Janitor ergonomics and injuries in the safe workload ergonomic exposure project (SWEEP) study.

INTRODUCTION: A Minnesota union identified to researchers at the University of Minnesota a concern relevant to a possible relation between their daily workload and outcome of occupational injuries among a population of janitors. OBJECTIVE: To assess if the ergonomic workload is related to injuries among janitors. METHODS: Following an initial group discussion among janitors, which identified common and hazardous tasks potentially leading to occupational injuries, a questionnaire was developed, pre-tested, and distributed to the janitors. Questions addressed various exposures, including workload, and comprehensive information regarding injury occurrence over two six-month sequential periods (May 2016-October 2016, November 2016-April 2017). Quantitative ergonomic analyses were performed on a sub-group of janitors (n = 30); these included data collection to identify Borg Perceived Exertion (Borg) and Rapid Entire Body Assessment (REBA) scores. Descriptive, multivariable with bias adjustment analyses were conducted on the resulting data. RESULTS: Eight tasks were found to be common for janitors. All average REBA scores for the tasks were identified in the high-risk category. The task of repeatedly emptying small trash cans (<25lb) was significantly related to injuries. Average Borg scores fell between the very light perceived exertion and somewhat difficult perceived exertion categories. Multivariable regression analyses indicated that age-sex-standardized ergonomic workload, measured by task frequencies and REBA or Borg scores, was positively related to injury occurrence. CONCLUSIONS: Standardized ergonomic workload was positively related to injury occurrence. This information serves as a basis for further research and potential intervention efforts.

Design with limited anthropometric data: A method of interpreting sums of percentiles in anthropometric design.

Occasionally practitioners must work with single dimensions defined as combinations (sums or differences) of percentile values, but lack information (e.g. variances) to estimate the accommodation achieved. This paper describes methods to predict accommodation proportions for such combinations of percentile values, e.g. two 90th percentile values. Kreifeldt and Nah z-score multipliers were used to estimate the proportions accommodated by combinations of percentile values of 2-15 variables; two simplified versions required less information about variance and/or correlation. The estimates were compared to actual observed proportions; for combinations of 2-15 percentile values the average absolute differences ranged between 0.5 and 1.5 percentage points. The multipliers were also used to estimate adjusted percentile values, that, when combined, estimate a desired proportion of the combined measurements. For combinations of two and three adjusted variables, the average absolute difference between predicted and observed proportions ranged between 0.5 and 3.0 percentage points.

An empirical description of the dispersion of 5th and 95th percentiles in worldwide anthropometric data applied to estimating accommodation with unknown correlation values.

BACKGROUND: Anthropometric data are assumed to have a Gaussian (Normal) distribution, but if non-Gaussian, accommodation estimates are affected. When data are limited, users may choose to combine anthropometric elements by Combining Percentiles (CP) (adding or subtracting), despite known adverse effects. OBJECTIVE: This study examined whether global anthropometric data are Gaussian distributed. It compared the Median Correlation Method (MCM) of combining anthropometric elements with unknown correlations to CP to determine if MCM provides better estimates of percentile values and accommodation. METHOD: Percentile values of 604 male and female anthropometric data drawn from seven countries worldwide were expressed as standard scores. The standard scores were tested to determine if they were consistent with a Gaussian distribution. Empirical multipliers for determining percentile values were developed.In a test case, five anthropometric elements descriptive of seating were combined in addition and subtraction models. Percentile values were estimated for each model by CP, MCM with Gaussian distributed data, or MCM with empirically distributed data. RESULTS: The 5th and 95th percentile values of a dataset of global anthropometric data are shown to be asymmetrically distributed. MCM with empirical multipliers gave more accurate estimates of 5th and 95th percentiles values. CONCLUSIONS: Anthropometric data are not Gaussian distributed. The MCM method is more accurate than adding or subtracting percentiles.

A method superior to adding percentiles when only limited anthropometric data such as percentile tables are available for design models.

PURPOSE: Designers and ergonomists may occasionally be limited to using tables of percentiles of anthropometric data to model users. Design models that add or subtract percentiles produce unreliable estimates of the proportion of users accommodated, in part because they assume a perfect correlation between variables. Percentile data do not allow the use of more reliable modeling methods such as Principle Component Analysis. A better method is needed. RESULTS: A new method for modeling with limited data is described. It uses measures of central tendency (median or mean) of the range of possible correlation values to estimate the combined variance is shown to reduce error compared to combining percentiles. Second, use of the Chebyshev inequality allows the designer to more reliably estimate the percent accommodation when the distributions of the underlying anthropometric data are unknown than does combining percentiles. CONCLUSION: This paper describes a modeling method that is more accurate than combining percentiles when only limited data are available.

The effect of tablet tilt angle on users' preferences, postures, and performance.

OBJECTIVE: Tablets and other mobile devices can be tilted during use. This study examined the effect of tablet tilt angles on reading performance, target-tapping performance, wrist and forearm posture, user comfort and users' tilt angle preferences. METHOD: Ten participants used tablets alternating among four different tilt angles: 0°, 30°, 45°, 60° and a user selected angle. Head, neck, wrist and forearm postural data were collected, along with reading and target-tapping performance. Subjective, perceived impressions were gathered via Likert scale questions. RESULTS: Neck flexion decreased significantly as tilt angle increased. The extreme tilt angles, 0° and 60°, were least preferred while the self-chosen tilt angle, averaging about 34°, was most preferred. Tapping performance was significantly better for the self-chosen tilt angle; however, this may be a practice effect. No effect of tilt was observed on reading performance or for forearm and wrist posture. CONCLUSIONS: Tablet tilt angles should include a range of 20° to 50° at minimum.

A method to improve the accuracy of pair-wise combinations of anthropometric elements when only limited data are available.

BACKGROUND: Designers and ergonomists occasionally must produce anthropometric models of workstations with only summary percentile data available regarding the intended users. Until now the only option available was adding or subtracting percentiles of the anthropometric elements, e.g. heights and widths, used in the model, despite the known resultant errors in the estimate of the percent of users accommodated. This paper introduces a new method, the Median Correlation Method (MCM) that reduces the error. OBJECTIVE: Compare the relative accuracy of MCM to combining percentiles for anthropometric models comprised of all possible pairs of five anthropometric elements. Describe the mathematical basis of the greater accuracy of MCM. METHODS: MCM is described. 95th percentile accommodation percentiles are calculated for the sums and differences of all combinations of five anthropometric elements by combining percentiles and using MCM. The resulting estimates are compared with empirical values of the 95th percentiles, and the relative errors are reported. RESULTS: The MCM method is shown to be significantly more accurate than adding percentiles. MCM is demonstrated to have a mathematical advantage estimating accommodation relative to adding or subtracting percentiles. CONCLUSIONS: The MCM method should be used in preference to adding or subtracting percentiles when limited data prevent more sophisticated anthropometric models.

Measuring the validity and reliability of ergonomic checklists.

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 checklist's validity.