Development of a cumulative trauma disorder risk assessment model for the upper extremities

Vishal Seth, Roberta Lee Weston, Andris Freivalds

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

A cumulative trauma disorders (CTD) risk assessment model was developed for predicting injury incidence rates. The model is unique in that it uses quantitative data such as hand motion frequencies and forces together to obtain a frequency factor score that is reflective of the strain imposed on the muscles and tendons of the wrist. Gross upper extremity postures are included in a posture factor score and various minor job stressors are included as a miscellaneous factor score. The individual frequency, posture and miscellaneous scores were regressed against CTD incidence rates for 24 jobs in a garment sewing and a printing company to yield weights for a predicted incidence rate or risk index. The miscellaneous score was not a significant predictor (p>0.05) in determining incidence rates and thus was dropped from the model. The results showed that the model was best suited for job tasks with cycle times greater than 4s. The final best fit model (p<0.001) explained 52.3% of the variance in predicted incidence rates and may be a useful tool in identifying jobs at risk for cumulative trauma disorders. Relevance to the industry: The CTD risk assessment model predicts CTD incidence rates or a relative risk potential for industrial jobs based on task and hand motion parameters. This allows appropriate medical and engineering personnel to intervene quickly to redesign the risky jobs and thus potentially reduce the injuries and medical costs. Copyright (C) 1999 Elsevier Science B.V.

Original languageEnglish (US)
Pages (from-to)281-291
Number of pages11
JournalInternational Journal of Industrial Ergonomics
Volume23
Issue number4
DOIs
StatePublished - Mar 15 1999

Fingerprint

Cumulative Trauma Disorders
Upper Extremity
Risk assessment
risk assessment
trauma
incidence
Incidence
Posture
Hand
Printing
Clothing
Tendons
Wounds and Injuries
Wrist
Muscle
Industry
personnel
Personnel
engineering
Weights and Measures

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Public Health, Environmental and Occupational Health

Cite this

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abstract = "A cumulative trauma disorders (CTD) risk assessment model was developed for predicting injury incidence rates. The model is unique in that it uses quantitative data such as hand motion frequencies and forces together to obtain a frequency factor score that is reflective of the strain imposed on the muscles and tendons of the wrist. Gross upper extremity postures are included in a posture factor score and various minor job stressors are included as a miscellaneous factor score. The individual frequency, posture and miscellaneous scores were regressed against CTD incidence rates for 24 jobs in a garment sewing and a printing company to yield weights for a predicted incidence rate or risk index. The miscellaneous score was not a significant predictor (p>0.05) in determining incidence rates and thus was dropped from the model. The results showed that the model was best suited for job tasks with cycle times greater than 4s. The final best fit model (p<0.001) explained 52.3{\%} of the variance in predicted incidence rates and may be a useful tool in identifying jobs at risk for cumulative trauma disorders. Relevance to the industry: The CTD risk assessment model predicts CTD incidence rates or a relative risk potential for industrial jobs based on task and hand motion parameters. This allows appropriate medical and engineering personnel to intervene quickly to redesign the risky jobs and thus potentially reduce the injuries and medical costs. Copyright (C) 1999 Elsevier Science B.V.",
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Development of a cumulative trauma disorder risk assessment model for the upper extremities. / Seth, Vishal; Lee Weston, Roberta; Freivalds, Andris.

In: International Journal of Industrial Ergonomics, Vol. 23, No. 4, 15.03.1999, p. 281-291.

Research output: Contribution to journalArticle

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