This article is reprinted with permission from The Ergonomics Report™ Archives, where it originally appeared on June 28 27, 2011.
Ergonomists have long struggled to develop and validate models, methods and evaluation tools with the power to predict future occurrence of pain, discomfort, musculoskeletal disorders and injuries. The NIOSH Lifting Equation (RNLE) and the University of Michigan 3D Static Strength Prediction Program (3DSSPP) are two well known evaluation methods that can be applied to understand the risk of low back pain and injury. Researchers Boda, Bhoyar and Garg conducted a study with the intention to validate the ability of the two methods in predicting the future occurrence of work-related low back pain (WLBP). The results are mixed.
In the introduction, the authors point out that numerous studies have shown a relationship between physical demands of lifting/lowering jobs and the incidence rate of LBP, but that for the most part, those studies used a cross-sectional design, not a longitudinal, or prospective cohort design, like that used in this study. In this context, cross-sectional studies look for incidence of LBP across a broad population at a single instance in time, whereas longitudinal studies look for the incidence of LBP over some time period. That is, this prospective cohort design allowed the researchers to track the incidence of LBP in the study population over time, giving them the ability to determine whether the RNLE or the 3DSSPP was able to predict the onset of work-related low back pain (WLBP).
The researchers used data from a previous study that tracked 750 workers at 30 companies performing a wide variety of tasks. 514 of those study participants had been assessed for LBP at the study outset and at least one monthly follow-up assessment. 258 of those workers were LBP free at the the study outset (baseline), so only those participants were used in this study. As with all controlled scientific studies, the authors present a great deal of detail regarding their methods, and interested readers are encouraged to read the full article, referenced below, to better understand the study methods.
Two separate assessment teams were used: (1) health outcome assessments that identified LBP at regular intervals throughout the study; and (2) job physical exposure assessments that established baseline and tracked and analyzed physical job variables at regular intervals. The teams were blinded from each other.
The physical assessment team tracked four primary variables that were used in this study:
- RNLE Lifting Index (LI) and Composite Lifting Index (CLI)
- L5/S1 Compressive Force (3DSSPP)
- Percent of Population Capable of producing the required muscle force (within gender; 3DSSPP); and
- Load-Moment (load weight x horizontal distance between hands and L5/S1)
The health outcome assessment team performed a variety of assessments to track and rate the occurence of WLBP. Part of their effort included methods to identify physical occurrences (e.g., a slip/fall, previous surgeries) and non-work activities that may contribute to WLBP, in which case the participant was removed from the final analysis.
For statistical analysis, the researchers calculated univariate hazard ratios (Cox proportional hazards model), which were then adjusted for categories of age, Body Mass Index (BMI), and gender.
Results and Conclusions
Primary findings include:
- Peak Load Moment was found to be the most statistically significant association (p=0.001) with the onset of WLBP, and there was a clear trend between increasing Peak Load Moment and increasing Hazard Ratios;
- There were statistical associations between WLBP and both the task that had the highest RNLE LI (p=0.005), and the job that produces the highest RNLE CLI (p=0.021);
- There was "suggestive evidence" (p=0.089) of association between WLBP and the RNLE CLI for jobs performed by a worker "most of the time";
- There was no statistical association between Peak Compressive Force and WLBP;
- There was no statistical association between gender specific Minimum Percent Capable and WLBP;
- There was "suggestive evidence" (p=0.097) of an association between age and WLBP;
- There was "suggestive evidence" (p=0.117) of an association between BMI and WLBP;
- There was no evidence of a gender association with WLBP; and
- A reference limit of 1.2 for both STLI and CLI may be more predictive of incident cases of WLBP as compared to the currently recommended NIOSH limit of 1.0.
Note that the researchers found associations with peak values and WLBP, but little or weak evidence for an association with average or typical physical exposures. The authors state that "It appears that peak stresses from a task or job are better predictors of incident cases of WLBP than those stresses from a job that workers perform most of the time," which they note is consistent with findings from other researchers.
They also note that other researchers have found statistically significant associations between back compressive forces and percent of capable population calculations from the 3DSSPP, but this study did not (see limitations, below, for a possible explanation).
This study appears to be a post-hoc analysis of data that was collected for a different study that was designed for different purposes. As such, the available data may not have been directed as well toward answering the questions posed by the authors as a study designed specifically for those questions. Also, even with a fairly large set of participants (258), the statistical power of this study is limited.
The authors also note that the 3DSSPP in particular is very sensitive to posture, and the methods used in this study to perform analysis, including review of video, may have led to analyst error in performing the calculations. They also suggest that some brief tasks that may have created peak exposures were not witnessed, and therefore not included in this analysis.
What This Means to Ergonomists
Validating our analysis models and methods is critical to an ergonomists ability to investigate risk and formulate reasonable recommendations. The RNLE and the 3DSSPP models are both prevalent methods used in the field, but this study and others suggest we still do not have strong validation to back their results. As with all evaluation tools, ergonomists must exercise great care and professional judgement in their application and interpretation. Ultimately, it is input from models like these and other available data, including such things as various types of surveys, existing incident rates, worker interviews and our own experience with risk assessment that form our understanding of the nature and level of risk.
Boda, Sruthi Vasudev; Bhoyar, Parag; Garg, Arun. Validation of Revised NIOSH Lifting Equation and 3D SSP Model to Predict Risk of Work-Related Low Back Pain, appearing in the 2010 Human Factors and Ergonomics Society Annual Meeting Proceedings, Industrial Ergonomics , pp. 1185-1189(5), last accessed from http://pro.sagepub.com/content/54/15/1185.abstract on August 28, 2013. doi: 10.1177/154193121005401521.
This article originally appeared in The Ergonomics Report™ on 2011-06-28.