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Purpose: Physical therapists are likely to be exposed to work-related musculoskeletal pain due to excessive repetitive tasks. This study was conducted to identify the relationship between work-related musculoskeletal pain and quality of life of physical therapists. Methods: A self-reported questionnaires was sent to 200 physical therapists at in Seoul and Kyoungido. The questionnaires was returned by 170 physical therapists. The questionnaire had included 4 items that coveringed demographic information, areas of musculoskeletal problems, pain rating scale, and WHOQOL-BREF. The analysis was completed using descriptive statistics, and differences between pain and demographic variables were identified using the chi-square test. The relationship between work-related musculoskeletal pain and quality of life was analyzed by t-test and Pearson's correlation. Results:The overall prevalence of work-related musculoskeletal pain was 76.8%. The most affected pain sites included the low back (48.8%), shoulder (45.,2%), hand and wrist (43.5%), and neck (33.3%). Pain ratings of subjects with pain was were moderate. There was a A significant difference for the subdomains of quality of life was observed between the subjects with musculoskeletal pain and those without pain. Weak negative correlations (r= -0.28) were observed between pain rating scale and QOL. Conclusion: These findings show that physical therapists appear to be at a higher risk for work-related musculoskeletal pain and physical domain of QOL. Therefore, Ffurther research is needed to investigate examine the effect of risk factors and ergonomics as physical load, general health status on prevalence of musculoskeletal pain.

This study was conducted to identify the characteristics of physical therapy service utilization and user satisfaction depending on the experience with therapeutic exercise or ultrasound intervention among elderly persons using senior centers. Methods: The subjects were adults aged over 60 years (total 215) recruited in 40 senior centers located in each province in South Korea. Subjects responded to questions concerning overall demographics factors, utilized characteristics of physical therapy service and 12 variety user satisfactions with effectiveness, facilities, and therapist using a survey instrument. The collected data were analyzed by Fishers' exact tests and t-tests using the SPSS 21.0 program to compare the results of elderly persons who had or had not experienced exercise therapy or ultrasound therapy. Results: The participants that experienced ultrasound therapy or therapeutic exercise reported significantly higher overall results pertaining to effectiveness of physical therapy and a clear explanation from physical therapist's satisfaction than those who were non-experienced. Satisfaction with pain relief was significantly higher among elderly who experienced therapeutic exercise than those who did not. Those who underwent ultrasound therapy showed significantly higher satisfaction with facilities and location than those who did not. Conclusion: The results of this study suggest that satisfaction among users differs by type of physical therapy. In the future, physiotherapy services provided in senior centers needs to be designed to improve the effectiveness of physical therapy, professionalism of physical therapists and comfort of facility.

This study examined the work-related musculoskeletal pain and quality of life of hospital workers. Methods: Self-reported questionnaires were sent to 350 hospital workers at Seoul and Gyeonggi-do, of which 341 were returned. The questionnaire had four items that covered the demographic information, areas of musculoskeletal problems, pain rating scale, and quality of life. The analysis was completed using descriptive statistic, and the differences between pain and demographic variables were identified using a chi-square test. The differences between the type of occupation and quality of life were analyzed by one-way analysis of variance and a Kruskal-Wallis test. Results: The 12-month prevalence of work-related musculoskeletal pain was 86.1% of physical therapists, 86.5% of occupational therapists, 77.1% of dental hygienists, and 75.8% of nurses. A significant difference in the general and work related variables was observed between the subject with a physical burden and type of occupation. The most affected pain sites of the physical therapist included low back, hand and wrist, shoulder, and neck. The occupational therapists included the hand and wrist, shoulder, neck, but the nurses and dental hygienists reported the shoulder, back, hand and wrist. A significant difference in the quality of life was noted between the subjects in physical therapists and dental hygienists and the subjects in the nurses and occupational therapists (p= 0.00). Conclusion: These findings show that hospital workers appear to be high risk for work-related musculoskeletal pain, and the quality of life of physical therapists and nurses was higher than that of dental hygienists and occupational therapists.

Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown. Methods:The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 normal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification performance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was performed. The motion data for wearing a gown were used for the test set. Results:The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF. Conclusion:We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we suggested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for rehabilitation which required determining the correctness of the motion. This is an Open Access article distribute under the terms of the Creative Commons Attribution Non-commercial License (Http:// creativecommons.org/license/by-nc/3.0.) which permits unrestricted non-commercial use, distribution,and reproduction in any medium, provided the original work is properly cited.