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model and 16 for a three-dimensional model. As an example, let us consider the anterior
posterior shear
forces. When spinal motion segments are tested to failure in anterior shear, the primary site of injury is
the pars interarticularis (Yingling and McGill, 1999a) whereas when tested in posterior shear the end-
plate is the most common injury site (Yingling and McGill, 1999b). This would indicate that the vari-
ables with a directional component should have each direction accounted for or accumulated separately.
This reasoning would also hold for the net joint moments where the musculature to generate an exten-
sion moment is completely different from the musculature for a flexor moment. Unfortunately this
greatly increases the number of variables and raises the potential problem that some variables could
show little or zero cumulative exposure depending on the task demands. The calculation of this
increased number of variables is a relatively simple addition to any biomechanical model used for asses-
sing cumulative loading. Modeling approaches that use sufficient data to generate force-time histories
can provide outputs for both acute and cumulative exposure, an example is presented in Figure 13.16.
The real challenge with generating so many variables is interpreting their relationship with the devel-
opment of pain. As mentioned previously, cumulative compression has been the most commonly used
variable in published studies and is also the most used peak exposure variable in ergonomic assessments.
It also has the added benefit of being unidirectional for joint compression. This is a direct result of the
imposed joint compression associated with activating trunk musculature to generate movement in any
dimension. These factors in combination with the epidemiological evidence and widespread use would
make it the forerunner in becoming the variable of choice when setting and implementing a cumulative
exposure TLV.
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13.9 Summary
Taking into account all of the potential barriers and processing difficulties associated with documenting
cumulative exposure, the fact that all of the studies that have examined the relationship between cumu-
lative loading and low back pain have found a significant positive relationship (Kumar, 1990; Norman
et al., 1998; Seidler et al., 2001, 2003) demonstrates the strength and potential of this approach. The posi-
tive association was independent of whether primary or secondary assessment methods between
exposure and developing injury
pain were used.
This chapter has focused on spinal cumulative loading exposure. This approach is not meant to replace
assessments of peak exposure. In fact, peak exposure is really just an instantaneous component of a
cumulative exposure dosage. Both peak and cumulative evaluations of work provide complimentary
but unique pieces of information. This is supported by the fact that in the calculation of multivariate
odds ratios for the reporting of low back pain, peak and cumulative exposure were independent com-
ponents in a factor analysis (Norman et al., 1998).
One of the primary barriers to the transfer of usable techniques for the assessment of cumulative
exposure to practicing ergonomists is the lack of consensus in data sets. Clearly, there is a need to deter-
mine a common method, or at the least provide an estimate of the error inherent in the approach used,
before any progress can be made towards developing a standard for assessing risk of injury from exposure
to cumulative loading. This chapter has attempted to highlight and where possible quantify the errors in
documenting cumulative loading. The knowledge presented spans from the theoretical foundations of
the link between injury and cumulative loading to applied techniques such as 3DMatch for quantifying
a worker's exposure. There is a strong evidence base to support the implementation of cumulative
loading assessments and highlights the need for a TLV and supporting assessment techniques for practi-
cing ergonomists.
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Acknowledgments
The author's research work reported in this chapter was funded by the Natural Sciences and Engineering
Research Council of Canada, and the AUTO21 Network Centers of Excellence whose funding is provided
by the Canadian federal government.
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