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http://rev.sagepub.com/ Ergonomics Reviews of Human Factors and http://rev.sagepub.com/content/7/1/149 The online version of this article can be found at: DOI: 10.1177/1557234X11410386 2011 7: 149 Reviews of Human Factors and Ergonomics Arun Garg and Jay M. Kapellusch Job Analysis Techniques for Distal Upper Extremity Disorders Published by: http://www.sagepublications.com On behalf of: Human Factors and Ergonomics Society at: can be found Reviews of Human Factors and Ergonomics Additional services and information for http://rev.sagepub.com/cgi/alerts Email Alerts: http://rev.sagepub.com/subscriptions Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://rev.sagepub.com/content/7/1/149.refs.html Citations: at MISSISSIPPI STATE UNIV LIBRARIES on November 25, 2013 rev.sagepub.com Downloaded from at MISSISSIPPI STATE UNIV LIBRARIES on November 25, 2013 rev.sagepub.com Downloaded from

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Reviews of Human Factors and

http://rev.sagepub.com/content/7/1/149The online version of this article can be found at:

 DOI: 10.1177/1557234X11410386

2011 7: 149Reviews of Human Factors and ErgonomicsArun Garg and Jay M. Kapellusch

Job Analysis Techniques for Distal Upper Extremity Disorders  

Published by:

http://www.sagepublications.com

On behalf of: 

  Human Factors and Ergonomics Society

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CHAPTER 4

410386 XXXXX10.1177/1557234X11410386Reviews of Human Factors and Ergonomics, Volume 7Distal Upper Extremity Disorders

Job Analysis Techniques for Distal Upper Extremity Disorders

Arun Garg & Jay M. Kapellusch

Distal upper extremity (DUE) work-related musculoskeletal disorders (WMSDs) are among the most costly injuries suffered in industry today. These WMSDs are reported in both office (computer use) and manufacturing environments. Job physical exposure analysis techniques for DUE WMSDs range from simple checklists to quantitative models. A summary of literature review of biomechanical, physiological, psychophysical and epidemiological bases for job physical exposure risk factors for DUE WMSDs is provided. Several job analysis methods suitable for manufacturing environments are reviewed and discussed. A comparative analysis of Rapid Upper Limb Assessment (RULA), Threshold Limit Value for Hand Activity Level (TLV for HAL), and the Strain Index is provided along with results from validation studies and advantages and disadvantages of each method. Three examples from industries are provided to demonstrate applications of RULA, TLV for HAL, and the Strain Index. Last, issues with current job analysis methods when a worker rotates to different jobs and/or when a job consists of several tasks are discussed as well as the need for more robust models to account for these variations in physical exposure in real-world environments.

M usculoskeletal disorders (MSDs) are disorders of the muscles, nerves, tendons,

ligaments, joints, cartilage, and spinal discs (U.S. Department of Labor, 2008). MSDs do not include disorders caused by slips, trips, falls, motor vehicle accidents, or other simi-lar accidents. Some examples of MSDs include carpal tunnel syndrome (CTS), de Quer-vain’s disease, trigger finger, tarsal tunnel syndrome, epicondylitis, tendonitis, Raynaud’s phenomenon, rotator cuff syndrome, sciatica, herniated spinal disc, low back pain, and carpet layer’s knee.

An injury or illness is considered to be work related if an event or exposure in the work environment either caused or contributed to the resulting condition or signifi-cantly aggravated a preexisting injury or illness (U.S. Department of Labor, 2008). Cases reported as MSDs by the U.S. Bureau of Labor Statistics (BLS) include those in which the nature of an injury is a sprain, strain, tear, soreness, hernia, CTS, or other similar type of injury to the soft tissue structures and in which the causal event is bodily movement, such as overexertion, repetition, reaching, bending, twisting, or climbing.

Keywords: distal upper extremity disorders, risk factors, job analysis methods, RULA, Strain Index, TLV for HAL

Reviews of Human Factors and Ergonomics, Vol. 7, 2011, pp. 149–196. DOI 10.1177/1557234X11410386. Copyright 2011 by Human Factors and Ergonomics Society. All rights reserved.

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In 2007, there were 335,390 days-away-from-work MSDs in the United States (BLS, 2009). The rate of MSD injuries declined by 8%, from 39 days-away-from-work cases per 10,000 workers in 2006 to 35 cases per 10,000 workers in 2007. This decrease in the number of MSDs was primarily responsible for the overall decline in days-away-from-work cases in 2007 (BLS, 2009). There were 264,930 days-away-from-work cases in 2007 attributed to overexertion. Sprains and strains accounted for 14% of total workplace injuries and illnesses (BLS, 2009).

Regarding event or exposure attributed to causing injuries and illnesses, repetitive motion (3.2% of all injuries and illnesses with days away from work in 2007) continued to be the event with the highest median days away from work in 2007 (BLS, 2009). CTS alone accounted for 11,940 cases (1.0% of all injuries and illnesses involving days away from work) and tendonitis for 4,380 cases (0.4%) involving days away from work in 2007 (BLS, 2009). Regarding nature of injuries and illnesses, CTS cases accounted for the second highest median days away from work (28 days) in all private industries, second only to fractures (30 days).

Overexertion injuries, including those associated with an upper extremity, are the most costly injuries to industry, accounting for $12.4 billion in 2007 (Liberty Mutual Insurance, 2009). Repetitive-motion injuries alone cost industry $2 billion dollars in 2007 (Liberty Mutual Insurance, 2009).

Silverstein (2005) analyzed workers’ compensation claims for Washington State from 1995 to 2003. Of these claims, 10% of all claims and 35.8% of all WMSD claims were for an upper extremity injury, resulting in a direct cost of $1.29 billion. Average direct cost per claim was $10,448. Average claim incidence rate was 104.3 per 10,000 workers, with an average lost time of 207 days.

From this brief literature review, it is clear that upper extremity disorders are among the most costly injuries suffered in industry today. These disorders are reported in both office (computer use) and manufacturing environments. This article provides a review of job physical exposure risk factors present and job analysis methods used in manufac-turing environments, with a focus on real-world applications.

Upper extremity includes shoulder, upper arm, elbow, lower arm, wrist, hand, and fingers. This chapter covers all upper extremity MSDs except those related to shoulder, and therefore, it refers to the discussed region of the body as the distal upper extremity (DUE).

BioMEchAnicAl, PhysioloGicAl, AnD PsychoPhysicAl BAsEs To QUAnTify JoB PhysicAl

sTrEssEs

An understanding of biomechanical and physiological stresses can be useful in develop-ing job analysis models that relate work stresses to strain on muscle-tendon units and nerves and the pathogenesis of associated disorders. Although there are many theories for the pathogenesis of DUE MSDs, precise injury and illness mechanisms are not well established (Moore, 1992a, 1992b, 2002; National Research Council & Institute of Medicine, 2001). Furthermore, there is a lack of explicit relationships between job

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physical exposure variables (such as force, repetition, and posture) and acceptable and unacceptable levels of biomechanical and physiological responses. Therefore, most job physical exposure risk factors as well as job analysis methods are based on concepts derived from biomechanics and physiology, psychophysical studies, epidemiological studies, and assumed risk factors rather than explicit relationships between job physical exposure variables and biomechanical, physiological, and/or psychophysical responses.

Wherever applicable, data such as those from biomechanical studies on strength, physiological studies on localized muscle fatigue, and psychophysical studies on maxi-mum acceptable frequencies and workloads can be used to determine acceptable combi-nations of job physical exposure variables. Unfortunately, there are only limited data available.

There are two approaches for job evaluation. The first approach requires evaluating job physical demands in relation to a given person’s work capacity, such as upper extrem-ity muscle strength, upper-body aerobic capacity, and/or endurance. The most com-monly used approach relates job demands to the capacities of the entire workforce by recommending acceptable levels that would be “safe” for a certain percentage of popula-tion. It is important to note, as the term implies, that job analysis methods are designed to analyze jobs and not to assess specific workers. The most commonly used recommen-dation in ergonomics is to design jobs that would be acceptable or safe to at least 75% of working population (National Institute for Occupational Safety and Health [NIOSH], 1981; Snook, 1978; Waters, Putz-Anderson, Garg, & Fine, 1994).

A functional description of the physical effort required to perform a job is a valuable tool. It can be used to show how a job should be modified to reduce physical stresses to acceptable levels and to prioritize jobs for which changes should be made. Therefore, quantifying stresses from job physical exposure on affected muscles, tendons, and joints in terms of force, duration, repetition, and other job physical exposure variables is of primary importance in developing job analysis methods.

Biomechanical Basis: Muscle strength

The primary applications of biomechanics in developing job analysis methods have been to determine (a) workers’ upper limb strength (in particular, grip strength) and (b) force requirements of jobs as a percentage of workers’ strength, often reported as percentage maximum voluntary contraction (%MVC). Maximum grip strength pro-vides a ceiling force limit or maximum force limit that workers can be exposed to infre-quently. There are extensive data available in the literature on workers’ grip strength for different types of grasps, and there are minor differences in grip strengths reported by different studies (Angst et al., 2010; Crosby, Wehbe, & Mawr, 1994; Gunther, Burger, Rickert, Crispin, & Schulz, 2008; Mathiowetz et al., 1985). Table 4.1 provides normative data for different types of grip strengths for U.S. adults from Mathiowetz et al. (1985).

Three conclusions can be drawn from Table 4.1: (a) Pinch strength is substantially lower than power grip strength (about 20% of grip strength), (b) there is a large varia-tion in population grip and pinch strength, and (c) females’ grip strength is about 60% of males’ grip strength. The mean pinch strengths seen in Table 4.1, considered in the context of large variations and relative reduction in strength for females, help to explain

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Table 4.1. Grip strength for Male and female Adults (in kilograms)

Males Females

Type of Grasp M sD Range M sD Range

Power grip 47.4 12.86 14.5–80.0 28.5 7.27 11.3–62.3Tip pinch 7.7 1.86 3.2–15.4 5.1 1.18 1.8–9.1Key pinch 11.1 2.09 4.1–18.6 7.4 1.36 3.6–11.3Palmer pinch 11.7 2.27 4.1–20.5 7.4 1.73 3.6–15.4

Source: Mathiowetz et al. (1985).

why pinching with force is considered to be a risk factor for DUE WMSDs. Grip strength is further affected by several variables, including (a) grip span, (b) forearm rotation, (c) elbow flexion, and (d) wrist posture (Garg, Cykana, & Hegmann, 2003; Harkonnen, Piirtomaa, & Alaranta, 1993; Richards, Olson, & Palmiter-Thomas, 1996).

Most studies have used a Jamar dynamometer with handle set to the second position to measure grip strength, and a significant percentage of participants have achieved maximum grip strength using the second position of the Jamar dynamometer handle (Driscoll et al., 1992; Firrell & Crain, 1996). Harkonnen et al. (1993) reported that both males and females had the highest grip strength at the third position of the Jamar dyna-mometer handle, although grip strength at the second position of the handle was fairly comparable to the strength at the third position. From these studies, it appears that opti-mum grip span is between 3.8 cm and 6.4 cm, depending on gender, hand size, and type of grip.

Hand and wrist posture has been reported to affect both grip and pinch strengths (Driscoll et al., 1992; Friedman, Palmer, Short, Levinsohn, & Halperin, 1993; Garg et al., 2003; Lamoreaux & Hoffer, 1995). A minimum of 25° of wrist extension is required for optimum grip strength (Driscoll et al., 1992), as is some ulnar deviation (Friedman et al., 1993). Wrist flexion has a profound effect on grip strength: An increase in wrist flexion causes a decrease in strength (Garg et al., 2003).

Grip strength is the highest with forearm in supination and the lowest with forearm in pronation (Richards et al., 1996). Elbow flexion has been reported to have a relatively minor effect on grip strength, with maximum strength at 0° elbow flexion and mini-mum (~6% reduction) at 135° elbow flexion (Beaton, O’Driscoll, & Richards, 1995; Catovic, Catovic, Kraljevic, & Muftic, 1991; Kuzala & Vargo, 1992).

Similarly, wrist posture and pinch span have been shown to affect pinch strength (Imrhan & Rahman, 1995; Shih & Ou, 2005; Shivers, Mirka, & Kaber, 2002). From these studies, it can be concluded that pinch strength is the lowest with the wrist flexed. It is not clear precisely what the optimum pinch span is; however, it should be ≥2 cm.

When supporting the weight of an object or applying force with the hands, the weight of the object and/or the applied force produces a torque (moment) at the wrist. In cer-tain situations, wrist strength required to counter this torque, rather than grip strength, may limit a person’s ability to perform the task. Garg et al. (2003) reported applied grip forces for holding different weights with the forearm in neutral position as well as maxi-mum grip and wrist strength. Applied grip forces ranged from 1.1 to 1.7 times the weight

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of the object held. The ratio of applied grip force to object weight was lower for heavier weights (1.1) and higher for lighter weights (1.7). Measured wrist strength was about 30% of measured grip strength. Thus, measured wrist strength was comparable to 3-point pinch strength as reported in the literature. Garg et al.’s study suggested that in many situations, wrist strength rather than grip strength might limit a person’s ability to perform a task.

A few studies have compared wrist flexion and wrist extension strengths (Al-Eisawi, Kerk, & Congleton, 1998; An, Askew, & Chao, 1986; Seo, Armstrong, Ashton-Miller, & Chaffin, 2010; Vanswearingen, 1983). In general, these studies have found that wrist flex-ors and ulnar deviators are strongest and wrist extensors are weakest.

One of the biomechanical applications for job analysis methods in estimating the risk of DUE WMSDs is to determine the magnitude of applied hand force. Normally, applied hand force is expressed as a percentage of a worker’s maximum hand force strength (%MVC). At present, the current biomechanical methods to determine hand force, such as electromyography (EMG), force transducers, and force sensors, as well as biomechan-ical models are not well suited for industrial applications. Therefore, most job analysis methods rely on analyst or worker force ratings measured by psychophysical scales, such as the Borg CR-10 scale (Borg, 1982) or a visual analog 10-cm scale, to estimate applied hand force when performing a job.

Physiological Basis: localized Muscle fatigue

Localized muscle fatigue from physical exertion is the most commonly studied response in laboratories to provide a basis for job analysis methods (Åstrand, Guharay, & Wahren, 1968; Byström & Fransson-Hall, 1994; Chaffin, 1973; Jørgensen, Fallentin, Krough-Lund, & Jensen, 1988; Kilbom, Gamberale, Persson, & Annwall, 1983; Lind, Taylor, Humphreys, Kennelly, & Donald, 1964; Potvin, 1997; Sjørgaard, Kiens, Jorgensen, & Saltin, 1986). The National Research Council and the Institute of Medicine (2001) pro-posed a conceptual model explaining how workplace factors might play a role in the development of WMSDs. The model suggests fatigue can cause discomfort and pain, which can lead to impairment and disability. Some researchers believe that persistent fatigue can lead to DUE MSDs (Hagberg et al., 1995; Moore & Garg, 1995; National Research Council & Institute of Medicine, 2001; Tichauer & Gage, 1977).

However, the role of localized muscle fatigue in DUE MSD causation is not clear. For example, fatigue and discomfort are often reported after strenuous exercise (Åstrand et al., 1968; Byström & Fransson-Hall, 1994; Garg, Hegmann, & Kapellusch, 2006; Garg, Hegmann, Schwoerer, & Kapellusch, 2002; Hagberg, 1981; Herberts, Kadefors, Andersson, & Petersen, 1981; Herberts, Kadefors, & Broman, 1980; Kilbolm et al., 1983; Lind et al., 1964; Potvin, 1997), and it is known that during eccentric contractions, repet-itive muscle activation produces fiber injury and a decline in generated force in the affected muscle (McCully & Faulkner, 1985). It is believed that persistent muscle fatigue and discomfort may result in a disease state (Kuorinka & Forcier, 1995); however, there is a lack of scientific evidence to generalize this model. This issue is further complicated in that the time for development of WMSDs can range from hours to months after expo-sure (Kuorinka & Forcier, 1995).

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A few studies in the literature provide guidance on acceptable levels of DUE physical workload. For example, Byström and Fransson-Hall (1994) studied several physiological responses (EMG, maximum voluntary handgrip contraction, potassium and lactate con-centration, local blood flow, blood pressure, and heart rate) for low-intensity, intermittent handgrip contractions with short contraction relaxation cycles. The study concluded that intermittent handgrip contractions of ≥17 %MVC were unacceptable. Similarly, continu-ous handgrip contractions of ≥10 %MVC were found to be unacceptable.

Hagg and Milerad (1997) studied forearm muscular exertion during intermittent grip-ping work using EMG. The study concluded that intermittent gripping work requiring 25 %MVC with equal proportions of work and rest time (10 s work followed by 10 s rest) was fatiguing. Bjorksten and Jonsson (1977) studied the endurance limit of force for inter-mittent static contractions. The study recommended a force of 14% of maximum for intermittent static contractions and 7.9% for (continuous) static contractions.

Astrand et al. (1968) studied circulatory response to arm exercise by nailing at bench level, into wall at head level, and into ceiling at 10 cm above head level. The authors reported that physiological responses (heart rate, blood pressure, and blood lactate) were higher for nailing into the wall than into the bench and higher still for nailing into the ceiling. Meanwhile, productivity, expressed as nails driven per minute, decreased from nailing into the bench to nailing into the wall to nailing into the ceiling. The authors attributed the increase in physiological responses and decrease in productivity to the intermittent static component required to perform increasingly elevated work. Thus, the study implies that intermittent static muscular exertion could lead to localized muscle fatigue and reduced productivity and should be minimized when designing work involving the upper limb.

Psychophysical studies: Maximum Acceptable Workload

Psychophysics is the study of relationships between physical stimuli and sensory responses. Workers participating in psychophysics studies for ergonomics applications use a method of self-adjustment to determine maximum acceptable workload (Snook & Irvine, 1967). Generally, workers are given control of one task variable (usually weight of the object or force applied or frequency of exertion) and are allowed to adjust that variable to the maximum value they are willing to accept on the basis of the instructions given to them. All other task variables are fixed to allow for realistic simulation of jobs.

Regarding instructions, often participants are asked to imagine that the task is per-formed for a set period of time, such as for 8 hr per day. However, typically, the experiment lasts for a much shorter time (25 min to 1 hr), and this difference raises some concern as to whether results are truly applicable for 8 hr of work. There is also a concern whether maximum acceptable forces and frequencies obtained from a single experiment can be tolerated when workers perform the same work every day. For example, Snook, Vaillancourt, Ciriello, and Webster (1995) reported that maximum acceptable torque for 5 days per week of exposure was 36.3% lower than that for 2 days per week of exposure.

Dahlan and Fernandez (1993) reported maximum acceptable frequencies for a simu-lated gripping task for four gripping forces (expressed as %MVC) and three gripping

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durations of exertion. Data such as these are very useful in designing job analysis meth-ods based on workers’ perceptions of maximum acceptable workloads. Similar studies were conducted by Abu-Ali, Purswell, and Schlegel (1996); Klein and Fernandez (1997); and Marley and Fernandez (1995). For example, Marley and Fernandez reported that maximum acceptable frequency decreased by 12% and 27% when a drilling task required 25° and 50° of wrist flexion, respectively. Ciriello, Webster, and Dempsey (2002) and Snook et al. (1995; Snook, Ciriello, & Webster, 1999; Snook, Vaillancourt, Ciriello, & Webster, 1997) have also reported studies on maximum acceptable force limits during repetitive wrist motions.

Armstrong, Punnett, and Ketner (1989) used a different psychophysical approach. In this approach, all task variables were fixed and workers were required to rate the level of discomfort, perceived exertion (most often using Borg CR-10 scale), fatigue, and/or pain. Armstrong et al. asked the workers to rate the weight, handle size, grip force, and posture comfort of hand tools that they used on a 0-to-10 scale.

Some of the findings that can be used in workstation design and tool selection included the following: (a) A tool weight of 0.9 kg to 1.75 kg was rated as “just right”; (b) grip force required to use tools that weighed ≤2.0 kg was rated as “very comfortable”; (c) use of gloves, as compared with bare hand when using tools, resulted in a more com-fortable grip force rating; (d) a tool handle diameter of ≤3.8 cm was rated as “just right”; (e) the most comfortable vertical location for using tools was between 102 cm and 153 cm; and (f) the most comfortable horizontal location for using hand tools was within 38 cm of the worker. Similarly, using ratings of perceived exertion, Ulin et al. (Ulin, Armstrong, Snook, & Franzblau, 1993; Ulin, Armstrong, Snook, & Keyserling, 1993; Ulin, Ways, Armstrong, & Snook, 1990) recommended that repetitive work should be placed near elbow height whenever possible.

Nussbaum and Johnson (2002) studied single-digit exertions by the thumb and index finger. They reported that the maximum acceptable force (a) was a little greater for thumb exertions than for index finger exertions and (b) decreased with an increase in exertion frequency.

Psychophysical studies, such as those just discussed, provide practical data and rec-ommendations that are useful for job design and analysis. These data are especially use-ful when biomechanical and physiological data are lacking.

JoB PhysicAl ExPosUrE risK fAcTors for DUE DisorDErs

Epidemiological studies determine correlations or associations between job physical fac-tors and prevalence or incidence of DUE MSDs. These studies provide an epidemiological basis for job analysis methods. Epidemiological data are also used to validate job analysis methods. Identifying and quantifying the effects of job physical exposure risk factors requires some means of quantifying job physical exposure (force, duration, posture, etc.) and of classifying a worker as a prevalent and/or incident case for DUE disorders (case definition). There is a lack of consistency within the literature with regard to the methods used for quantifying job physical exposure and case definition.

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Several assessment methods have been used to quantify job physical exposure, includ-ing (a) self-reported questionnaires, (b) classification by job title, (c) simple observation and/or professional opinion, and (d) measurements from video, force gauges, EMG, and/or electrogoniometry. Thus, there is no “standard” methodology for quantification of physical exposure. Determination of prevalent or incident cases of DUE disorders also varies depending on the study. The methods used to determine whether a worker is a case or noncase include (a) self-reporting of disorder by the study participants, (b) physical examination by a qualified health professional, and (c) physical examination plus related maneuvers and/or tests, such as nerve conduction studies for nerve-related disorders (e.g., CTS; Rempel et al., 1998).

This lack of consistency in definitions of physical exposure assessments and determi-nation of cases among studies might be responsible for inconsistencies among studies in identification of job physical exposure and other risk factors. However, when evaluated as a whole, despite these inconsistencies, the literature shows substantial evidence of association between job physical demands and DUE MSDs.

Among DUE disorders, CTS, although not the most common disorder, is the most commonly studied disorder for prevalence, incidence rate, and risk factors (Bernard, 1997; Bonfiglioli et al., 2007; Gell, Werner, Franzblau, Ulin, & Armstrong, 2005; Katz & Simmons, 2002; Melchior et al., 2006; Moore & Garg, 1994; Roquelaure et al., 1997; Roquelaure, Mariel, Dano, Fanello, & Penneau-Fontbonne, 2001; Silverstein et al., 2010; Spielholz et al., 2008; Thomsen, Hansson, Mikkelsen, & Lauritzen, 2002; Werner et al., 2005a). Generic risk factors for DUE disorders include forceful exertion, high repetition, awkward hand-wrist posture, and exposure to hand or arm vibration (Bernard, 1997; Mani & Gerr, 2000; Moore & Garg, 1995). What is not clear is how to define “high force,” “high repetition,” and “awkward posture” and precisely what effect they have on rate of DUE MSDs, because different studies have used different measures and different values for high force, high repetition, and awkward posture.

Furthermore, it appears that these risk factors interact in a multiplicative manner. For example, a few studies have reported that exposure to those jobs that require both high force and high repetition have greater association with DUE MSDs than those jobs that require exposure to high force or high repetition alone (Armstrong, Fine, Goldstein, Lifshitz, & Silverstein, 1987; Chiang et al., 1993; Melchior et al., 2006; Moore, Rucker, & Knox, 2001; Osorio et al., 1994; Silverstein, Fine, & Armstrong, 1987). Similarly, those jobs requiring a combination of high repetition and nonneutral posture had a greater risk for DUE MSDs that those jobs requiring exposure to either high repetition or non-neutral posture alone (Moore et al., 2001). Also, Haahr and Andersen (2003) reported that the combination of forceful work, nonneutral posture, and repetition was associ-ated with new cases of lateral epicondylitis.

Several studies have identified that high force is associated with DUE symptoms and/or disorders (Chiang et al., 1993; Gardner, Dale, VanDillen, Franzblau, & Evanoff, 2008; Haahr & Andersen, 2003; Leclerc et al., 2001; Loslever & Ranaivosoa, 1993; Miranda, Heliovaara, & Viikari-Juntura, 2009; Moore et al., 2001; Moore & Garg, 1994; Roquelaure et al., 1997; Silverstein et al., 2010). Similarly, highly repetitive work has been reported to be a risk factor by several studies (Andersen, Hahr, & Frost, 2007; Chiang et al., 1990,

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1993; Moore et al., 2001; Roquelaure, 1997; Silverstein et al., 1987). Working with non-neutral posture of hands and/or arms has been reported to be a risk factor by several studies (Feveile, Jensen, & Burr; 2002; Gardner et al., 2008; Haag, Oster, & Bystrom, 1997; Haahr & Andersen, 2003; Loslever & Ranaivosoa, 1993; Moore et al., 2001; Moore & Garg, 1994; Werner et al., 2005a).

Exposure to hand or arm vibrations from vibrating hand tools has been reported to be a risk factor for DUE MSDs by a few studies (Bovenzi, Della, Nataletti, Alessandrini, & Poian, 2005; Silverstein et al., 2010). It should be noted that there are also studies that have reported a lack of association between force, repetition, posture, and/or vibration and risk of upper limb MSDs. Unfortunately, inconsistencies among study definitions and lack of consensus among studies make it difficult to determine exactly what consti-tutes high force, high repetition, and awkward posture and what combinations of these are and are not acceptable in any given situation.

Numerous other risk factors for DUE MSDs have been postulated, and some have been studied. For example, many people believe that lack of job rotation is a risk factor, but there are few data on job rotation and risk of DUE disorders. Other generic risk fac-tors for DUE MSDs mentioned in the literature include unaccustomed work; stress con-centration; hand, wrist, or forearm contact with sharp surfaces; duration of task per day; insufficient recovery time; static work; pinch grasp; exposure to cold temperature; and poorly fitting gloves. Unfortunately, there are insufficient epidemiological studies on many of these potential risk factors to draw firm conclusions.

A few studies that have identified additional risk factors include Schoenmarklin, Marras, and Leurgans (1994), who found that acceleration in the flexion-extension plane was a risk factor for DUE MSDs; Moore and Garg (1994) and Roquelaure (1997), who reported that a lack of recovery time was associated with an increased risk of DUE MSDs; and Moore et al. (2001), who reported that whereas use of gloves was associated with increased risk of DUE MSDs, pinch grasp and localized mechanical compression had no association.

JoB AnAlysis METhoDs

Over the years, several job analysis methods have been developed to quantify job physi-cal stresses to the DUE and classify those stresses into “acceptable or safe” and “unac-ceptable or hazardous” zones. Practically all job analysis methods, with a few exceptions, include force, repetition, and hand and wrist posture as risk factors. These job analysis methods provide a mechanism to analyze physical exposure resulting from a combina-tion of risk factors. Depending on the risk factors present in the job, these job analysis methods either provide a semiquantitative or quantitative score for the entire job. When this score exceeds a predefined value, the job is believed to be unacceptable or hazard-ous. With some job analysis methods, it is possible to study what the most important risk factor present in the job is and how modifying this risk factor would affect the score and, therefore, risk of DUE MSDs.

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rodgers Muscle fatigue Model

Rodgers (1988) developed a job analysis technique to identify the muscle groups and type of physical effort that could contribute to an injury or illness. It is a simple method, primarily designed to define physical exposure problems and help with making job changes. Rodgers’s model consists of three factors: (a) effort intensity (light, moderate, or heavy), (b) time of continuous muscle effort (<6 s, 6 s to 20 s, and >20 s), and (c) repetition frequency <1, 1 to 5, and >5 cycles per minute). These categories were cho-sen to avoid accumulation of muscle fatigue on the basis of data from Rohmert (1973). Each of the three factors is assigned a rating of 1, 2, or 3 for arms and elbow and for hand and wrist separately (i.e., separate ratings for separate muscle groups). A table is used to assign priority for job changes. A review of the table shows that the combination of high force and long continuous effort time has the highest rating. In other words, the model states that higher forces exerted for longer durations have the highest risk for injury.

Ergonomic Job Measurement system (EJMs)

EJMS (Ridyard, Tapp, & Wylie, 2001) is an easy-to-use job analysis method developed for facility-based ergonomic teams to assess workplace ergonomic risk factors. It requires only a basic understanding of ergonomic principles and limited practical field experience (Ridyard et al., 2001). EJMS has two separate job evaluation sections: evalu-ation of (a) repetitive motion and awkward posture and (b) lifting tasks.

In Section 1, combinations of force (low, medium, and high) and frequency (low, medium, and high) ratings are assigned for each body part (eye strain, neck and shoul-der bend, trunk twist and bend, wrist bend, finger and hand motion, push and pull, and static posture) separately. Force-frequency combination scores for each body part can range from 0 to 20. Scores for all body parts are added to determine a repetitive motion and awkward body posture subtotal score. A score for ergonomic complaints or injuries (1% to 5%, 6% to 19%, and ≥20%) is added to the subtotal score to determine the total risk score. On the basis of total risk score, jobs are classified as low risk, moderate risk, or high risk. Similarly, lifting tasks are evaluated in Section 2.

Criteria or research used for assigning force-frequency scores for different body parts as well as those used for classifying total risk scores into three categories are not clear. However, Ridyard et al. (2001) state that one industrial division of an international com-pany achieved a 50% reduction in workers’ compensation cost using EJMS. Furthermore, the paper states that EJMS has proved to be an effective tool in presenting risk assess-ment data to management.

state of Washington industrial safety and health Act (WishA) checklist

Generally, checklists are used as a surveillance tool. They tend to have high sensitivity because they flag potentially all risk factors present in a job. One of the most compre-hensive checklists is the WISHA checklist from the Washington State Department of Labor and Industries (2000). The purpose of this checklist is to identify worker exposure

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to those workplace hazards that can cause or aggravate WMSDs. The WISHA checklist first determines whether a job is defined as a “caution zone job.” A caution zone job might be later defined as a “hazard zone job” in secondary evaluation. A caution zone job is a job for which employees’ typical work activities include any of the specific physical risk factors outlined in the caution zone checklist. These jobs are believed to have a sufficient degree of risk to require ergonomics awareness education and evalua-tion to determine whether the job should be classified as a hazard zone job (secondary evaluation).

Typical work activities are those that are performed more frequently than 1 day per week and more often than 1 week per year. Risk factors are identified on the basis of whether there is exposure to (a) awkward posture, (b) high force, (c) high repetition, or (d) hand or arm vibration. Awkward posture is a WMSD hazard provided the exposure to awkward posture exceeds a minimum amount of duration per day (hours per day). High hand force is a WMSD hazard based on employee exposure to a combination of (a) nature of exertion (e.g., gripping vs. pinching), (b) amount of force required to perform the task, (c) duration of exposure to risk factor per day (hours per day), (d) posture of body limb, and (e) highly repetitive motion. Highly repetitive motion is a WMSD hazard provided the exposure includes a combination of posture, force, and duration.

In this regard, this checklist has some unique characteristics: (a) It evaluates physical exposure to a combination of risk factors rather than each single risk factor separately and adds scores for each risk factor together to determine risk, (b) duration refers to the total amount of time per day an employee is exposed to the risk factor and not to dura-tion an employee spends performing the work activity that includes the risk factor, and (c) potential for a WMSD hazard is analyzed for each body part separately rather than by adding together scores from all different body parts.

To use the checklist, the evaluator first determines whether any physical risk factors that apply are present in the job that would cause categorization of the job as a caution zone job. For caution zone jobs, if additional risk factors are identified during secondary analysis, an MSD hazard exists (hazard zone job). Furthermore, the checklist clearly states that a caution zone job may not be hazardous, but it does require further evalua-tion with the use of more comprehensive job evaluation methods.

rUlA: rapid Upper limb Assessment

McAtamney and Corlett (1993) developed a survey method for the investigation of work-related upper limb disorders. RULA is applicable to a variety of manufacturing jobs as well as computer work. RULA provides a quick assessment of the postures of the neck, trunk, legs, upper arms, lower arms, and wrists. Given that rating scores are assigned to posture, repetition, and force, it is clear that RULA is a posture-driven model to determine risk of upper limb MSDs. The model authors emphasize that (a) RULA is a guide, (b) RULA is not a substitute for an understanding of ergonomics, and (c) RULA primarily provides a method to prioritize jobs or tasks for further investigation.

A RULA score is based on three variables: (a) posture of body joints, (b) muscle use, and (c) force. Each side (left and right arms) is evaluated separately. Postures for upper limb (upper arm, lower arm, and wrist) are scored as one group (Group A), and postures for

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neck, trunk, and legs are scored in a second group (Group B). Posture scores can range from 1 to 9 (Figure 4.1). Muscle use includes static exertion (object held for longer than 1 min) and repetitive action (action repeated more than four times per minute) and can have a value of 0 or 1 (Table 4.2). The force or load score depends on forceful actions or weights of the objects held and length of holding (intermittent vs. static or repetitive).

RULA requires the following steps to analyze a job (Figure 4.2): (a) Observe the job or videotape of the job for several cycles; (b) rate postures for upper limbs (upper arm, lower arm, and wrist) and neck, trunk, and legs (Figure 4.1); (c) determine posture scores for upper limbs (Score A, Table 4.3) and for neck, trunk, and feet (Score B, Table 4.4); (d) determine scores for muscle use and force (Table 4.2); (e) calculate total score for upper limb (Score C) and for neck, trunk, and legs (Score D; Figure 4.2); and (f) deter-mine grand score and recommended action (Table 4.5). The authors recommend that postures selected for assessment should be the postures used either for the greatest length of the work cycle or where highest loads occur.

McAtamney and Corlett (1993) studied 16 data entry operators in two postures: (a) ideal posture, with RULA score of 1, and (b) a posture requiring neck flexion, lower arm flexed more than 90°, and wrist extended and in ulnar deviation, with RULA score >1. There was an association between the two RULA scores and reported pain for neck and lower arm. Similarly, Fountain (2003) studied 20 participants for a 30-min typing task and reported significant association between RULA scores and perceived discomfort but not between RULA scores and EMG. Jones and Kumar (2010) studied 87 sawmill work-ers on four repetitive jobs to determine agreement between different ergonomic risk assessment methods and their ability to classify “at-risk” jobs. The authors concluded that RULA and the Strain Index (discussed later) were best in classifying jobs into three levels of risk.

Kilroy and Dockrell (2000) used RULA to determine the effect of an ergonomic inter-vention on working posture and musculoskeletal symptoms in female biomechanical scientists. The study concluded that RULA scores generally corresponded with reporting of symptoms and discomfort. RULA has also been used to study working postures of trunk and neck in professional truck drivers (Massaccesi et al., 2003), surgeon body postures during microlaryngeal surgery (Statham et al., 2010), and body postures during carpet mending operations (Choobineh, Tosian, Alhamdi, & Davarzanie, 2004).

Threshold limit Value (TlV) for hand Activity level (hAl)

The American Conference of Government Industrial Hygienists (ACGIH; 2001) estab-lished the TLV for HAL to evaluate hand-intensive jobs. The TLV for HAL is based on biomechanical load, fatigue, and epidemiological data (ACGIH, 2005; Violante et al., 2007). The TLV for HAL is designed to evaluate monotask jobs that are performed for 4 or more hours per day. A monotask job is defined as a job that requires performing the same set of motions and/or exertions repeatedly.

The TLV is a function of two variables: HAL and normalized peak hand force. HAL is based on frequency of hand exertions and duty cycle (percentage of exertion time in a job cycle time) and thus characterizes repetition and the associated decrease in duration

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Figure 4.1. Rapid Upper Limb Assessment scoring system for postures. Source: Moore (2006).

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Table 4.2. rapid Upper limb Assessment Muscle Use and force score criteria

Muscle use score added to Posture Score A and BGive a score of 1 if the posture isMainly static, e.g., held for longer than 1 minRepeated more than four times per minuteForce score added to Posture Score A and B

0 1 2 3

No resistance or less than 2-kg intermittent load or force

2- to 10-kg intermittent load or force

2- to 10-kg static load 2- to 10-kg repeated load

10-kg or more static load10-kg or more repeated loads

or forcesShock or forces with a rapid

buildup

Source: McAtamney and Corlett (1993).

Upper ArmMuscle ForceScore A Score C

Lower Arm

Wrist=+ +

Muscle ForceScore A Score C

Wrist Twist Use Table A

Grand Score

N k

Use Table C

Neck

Trunk

Score B Muscle Force

+ =

Score D

+

Legs Use Table B

Figure 4.2. Rapid Upper Limb Assessment scoring sheet. Source: McAtamney and Corlett (1993).

and frequency of rest pauses as the repetition rating increases (Latko et al., 1997). HAL is independent of cycle time. There are two options to determine HAL rating: (a) rating of HAL by a trained observer using a 0-to-10 scale with verbal anchors shown in Figure 4.3 or (b) calculating HAL on the basis of frequency of exertions and duty cycle from Table 4.6. It is important to note that there are situations in which Table 4.6 cannot be used to determine HAL rating, as the table provides HAL ratings only for selected combinations of frequency and duty cycle.

Peak hand force is the maximum force exerted during performance of the job nor-malized on a scale of 0 to 10, which corresponds to 0% to 100% of the applicable popu-lation reference strength. Ebersole and Armstrong (2006) recommend using the 90th

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Table 4.3. rapid Upper limb Assessment Table A to Determine Posture Group A score

Wrist Posture Score

1 2 3 4

Wrist Twist Wrist Twist Wrist Twist Wrist Twist

Upper Arm Lower Arm 1 2 1 2 1 2 1 2

1 1 1 2 2 2 2 3 3 3 2 2 2 2 2 3 3 3 3 3 2 3 3 3 3 3 4 42 1 2 3 3 3 3 4 4 4 2 3 3 3 3 3 4 4 4 3 3 4 4 4 4 4 5 53 1 3 3 4 4 4 4 5 5 2 3 4 4 4 4 4 5 5 3 4 4 4 4 4 5 5 54 1 4 4 4 4 4 5 5 5 2 4 4 4 4 4 5 5 5 3 4 4 4 5 5 5 6 65 1 5 5 5 5 5 6 6 7 2 5 6 6 6 6 7 7 7 3 6 6 6 7 7 7 7 86 1 7 7 7 7 7 8 8 9 2 8 8 8 8 8 9 9 9 3 9 9 9 9 9 9 9 9

Source: McAtamney and Corlett (1993).

Table 4.4. rapid Upper limb Assessment Table B to Determine Posture Group B score

Trunk Posture Score

1 2 3 4 5 6

Legs Legs Legs Legs Legs LegsNeck Posture Score 1 2 1 2 1 2 1 2 1 2 1 2

1 1 3 2 3 3 4 5 5 6 6 7 72 2 3 2 3 4 5 5 5 6 7 7 73 3 3 3 4 4 5 5 6 6 7 7 74 5 5 5 6 6 7 7 7 7 7 8 85 7 7 7 7 7 8 8 8 8 8 8 86 8 8 8 8 8 8 8 9 9 9 9 9

Source: McAtamney and Corlett (1993).

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Table 4.5. rapid Upper limb Assessment (rUlA) Table c to Determine rUlA Grand score

Neck, Trunk, and Leg Score

Wrist and Arm Score 1 2 3 4 5 6 7+

1 1 2 3 3 4 5 52 2 2 3 4 4 5 53 3 3 3 4 4 5 64 3 3 3 4 5 6 65 4 4 4 5 6 7 76 4 4 5 6 6 7 77 5 5 6 6 7 7 78+ 5 5 6 7 7 7 7

Source: McAtamney and Corlett (1993).

Figure 4.3. Verbal anchors for hand activity level scale. Source: American Conference of Government Industrial Hygienists (2001).

Table 4.6. Table to Determine hand Activity level Using Duty cycle and Effort frequency

Duty Cycle

Frequency Period 0%–20% 20%–40% 40%–60% 60%–80% 80%–100%

0.12/s 8.0 s 1 1 — — —0.25/s 4.0 s 2 2 3 — —0.5/s 2.0 s 3 4 5 5 61.0/s 1.0 s 4 5 5 6 72.0/s 0.5 s — 5 6 7 8

Source: American Conference of Government Industrial Hygienists (2001).

percentile force exerted by the workers to eliminate inclusion of random or spurious elements. Methods recommended for assessing hand forces include (a) analyst or worker force ratings on a visual analog scale with two anchor points: 0 (none at all) and 10 (greatest imaginable); (b) analyst or worker force rating on Borg CR-10 scale; (c) force

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measured with the use of instrumentation; (d) force estimated from EMG; and (e) force calculated from biomechanical analyses.

Using peak force and HAL rating, TLV for HAL defines two limits: (a) action limit (AL) and (b) TLV. Physical exposure above the TLV is believed to be unsafe and is referred to as hazardous. The AL represents an upper limit of force and repetition com-binations to which it is believed that nearly all workers may be repeatedly exposed with-out adverse health effects. Physical exposure above the AL and below the TLV is treated as a concern zone (Figure 4.4). Professional judgment is required if the physical exposure involves one or more of the following: (a) sustained nonneutral posture (wrist flexion, extension, wrist deviation, or forearm rotation, (b) contact stresses, (c) low temperature, and/or (d) vibration.

Wurzelbacher et al. (2010) studied different methods for determining HAL rating and peak force for TLV for HAL on 484 workers. HAL was measured on site by a trained observer using a 10-point visual analog scale and off site using video analysis of the same jobs and the HAL table. Hand force was measured on site by a trained observer using the Borg CR-10 scale and with ratings of perceived exertion by the worker performing the task. The two methods for assessing HAL were correlated (Spearman rank = 0.49), and the two methods of rating perceived exertion were correlated (Spearman rank = 0.47 to 0.69). Similarly, Ebersole and Armstrong (2006) concluded that a single analyst is appro-priate for basic job assessment using TLV for HAL because of high interrater reliability of the repetition and force metrics.

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8 9 10

Nor

mal

ized

Pea

k Fo

rce

Hand Activity Level

TLV

Action Limit

Figure 4.4. Threshold Limit Value (TLV) for Hand Activity Level evaluation graph. Source: American Conference of Government Industrial Hygienists (2001).

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Werner et al. (2005b) used TLV for HAL to study 279 auto assembly workers in a plant to identify which job factors influenced visits to the plant medical department because of DUE musculoskeletal problems. The study concluded that peak force and TLV for HAL scores exceeding the TLV were significant predictors of DUE MSDs. Franzblau, Armstrong, Werner, and Ulin (2005) assessed prevalence of symptoms and selected upper extremity disorders among 908 workers from seven job sites using TLV for HAL. The study concluded that although symptoms in the DUE and wrist, hand, and fingers tendonitis did not vary by TLV category, all measures of CTS and elbow and forearm tendonitis were significantly associated with TLV category.

Bonfiglioli et al. (2007) used TLV for HAL to study ergonomic risk for prevalence of CTS in cashiers and office workers. The study concluded that there was possible expo-sure to biomechanical risk factors for CTS. Violante et al. (2007) followed 2,092 workers for 1 year and found that workload above the TLV was associated with an almost three-fold risk of CTS.

The strain index

The Strain Index is a semiquantitative job analysis method designed to identify jobs that are associated with DUE MSDs versus those that are not (Moore & Garg, 1995). It is based on principles derived from physiology, biomechanics, and epidemiology as related to DUE disorders. The Strain Index is based on multiplicative interactions among six task variables, consistent with physiological, biomechanical, and epidemiologi-cal principles. The Strain Index score (SI score) represents the product of six multipliers, each corresponding to one of the six task variables. The six task variables are (a) intensity of exertion, (b) percentage duration of exertion, (c) number of efforts (exertions) per minute, (d) hand and wrist posture, (e) speed of work, and (f) duration of task exposure per day. All six variables are assigned a rating ranging from 1 to 5 (Table 4.7). The multipliers for the six variables related to their ratings are given in Table 4.8.

Intensity of exertion reflects the muscular effort required to perform a task one time, and it is the most important variable in the Strain Index job analysis methodology. Percentage duration of exertion is a measure of how long an exertion is maintained dur-ing a cycle and reflects biomechanical and physiological strain on the DUE. Efforts per minute is the number of exertions per minute and is synonymous with frequency of exertion. Posture is defined as position of the wrist and hand relative to anatomical neu-tral position and accounts for intrinsic compressive stresses to the contents of flexor and extensor compartments about the wrist. Speed of work refers to pace of the task or job and accounts for incomplete recovery during successive exertions. Duration of task exposure per day refers to number of hours spent on the task per day.

In a longitudinal study, Moore and Garg (1995) studied 32 pork processing jobs using the Strain Index. The study demonstrated that with a score of greater than 5.0, the Strain Index was able to identify those jobs that were associated with upper extremity disor-ders. Knox and Moore (2001) tested the Strain Index on 28 jobs in a turkey processing plant, using the previously established SI score cutoff point of 5.0, and found that the Strain Index had sensitivity of 0.86 and specificity of 0.79. Moore et al. (2001) studied the performance of the Strain Index in comparison to several “generic” risk factors (e.g.,

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high forcefulness, high repetitiveness, pinch grasp) for 56 jobs (28 turkey processing jobs and 28 manufacturing jobs). The authors concluded that the odds ratio for the Strain Index was 3 to 16 times larger than for any other job physical factors studied.

Rucker and Moore (2002) suggested that a cutoff score of approximately 9.0 (as opposed to 5.0) would likely perform better for manufacturing jobs, noting that in gen-eral, manufacturing jobs seemed to have higher duration of exertion and efforts per minute but lower force requirements than the turkey and pork jobs previously studied. On the basis of an analysis of combined data from the three previous studies, Moore, Vos, Stephens, Stevens, and Garg (2006) reported that an SI score of ≥6.1 was able to best identify “positive” jobs, in other words, jobs associated with one or more DUE morbidities.

Bloswick et al. (2003) evaluated the predictive performance of the Strain Index on 698 automotive assembly jobs in six assembly plants. They reported sensitivity for the Strain Index ranging from 0.66 to 0.88 and specificity from 0.40 to 0.52. It should be noted that SI scores were computed with an “observational” rather than video assess-ment of jobs. In a cross-sectional study, Bovenzi et al. (2005) used the Strain Index meth-odology to assess risk of distal upper limb MSDs in 100 female orbital sanders in a furniture industry and in a control group of 100 female office workers. The authors reported that the occurrence of soft-tissue disorders of the DUE increased significantly with increase in the SI score. Marras, Murgia, and Pazzona (2005) used the Strain Index to quantify biomechanical loads for the DUE in two dairy industries with different levels

Table 4.7. strain index Task Variable Table

RatingIntensity of

ExertionDuration of

ExertionEfforts per

MinuteHand-Wrist

PostureSpeed of

WorkDuration per Day

1 Light <10 <4 Very good Very slow <12 Somewhat

hard10 to 29 4 to 8 Good Slow 1 to 2

3 Hard 20 to 49 9 to 14 Fair Fair 2 to 44 Very hard 50 to 79 15 to 19 Bad Fast 4 to 85 Near maximal ≥80 20 Very bad Very fast ≥8

Source: Moore and Garg (1995).

Table 4.8. strain index Multiplier Table

RatingIntensity of

ExertionDuration of

ExertionEfforts per

MinuteHand-Wrist

PostureSpeed of

WorkDuration per

Day

1 1 0.5 0.5 1.0 1.0 0.252 3 1.0 1.0 1.0 1.0 0.503 6 1.5 1.5 1.5 1.0 0.754 9 2.0 2.0 2.0 1.5 1.005 13 3.0 3.0 3.0 3.0 1.50

Source: Moore and Garg (1995).

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of mechanization. The authors reported that mechanization significantly lowered the SI score, especially for salting and cheese shaping operations.

commonly Used Job Analysis Methods

Dempsey, McGorry, and Maynard (2005) conducted a survey of Certified Professional Ergonomists to gather information on job analysis methods used by practitioners. The most commonly used observational job analysis methods were those involving manual materials handling. Regarding upper extremity, the most widely used job analysis method was some form of a checklist (used by 70.5% respondents), followed by RULA (51.6%), the Strain Index (39.3%), and the TLV for HAL (22.1%). What is not clear is whether the Certified Professional Ergonomists prefer to use checklists and RULA because they are simple and less time-consuming or whether lack of use of more com-plicated methods (such as the Strain Index and the TLV for HAL) is because of lack of training on these methods.

Similarly, Pascual and Naqvi (2008) conducted a survey of Canadian certified ergon-omists, Joint Health and Safety Committees, and health and certification trainers to bet-ter understand which ergonomic analysis tools were used in industry to identify jobs associated with increased risk of MSDs. The study reported that the most commonly used job analysis method for determining risk for DUE MSDs was RULA and that most curricula did not include ergonomics analysis tools.

coMPArison of JoB AnAlysis METhoDs

The DUE job analysis methods most often used in American industrial settings are (a) checklist (of which there are many), (b) RULA, (c) TLV for HAL, and (d) Strain Index (Dempsey et al., 2005). (Given a lack of recent references to the Rodgers Muscle Fatigue Model and the EJMS, it is not clear whether these methods are currently used in industry; therefore, they are not included in the following discussions.) Out of the remaining four methods, checklists are the simplest to use and require minimal training and time to collect and analyze data. Because of these characteristics, checklists are valuable as surveillance tools to help identify and prioritize jobs potentially needing ergonomic improvement.

It is important to note that there are no epidemiologic studies on how effective check-lists are in discriminating between jobs associated with development of DUE WMSDs and jobs that are safe. Therefore, jobs identified by a checklist as potentially hazardous should be further assessed with either a more comprehensive job analysis method or the professional opinion of a trained ergonomist prior to committing capital resources to improving the job.

RULA, the next most widely used method, does not require any special equipment, is simple to use, and requires minimal training and the least amount of time to collect and analyze data as compared with TLV for HAL and the Strain Index. RULA is a posture-driven model with much less emphasis on repetition and hand force required to perform the job. It should be noted that force and repetition have been shown to be important

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risk factors associated with upper extremity WMSDs (Andersen et al., 2007; Armstrong et al., 1987; Bernard, 1997; Chiang et al., 1990, 1993; Gardner et al., 2008; Haahr & Andersen, 2003; Miranda et al., 2009; Moore et al., 2001; Moore & Garg, 1994; Roquelaure, 1997; Silverstein et al., 1987, 2006, 2010). Regarding epidemiologic evidence, as stated earlier, a correlation between RULA scores and perceived discomfort has been reported for a 30-min typing task (Fountain, 2003), and RULA’s ability to classify risk into three categories for sawmill workers on four jobs has also been reported (Jones & Kumar, 2010).

There are a few studies that have compared different ergonomic job analysis methods by assessing tasks for DUE WMSD risk. For example, Drinkaus et al. (2003) compared RULA and the Strain Index for ergonomic risks to the DUE of 244 automotive assembly plant tasks. The authors concluded that there is little agreement between the outputs of the two methods. Bloswick et al. (2003) compared three job analysis methods (Rodgers Muscle Fatigue Model, RULA, and the Strain Index) and concluded that the Strain Index performed the best of the three models used for DUE risk prediction.

Bao (2004) compared the risk predictions of the Strain Index, the ACGIH TLV for HAL, and the WISHA checklist using 23 workshop participants as analysts who had received half a day’s training on practical use of the methods. The jobs studied were very diverse, including (a) wire cutter, (b) paper mover, (c) electronics assembler, (d) laundry worker, (e) sawmill worker, (f) pharmacist, and (g) poultry processor. Bao observed that the Strain Index and TLV for HAL had very good agreement (95.2%) with each other. Also, evaluations conducted via professional judgments were less predictive compared with those based on structured methods. Furthermore, Bao observed that the variability among the different methods was high when either the hand force or the repetition was not very high. In a subsequent study, Bao, Howard, Spielholz, and Silverstein (2006) compared TLV for HAL and the Strain Index. The study concluded that there were poor correlations between different methods for measuring hand force and repetition.

Silverstein et al. (2006) studied 733 full-time employees from 12 work sites in a pro-spective cohort study. They used the Composite Strain Index (CSI; a proposed modifica-tion to the Strain Index to better allow for multitask job analysis) and TLV for HAL to predict risk of CTS. They found the log of CSI to be predictive of CTS when adjusted for age (years), gender, and body mass index (a measure of human body fat, calculated as mass divided by height squared, or kg/m2), whereas TLV for HAL was not found to be associated with incidence of CTS. Spielholz et al. (2008) studied 567 workers from 12 companies using the Strain Index and the TLV for HAL. Overall, the two methods agreed half of the time (56%) on classifying jobs for risk categorization, and the differ-ences were statistically significant (p < .05). In general, the TLV for HAL categorized more jobs as safe, and the Strain Index categorized more jobs as hazardous.

None of the three methods provides adequate guidance on detailed variable defini-tions and how to estimate these variables. In this regard, RULA has the weakest docu-mentation and many decisions are left to the analyst. For example, muscle use is defined as repetitive if the action is repeated more than four times per minute. It is not clear whether “the action” refers to exertions per minute or number of job cycles per minute. Both muscle use and force are assessed for Body Part Groups A and B separately. Body Part Group B includes neck, trunk, and legs. It is difficult to understand where applied

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force would come from given these diverse body parts, but one would assume that force application must be from the feet for Body Part Group B.

The normalization procedure for peak force for TLV for HAL requires determination of worker strength as well as 50th percentile strength for the “work population.” If the workforce contains a mix of male and female workers, it is not clear how to determine 50th percentile strength for that work population. Also, the TLV for HAL recommends use of professional judgments when the job requires nonneutral hand and wrist pos-tures; however, no guidance is provided. Furthermore, a strict interpretation of peak force would imply that one should use peak force irrespective of whether one of many exertions is at peak force level or all exertions are at peak force level. This interpretation potentially results in the same TLV for HAL score for jobs with very different overall exposure.

The Strain Index suffers from a similar problem. Although five of the six variables are well defined in the Strain Index, little guidance is provided on intensity of exertion if the job cycle involves exertions occurring at different force levels. This problem is exacer-bated by the fact that intensity of exertion contributes the most penalty within the Strain Index methodology.

For all three of these methods, an applications guide or users guide would be helpful so that analysts could better reflect the developer’s thought process when applying the job analysis method. Such guides would likely reduce inconsistencies seen in application and perhaps improve the reliability of the methods for everyday use.

strengths and Weaknesses of rUlA, TlV for hAl, and the strain index

From the preceding discussions, it is clear that although different job analysis methods have some similarity, there are also significant differences among these methods. It is likely that all three methods would classify jobs requiring low force, low repetition, and neutral posture as acceptable or safe. Similarly, all three methods should classify most jobs that require exposure to high force, high repetition, and awkward posture as unacceptable or unsafe. Predictions from the three job analysis methods may differ for those jobs that have physical exposures between these two extremes.

As stated earlier, RULA is a posture-driven model with maximum penalty assigned to nonneutral posture. Thus, jobs performed in an awkward posture but with low force and low repetition would probably be classified as unacceptable. On the other hand, infre-quent exposure to high force in neutral posture would most likely be rated as acceptable. There is a lack of validation studies on RULA that relate exposure scores from RULA to upper extremity MSDs. Also, RULA considers postures of all body limbs (lower extrem-ity, trunk, neck, etc.). It is not clear how postures of lower extremity and trunk contrib-ute to DUE MSDs. However, RULA is the simplest of the three methods, requires minimum training for job analysis, requires the least amount of time for job analysis, and is easy to use.

TLV for HAL is designed to assess monotask jobs performed for at least 4 hr per day and evaluates risk from normalized peak force and HAL (a combined measure of repeti-tions and percentage duty cycle). The method provides about equal penalty for force and

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repetition while ignoring posture in its computation. From a review of job physical risk factors discussed earlier, a combination of force and repetition is probably the most important risk factor for DUE MSDs. Professional judgments are required for non- neutral hand and wrist postures, contact stresses, exposure to vibration, and so on. Thus, TLV for HAL considers the likely most important risk factors and is relatively simple to use. Although Gell et al. (2005) and Werner et al. (2005a) did not find TLV for HAL to be associated with CTS, other studies have reported an association between TLV for HAL and upper extremity MSDs, including CTS (Franzblau et al., 2005; Violante et al., 2007; Werner et al., 2005b).

Normalized peak force in TLV for HAL refers to the relative level of effort on a scale of 0 to 10 (or Borg CR-10 scale as a substitute) that a person of average strength (50th percentile) would exert in the same posture to perform the task. It is difficult to normal-ize peak force rating given by a worker or even by an analyst, and it is often ignored. In the TLV for HAL graph, both the AL and the TLV lines end at the normalized peak force of 5 and 7 (Figure 4.4). It is not clear how to analyze infrequent exertions with a force rating exceeding these values. For example, if one assigns a HAL rating of 1, these jobs will be considered unacceptable despite having only infrequent, submaximal exertions. Another option would be to interpolate AL and TLV lines to a HAL rating of 0. Furthermore, the HAL table covers a limited number of combinations of frequency of exertion and percentage duty cycle (percentage duration of exertion). Therefore, one would often have to use the HAL scale with verbal anchors to assign a HAL rating.

The Strain Index is perhaps the most comprehensive model of the three job analysis methods in that it incorporates interactive effects of force, frequency, duration, and pos-ture. However, of the three methods, the Strain Index would also require the most training to use and would take the maximum amount of time to study and analyze jobs. Four lon-gitudinal studies (Knox & Moore, 2001; Moore et al., 2001; Moore & Garg, 1995; Silverstein et al., 2006) have reported that the SI score is a significant predictor of DUE MSDs.

One of the problems in using the Strain Index is in the assignment of force (intensity of exertion) rating when the force requirements vary significantly from exertion to exer-tion within a job cycle. The Strain Index methodology does not use average or peak force; rather, it relies on the analyst’s overall rating of force required to perform the job. An analyst has to assign a force rating that is “most appropriate” for that job. Thus, providing a force rating for jobs with different force-level exertions requires biomechanical understanding of stresses to the body and experience. This requirement can be problematic, as force rating in the Strain Index provides the largest penalty in the model.

APPlicATions AnD ExAMPlEs of JoB AnAlysis METhoDs

Following are three examples of manufacturing jobs with varying levels of risk for DUE injury. The physical activities of each job are verbally described, and temporal plots of force and posture are provided. We analyzed the jobs using RULA, the Strain Index, and the ACGIH TLV for HAL and used the methods described by each model’s respective authors. All analyses are for dominant hand. For RULA, ratings were provided both for

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the worst posture observed and for the worst posture observed during peak force. For TLV for HAL, HAL was rated with the verbal anchor scale (Figure 4.3) as well as the HAL table (Table 4.6). Our professional opinion of improvements necessary to reduce the risk of DUE injury are provided for each job and compared with the objective con-clusions drawn from RULA, the Strain Index, and TLV for HAL.

The three examples discussed in this section have been selected from data collected on thousands of manufacturing jobs for a large-scale prospective cohort study of DUE MSDs (Garg, Kapellusch, Hegmann, & Merryweather, 2010).

Example 1: flywheel subassembly

Flywheel subassembly is a very-high-repetition and moderate-force job. This job con-sists of picking up a flywheel weighing 3.8 kg from a pallet with both hands, placing it into a fixture, placing a magnet on the flywheel, placing screws in the magnet, and tight-ening screws using a suspended, inline, power screwdriver. After completion, the fly-wheel is removed from the fixture and stacked on a separate pallet. Cycle time is 15 s; two cycles were analyzed.

Strain Index analysis: Example 1. Force requirements for the job vary from 1 to 5 on the Borg CR-10 scale (Figure 4.5), corresponding to Strain Index ratings (SI ratings) of 1 to 3. The Strain Index methodology calls for an overall rating of force that best repre-sents the requirements of the job; thus the force rating is not necessarily a pure measure of peak or average force. In this example, a majority of time is spent at a force level with an SI rating ≤2; however, nearly half of efforts during the cycle occur at an SI rating of 3. These higher force requirements correspond to when the worker is using a multipoint pinch to lift the flywheel. The worker cannot grasp the flywheel in a balanced fashion. This task creates a torque on the hand and leads to greater pinch forces than would nor-mally be required to manipulate a 3.8-kg object. These higher forces occur every cycle and represent a material portion of the cycle time; therefore, the Strain Index intensity of exertion for this example is hard, an SI rating of 3.

Each time a worker grasps an object, regrasps an object, or substantially increases the force required while grasping an object, an exertion has occurred. By counting the peaks in the force plot of Figure 4.5, one will find that the worker exerts force 17 times in 30 s. Thus, the Strain Index number of exertions per minute is 17 per 30 s, or 34 efforts per minute, corresponding to an SI rating of 5.

Duration of exertion is determined by comparing the time one spends exerting effort with the total observation time. In this example, the worker is exerting effort for approx-imately 24 out of 30 s. This amount of time equates to 79% duration of exertion and received an SI rating of 4.

Posture rating is determined on the basis of the most representative posture used during the job. For this example, posture ranges from SI ratings of 1 (very good) to 4 (bad) during the cycle. A majority of time is spent with postures that are very good or good. The occasional bad postures occur with generally lower force requirements. Thus, the representative Strain Index posture rating for the job is fair, corresponding to an SI rating of 3.

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Speed of work is used to account for very fast motions that interfere with recovery time during a cycle. In this example, it is clear that the worker is exerting effort during most of the cycle and has essentially no opportunity for meaningful rest. Furthermore, during observation, the worker appeared to be “rushing” the work. Thus, speed of work is fast, corresponding to an SI rating of 4.

This job is performed for 8 hr per day, corresponding to an SI rating of 4. A summary of all SI ratings and their corresponding multipliers is provided in Table 4.9. The SI score for this job is 81, indicating that workers performing this job are at high risk for injury.

TLV for HAL analysis: Example 1. Peak force for this example, rated on the Borg CR-10 scale, is 5 (hard). Thus normalized peak force is 5 of 10. The HAL rating can be determined with the verbal anchor scale (Figure 4.3) or the HAL table (Table 4.6). On the verbal anchor scale, this job was described as having “rapid steady motion/exertions; no regular pauses” and received a HAL rating of 8. The job has 34 efforts per minute (about 0.5 efforts per second) and a duty cycle of 79% (between 60% and 80%); accord-ing to Table 4.6, the HAL rating is 5. As seen in this example, HAL ratings can be very different depending on whether verbal anchor scale or table is used. One can use Figure 4 to determine risk level. For this example, normalized peak force is 5. Regardless of whether HAL rating is 5 or 8, the job will fall above TLV; thus, the job is considered hazardous. TLV for HAL results are summarized in Table 4.10.

RULA analysis: Example 1. Temporal charts of RULA DUE postures are provided in Figure 4.6. This job requires intermittent application of force with hands, greater than 2 kg, resulting in a force score of 1. More than four efforts per minute are performed, so the hand-arm muscle use score is also 1. The worst hand-wrist posture seen is greater than 15° flexion and extension, resulting in a score of 3 (Figure 4.1). In addition, the worker typically has some ulnar deviation (not shown in Figure 4.6), resulting in an additional point. This posture also occurs during peak force; thus, worst hand-wrist posture is 4 and worst hand-wrist posture during peak force is also 4. During peak force,

Figure 4.5. Temporal plots of force and posture for dominant hand in Example 1; used for Strain Index and Threshold Limit Value for Hand Activity Level analyses.

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the forearm is nearly pronated, resulting in a rating of 2 for wrist twist for both worst posture and worst posture during force.

While the worker reaches to pick up parts, the worker’s shoulder flexes, resulting in an upper arm posture between 45° and 90°. This reaching occurs during peak force; thus, upper arm posture is initially scored as 3 for both worst posture and worst posture dur-ing force. The worker’s shoulder was not elevated; however, there was shoulder abduc-tion during the reach, adding an additional point. Therefore, the upper arm posture score for both worst posture and worst posture during force is 4. For lower arm posture, there is a difference between the worst posture observed and the worst posture that occurs during peak force. The worst posture observed is elbow flexion greater than 100°; the arm is not outside the body or across the midline at the time. Thus the score is 2. The worst posture during peak force is between 60° and 100° of elbow flexion. Again, the arm is not outside the body or across the midline; thus, the score is 1.

RULA scores for neck, trunk, and legs as well as a summary of the aforementioned ratings and RULA grand score calculation for this job are provided in Table 4.11. The RULA grand score for this example is 6, indicating the job is in need of “further investi-gation” and should be “changed soon.”

Professional judgment: Example 1. This job poses high risk for DUE injury to work-ers, predominantly from high force requirements and high frequency of exertion result-ing in insufficient recovery time. Poor workstation design has led to extended reaching to pick up heavy flywheels. Furthermore, flywheels are palletized such that they must be picked up from one end, leading to high torque on the hand and wrist. This problem is exacerbated by the need to pick up parts with multifinger pinch as opposed to power grip, leading to much higher %MVC requirements for the job. Work pace is unacceptably

Table 4.9. strain index (si) ratings, Multipliers, and overall score for Example 1

Intensity of Exertion

Number of Exertions per Minute

Duration of Exertion

Hand-Wrist Posture

Speed of Work Hours/Day

Measurement Hard 34 79% Fair Fast 8Rating 3 5 4 3 4 4Multiplier 6.0 3.0 2.0 1.5 1.5 1.0

Note: SI score = 6.0 × 3.0 × 2.0 × 1.5 × 1.5 × 1.0 = 81.0 (hazardous).

Table 4.10. Threshold limit Value (TlV) for hand Activity level (hAl) results for Example 1

HAL

Normalized Peak Force (Borg

CR-10) HAL Rating Risk Level

HAL by verbal anchor 5 8 >TLV; hazardousHAL by table 5 5 >TLV; hazardous

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high. Although the job is self-paced, production demands require workers to perform operations very quickly, leaving little time for suitable recovery. In general, hand and wrist postures are acceptable, and it is believed that hand and wrist posture does not materially contribute to increased risk on this job.

Reducing risk for this job would require workstation improvements, in particular, improved presentation of the flywheels to allow the workers to use power grasp when moving them as well as to allow the workers to grasp them uniformly, eliminating unnecessary torque. Even with substantial force requirement reductions, the frequency of efforts would need to be reduced. This reduction could be achieved though design changes, semiautomation, or simple lowering of each worker’s production demands. Workstation design improvements could also all but eliminate nonneutral hand and wrist postures on the job.

Figure 4.6. Temporal plots of force and posture for dominant hand in Example 1; used for Rapid Upper Limb Assessment analysis.

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Comparison of analysis methods: Example 1. All methods, including professional judgment, agree that this job has high risk for injury. Both the TLV for HAL and the Strain Index agree with professional judgment that force and frequency are the primary drivers of this risk (although it should be noted that TLV for HAL determined with the use of tabulated HAL suggests that frequency is less of a concern). The Strain Index sug-gests that hand and wrist posture could be improved, but is not a serious problem on this job. RULA contradicts professional judgment, showing that posture is the primary prob-lem (note that RULA is assessing shoulder, neck, and trunk in addition to hand and wrist posture). RULA finds no problem with force or frequency; had the number of exertions been very small and force been very low, the RULA grand score (4 or 5 depending on the level-of-force and frequency reduction) would still be rated as potentially unacceptable. A summary of the comparison of analysis methods results is provided in Table 4.12.

Example 2: Assembling Wiring harness

Assembling a wiring harness requires mixed force and a long cycle time. In this job, a worker is assembling a wiring harness and attaching it to a cylindrical indoor lighting fixture. At the beginning of the cycle, the worker picks up wires with the right hand, picks up a plastic plug with the left hand, and inserts two wires into the plug. Next, the worker places the plug onto a metal bracket and secures the plug by driving two screws using a suspended, inline screwdriver. This process is repeated, resulting in two subas-semblies. The worker then picks up a metal can with his or her left hand and inserts the plug assemblies into the can with his or her right hand. The finished assembly moves away on a conveyor. Cycle time is 60 s. Analyses are presented for the right hand.

Strain Index analysis: Example 2. Force requirements for the job vary from 0.5 to 3 on the Borg CR-10 scale (Figure 4.7), corresponding to SI ratings of 1 to 2. In this exam-ple, more than one third of efforts occur with SI rating = 2; therefore, an SI rating of 2 (somewhat hard) is assigned. Each cycle requires 29 exertions, and these exertions con-sume 72% of the cycle. Thus, the efforts-per-minute rating is 5, and duration-of-exertion rating is 4. The worker is predominantly working with very good or good posture; how-ever, six exertions are performed with fair posture, and one is performed with bad pos-ture, although the force is not high. These fair and bad postures are required to perform the task, and therefore the posture rating for this job is 3 (fair).

This job is self-paced and the worker does not appear to be rushed. The duty cycle is high and opportunities for rest are few; therefore, the speed rating for this job is fair and receives a rating of 3. The job is performed for 8 hr per day, resulting in a rating of 4 for hours per day. The SI score for this job is 27, indicating that workers performing this job are at high risk for injury (Table 4.13).

TLV for HAL analysis: Example 2. Peak force for this example, rated on the Borg CR-10 scale, is 3 (moderate). Thus, normalized peak force is 3 of 10. On the verbal anchor scale, this job was described as having “steady motion/exertion; infrequent pauses” and received a HAL rating of 6. The job has 29 efforts per minute (about 0.5 efforts per sec-ond) and a duty cycle of 72% (between 60% and 80%); according to Table 4.6, the HAL

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Table 4.11. rapid Upper limb Assessment Analysis results for Example 1

Worst PositionWorst Position During

Peak Force

Segment Category Score Category Score Comments

Upper arm 45°–90° with abduction

4 45°–90° with abduction

4 Flexion while reaching for parts with shoulder abduction

Lower arm 100°+ 2 60°–100° 1 Elbow flexion while carrying (worst) and reaching for (worst at peak force) parts

Wrist 15°+ with deviation

4 15°+ with deviation

4 High flexion w/radial deviation

Wrist twist Near-max pronation

2 Near max pronation

2 Full pronation while lifting parts

Hand forcea >2 kg 1 — — Pinch force to pick up parts

Arm muscle usea

>4 times per minute

1 — — Repetitive, near-continuous use of hands

Necka 0°-10° 1 — — Minimal neck flexionTrunka 0° 1 — — No observable truck

flexionLega No leg

support, no muscle use, no force

2 — — Continuous standing, a few steps walking during each cycle

Neck/trunk/leg force and muscle usea

No muscle use, no force

2 — — Continuous standing, a few steps walking during each cycle

Posture Score A

5 5

Posture Score B

3 3

Score C 5 + 2 = 7 5 + 2 = 7 Score D 3 + 0 = 3 3 + 0 = 3 Grand score 6 (further investigation,

change soon)6 (further investigation,

change soon)

a. Worst position ratings are used for calculations of both worst position and worst position during peak force.

rating is 5. For this example, regardless of whether HAL rating is 5 or 6, the job will fall between the AL and TLV; thus, the job is considered moderately hazardous. TLV for HAL results are summarized in Table 4.14.

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Table 4.12. comparison of Analysis results for Example 1

RULA TLV for HAL

Worst PositionWorst Position

During Force Table HALVerbal

Anchor HAL SI

Force 1 of 3 1 of 3 5 of 10 5 of 10 3 of 5Repetition duration

1 of 1 1 of 1 5 of 10 8 of 10 5 of 5 (efforts); 4 of 5 (duration)

Posture Hand/wrist 4 of 4 4 of 4 Not assessed 3 of 5 Forearm 2 of 2 2 of 2 Upper arm 4 of 6 4 of 6 Lower arm 2 of 3 1 of 3 Risk 6 of 7, further

investigation, change soon

6 of 7, further investigation, change soon

>TLV; high risk

>TLV; high risk

SI = 81; high risk

Suggested improvements

Improve postures

Improve postures

Reduce force Reduce force and hand activity

Reduce force and frequency

Note: RULA = Rapid Upper Limb Assessment; TLV = Threshold Limit Value; HAL = Hand Activity Level; SI = Strain Index.

RULA analysis: Example 2. Temporal charts of RULA DUE postures are provided in Figure 4.8. This job requires intermittent application of force greater than 2 kg, resulting in a force score of 1. More than four efforts per minute are performed, so the hand-arm muscle use score is also 1. The worst hand-wrist posture is greater than 15° extension and occurs during application of peak force. Thus, worst hand-wrist posture is 3, and worst hand-wrist posture during peak force is also 3. During peak force, the forearm is nearly pronated, resulting in a rating of 2 for wrist twist for both worst posture and worst posture during force. While the worker reaches to pick up wires, the worker’s shoulder flexes, resulting in an upper arm posture more than 90° with shoulder abduc-tion. This posture results in a score of 5. Worst upper arm position during peak force is <20° flexion with no abduction, resulting in a score of 1. Worst lower arm posture of >100° flexion occurs during peak force. The hand is not outside the body or crossing over the body midline; therefore, the upper arm score is 2.

RULA scores for neck, trunk, and legs as well as a summary of the aforementioned ratings and RULA grand score calculation for this job are provided in Table 4.15. The RULA grand score for this example is 6 when based on worst postures observed and 4 when based on worst postures during force. Depending on the methods used, the job is in need of “further investigation” and should be “changed soon.”

Professional judgment: Example 2. This job poses moderate risk for DUE injury to workers, predominantly from high force requirements and high frequency of exertion resulting in insufficient recovery time. Workers must insert wires into plastic plugs using

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a forceful two-point pinch. They also must drive Phillips screws that have a tendency to strip unless large amounts of thrust force are applied, requiring high grip force. The worker is not rushed, but there is not adequate recovery time given the force demands. Some wrist extension is present while inserting wires but only to a small degree, and it is not believed to increase risk.

Reducing risk for this job would require a reduction in applied force or a reduc-tion in required efforts (and/or the duration of those efforts). A reduction of both is probably not necessary, as the current force requirements would be acceptable with more rest, and the current frequency requirements would be acceptable with reduced force. In this example, semiautomation to reduce frequency would likely be difficult and expensive. Providing a tool to help insert wires (i.e., eliminating forceful pinch-ing) and changing to a Torx or an other positive engaging fastener system in place of Phillips screws (i.e., reducing forceful grip requirement) would reduce force require-ments and make the rate of exertions and duty cycle acceptable, resulting in low risk for DUE MSDs.

Table 4.13. strain index (si) ratings, Multipliers, and overall score for Example 2

Intensity of

Exertion

Number of

exertions/min

Duration of

Exertion

Hand/Wrist

PostureSpeed of

WorkHours/

Day

Measurement Somewhat hard

29 72% Fair Fair 8

Rating 2 5 4 3 4 4Multiplier 3.0 3.0 2.0 1.5 1.0 1.0

Note: SI score = 3.0 × 3.0 × 2.0 × 1.5 × 1.0 × 1.0 = 27.0 (hazardous).

Figure 4.7. Temporal plots of force and posture for dominant hand in Example 2; used for Strain Index and Threshold Limit Value for Hand Activity Level analyses.

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Figure 4.8. Temporal plots of force and posture for dominant hand in Example 2; used for Rapid Upper Limb Assessment analysis.

Table 4.14. Threshold limit Value (TVl) for hand Activity level (hAl) results for Example 2

Normalized Peak Force (Borg

CR-10) HAL Rating Risk Level

HAL by verbal anchor

3 6 ≥AL, ≤TLV; moderate hazard

HAL by table 3 5 ≥AL, ≤TLV; moderate hazard

Note: AL = action limit.

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Comparison of analysis methods: Example 2. For this job, the methods disagree about level of risk. RULA indicates the job should be further investigated and will require changes. TLV for HAL and professional opinion agree that the risk for DUE injury is moderate. The Strain Index indicates the risk is high. Similar to Example 1, the methods disagree on the source of risk. The TLV for HAL and Strain Index both show that high force and/or high repetition are problems (the Strain Index suggests both should be improved; TLV for HAL suggests that one or the other should be improved). RULA shows that increased risk is the result of poor postures. For this example, if force and frequency were reduced to RULA ideal levels, the RULA grand score would not be mate-rially changed; thus, RULA finds no problem with the force or frequency requirements of this job (Table 4.16).

Example 3: Machine operator

Machine operator is a low-force, moderate-frequency job. Operations for this job are predominantly performed by semiautomated machines. The worker places a small tubular sensor into an automatic testing fixture, then removes the sensor following the test and places it into an automatic welding machine. After welding is complete, the worker removes the sensor and, using a small handheld media blaster, removes debris from the welded tip of the sensor. After it is cleaned, the finished sensor is placed in a basket, and the cycle repeats. Cycle time is 20 s. Analyses are for the worker’s right hand.

Strain Index analysis: Example 3. Force requirements for the job vary from 0.5 to 1 on the Borg CR-10 scale (Figure 4.9), corresponding to an SI rating of 1. Each cycle requires 6 exertions, and there are three cycles per minute, resulting in 18 exertions per minute and an SI rating of 4. The duty cycle for the job is 43% of the cycle and receives an SI rating of 3. The worker is predominantly working with very good or good posture, with only the occasional exertion occurring with fair posture; thus posture rating is 2 (good). This job is paced by semiautomated machines and results in a somewhat relaxed work pace with noticeable idle pauses. Speed of work is rated as 2 (slow). The job is performed for 8 hr per day, resulting in a rating of 4 for hours per day. The SI score for this job is 3, indicating that workers performing this job are at low risk for injury (Table 4.17).

TLV for HAL analysis: Example 2. Peak force for this example, rated on the Borg CR-10 scale, is 1 (very light). Thus normalized peak force is 1 of 10. On the verbal anchor scale, this job was described as having “consistent conspicuous long pauses; or very slow motions” and received a HAL rating of 2. The job has 18 efforts per minute (about 0.25 efforts per second) and a duty cycle of 43% (between 40% and 60%); according to Table 4.6, the HAL rating is 3. For this example, regardless of whether HAL rating is 2 or 3, the job will fall below the AL; thus the job is considered low risk for injury. TLV for HAL results are summarized in Table 4.18.

RULA analysis: Example 3. Temporal charts of RULA DUE postures are provided in Figure 4.10. This job does not require application of force >2 kg and therefore receives a

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Table 4.15. rapid Upper limb Assessment Analysis results for Example 2

Worst PositionWorst Position During

Peak Force

Segment Category Score Category Score Comments

Upper arm 90°+ w/abduction

5 ±20° 1 Flexion while reaching for wires with shoulder abduction, near-neutral posture while inserting wires

Lower arm >100° 2 >100° 2 Elbow flexion while reaching for wires (worst) and driving screws (worst at peak force)

Wrist 15°+ 3 15°+ 3 High extension while inserting wires

Wrist twist Near-max pronation

2 Near-max pronation

2 Near-full pronation while inserting wires

Hand forcea >2 kg 1 — — Pinch force to insert wires/drive screws

Arm muscle usea

>4 times per minute

1 — — Repetitive, near-continuous use of hands

Necka 0°-10° 1 — — Minimal neck flexionTrunka 0° 1 — — No observable truck

flexionLega No leg

support, no muscle use, no force

2 — — Continuous standing, a few steps walking during each cycle

Neck/trunk/leg force and muscle usea

No muscle use, no force

2 — — Continuous standing, a few steps walking during each cycle

Posture Score A

7 3

Posture Score B

3 3

Score C 7 + 2 = 9 3 + 2 = 5 Score D 3 + 0 = 3 3 + 0 = 3 Grand score 6 (further investigation,

change soon)4 (further investigation,

change may be needed)

a. Worst position ratings are used for calculations of both worst position and worst position during peak force.

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hand force score of 0. More than four efforts per minute are performed, so the hand-arm muscle use score is 1. The worst hand-wrist posture seen is greater than 15° flexion and occurs when reaching into the media bowl to grasp the media blasting wand; this results in a score of 3. Worst hand-wrist posture during force occurs ±15° from neutral and receives a score of 2. The job does not require deviation of the wrist. During peak force, the forearm is nearly pronated, resulting in a rating of 2 for wrist twist for both worst posture and worst posture during force.

While the worker reaches to place sensors into the machines, the worker’s shoulder flexes, resulting in an upper arm posture between 20° and 45°. This action typically occurs with shoulder abduction, and thus the upper arm score is 3 for both worst pos-ture and worst posture during force. Worst lower arm posture of <60° flexion occurs during peak force and typically with the hand outside the body; thus the score is 3. RULA scores for neck, trunk, and legs as well as a summary of the aforementioned rat-ings and RULA grand score calculation for this job are provided in Table 4.19. The RULA grand score for this example is 4 for both worst posture and worst posture during force. According to RULA, the job is in need of “further investigation” and might need to be “changed soon.”

Table 4.16. comparison of Analysis results for Example 2

RULA TLV for HAL

Worst

Position

Worst Position

During Force Table HAL

Verbal Anchor

HAL SI

Force 1 of 3 1 of 3 3 of 10 3 of 10 2 of 5Repetition duration

1 of 1 1 of 1 5 of 10 6 of 10 5 of 5 (efforts); 4 of 5 (duration)

Posture Hand/wrist 3 of 4 3 of 4 Not assessed 3 of 5 Forearm 2 of 2 2 of 2 Upper arm 5 of 6 1 of 6 Lower arm 2 of 3 2 of 3 Risk 6 of 7, further

investigation, change soon

4 of 7, further investigation, change may be needed

≥AL, ≥TLV, moderate risk

≥AL to ≥TLV moderate risk

SI = 27 high risk

Suggested improvements

Improve postures

Improve postures

Reduce force or hand activity

Reduce force or hand activity

Reduce force or frequency

Note: RULA = Rapid Upper Limb Assessment; TLV = Threshold Limit Value; HAL = Hand Activity Level; SI = Strain Index; AL = action limit.

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Professional judgment: Example 3. This job poses very low risk of DUE injury to work-ers. Force requirements are very low. Frequency is somewhat high because of short cycle time, but adequate rest is provided during the cycle to accommodate the exertion rate. Hand and wrist postures are good except for required wrist flexion to reach for the media-blasting wand that is typically left lying in the media blast bowl. The wand weighs approxi-mately the same as a ballpoint pen, and the activity is not believed to increase risk.

This job does not require improvement; however, to increase comfort, a holder should be added for the media-blasting wand. This holder would place the wand in a precise location, providing a slight improvement in movement efficiency and eliminating wrist flexion.

Comparison of analysis methods: Example 3. For this job, the TLV for HAL, the Strain Index, and professional opinion agree that the risk for DUE injury is very low. RULA indicates that the job should be further investigated and that change may be needed. RULA shows that increased risk might be attributable to poor postures. In par-ticular, lower arm (elbow) flexion and wrist twist (pronation) are scored high. However, reducing these scores to their ideals would not change the RULA conclusion (although the grand score would be reduced to 3; Table 4.20).

summary of Examples of Job Analysis Methods

These examples show that these three methods have strengths and weaknesses. Although the three methods might lead to the same conclusion regarding whether a given job is safe or unsafe, the reasoning could be quite different and contradictory in terms of identifying different potential risk factors and therefore ergonomic solutions to make the job safe. This different reasoning poses a problem for practicing ergonomists and health and safety professionals.

Figure 4.9. Temporal plots of force and posture for dominant hand in Example 3; used for Strain Index and Threshold Limit Value for Hand Activity Level analyses.

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Table 4.17. strain index (si) ratings, Multipliers, and overall score for Example 3

Intensity of

Exertion

Number of Exertions/

Min

Duration of

Exertion

Hand/Wrist

PostureSpeed

of WorkHours/

Day

Measurement Light 18 43% Good Slow 8Rating 1 4 3 2 3 4Multiplier 1.0 2.0 1.5 1.0 1.0 1.0

Note: SI score = 1.0 × 2.0 × 1.5 × 1.0 × 1.0 × 1.0 = 3.0 (low risk).

Table 4.18. Threshold limit Value for hand Activity level (hAl) results for Example 3

Normalized Peak Force (Borg CR-10) HAL Rating Risk Level

HAL by verbal anchor 1 2 <AL; low hazardHAL by table 1 3 <AL; low hazard

Note: AL = action limit.

The TLV for HAL might be the best choice for facilitywide audits for DUE risk, as it is fairly quick and easy to use and clearly defines risk levels. RULA might be the best choice to assess whole-body postural stresses, particularly on low-force, lower-frequency jobs. The Strain Index might be the best for more comprehensive analyses of DUE risk, particularly prior to ergonomics interventions, when analysts may like to determine the benefits of various improvement strategies. Ultimately, practicing professionals should have a clear picture of their goals and objectives prior to selecting a model to assess risk.

fUTUrE rEsEArch

Estimating stresses: complex Jobs

There are many jobs in industry in which the weight of the object or the applied hand force, the duration of the applied force, and/or the hand-wrist posture frequently change during a job cycle. Traditional approaches to account for variation in physical exposure within a job cycle are (a) simple averaging of each job’s exposure variables, (b) use of the most common (typical) value of exposure variable to represent all exposure levels, (c) use of peak values of exposure variables to represent all exposure levels, and (d) time-weighted or frequency-weighted averaging of either exposure variables or exposure dose.

It is clear from the literature that these approaches often underestimate or overesti-mate the physical exposure to the worker (Dempsey, 1999, Garg & Kapellusch, 2009b). Herrin, Jaraieidi, and Anderson (1986) concluded that averaging or pooling of stressful

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Figure 4.10. Temporal plots of dominant hand force and posture in Example 3; used for Rapid Upper Limb Assessment analysis.

and nonstressful tasks tended to obscure the differences between tasks that contributed most to overexertion injuries and those tasks that did not contribute to overexertion injuries. Techniques such as time or frequency weighting, using only peak exposure, and determining cumulative exposure have been suggested to overcome problems associated with simple averaging. Unfortunately, each of these techniques has weaknesses.

Time-weighted and frequency-weighted approaches assume that an increase in time or frequency is equivalent to the same increase in force. Garg and Kapellusch (2009b) showed that this assumption may result in exposure misclassification. The peak force approach assumes that all exertions are at the peak force level and thus tends to overes-timate stresses, particularly in jobs with long cycles and infrequent use of high force. Cumulative exposure accounts for all exertions performed by a worker, but it is not clear

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Table 4.19. rUlA Analysis results for Example 3

Worst PositionWorst Position During

Peak Force

Segment Category Score Category Score Comments

Upper arm 20°–45° with abduction

3 20°–45° with abduction

3 Flexion with shoulder abduction while reaching to place sensors into machines.

Lower arm <60° 3 <60° 3 Elbow is extended while reaching into machines

Wrist ±15° 2 ±15° 2 High flexion while reaching for media blaster

Wrist twist Near max pronation

2 Near max pronation

2 Near full pronation while inserting wires

Hand forcea <2 kg 0 — — Very light force levels required

Arm muscle usea

>4 time per minute

1 — — Short cycle time leads to high frequency of exertion

Necka 0°-10° 1 — — Minimal neck flexionTrunka 0° 1 — — No observable truck

flexionLega No leg

support, no muscle use, no force

2 — — Continuous standing, a few steps walking during each cycle

Neck/trunk/leg force and muscle usea

No muscle use, no force

2 — — Continuous standing, a few steps walking during each cycle

Posture Score A

4 4

Posture Score B

3 3

Score C 4 + 1 = 5 4 + 1 = 5 Score D 3 + 0 = 3 3 + 0 = 3 Grand score 4 (further investigation,

change may be needed)

4 (further investigation, change may be needed)

a. Worst position ratings are used for calculations of both worst position and worst position during peak force.

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whether upper limb MSDs are caused by cumulative exposure or peak exposure (Dempsey, 1999). Furthermore, cumulative exposure is generally expressed in units of force and time. Thus, the implied assumption is that an increase in force magnitude has the same effect on injury potential as an equivalent increase in duration of force caused by an increase in either repetition or duration of force exertion.

Although both peak and cumulative loads have been shown to be related to MSDs, there are some major concerns with these approaches (Garg & Kapellusch, 2009a, 2009b). Thus, there is a need to develop job analysis methods that account for significant variations in important job physical risk factors, such as force, posture, and duration per exertion in a job cycle. Garg and Kapellusch (2009b) have proposed use of index compu-tation methods that divide a job into subtasks. A baseline strain score is assigned from the most stressful task, and then some form of incremental increase in strain is added to the baseline from the remaining subtasks. Although the general technique has shown some promise, it is not at all clear what this incremental increase in strain should be from performing other subtasks.

Estimating stresses: Job rotation

Job rotation is commonly used in many workplaces to provide production flexibility and to control MSDs. More than 42% of manufacturing companies use job rotation

Table 4.20. comparison of Analysis results for Example 3

RULA TLV for HAL

Worst

Position

Worst Position

During Force Table HAL

Verbal Anchor

HAL SI

Force 0 of 3 0 of 3 1 of 10 1 of 10 1 of 5Repetitionduration 1 of 1 1 of 1 3 of 10 2 of 10 4 of 5

(efforts); 3 of 5 (duration)

Posture Hand/wrist 2 of 4 2 of 4 Not assessed 2 of 5 Forearm 2 of 2 2 of 2 Upper arm 3 of 6 3 of 6 Lower arm 3 of 3 3 of 3 Risk 4 of 7, further

investigation, change may be needed

4 of 7, further investigation, change may be needed

<AL; low risk

<AL; low risk

SI = 3; low risk

Suggested improvements

Improve postures

Improve postures

None required

None required

None required

Note: RULA = Rapid Upper Limb Assessment; TLV = Threshold Limit Value; HAL = Hand Activity Level; SI = Strain Index; AL = action limit.

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(Jorgensen, Davis, Kotowski, Aedla, & Dunning, 2005). There is no clear methodology to account for variation in biomechanical exposure from job rotation (Dempsey, 1999; Garg & Kapellusch, 2009a, 2009b; Mathiassen, 2006). All three methods discussed ear-lier (RULA, the TLV for HAL, and the Strain Index) do not provide any guidance on how to deal with job rotation. There is a need to develop a sound methodology to inte-grate the strain to the worker from multiple jobs performed during an entire work shift. This integration would help industries to develop job rotation patterns that would minimize physical stresses and strains to the workers.

For example, Garg and Kapellusch (2009b) have proposed a Cumulative Strain Index that would include stresses to the worker from all different jobs performed during a work shift. According to their methodology, the Cumulative Strain Index score is equal to the sum of the largest single-job SI score and the incremental Strain Index (ΔSI) from each subsequent job in the job rotation. Once again, it is not clear what this ΔSI should be.

improved instrumentation for Data collection and Analysis

At present, collecting and analyzing data using biomechanics-based methods, such as TLV for HAL and the Strain Index, for upper extremity job analysis in industrial settings is very time-consuming. There is no reliable and easy-to-use method to measure applied hand forces in the workplace for handwork affecting the upper extremity. Therefore, either analyst or worker ratings of perceived exertion are commonly used for applied hand force. These ratings, although easy to obtain and analyze, might not measure the hand force accurately and thus affect risk assessment by the job analysis method used.

To determine number of exertions, duration of exertion, and hand-wrist postures, most researchers rely on videotaping the job, then analyzing the videotape in slow motion to determine frequency of exertion, duration of exertion, and hand-wrist pos-ture. This is a very time-consuming and tedious process especially for those jobs that have long cycle times (more than a few seconds). There could be significant errors from both inaccuracy in obtaining three-dimensional measurements from two-dimensional video (parallax error) and interobserver variability. Although electronic goniometers are available to measure and record hand-wrist posture, they are rarely used in the field, as they are expensive, fragile, and/or might interfere with job performance. Better technol-ogy is needed that would allow easy collection and analysis of both kinetic and kine-matic data in workplaces. Instrumentation should be user-friendly, should cause least interference to the worker, and should allow automatic collection and storage of measurements.

conclUsion

The most commonly used method for job analysis to determine the risk of DUE MSDs is a checklist (of which there are several), followed by RULA. The current scientific lit-erature reviewed in this chapter addresses RULA, the TLV for HAL, and the Strain

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Index. RULA is a posture-driven model and considers the posture of all body limbs in determining RULA grand score. It appears that repetition and force are given much less weight than body posture in determining the RULA grand score. There is little valida-tion of RULA on how effective this method is in discriminating between safe and unsafe jobs. The TLV for HAL is a two-variable model (peak force and HAL). There is contra-dictory epidemiological evidence on validation of the TLV for HAL. The Strain Index is the most complex of the three models, with greater consistency in epidemiological stud-ies on its ability to predict risk of upper extremity WMSDs.

RULA and checklists can be used as screening tools. For complete ergonomic assess-ments, it is recommended that ergonomists and health and safety professionals should use either the TLV for HAL or the Strain Index.

Real-life work environments commonly make use of jobs that consist of several bio-mechanically different tasks. In addition, many employers and work environments require that workers perform more than one job per day (job rotation). None of the current job analysis methods available are particularly well suited to deal with these scenarios. As such, there is need for more robust models to account for these variations in physical exposure in real-life environments.

At present, collecting and analyzing data for DUE job analysis in industrial settings using biomechanics-based methods, such as TLV for HAL and the Strain Index, is a tedious, time-consuming process. Better instrumentation to collect and analyze data in industry is needed. Much more research is needed to (a) better understand safe tissue tolerance in different loading conditions of hand, wrist, and forearm; (b) develop job analysis methods to accurately quantify stresses to the DUE; and (c) integrate stresses to the DUE into a meaningful assessment of risk in different working conditions, such as to account for job rotation and complex jobs with varying hand and wrist force during a job cycle. These improvements would help ergonomists in understanding the causes of WMSDs of the DUE and their prevention in workplaces.

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ABoUT ThE AUThors

Arun Garg is a professor and chair of the Industrial and Manufacturing Engineering Department and director of the Center for Ergonomics at the University of Wisconsin–Milwaukee. He is a Board Certified Professional Ergonomist. He received his PhD from the University of Michigan in 1976. He has more than 150 publications. He has devel-oped several job analysis methods used worldwide. These include the Revised NIOSH Lifting Equation, 3-D static strength biomechanical model, the energy expenditure model, the Strain Index, and the human strength prediction model.

He has served as a consultant to many industries and government organizations. He is currently leading two prospective cohort studies addressing risk factors for distal upper extremity and low back pain. His areas of expertise include ergonomics, biomechanics, work physiology, office ergonomics, and design of workplace and hand tools to reduce musculoskeletal injuries and illnesses.

Jay M. Kapellusch is an assistant professor of occupational science and technology and a member of the Center for Ergonomics at the University of Wisconsin–Milwaukee. His interests and expertise are in studying the effects of job physical exposure on incidence of musculoskeletal injuries, job analysis methods, and job design. He has more than 15 years of research and consulting experience in ergonomics and has analyzed in excess of 2,000 jobs in more than 150 companies.