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Identifying safe load moment exposures for the back.
Marras WS; Lavender SA; Ferguson SA; Splittstoesser RE; Yang G; Schabo P; Burr DL
Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, U01-OH-007313, 2004 Apr; :1-413
Introduction: Low back disorders continue to be the most common and significant work-related musculoskeletal disorders in the US. Identifying what constitutes a "safe" physical workload from both the magnitude and temporal perspectives has been the biggest challenge facing injury prevention efforts. The goal of this project was to identify and quantify biomechanical risk factors, such as the load moment and duty cycle variables, that are predictive of low back disorders in distribution centers. The specific aims of this project were: (1) To develop new models based on an existing industrial exposure database. (2) To develop instrumentation so that the horizontal distance between the hands and the spine can be accurately measured for each lift performed over the course of an entire work shift. (3) To develop a database that contains the load moment exposures, hand position data, duty cycle parameters, and injury experience data. (4) To define via a prospective study the relationship between moment exposure parameters and the low back disorder incidence rates and disability measures. (5) To develop a simplified approach that can be used by practitioners to easily evaluate work tasks. Methods: A sonic-based Moment Exposure Tracking System was developed to measure the physical exposure of the job continuously over an entire shift. A prospective study was designed to examine the low back health effect of workers in distributions centers. The physical exposure was measured on 195 workers on 50 jobs in 21 distribution centers. Low back injury rates were collected from the company's records. The health effects measures included a Clinical Lumbar Motion Monitor (CLMM) functional performance, low back symptom survey, and psychosocial measures administered at baseline and 6-14 months follow-up. Results: A new model based on the existing industrial database was built by dichotomizing the original variables. The sensitivity and specificity of the model have been improved. The accuracy of the Moment Exposure Tracking System (METS) has been tested and validated. The average error of the moment arm measurement was within 1.5 inches. For the prospective study, 451 workers who stayed on the same job at follow-up completed the low back functional performance test and the survey. The data captured by the METS was used to create a database that contains over 66,000 exertions quantifying load moment exposure, trunk and box kinematics, and duty cycle parameters from the 50 distribution center jobs. Multiple logistic regression models capable of predicting high risk jobs were developed based on both the incidence rate and the CLMM functional assessment. A combination of the mean of a job's maximum resultant dynamic moments, mean of the maximum lateral trunk accelerations, and the mean duration of the exertions predicted the prospective change of low back functional performance with 70% sensitivity and 93.8% specificity. A combination of the average horizontal dynamic lift moment, the integrated lateral trunk acceleration, and the mean duration of the non-load exposure periods were predictive of incidence rate with 80% sensitivity and 87% specificity. Consistent with the final aim of the study, simplified models were developed to be used without specialized instrumentation. The average box weight and duration of exertion can be used to predict the high risk jobs defined by change of low back functional performance. The average of the peak horizontal static lift moments and the mean duration of the non-load exposure periods could be used to assess the job risk defined by the incidence rate. Discussion: The models created represent the interactions between the force, motion, and frequency nature of the physical exposure. The correspondence between load moment, kinematic, and duty cycle variables entering the models based upon low back functional status and the incidence rate data is strong despite the longitudinal nature of the low back functional status and the cross-sectional nature of the incidence rate data. Conclusions: Low back disorder risk can be characterized by combinations of the load moment exposure, trunk kinematics, and duty cycle descriptors. The specialized instrumentation developed in this study allows us to accurately quantify the physical exposure of manual materials handling jobs, and hence low back disorder risk in distribution centers. The combinations of dynamic moment, lateral trunk acceleration, and temporal variables are predictive of the change of low back functional status and injury rate. The simplified models presented in this report that do not require instrumentation can be used to identify high back injury risk distribution center jobs with reasonable sensitivity and specificity.
Back-injuries; Injuries; Manual-lifting; Manual-materials-handling; Motion-studies; Muscle-stress; Musculoskeletal-system; Musculoskeletal-system-disorders; Work-environment; Ultrasound; Monitors; Biomechanical-modeling; Biomechanics; Risk-analysis; Warehousing; Job-analysis; Force; Mathematical-models; Analytical-processes; Biodynamics; Exposure-assessment; Models; Physical-capacity; Task-performance; Human-factors-engineering; Author Keywords: back injury; low back pain; musculoskeletal disorders; MSD; distribution center; load moment
William S. Marras, Ph.D. The Ohio State University, Institute for Ergonomics, Biodynamics Laboratory, 1971 Neil Avenue, Columbus, OH 43210
Final Grant Report
NTIS Accession No.
National Institute for Occupational Safety and Health
The Ohio State University
Page last reviewed: March 25, 2022
Content source: National Institute for Occupational Safety and Health Education and Information Division