• Journal of Korean Orthopaedic Sports Medicine
• /
• v.3 no.1
• /
• pp.10-14
• /
• 2004

There is a large number of old athletes participating golf, and the shoulder, especially the nondominant or lead arm, appears to be at greatest risk for golf-related injury during extremes of motion. To reduce and prevent the risk of injury and improve the performance of golf, golfer should understand the biomechanics of the golf swing, increase flexibility, and perform stretching and strengthening exercises regularly .

• Korean Journal of Applied Biomechanics
• /
• v.25 no.2
• /
• pp.149-155
• /
• 2015

Purpose : Recently, many researchers and golf coachers demonstrated that X-factor and X-factor stretch had a co-relationship with driving distance. However, its relationship is still controversial and ambiguous. Thus, the aim of this study was to examine the relationship among X-factor, X-factor stretch and swing-related factors, including driving distance in elite golfers. Method : Seventeen male elite golfers (handicap: ${\leq}4$) with no history of musculo-skeletal injuries participated in the study. Thirty spherical retro-reflective markers were placed on including the middle point of PSIS, the right/left ASIS, the right/left lateral acromion of the scapula, driver head and shaft grip. All motion capture data was collected at 100Hz using 6 infrared cameras. Carry distance, club speed, ball speed, smash factor, launch angle, and spin rate were collected from radar-based device, TrackMan. Results : Pearson's correlation coefficient method was used to find the correlations among X-factor, X-factor stretch and swing-related factors. Positive correlations between driving distance and other swing-related factors which include club speed(r=.798, p<.001), and ball speed(r=.948, p<.001) were observed. In contrast to the swing-related factors, X-factor and X-factor stretch had no relationship to driving distance. Conclusion : These results indicate that X-factor and X-factor stretch are not key regulators in driving distance.

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