Quantitative approaches to physical
ergonomic issues encountered while
assessing workplace designs
Quantitative approaches to physical
ergonomic issues encountered while
assessing workplace designs
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op
maandag 23 juni om 1500 uur
door
Thomas Joseph ALBIN
Master of Science
Industrial and Management Systems Engineering,
University of Nebraska, Lincoln
Dit proefschrift is goedgekeurd door de promotor: Prof. dr. P. Vink
Copromotor: Dr. ir. J.F.M. Molenbroek
Samenstelling promotiecommissie:
Rector Magnificus, Voorzitter
Prof. dr. P. Vink, Technische Universiteit Delft, promotor
Dr. ir. J.H.M. Molenbroek, Technische Universiteit Delft, copromotor
Prof. dr. ir. R.H.M. Goossens, Technische Universiteit Delft
Prof. dr. M.S. Hallbeck, University of Nebraska, Lincoln, USA Prof.dr. V. Hermans, Vrije Universiteit Brussel, België Professor dr. ir. D.V. Keyson, Technische Universiteit Delft
Dr. M.M. Robertson, Liberty Mutual Research Institute for Safety, USA
1. Throw checklists away if they cannot predict better than randomly guessing does.
2. Everyone knows that you can’t reliably add or subtract percentiles of anthropometric data, but it is done often.
3. The use of musculoskeletal risk assessment tools can and must be managed as a dynamic measurement process.
4. Assuming that anthropometric data are always normally distributed is a grievous error.
5. The placebo effect plays an important role in ergonomics.
6. Increasingly ergonomic practitioners are generalists who are rule-bound in their approach to problem solving, rather than autonomous.
7. Ergonomics lacks an efficient flow of feedback from practitioners to researchers.
8. Ergonomics is widely perceived as dealing only with risk of injury or illness, rather than with all aspects of the interaction between humans and the things that they use in a context.
9. A user with too many controls of things and too little control of his/her work is likely to report discomfort.
10. Stubbornness is a good trait in a researcher. 1
Checklists kunnen beter weggegooid worden als deze niet een betere voorspelling doen dan volgens toeval ook te beredeneren is.
2
Iedereen weet dat je percentielen van antropometrische gegevens niet mag optellen en aftrekken. Toch wordt het vaak gedaan.
3
Het gebruik van RSI risico meetinstrumenten dient als een dynamisch meetproces gezien te worden.
4
De aanname dat antropometrische data altijd normaal verdeeld zijn is een ernstige fout.
5
Het placebo effect speelt een belangrijke rol in de ergonomie. 6
In hun benadering van ergonomie zijn de ergonomen in toenemende mate meer regel gebonden in plaats van autonoom.
7
Ergonomie lijdt aan gebrekkige feedback van de praktiserenden naar de
onderzoekers.
8
Ergonomie wordt te vaak gezien als vooral bezig zijn met de preventie van
letsel of ziekte in plaats van met alle aspecten van de interactie tussen mensen
en de dingen die ze gebruiken in een bepaalde omgeving.
9
Iemand, die veel moet regelen en weinig controlemogelijkheden over zijn werk
heeft gaat eerder discomfort rapporteren.
10
Eigenwijsheid is een goede eigenschap van een onderzoeker .
These propositions are regarded as opposable and defendable, and have been approved as such by the supervisors.
Deze stellingen worden opponeerbaar en verdedigbaar geacht en zijn als zodanig goedgekeurd door de promotoren.
Table of Contents
Introduction
Chapter 1: Musculoskeletal disorders, an important societal issue
3
Part I: Understanding checklist reliability and validity in order to correctlyidentify at-risk jobs
30
Chapter 2: Measuring the validity and reliability of ergonomic checklists
31
Part II: Getting the fit right: Designing with limited anthropometric data41
Chapter 3: A method to improve the accuracy of pair-wise combinations of
anthropometric elements when only limited data are available
42
Chapter 4: A method superior to adding percentiles when only limited
anthropometric data such as percentile tables are available for
design models
54
Chapter 5: An Empirical Description of the Dispersion of 5th and 95th Percentiles in Worldwide Anthropometric Data Applied to Estimating
Accommodation with Unknown Correlation Values
73
Part III: Let the user speak: Finding the preferred tilt angle for tablets
89
Chapter 6: The effect of tablet tilt angle on users’ preferences, postures, andperformance
90
Epilog
101
Curriculum Vitae
123
Publications
124
Chapter 1:
Introduction: Musculoskeletal disorders,
an important societal issue
Musculoskeletal Disorders (MSDs) are a common problem worldwide. Woolf and Pfleger (2003) report that the most common example of MSD is low back pain, which is experienced by nearly every person at some time in his or her life. Woolf and Pfleger estimate that the point prevalence of low back pain in the world population is about one of every three persons, or in other words, approximately 2.4 x 109 individuals are currently experiencing low back pain. A survey study in the United Kingdom found that, in a representative sample of men and women aged 16 years or more, nearly one half had experienced significant musculoskeletal pain of any kind during a one-month period prior to completing the survey (Urwin et al, 1998). Musculoskeletal Disorders are the predominant cause of physical disability worldwide (Wolf and Pfleger, 2003; Woolf et al, 2012)
Work Related Musculoskeletal Disorders
Work-Related Musculoskeletal Disorders (WRMSDs) are also a global occupational health concern. The World Health Organization [2003] has defined WRMSD as:
“all musculoskeletal disorders that are induced or aggravated by work and the circumstances of its performance”.
In a review of the epidemiology of WRMSDs, Punnett and Wegman [2004] note that WRMSD are:
“… the single largest category of work-related illness, representing a third or more of all registered occupational diseases in the United States, the Nordic countries, and Japan.”
Similarly, WRMSDs constituted 38 percent of occupational diseases in the European Union, as reported by the Executive Agency for Health and Consumers (2005).
Careful attention to the physical layout of the workplace is a way to reduce the risk of WRMSDs. Such analyses may be applied to existing workstations, to ab initio designs and to prototypes. However, in practice, the time and other
resources available to ergonomists and designers with which to analyze existing workplaces with regard to WRMSD risk, or to evaluate new workplace design with regard to WRMSD risk are generally limited. In this PhD thesis, we set the objective of demonstrating some readily practicable methods of analyzing the workplace that are intended to increase the efficiency with which designers and ergonomists can utilize limited resources during several phases of designing improvements to the physical workplace. We first discuss a means of evaluating the reliability and validity of checklists or other assessment tools used to measure the risk of WRMSDs in existing workplaces. The second methodology described is a means of improving the prediction of
anthropometric accommodation when the data available are severely limited, for example, when only summary percentiles are available. This methodology might be applied when developing designs ab initio. Finally, we describe a user test of various design form factors in order to find a user-preferred solution when design criteria for user comfort and performance are somewhat contradictory.
Approaches to design of the workplace based on
physical ergonomics
The physical design of the workplace, including the physical layout and the design of the actions necessary to perform the work, plays a role in the causation of WRMSDs. A poorly designed workstation layout may cause an awkward posture by a mismatch between the physical size of the user and the workstation dimensions, or it may lead to repetitive, stressful motions, which may increase the risk to workers of a musculoskeletal disorder.
For example, the design of a work surface in a compter workstation may not provide a sufficient range of depth and height adjustment to appropriately accommodate placement of the users’ hands, input devices and displays. Much research (Marcus et al, 2002; Keir et al, 2007: Berqvist et al, 1995) implicates the resultant constrained postures as causal factors of WRMSDS. Similarly, in manufacturing environments, the horizontal distance between the worker’s spine and the center of mass of an object lifted has been implicated as a causal factor of low back pain and injury (Chaffin and Andersson, 1984; Marras et al, 1993; and consequently is included as a risk factor in technical guidelines such as the NIOSH lifting guide (Waters et al, 1994) and in European Standard EN 1005-2 (CEN, 2003). Accurate anthropometric models provide a basis for designs that will accommodate the intended users.
An example of the interaction between the individual worker and the workspace in described in the model shown in Figure 1, adapted from Hildebrandt (2001). Other models are specific in regard to postural evaluation and design. For example, ISO 11226 provides guidance for the evaluation of working postures in existing workspaces and European Standard EN 1005-4 sets design criteria for working postures at new workspaces.
Hence it behooves designers and ergonomists to be cognizant in making design decisions with regard to the musculoskeletal risks present as well as the physical fit between the design and the users’ anthropometry.
Figure 1. A model suggested by Hildebrandt (2001) and others describing the effect of workspace design on occurrence of WRMSDs
Finally, a design decision may have a direct impact on workers’ ability to perform the work as efficiently and economically as possible. For example, Jonsson (1988) suggested that the static load or muscular exertion should not exceed 5 percent of the Maximum Voluntary Contraction (MVC) for extended work as it has been shown (Sjogaard et al, 1988) that muscular fatigue can occur when exertion levels of 5 percent of MVC are maintained for long durations; Forestier and Nougier (1998) have shown that muscular fatigue adversely affects performance of even such simple tasks as throwing a ball. Perhaps a more immediate design effect familiar to computer users is the movement of a cursor from some starting location on a display screen to click a target at another location on the screen using an input device such as a mouse. MacKenzie (1992) used Fitts’ law to model the movement time required for the user to execute the cursor movement as a function of the distance between the starting and ending points and the width of the target. The greater the distance, or the smaller the target, the more time is required for the desired movement.
The effect of this design feature on performance is so consistent, that some technical standards, such as ISO 9241-410 (2012), use the Fitts’ law model applied to various movement protocols to compare the relative efficacy of input device designs.
An ideal approach to physical ergonomics might follow an iterative process: first a task analysis to completely characterize and describe the process being studied in terms of the actions of the human user, both as input to the system and in response to output from the system, then an analysis or assessment of the actions as initially designed or observed with respect to items of concern, e.g. musculoskeletal risk factors, anthropometric accommodation, than a generation of solutions for the problems identified, next implementation of the solutions, and finally, verification that the solutions have been effective. However, practitioner ergonomists and designers will inevitably find that their ability to implement this ideal process is constrained by limits on resources, such as time, money, and data. In order to be as efficient as possible, practitioner ergonomists and designers need to be able to base their design decisions on a solid basis.
Measurement
Dul et al (2012) note that Human Factors and Ergonomics
“has great potential to ensure that any designed artefact, ranging from a consumer product to an organisational environment, is shaped around the capacities and aspirations of humans, such that performance and well-being are optimised”.
It is entirely possible to design solutions to issues that have been identified based on “feeling and experience”; however, there is an inherent risk that the resulting design will not be an improvement except in the mind of the designer. The use of assessment tools provides an approach to form a quantitative basis for designs.
In this PhD thesis, we describe some quantitative approaches to support and facilitate the design process, particularly with regard to the characterization of musculoskeletal risks, anthropometric accommodation of the desired proportion of a group of intended users, and finally, with regard to the effect of possible design form solutions on users’ preferences, comfort, musculoskeletal risk exposure and performance.
By introducing more objective measurement techniques, we seek to facilitate management of the design process and to implement the insight of Peter Drucker, the management guru, who said:
“… the measurement used determines what one pays attention to.” This has morphed into a popular aphorism, “what gets measured gets managed”.
Part I: Characterization and assessment of
musculoskeletal risk exposure in existing
workstations
If we seek to manage users’ exposure to musculoskeletal risk by paying attention to measurements that attempt to characterize the risk, we naturally want to be as certain as possible that the things we pay attention to truly describe the risk present. The data regarding WRMSDs are typically described in terms of incidence or prevalence.
Incidence is defined as the number of new cases occurring during some period of time, prevalence is defined as the number of cases observed at some point in time, or within some time period. Consequently the incidence of new WRMSD cases in a population of factory workers might be 1 per 100 workers, but the prevalence of WRMSD symptoms might be 2.5 per 100 workers if we asked today.
The incidence or prevalence of WRMSDs among individuals is extremely useful to describe the scope of the problem to society. Data regarding the incidence and prevalence of WRMSDs typically focus on the affected individuals, for example in the United States the Bureau of Labor Statistics (BLS) routinely reports the incidence of lost-time WRMSD cases per 10,000 individuals per standard year worked. A standard year is 2,000 labor hours. However, the first question that we might ask is; are we gathering the right data for our purposes? In contrast to physicians and physical therapists, ergonomists, engineers and designers are typically concerned with the analysis and design of things, not people. Since we analyze and design jobs and environments, it would be more consistent, logically, to think about the incidence and prevalence of WRMSDs in jobs rather than the incidence or prevalence in people.
We might define a work process as a group of actions and decisions sufficient to accomplish some goal. A work process consists of tasks. A process may be described in a task analysis, which lists the actions and decisions required to
accomplish the desired goal, for example, manufacturing widgets. Similarly, in manufacturing it is common to have a standard process description, which lists the steps and actions that a worker is expected to perform in the course of his or her work, and in lean engineering, which uses “value stream mapping” to describe the actions and decisions necessary to accomplish a goal, but also adds a quantitative judgment as to the value that such actions and decisions add to accomplishing the goal.
As the term “task analysis” implies, a work process may be described as a series of tasks that, in aggregate, accomplish a desired goal, such as
manufacturing a widget. A worker’s job is typically to perform some subset of the tasks that comprise a work process.
Risk exposure of individuals performing a job may vary, as many different jobs are possible within a process. For example, if multiple people are assigned to a job, then it is possible that not all of them will perform the same tasks. Similarly, different individuals performing the same tasks may have different risk
exposures. For example, a small individual may be required to reach further than a larger person and a larger person may be required to bend further than a smaller individual.
Ways to assess WRMSD risk
In Chapter 2 of this thesis, we discuss means of evaluating the reliability and validity of checklists or other assessment tools used to assess workplace designs. Checklists are commonly used to analyze WRMSD risk factors in jobs that consist of a persistent set of tasks. In order to be useful tools for the identification of WRMSD risks, checklists must be reliable and valid. We might define reliability as the consistency of checklist results and validity as the accuracy of checklist results.
However, the reliability and validity of checklists are generally not well
established. If information regarding checklist reliability and validity are known, it is generally within a specific context of use and it is an open question as to whether or not a checklist will perform in the same way in different working environments, especially if the jobs differ greatly one from the other. There are three issues of interest.
1. In the United States, the Bureau of Labor Statistics annually publishes statistics regarding workplace injuries and illnesses. In the most recent data (BLS, 2012), the incidence rates for one type of WRMSD, carpal tunnel syndrome, the incidence rate of cases resulting in days away from work vary between 0.2 to 5.1 cases per 10,000 employees per year worked.
Similarly a report from the EU (European Agency for Safety and Health at Work, 2010) reports the prevalence of WRMSDs among different
occupations as a percent of the mean prevalence of WRMSD in the EU. The prevalence varies by occupation between 65 and 140 percent of the mean prevalence. Clearly jobs do differ with regard to WRMSDs and we need to understand how checklists are affected by different rates or prevalence of WRMSDs.
2. A second concern with checklist reliability and validity is that, when some information is known regarding these properties, it is often assumed to be a static and unchanging property of the checklist.
3. Finally, Kanis (2012, 1997) has drawn a distinction between observing “at” humans rather than “through” humans. Fortunately for the physical actions typically assessed by WRMSD checklists, he concludes that it is possible to reliably make measurements directed at humans, such as anthropometric dimensions and postural angles, but that is much more problematic to reliably measure “through” the agency of a human observer, for example asking a worker to subjectively rate exertions.
Reliability of checklists
Reliability is the most fundamental property that a checklist must have. Without knowledge of the reliability of a checklist, a practicing ergonomist or designer cannot determine the validity or the utility of a checklist that he or she is using. There are two types of reliability with which we must be concerned. The first is intra-rater reliability. An ergonomist or designer must be certain that he or she consistently evaluates WRMSD risk present in jobs when using a checklist, and that, all other things being equal, two evaluations of the same job done at different times will consistently produce the same results. Similarly inter-rater reliability or consistency is necessary if we are to be able to compare the results of WRMSD checklist assessments performed by two or more different raters. We might re-envision the process of ergonomic assessments by considering reliability as a description of the quality of a measurement. When seen in this way, reliability is treated as a dynamic quality to be measured and managed, rather than a static process. From this viewpoint, should the reliability of a measurement be less than satisfactory, actions are taken to improve it.
Validity of checklists
Nunnally (1967) describes three kinds of validity for measuring instruments such as checklists. They are predictive validity, content validity and construct validity. He describes predictive validity as
“determined by, and only by, the degree of correspondence between the two measures involved. Thus if it were found that accuracy in horseshoe pitching correlated highly with success in college, horseshoe pitching would be a valid measure for predicting success in college”.
Nunnally states that content validity
“depends primarily on the adequacy with which a specified domain of content is sampled. A prime example would be a final examination for a course in introductory psychology. Obviously the test would not be validated in terms of predictive validity, because the purpose of the test is not to predict something else but to directly measure performance in a unit of instruction. The test must stand by itself as an adequate measure of what it is supposed to measure. Validity cannot be determined by correlating the test with a criterion, because the test itself is the criterion of performance”.
Finally, Nunnally describes construct validity by distinguishing it from predictive and content validity by the abstractness of the variables with which it deals. He uses the example of the analysis of a study of heredity and environment on intelligence in identical twins, fraternal twins and nontwin siblings via use of an intelligence test. Quoting Nunnally
“The intelligence test cannot be validated by correlating it with a “criterion”, because there is no better measure known. (If there was, why not use it in the study?) The test cannot be validated in terms of content validity, because the “content” (relevant behavior) is
somewhat in dispute, and even more in dispute is how to turn such content into workable measures”.
Nunnally extensively describes issues of concern in assessing construct validity. More recently Kanis (2013) has also discussed the difficulties inherent in measuring construct validity. However, in this thesis we deal with predictive validity, that is, assessment of how well an instrument such as a checklist predicts risk of WRMSD.
As discussed in this thesis, checklist validity is a measure of how well the checklist does what it is intended to do, that is, how well does it perform in correctly identifying problem jobs, jobs that are at risk for musculoskeletal
disorders? That is, does it have predictive validity when compared to some criterion?
A person responsible for assessing WRMSD risk needs dynamic information regarding the intra- and inter-rater reliability and the validity of the
measurements that he or she makes when using assessment tools such as a checklist, and it should be information specific to his or her own individual context of use, current as of the time of use.
If he or she oversees other individuals who are engaged in the collection of data using a checklist, then he or she must know that those individuals produce reliable, consistent measurement data, individually and as a group. Secondly, it is essential to know what level of confidence may be placed in the checklist’s identification of a job as a problem job. Again, that information must be dynamic and current as of the time of use if the process of identifying jobs at risk for WRMSD is to be effectively managed. With this information, if the reliability or validity of a checklist’s results is less than is desired, then the ergonomist or designer can take steps to manage measurement process. As Nunnally (1967) says “whereas measure of length and of some other simple physical attributes may have proved their merits so well that no one seriously considers changing to other measures, most measures should be kept under constant surveillance to see if they are behaving as they should. New evidence may suggest modifications of an existing measure or the development of a new and better approach to measuring the attribute in question…”
At its most basic, predictive validity is a measure of how well a checklist or assessment tool does what it is intended to do, correctly identify jobs that are at risk for WRMSD.
The problem of accurately identifying jobs that are at risk in regard to WRMSD using checklists is conceptually similar to using diagnostic tests to determine if an individual has some disease. The presence of the disease is a “gold standard” used to evaluate the performance of the test.
The effectiveness of tests at diagnosing disease is commonly described by sensitivity and specificity. Sensitivity is the proportion of the total number of individuals with the disease correctly identified by the test as having the disease and specificity is the proportion of individuals without the disease who are correctly identified by the test as not having the disease. Finally prevalence is the proportion of individuals in the population who have the disease (Spitalnic, 2004). As a general rule, the closer sensitivity and specificity are to 100 percent, the better the test is rated.
Disease is present Disease is not present Checklist is
positive A B
Checklist is
negative C D
Figure 1.1. Two by two table describing possible outcomes when a checklist is used to evaluate a sample of jobs
Figure 1.1 describes all possible outcomes when a diagnostic test is given to a group of individuals, some of whom have the disease that is being tested for. The cell labeled A is the number of individuals who both have the disease and tested positive for the disease, the cell labeled B is the number of individuals who tested positive but don’t have the disease, cell C is the number of
individuals who have the disease but tested negative, and cell D is the number who both tested negative and do not have the disease.
Mathematically sensitivity is expressed as the number of individuals in cell A divided by the sum of the number of individuals in cells A and C (A/A+C). Specificity is the number of individuals in cell D divided by the sum of cells B and D (D/B+D). Prevalence is the sum of cells A and C divided by the sum of all cells (A+C)/(A+B+C+D).
While it might appear that a checklist with high sensitivity and specificity in regard to identifying WRMSD would be satisfactory for our purposes, these quantities don’t tell us directly how well our checklist or test does at identifying a problem job.
Sensitivity tells us the proportion of problem jobs that are correctly identified by the checklist, not the proportion of jobs identified as problem jobs that are truly problem jobs. This is a subtle distinction, but an important one with regard to the allocation of resources. The resources available to a practicing ergonomist or designer are nearly always constrained. There is a limit on the amount of time available to allocate to determining how to reduce the risk exposures in the jobs identified as problem jobs, a limited amount of money to finance the changes, etc.
Ideally the practitioner would be completely efficient and allocate resources only to fixing jobs that need to be fixed, but if the probability that a job identified as at risk for WRMSD by the checklist is low, the checklist is not efficient as a means of identifying problem jobs and the allocation will likely be inefficient. In order to be useful, a checklist must give a probability of correctly identifying at risk jobs better than random guessing.
It is readily practicable to determine the probability that a checklist or
assessment tool correctly identifies problem jobs in a manner specific to a given context of use, that is, taking into account the local prevalence rate of WRMSD. Consequently it is entirely feasible for a designer or ergonomist to dynamically track the performance of a checklist with regard to validity as applied in his or her area of interest by dynamically calculating the probability that a checklist correctly identifies problem jobs. The probability can be updated continuously, each time that a new analysis is completed. The probability can also be used to manage the use of the checklist, for example, by determining an optimal score to discriminate between problem jobs and non-problem jobs. The probability might also be used as a dependent measure to evaluate the effect of
modifications to the checklist; for example, does a simpler format yield equivalent results?
Similarly it is possible for a designer or ergonomist to dynamically track and manage the reliability of the measurements made using a checklist in order to be confident that the data gathered are both reliable and valid. The two concepts of dynamically measuring and managing the reliability and validity of checklists are introduced in chapter 2.
Three critical roles for prevalence in the identification of jobs at risk for WRMSD First, as described in chapter 2, random guessing as to whether or not a job is at risk for WRMSD will result in a percent of jobs correctly identified that is equal to the percent prevalence. That is, if the prevalence of WRMSD among a group of jobs is 10%, then randomly guessing has a probability of 10% of correctly identifying jobs at risk for WRMSD, if the prevalence is 1% then guessing has a probability of correctly identifying 1%. This sets a floor with regard to the effectiveness of a checklist at identifying jobs at risk for WRMSD; in order for a checklist to be useful, it must yield a higher probability of correctly identifying a job at risk for WRMSD than would guessing.
Second, while sensitivity and specificity might be thought at first to be the relevant characteristics to be considered for evaluating a checklist, they are insensitive to prevalence and consequently might lead a user to over estimate the performance of a checklist in terms of a positive result on the checklist indicating a job at risk. The probability that a positive checklist result correctly identifies a job at risk increases as the prevalence increases.
To illustrate this, consider a hypothetical case where 1,000 jobs have been analyzed with a checklist to determine which, if any, jobs are at risk for WRMSDs. We have independently used some “gold standard” to determine which of the jobs are at risk with regard to WRMSD. The test has good sensitivity, 90 percent, and good specificity, 87 percent. Although sensitivity
and specificity remain constant, as shown in Figure 1.2, the probability of correctly identifying a job as at risk with regard to WRMSD increases as the prevalence increases.
Prevalence of WRMSD jobs
1% 5% 10% Number of jobs analyzed 1000 1000 1000 Jobs at risk 10 50 100 Jobs not at risk 990 950 900 Jobs correctly identified
as at risk
9 45 90
Jobs incorrectly identified as at risk
129 124 117 Total Jobs identified as at
risk 138 169 207 Probability Job is Correctly Identified 7% 27% 57% Sensitivity 90% 90% 90% Specificity 87% 87% 87%
Figure 1.2 Effect of prevalence on probability of correctly identifying a job as at risk for WRMSD
Finally, Musculoskeletal Disorders (MSDs) are common in the population at large. Bot et al (2005) report estimates for the 12-month prevalences among the patients of general medical practitioners in the Netherlands of 31.4 % for neck pain, 30.3 % for shoulder pain, 11.2% for elbow pain, and 17.5% for wrist or hand pain. Huisstede et al (2006) conducted a systematic review of 13 studies of the prevalence of upper extremity disorders; they report that the point prevalence varied between 1.6 % and 53%, 12-month prevalence ranged between 2.3% and 41%, and one study reported lifetime prevalence among dentists of 29%. Andersson (1999) reviewed 15 studies reporting back pain incidence or prevalence; 5 reported point prevalences ranging from 12% to 30.2% and six studies of unknown period length had period prevalences ranging from 25% to 42%. Walker (2000) performed a systematic review of 30 studies of the prevalence of low back pain and reported that point prevalences
ranged between 12% to 33%, 12-month prevalences ranged between 22% to 65% and lifetime prevalence ranged from 11% to 84%.
What proportion of these MSDs result from workplace exposures? Punnett and Wegman (2004) observe that MSDs have multiple risk factors, including, but not limited to occupational risk factors.
“… the etiology of these disorders in the population as a whole is multifactorial. Not everyone with MSDs has ergonomic exposures at work, and not everyone exposed at work develops a MSD. The appropriate concept here is ‘‘work- related’’ disorders, as distinguished from specifically ‘‘occupational’’ disorders where a single factor is both necessary and sufficient to cause the disease.” They refer to the study of Tanaka et al (2001), which concluded that
“… about 40% of all upper extremity MSDs in the total US employed population were attributable to occupational exposures…”
In order to discuss the contribution of work-related risk factors to the occurrence of MSD, Punnett and Wegman introduce the concept of the Attributable Fraction (AF) alluded to by Tanaka et al, defining it as
“The AF is an estimate of the proportion of disease that would be reduced in the exposed population if the exposure were eliminated and represents the relative importance of exposure reduction in those settings where the exposure is prevalent”.
Punnett and Wegman summarize the findings of the National Academy of Sciences with regard to the AF for physical occupational risk factors and the occurrence of upper extremity disorders and back disorders. For the upper extremity, the range of AF estimates for Repetition was 53-71%, Force 78%, Repetition and Force 88-93%, Repetition and Cold 89%, Vibration 44-95%. For back disorders, the range of AF estimates for manual material handling was 11-66%, Frequent bending and twisting 19-57%, Heavy physical load 31-58%, Static work posture 14-32%, Whole body vibration 18-80%.
The National Academy of Sciences (1997) provides a definition of AF, “For example, if workers exposed to frequent bending and twisting have a prevalence of low back pain that is 3 times that of those not exposed, then among the exposed the attributable fraction will be: AFe =(3–1)/3=0.67
By this hypothetical calculation, 67 percent of low back pain in the exposed group could be prevented by eliminating work that requires bending and twisting.”
Consequently a checklist that assessed only the work-related upper extremity exposures such as posture, force, duration, frequency for our hypothetical occupational group may at best be able to identify only the AF of risk factors, or about 67% of the jobs in which individuals will present with MSDs, as some individuals will develop MSDs for reasons other than workplace exposures.
Part II: Designing for anthropometric
accommodation in new workstations when only very
limited anthropometric data are available
A fundamental objective towards which a designer or ergonomist must work is that the workspace should physically fit the intended users, that is, the
workplace should fit the user, just as one would expect his or her shoes to fit. Anthropometric data describing the intended users is essential in order to accomplish this objective.
“Anthropometric data are fundamental to occupational biomechanics. Without it, biomechanical models to predict human reach and space requirements cannot be developed” (Chaffin and Andersson, 1984). The problem of poor anthropometric fit between the worker and the workplace can lead to discomfort or injury, just as poorly fitting shoes may lead to discomfort and injury.
A poor anthropometric fit in the workplace often leads the individual user to assume “awkward” postures that limit his or her ability to perform the work as efficiently as possible, or that place him or her at risk for WRMSD.
Working postures are strongly implicated as causal factors of WRMSD (Putz-Anderson et al, 1997; Hildebrandt, 2001; Hildebrandt et al, 2001). Practitioners have long recognized this; Delleman and Dul (2005) describe two international standards developed to guide evaluation of working postures and movements in regard to risk of WRMSD, ISO 11226 to be used in the evaluation of existing work situations and EN 1005-4 “…for evaluation during a design/engineering process.”
In chapters 3, 4 and 5 of this thesis we develop a new method of improving workplace dimensions when the anthropometric information available is limited. The improved anthropometric information can be used to produce workplace designs that improve the match between the users’ anthropometry and the safety of postures and movements required to perform the work and that are superior to adding or subtracting percentiles.
Delleman et al (2004) note that
“People adopt postures (mostly without any conscious decision) to deal with the workplaces and surrounding environments that they find” and “The redundancy provided by the multiple degrees of freedom” of the human body “also means that several alternative postures may be possible for the performance of a given task. Not all, however, are healthy postures… The posture adopted for a task is most directly determined by the dimensions and arrangement of the workplace and the equipment used (particularly in relation to work height, reach distance, field of view, and space to move freely). The dimensions and arrangements constrain the range of postures that are possible while performing the task and, in poorly designed workplaces, are often found to make healthy posture difficult or impossible.”
Limited anthropometric data constrain designs
Designers and ergonomists seeking to utilize anthropometric data to evaluate an existing workplace or to design one ab initio may encounter several problems:
the anthropometric data pertinent to the intended users may be difficult to find, especially when the designer and the intended users are from different cultures, reliably combining anthropometric elements to order to develop a necessary dimension that has not been measured directly. For example, a designer or ergonomist might wish to combine chair seat height and elbow height above the chair seat in order to accommodate the seated elbow height above the floor for a specific range of users,
workspace layouts typically have multiple dimensions, for example, a chair might be specified in terms of five product dimensions and their related body dimensions: seat height (popliteal height), seat width (seated hip breadth), seat depth (buttock-popliteal length), armrest height (seated elbow rest height), and backrest height (seated acromial height), where it is desired to concurrently accommodate 90% of the intended users on all five dimensions.
The second and third problems described above are closely related, as they each require the combination of two or more anthropometric elements. The difficulty of the problem is further compounded when only limited data, such as summary tables of percentiles, are available.
In such cases, it is sometimes assumed, erroneously, that specifying a percentile value equal to the desired level of accommodation for each anthropometric element, e.g. for seat height, seat width, etc., will produce the desired level of accommodation. However, this is not the case (Gordon, 2002; Robinette and McConville, 1981; Robinette, 2012; Zehner et al, 1993). Combining percentiles (adding or subtracting) will introduce error into the estimated dimensions necessary to accommodate the desired proportion of the user population.
Kreifeldt and Nah (1995) have parameterized the error in such estimates of combinations of two anthropometric elements in terms of the correlation value and the individual variances of the two elements. They note that the worst case is encountered when subtracting two elements; only 50% accommodation may be achieved rather than the 95% expected.
Accurately combining pairs of elements
Roebuck et al (1975), Pheasant (1986) and Roebuck (1993) note that it is possible to accurately combine pairs of anthropometric elements (add or subtract) when the individual means, individual variances and the correlation value for the two elements are known.
The combined mean of two elements A and B is the sum or difference of the two means (MeanAB = MA ± MB). When adding two elements, the combined
variance is the sum of the two individual variances, plus the product of the individual standard deviations multiplied by the correlation value. When
subtracting two elements, the product of the correlation value and the standard deviations is subtracted [SAB =SA+SB ± (rABsAsB)].
Multiple anthropometric elements may be combined using this process by concatenation, for example, in order to combine three elements, first two of the three are combined as described by Pheasant and Roebuck, and then the third is combined with the result. For example, one might add elements A and B, then combine C with (A+B).
Once the aggregated mean and variance are determined, then, with knowledge of the underlying distribution of the data, the aggregate percentiles can be developed. For example, Pheasant (1986) suggests that anthropometric data, such as stature, are normally distributed and that percentiles can be estimated using the combined mean, standard deviation and a multiplier based on the normal table. For example, the 5th percentile should be equal to the mean minus 1.645 times the standard deviation and the 95th percentile should be equal to the mean plus 1.645 times the standard deviation.
In summary, if the means, variances and correlations between all possible pairs of elements for the anthropometric elements are known, then it is possible to accurately combine them into a single variable in a pair wise manner. If the underlying distributions of the data are known, then the percentiles of the combined variable can also be calculated.
Further constraints
Unfortunately, while summary tables of percentiles of anthropometric
information for various nationalities, cultures and age groups are fairly common (Pheasant, 1986; Gordon et al, 1988; Roebuck, 1993; Peebles and Norris, 1998; Norris et al, 1999; Harrison and Robinette, 2002; Steembekkers and Beijsterveldt, 1998; etc.), much of the information necessary to combine
multiple anthropometric elements is not available to ergonomists and designers. Robinette (1981), Gordon (2002) and others note that alternate, multivariate approaches such as Principle Component Analysis may be a more efficient means to combine anthropometric elements than the pair-wise method discussed in this thesis. However, these multivariate techniques also require much more extensive information than is generally available in such summary tables, requiring, for example, at least knowledge of the correlations between all the elements of concern.
A lack of correlation data
Although some anthropometric summaries, such as that of Roebuck, provide some correlation data, or it is available for others, e.g. Gordon et al, most do not. Even for those that provide some correlation values, it is not generally practicable to provide all possible pairs of correlations. For example, an anthropometric summary might provide information for 100 different elements. There would then be 4950 possible correlations for all possible combinations of pairs of the first 100 elements, then 4851 correlation values for all possible combinations of pairs for the resultant 99 elements, etc.
In chapter 3, 4 and 5 of this thesis, we extend the method of combining the variances of two anthropometric elements suggested by Pheasant and Roebuck to cases where the correlation between the two elements is unknown and must be estimated.
Reducing the error in the estimate of the combined
variance
That method defines the combined variance as
Variance(AB) = VarianceA + VarianceB ± [(2rAB)(SDA)(SDB)] Eq. 1
We first show that combining percentiles by addition or subtraction implicitly assumes that the correlation between the two elements (rAB) is 1.0. Clearly this
is not always the case, which means that this technique introduces error into the resulting anthropometric models.
The error in the combined variance is the difference between the implicitly assumed correlation value of 1.0 and the actual correlation value, (1-rAB). By a
process of integration over the range of possible values of rAB (-1.0 to 1.0), it is
shown that the average value of the error in the estimate of the combined variance when adding or subtracting two percentiles is two times the product of the standard deviations of the two elements, or 2(SDA)(SDB).
However, if the correlation value rAB is assumed to be equal to the median value
of the range of possible values when combining only two variables, or the mean value when combining more than two variables, then the error is considerably reduced. When the values are uniformly distributed, both the median and mean value for the range of possible rAB values of 1.0 to -1.0 is 0. Consequently, the
error is described as 0 - rAB, and integration of this function shows that the
average value of the estimate of the combined variance is also 0.
Consequently, if the correlations between the anthropometric data that it is desired to combine are unknown, assuming the correlation value to be the median of possible correlation values will reduce the average error. We refer to this method as the Median Correlation Method (MCM).
Questions about the underlying distribution of the
data
It is sometimes assumed that anthropometric data are normally distributed. If data such as stature are normally distributed, then the 5th and 95th percentile values should be the same distance from the mean. However, a cursory inspection of some summary tables suggests that this is not always so. For example, a quick check of the US anthropometric database CAESAR (Harrison and Robinette, 2002) finds that the 5th and 95th percentiles are not symmetric. The mean male stature is 1766.6 mm and the 5th percentile is 1650.2 mm and
the 95th percentile is 1900.8 mm. The differences between the mean and these endpoints are obviously not equal, 116 mm vs. 134 mm, respectively.
If the anthropometric data are not normally distributed, how then might one reliably estimate the proportion of individuals accommodated, if the mean and variance are known or have been estimated?
In chapters 3, 4 and 5 of this thesis, we discuss this issue. We first show that the Chebyshev Inequality (Walpole and Myers, 1978) allows us to determine the proportion accommodated based solely on the mean and variance
independently of the underlying distribution of the data. However this method results in very wide accommodation intervals. For example, if the data for an anthropometric element were normally distributed, then 90% of the data points would be expected to fall between the mean plus or minus 1.645 times the standard deviation. In contrast, in order to assure accommodation of the same proportion, the Chebyshev interval estimate would require the sum of the mean plus or minus 3.16 times the standard deviation.
We then examined the 5th and 95th percentile values of a global collection of anthropometric data provided in an International Standards Organization publication (ISO, 2010) with regard to the symmetry that would be expected if the underlying data were normally distributed, that is, if they are 1.645 standard deviations from the mean. We found that they are not distributed as would be expected of normally distributed data, but that 99% are empirically shown to be within two standard deviations of the mean. From this we infer an empirical distribution in which the 5th and 95th percentile values of global anthropometric data are reliably 2 standard deviations from the mean.
The combined mean and standard deviation are useful in determining the percentiles of the combined data, but they appear to offer limited information regarding the values of the individual elements necessary to concurrently accommodate the desired proportion of the population.
In chapters 4 and 5 we describe methods of determining the contribution of each individual anthropometric element to the combined mean and variance. Once these values are known, the values of each individual element necessary to achieve the desired level of accommodation can be estimated using the mean, variance and empirical distribution.
In chapter 5 we illustrate this method using a five-element chair model, contrasting the percent accommodated via a virtual-fit test for three different versions of the five-element model: one combining percentile values, a second assuming a normal distribution of the data (5th and 95th percentile values 1.645 standard deviations from the mean), the third assuming that 5th and 95th percentile values are 2 standard deviations from the mean. The virtual fit test
consisted of checking the accommodation for each of 2,208 females in the ANSUR database for concurrent accommodation on each of the five elements. Adding percentiles for each of the five variables achieved accommodated about 81 percent of all individuals for both addition and subtraction models. The MCM method assuming normally distributed data accommodated about 83 percent of all individuals for the addition and subtraction models. Finally, the MCM method assuming an empirical distribution achieved about 90% accommodation for both addition and subtraction models.
Part III: Let the user speak: Assessing user
preferences, performance and comfort
Part I of this thesis considers the reliability and validity of MSD assessment tools in existing jobs and described methods for practitioners to use to
dynamically evaluate and manage the reliability and validity of such tools. Part II describes a methodology to improve the quality of anthropometric
accommodation relative to adding percentiles when only very limited anthropometric data are available.
In part III of this thesis, we evaluate different design forms for tablet computer stands in regard to their effect on user performance and user comfort. There are many different approaches available to guide design practices, such as design for manufacturability, participatory design, Kaizen, participatory ergonomics, etc. However, according to Kok et al (2012) attention to user tests is often neglected, possibly because the testing is too complex. In chapter 6, we report the utilization of user testing to determine users’ preferences between two, somewhat contradictory recommendations for tablet tilt angle, one
regarding MSD risks for users’ wrists, the other regarding reading performance. The number of tablet computers in use has grown tremendously since the introduction of the iPad. While tablets are generally small and light, and can readily be repositioned and held at different angles, physically holding the tablet at different tilt angles in the hands can be fatiguing.
One possible design solution to this problem is to provide cases or other means of tilting and positioning tablets on desks or tables during use that don’t require the user to hold the device in the hands. However, although prior research suggested that tilt angles would affect both user discomfort and performance while entering data and reading output data, the effect on discomfort and performance while using tablets was not directly known.
Testing for MSD risks is one example of complexity in user testing; as such disorders may develop over an extended period of time and research studies are of much shorter duration. However, musculoskeletal discomfort may be a useful proxy for MSD risk (Hamburg et al, 2008).
Another layer of complexity arises when design criteria suggest contradictory optimal criteria. Such is the case with tilt angle and its effects on users’ wrist and reading performance. Previous research suggests differing optimal tilt angles with regard to wrist discomfort or injury risk during data input and reading performance during data output.
With regard to discomfort or injury risk, Marklin et al (1999) suggested that, when resting the forearms or wrists, tilting the input surfaces, such as
keyboards, leads users to extend their wrists more in response to the angle of the input device. Keir et al (2007) have recommended that wrist extension postures should be limited to angles less than 32º in order to avoid too high intracarpal tunnel pressure. This implies that, when users rest their forearms and wrists on a table or desk surface, the tablet tilt angle should be 32º or less in order to avoid discomfort or risk of injury due to wrist posture while entering data.
Physical discomfort resultant from tablet tilt angles is not limited to hands and wrists. Recently Young et al (2012) have noted that tablet tilt angles may produce greater neck flexion postures than desktop computing, a possible concern with regard to head and neck discomfort.
With regard to reading performance, Tinker (1956) reported that tilt angle affected the readability of text passages, noting a 6% decrease in reading performance when a book page was held vertically. Skordahl (1958) reports that positioning a book flat on a tabletop decreases reading performance by about 10%. Finally, Chandler (2001) suggests that reading performance is optimized when books are tilted about 45º, but does not draw any conclusion with regard to a similar effect of tilt angles of computer display screens. In the third section of this thesis, we examine the effect of tablet tilt angles on reading performance, target-tapping performance, wrist and forearm posture, user comfort as well as users’ tilt angle preferences. Participants used tablets while alternating among four different tilt angles: 0º, 30º, 45º, 60º and a self-selected angle. Head, neck, wrist and forearm postural data were collected, along with reading and target-tapping performance. Finally, subjective, perceived impressions were gathered via Likert scale questions.
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Part I:
Understanding checklist reliability and
validity in order to correctly identify
Chapter 2: Measuring the validity and
reliability of ergonomic checklists
Abstract
Objective: Ergonomic practitioners commonly use observational assessment
tools, also known as checklists, to identify job hazards with regard to musculoskeletal disorders. However, it is often difficult to determine how effective such checklists are at identifying jobs in which workers are at risk, which complicates resource allocation. A means of dynamically assessing validity is needed.
Method: This paper focuses on a simple technique with which practitioners can
assess the probability that a positive checklist indication accurately identifies an at-risk job. The technique can also be used to study the effect of changes to the checklist and determine whether or not they improve the practical utility of the checklist. Similarly, by manipulating the role of different risk factors assessed on the checklist, it may guide hypotheses as to the relative importance of the risk factors. Finally, the paper briefly suggests the use of control charts to assess and manage inter- and intra-rater reliability rather than more traditional assessment methods such as correlations, Cohen’s and Fleiss’ kappa.
Conclusion: The probability that a checklist correctly identifies jobs with regard
to risk of musculoskeletal injury is a useful means of assessing the checklists’s validity.
2.1 Introduction
Observational assessment tools, often called checklists, are commonly used as a means to determine the presence and severity of risk factors for
Musculoskeletal Disorders (MSDs) present within jobs. Checklists can serve many useful functions. They provide ergonomics specialists with a consistent structure for analyzing risk factors present in jobs. They may also be used to devolve the ability to assess jobs for musculoskeletal risks to non-specialists. Finally, they provide a means of comparing jobs based on the risk factors present and prioritizing which jobs should get limited resources to make improvements.
Some examples of commonly assessed risk factors are the magnitude and duration of exertions, joint postural angles, and repetition of motions. As data, the risk factors are generally ordinal in nature, for example, a checklist might assess three ranges of joint postural angles, such as 0-15º, 16-30º, > 30º, or it might assess force with low, medium or high categories.
The risk factors are generally assessed at multiple body locations, such as at fingers, hands, shoulders, and for both sides of the body.
Typically a score is assigned to the risk factors that are identified; the greater the magnitude of the risk factor, the greater the weight or score assigned in that instance for that body part. For example, a joint postural angle observed to be in the 0-15º range might get a weight of 1, an observed angle in the 16-30º range a weight of 2, and a weight of 3 given to angles greater than 30º. Often the observed risk factor weights are combined to produce an overall rating, for example, by summing the weighted scores for all risk factors for all body locations. These overall scores are commonly used to prioritize jobs, based on the assumption that the greater the number of risk factors present in a job, or the greater the magnitude of the risk factors, or for some combination of the two, the greater is the risk associated with the job under scrutiny.
However, when utilizing checklists, an ergonomics practitioner must ask him- or herself two questions, first, how consistent are the checklist results, either from time to time for an individual user or between multiple users? Secondly, he or she must also ask, how accurate is the checklist that I am using at identifying at-risk jobs, and can it or should it be improved?
Their decision regarding risk factors and the need for remediation of a job directly affects the allocation of available resources. An inefficient allocation of resources may have adverse effects on the entire production process as well as decreasing confidence in the ergonomist’s recommendations. Consequently, it
