase as a result of this multiplier. Increases in RNLE performance were achieved primarily by decreasing the number of RNLE false positives (e.g., some CLIs for uninjured subjects were reduced below 3.0). Results are promising, but confidence intervals are broad and additional, prospective research is warranted to validate findings.

Keywords

Revised NIOSH Lifting Equation (RNLE) Personal characteristics BMI Age Gender Low back pain Intervertebral disc cross sectional area L5/S1 

Notes

Acknowledgement

This publication was partially supported by Grant # 2T420H008436 from NIOSH. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names is for identification only and does not constitute endorsement by the Public Health Service or by the U.S. Department of Health and Human Service.

References

  1. 1.
    Woolf AD, Pfleger B (2003) Burden of major musculoskeletal conditions. Bull. World Health Organ. 81(9):646–656Google Scholar
  2. 2.
    Lidgren L (2003) The bone and joint decade 2000–2010. Bull. World Health Organ. 81(9):629Google Scholar
  3. 3.
    Institute of Medicine and National Research Council (2001) Musculoskeletal disorders and the workplace. The National Academies Press, Washington, DCGoogle Scholar
  4. 4.
    Shiri R, Karppinen J, Leino-Arjas P, Solovieva S, Varonen H et al (2007) Cardiovascular and lifestyle risk factors in lumbar radicular pain or clinically defined sciatica: a systematic review. Eur. Spine J. 16:2043–2054CrossRefGoogle Scholar
  5. 5.
    Schumann B, Bolm-Audorff U, Bergmann A, Ellegast R, Elsner G et al (2010) Lifestyle factors and lumbar disc disease: results of a German multi-center case-control study (EPILIFFT). Arthritis Res Ther 12:193CrossRefGoogle Scholar
  6. 6.
    Da Costa BR, Vieira ER (2010) Risk factors for work-related musculoskeletal disorders: a systematic review of recent longitudinal studies. Am. J. Ind. Med. 53:285–323Google Scholar
  7. 7.
    Dempsey PG (2002) Usability of the revised NIOSH lifting equation. Ergonomics 45(12):817–828 Liberty Mutual Research Center for Safety & Health, 71 Frankland Road, Hopkinton, Massachusetts 01748, USACrossRefGoogle Scholar
  8. 8.
    Dempsey PG, McGorry RW, Maynard WS (2005) A survey of tools and methods used by certified professional ergonomists. Appl Ergon 36:489–503CrossRefGoogle Scholar
  9. 9.
    Waters TR, Putz-Anderson V, Garg A, Fine L (1993) Revised NIOSH equation for design and evaluation of manual lifting tasks. Ergonomics 36(7):446–749CrossRefGoogle Scholar
  10. 10.
    Waters TR, Putz-Anderson V, Garg A (1993) Applications manual for the revised NIOSH lifting equation. US Department of Health and Human Services, CincinnatiGoogle Scholar
  11. 11.
    Waters TR, Putz-Anderson V, Garg A, Fine LJ (1993) Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics 36:749–776CrossRefGoogle Scholar
  12. 12.
    Waters TR, Putz-Anderson V, Garg A (1994) ‘Applications manual for the revised NIOSH lifting equation’ DHHS (NIOSH) Publication No. 94–110, U. S. Department of Health and Human Services, National Institute for Occupational Safety and Health, Cincinnati, OHGoogle Scholar
  13. 13.
    Gallagher S, Sesek RF, Schall M Jr, Huangfu R (2017) Development and validation of an easy-to-use risk assessment tool for cumulative low back loading: the lifting fatigue failure tool (LiFFT). Appl Ergon 63(142–150):13Google Scholar
  14. 14.
    Garg A (1995) Revised NIOSH equation for manual lifting: a method for job evaluation. AAOHN J 43(4):211–216Google Scholar
  15. 15.
    Sesek RF (1999) Evaluation and refinement of ergonomic survey tools to evaluate worker risk of cumulative trauma disorders. Doctoral Dissertation. University of Utah, Salt Lake City, UTGoogle Scholar
  16. 16.
    Sesek R, Gilkey D, Drinkaus P, Bloswick DS, Herron R (2003) Evaluation and qualification of manual materials handling risk factors. Int J Occup Saf Ergon 9(3):271–287CrossRefGoogle Scholar
  17. 17.
    Sesek R, Tang R, Gungor C, Gallagher S, Davis GA, Foreman KB (2014) Using MRI-derived spinal geometry to compute back compressive stress (BCS): a new measure of low back pain risk. In: Duffy V (ed) Proceedings of the 5th AHFE Conference, pp 13–18Google Scholar
  18. 18.
    Tang R (2013) Morphometric analysis of the human lower lumbar intervertebral discs and vertebral endplates: experimental approach and regression models. Doctoral Dissertation. Auburn University, Auburn, ALGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Menekse Salar Barim
    • 1
  • Richard F. Sesek
    • 2
  • M. Fehmi Capanoglu
    • 2
  • Sean Gallagher
    • 2
  • Mark C. SchallJr.
    • 2
  • Gerard A. Davis
    • 2
  1. 1.Oak Ridge Institute for Science and Education (ORISE) Research FellowCincinnatiUSA
  2. 2.Auburn UniversityAuburnUSA

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