• Journal of the Ergonomics Society of Korea
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• v.35 no.6
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• pp.595-609
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• 2016

Objective: The purpose of this study was to develop and validate the guidelines for Medic Work Table (MWT) based on the anthropometric data of medical technologists. Background: Users' anthropometric data such as sitting height, sitting elbow height, knee height, and so on are significant factors for designing comfortable and useful furniture. Thus, many guidelines for different types of desks and chairs based on the users' anthropometric data have been suggested to many researchers. However, few researches have been conducted to provide design guidelines for MWT for blood collecting task. Medical technologists often use their upper extremities to perform blood collecting task with high repetitions. These repeated motions could be a critical factor in the prevalence rate of Work-related Musculoskeletal Disorders (WMSDs). Therefore, a study on ergonomic design of MWT would be essential in preventing the WMSDs and improving the quality of the working environment of medical technologists. Method: This study suggested design guidelines for ergonomic MWT by focusing on the heights of the upper side and underside, depths of the inside and outside, and width of MWT through anthropometric studies and literature reviews. Afterwards, a new MWT was made using the suggested design guidelines for this study. Five healthy medical technologists participated to evaluate the original MWT and new MWT. All participants took part in the range of motion (ROM) test, electromyography (EMG) muscle activity test, and usability test to validate the suggested guidelines in this study. EMG signals of related muscles (Flexor Carpi Ulnaris, Extensor Carpi Ulnaris, Deltoid Anterior, and Biceps Branchii) were recorded through the surface electromyography system from both the original MWT and the new MWT. The ROM test of the shoulder and elbow flexion was also assessed using motion sensors. Results: The newly designed MWT showed decreased ROMs of the shoulder and elbow up to 22% and 18% compared to the original MWT. The muscle activities in the new MWT also showed a decrease of 13% in Anterior Deltoid, 6% in Biceps Brachii, 5% in Flexor Carpi Ulnaris, and 8% in Extensor Carpi Ulnaris muscle groups, compared to the original MWT. In the usability test, the satisfaction score of the new MWT was also 56.1% higher than that of the original MWT. Conclusion: This study suggested guidelines for designing MWT and validating the guidelines through qualitative and quantitative analyses. The results of motion analysis, muscle activity, and usability tests demonstrated that the newly designed MWT may lead to less physical stress, less awkward posture, and better physical user interface. Application: The recommended guidelines of the MWT would be helpful information for designing an ergonomic MWT that reduces physical loads and improves the performance of many medical technologists.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.