OBJECTIVES: This study was conducted to investigate the prevalence and risk factors of osteopenia and osteoporosis in 40-59 year-old male workers working at nuclear power plants. METHODS: Bone density, body composition, and anthropometry data were analyzed for 2,073 subjects were carried out from March 2004 to July, 2004. Educational level, smoking status, drinking status and frequency of physical activity were investigated by self-reported questionnaires through internet to identify lifestyles related to bone mass density. Bone mass density was measured by EXE-3000 of Osteosys Co. and body fat percentage, and waist-to-hip ratio by Inbody 3.0 of Biospace Co. RESULTS: The prevalence of ostepeniaosteopenia and osteoporosis was 22.8% and 3.0%, respectively. According to multinominal multinomial logistic regression analysis, age, smoking status (current smoker and former smoker), low body mass index (< 25 kg/m2), and frequency of physical activity were significantly associated with osteopenia and osteoporosis. CONCLUSIONS: The prevalence of osteopenia and osteoporosis wasseemed high for the, when it was inferred from subjects' age compared to the results of previous studies. The This study results suggeststhat it is necessary for 40-59 year-old male workers to stop smoking, and exercise regularly in order to prevent osteopenia and osteoporosis.
Adipose Tissue ; Anthropometry ; Body Composition ; Body Mass Index ; Bone Density ; Bone Diseases, Metabolic* ; Drinking ; Humans ; Internet ; Life Style ; Logistic Models ; Male* ; Motor Activity ; Nuclear Power Plants ; Osteoporosis* ; Prevalence* ; Questionnaires ; Risk Factors* ; Smoke ; Smoking ; Waist-Hip Ratio

Purpose: To assess characteristics the application of mobile medication system and medication administration error (MAE) alerts in a general hospital. Methods: The subject hospital adopted a mobile medication system in 2016. All medication administrations in the general wards and ICUs were automatically recorded in real-time using identification barcodes, drug barcodes, and hand-held point-of-care devices. MAE alert logs were recorded from April 1st 2017 to March 31st 2018. For this study analysis was done using Pearson’s chi-squared test for potentially related factors of MAE alerts included administration time, order type, medication route, and length of nurse’s employment. Results: The total number of medications during the period of this study was 3,227,990. Among them, 2,698,317 medication doses were recorded, resulting in the system application rate of 83.6%. The system application rate was significantly correlated with all factors related to potential MAE alters. In this study 23,314 MAE alerts(0.9% of the total medication doses) were identified. The MAE alerts were related to new (OR=2.26, p<.001) and emergency (OR=2.25, p<.001) orders, and administration at a non-standard time (OR=2.032, p<.001). Medication route (p<.001), and nurse’s employment duration(p<.001) were also related. Conclusion A mobile medication system contributes to improving patient safety by preventing potential MAEs. The MAE alerts were related to administration time, order type, medication route, and duration of nurse’s employment. In order to prevent medication administration errors, it is necessary to standardize the process of medication and create an environment in which medication administration can be performed in a planned situation.