Article Highlights:
- Fatigue Failure theory is a new ergonomics theory about how workers develop MSDs
- Three recently developed ergonomics assessment tools — LiFFT, DUET and The Shoulder Tool — can be applied by practitioners to predict risk of injury for single task jobs as well as the cumulative risk for multi-task jobs
- Validation shows a dose-response relationship between the prevalence of certain musculoskeletal injuries and the cumulative damage calculation from each tool
- Ergonomics practitioner benefits include: simplicity, ease of use, cumulative risk assessment for both single and multi-task jobs, enablement of prioritized improvement strategies, and better informed job rotation strategies
What is Fatigue Failure?
Recently, several new musculoskeletal disorder (MSD) risk assessment tools have been developed based on an award-winning advance in understanding the mechanisms by which MSDs are likely caused. This new concept suggests that MSDs develop from the cumulative damage resulting from a material fatigue failure process.
In this case, “fatigue” is not the exhaustion one experiences after a hard day of work – feeling energy depleted and tired muscles. Instead, this is material fatigue which represents a reduced capacity to withstand loading.
A common example of a fatigue failure process is taking a paper clip and bending it back and forth until it breaks. The paper clip will not break with the first bend, but with repeated bending eventually a very small failure (a micro-failure) will develop in the metal. With increasing repetitions of bending this small micro-failure will grow until the paper clip ultimately fails. All materials, including musculoskeletal tissues, experience this process if they are exposed to enough repeated stress.
Recent studies have demonstrated that this type of repetitive loading leads to damage and pain, resulting in the development of MSDs. However, if MSDs are the result of a fatigue failure process (as it appears they are), risk models should include such methods.
How Does Fatigue Failure Theory Differ from Other MSD Theories and Assessment Tools?
There are several theories that researchers have identified as potential causes and contributors to the development of MSDs. Ergonomics practitioners apply assessment tools that are based on these various theories to assess hazards and predict risk of injury. Examples include:
- Biomechanics (e.g., 2- and 3-dimensional biomechanical calculators)
- Psychophysics (e.g., Liberty Mutual “Snook” tables and equations)
- Physiology (e.g., Garg or Dempsey energy expenditure equations)
Some assessment tools, like the NIOSH Lifting Equation, incorporate multiple theories.
3 New Fatigue Failure Ergonomics Assessment Tools Now Available
Three new fatigue failure MSD risk assessment tools have been deployed:
- LiFFT — Lifting Fatigue Failure Tool
- DUET — Distal Upper Extremity Tool
- The Shoulder Tool
All 3 of these tools have been integrated into the assessment tool set available in Ergoweb’s proprietary software system, Ergoweb Enterprise™.
Peter budnick
Unlike most other ergonomics risk assessment tools, these new tools predict the probability that a task or job presents a high risk of injury. For example, LiFFT can be applied to a lifting task and conclude that “there is a 45% likelihood that this is a high risk job” for low back pain or injury (“High risk job” is defined by the researchers as having at least 12 injuries per 100 workers annually. “Low-risk jobs” were those having no injuries and no turnover for the preceding three-year period).
Most ergonomic assessment methods focus on specific risk factors and provide a measure of risk that falls into broad categories of red, yellow and green; or sometimes just red and green. In contrast, fatigue failure tools like LiFFT, DUET and The Shoulder Tool provide a more continuous measure of risk that allows practitioners the ability to better see tangible changes to risk associated with changes to the work tasks. These tools also identify the riskiest sub-tasks for each job allowing for targeted ergonomic interventions that result in the greatest reductions in risk.
There are several benefits associated with the use of fatigue failure principles to assess MSD risk:
- Simplicity and Ease of Application.
Fatigue failure theory indicates that two primary measures (a measure of loading along with the number of repetitions) are the two key factors needed for risk assessment.
- Ability to Assess Cumulative Risk over multiple tasks (i.e., Multi-Task Risk).
Fatigue failure tools can easily assess the cumulative risk associated with multiple multi-task jobs.
- Prioritizing Interventions and Improvements.
These tools provide the proportion of risk associated with each individual sub-task in multi-task jobs. This can help in prioritizing ergonomic interventions by targeting the jobs and even specific sub-tasks having the highest risk.
- Other Benefits.
These tools can also evaluate the risks associated with job rotation strategies, providing risk assessment for the individuals rotating through those jobs. Additionally, these tools can evaluate the effectiveness of exoskeletons, and other aspects affecting musculoskeletal disorders.
How Has Fatigue Failure Theory MSD Risk Prediction Been Validated?
Validation is important for any ergonomics risk assessment tool. Validation is a measure of confidence that an assessment tool is predicting accurately, keeping practitioners from overestimating — or underestimating — the level of risk a task or job presents. Ideally, researchers would like to conduct prospective studies that allow them to control variables like force, posture, duration and frequency of exposure to learn exactly how such factors affect injury outcomes. However, since it is impossible to control real-world production environments, and unethical to knowingly allow people to become injured, ergonomics researchers must use epidemiological databases to validate their theories and resulting assessment tools.
For LiFFT, Gallagher et al (2017) used two different epidemiology data sets:
- LMM database described by Marras et al. (1993) and presented in Zurada et al. (1997), which includes peak spinal load moments and repetition rates as well as Low Back Disorder outcomes for a wide variety of industrial lifting tasks, and
- An epidemiological study examining MSD outcomes from a large automotive manufacturer (Sesek, 1999) that includes historical injury data and symptom surveys for the analyzed jobs
For DUET and The Shoulder Tool, only the automotive manufacturer database was used for validation, since the LMM database focuses solely on low back outcomes, while these tools focus on the hand, wrist, arm and shoulder.Interested readers are encouraged to read the original publications, cited below, or watch this webinar for more validation details.
What’s Next for Fatigue Failure in Ergonomics?
Effectiveness of Exoskeletons – A new study by Zelik et al. (2021) has demonstrated that fatigue failure tools can help assess the effectiveness of exoskeletons. Specifically, the LiFFT model has been utilized to create a new tool (Exo-LiFFT) that evaluates the decrease in risk associated with low back exoskeleton use during lifting tasks. Basically, the decrease in low back moment due to exoskeleton use can be calculated and the LiFFT model can be used to estimate the decrease in risk. Previously, researchers relied on electromyography to estimate the decrease in risk; however, this procedure requires specialized equipment and expertise. Exo-LiFFT requires no specialized equipment or expertise and provides easy assessment of the decrease in risk due to exoskeleton use.
The Cost of Job Rotation – Fatigue failure tools were used to assess the effects of job rotation in a recent award-winning paper in the journal Ergonomics (Mehdizadeh et al., 2020). This paper demonstrated that job rotation is not effective when attempting to “balance” the exposure of workers to jobs having different physical demands (high versus low). This paper demonstrated that the increase in risk to workers in low stress jobs is greater than the decrease in risk to those at high risk. The overall MSD risk for the job rotation pool as a whole was increased. While job rotation may have benefits in terms of reducing boredom, expanding a worker’s skill set, etc. it might not hold benefits in terms of reducing MSD risk.
Automated risk assessment – New technologies are being developed (such as video-based methods and wearable sensors) that will soon allow for continuous or real-time assessment of MSD risk. Previous MSD risk models are generally not well-equipped to make the transition to continuous risk assessment, due to the lack of methods to assess the highly variable loading exposures that occur with continuous risk assessment. However, fatigue failure theory has validated methods that can be used to specifically address the highly variable loading conditions that will be encountered in continuous risk assessment. Thus, the fatigue failure method is well-positioned to incorporate such advances in risk assessment technologies
Personalized risk assessment – Personal characteristics (age, gender, anthropometry) can play an important role in terms of an individual’s risk of getting an MSD. Fatigue failure theory suggests that individuals with stronger tissues would be more resistant to MSDs, for example. It may be possible to use fatigue failure techniques to develop “personalized” assessments of risk. This may assist with the development of risk assessment tools that can take factors such as those listed above into account when evaluating a specific individual’s risk of experiencing an MSD.
Taking Healing into Account – the fatigue failure process in musculoskeletal tissue can be considered a “modified” fatigue failure process because the body not only experiences damage but can also heal damage after it occurs. In fact, it is even possible for structures to increase in strength and capability. Researchers are exploring how tissue healing and remodeling can be incorporated into fatigue failure based models. There are a number of personal characteristics which are associated with differences in healing and remodeling capability. Age, gender, obesity and psychological stress are four such measures that are known to affect the rates of healing and adaptive tissue remodeling. These personal characteristics are likely to improve the risk assessments from future versions of these tools.
References
Sean Gallagher, Richard F. Sesek, Mark C. Schall Jr., Rong Huangfu, Development and validation of an easy-to-use risk assessment tool for cumulative low back loading: The Lifting Fatigue Failure Tool (LiFFT), Applied Ergonomics 63 (2017) 142-150, https://doi.org/10.1016/j.apergo.2017.04.016
Sean Gallagher, Mark C. Schall, Jr., Richard F. Sesek, and Rong Huangfu, An Upper Extremity Risk Assessment Tool Based on Material Fatigue Failure Theory: The Distal Upper Extremity Tool (DUET), HUMAN FACTORS, Vol. 60, No. 8, December 2018, pp. 1146–1162, DOI: 10.1177/0018720818789319
Dania Bani Hani, Rong Huangfu, Richard Sesek, Mark C. Schall Jr., Gerard A. Davis & Sean Gallagher (2021), Development and validation of a cumulative exposure shoulder risk assessment tool based on fatigue failure theory, Ergonomics, 64:1, 39-54, DOI: 10.1080/00140139.2020.1811399
Marras, W.S., Lavender, S.A., Leurgans, S.E., Rajulu, S.L., Allread, W.G., Fathallah, F.A., Ferguson, S.A., 1993. The role of dynamic three-dimensional trunk motion in occupationally-related low back disorders: the effects of workplace factors, trunk position, and trunk motion characteristics on risk of injury. Spine 18, 617-628.
Sesek, R.F., 1999. Evaluation and Refinement of Ergonomic Survey Tools to Evaluate Worker Risk of Cumulative Trauma Disorders (Dissertation).
Zurada, J., Karwowski, W., Marras, W.S., 1997. A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design. Appl. Ergon. 28, 49-58.
Amir Mehdizadeh, Alexander Vinel, Qiong Hu, Mark C. Schall Jr., Sean Gallagher & Richard F. Sesek (2020) Job rotation and work-related musculoskeletal disorders: a fatigue-failure perspective, Ergonomics, 63:4, 461-476.