ABSTRACT
PURPOSE: This study aimed to investigate variables affecting health-related quality of life of manufacturing industry workers. METHODS: We used data from 200 survey questionnaires collected from a vehicle manufacture factory located in Ulsan metropolitan area. The participants were manufacturing workers. The data were analyzed using t-test, one-way analysis of variance, and Scheffé's test to identify differences among variables. Pearson's correlation coefficient and stepwise multiple regression were used to identify variables that affected health-related quality of life. RESULTS: The findings revealed that occupational stress and quality of sleep were statistically significant variables of the physical component score with an explanatory power of 42.2%. Occupational stress, depression, and religion were statistically significant variables of the mental component score with an explanatory power of 43.3%. CONCLUSION: Continuous monitoring and appropriate intervention for occupational stress, quality of sleep, and depression are needed to improve manufacturing workers' health-related quality of life.
Subject(s)
Depression , Quality of LifeABSTRACT
PURPOSE@#This study aimed to investigate variables affecting health-related quality of life of manufacturing industry workers.@*METHODS@#We used data from 200 survey questionnaires collected from a vehicle manufacture factory located in Ulsan metropolitan area. The participants were manufacturing workers. The data were analyzed using t-test, one-way analysis of variance, and Scheffé's test to identify differences among variables. Pearson's correlation coefficient and stepwise multiple regression were used to identify variables that affected health-related quality of life.@*RESULTS@#The findings revealed that occupational stress and quality of sleep were statistically significant variables of the physical component score with an explanatory power of 42.2%. Occupational stress, depression, and religion were statistically significant variables of the mental component score with an explanatory power of 43.3%.@*CONCLUSION@#Continuous monitoring and appropriate intervention for occupational stress, quality of sleep, and depression are needed to improve manufacturing workers' health-related quality of life.
ABSTRACT
BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep disorder that causes daytime dysfunction and cardiovascular diseases. Nocturnal polysomnography (NPSG) is the standard method of evaluating OSA; however, it is time-consuming, inconvenient, and expensive. Selective performance of NPSG would be possible if we could better predict those who are more likely to have clinically significant OSA. The aim of this study is to define clinical and anthropometric predictors of OSA. METHODS: We included 100 consecutive patients in whom OSA was clinically suspected. Structured sleep interview, anthropometric measurement, and NPSG were performed in all subjects. Presence of OSA was defined when the apnea-hypopnea index was five or more. Parameters from sleep interview and anthropometric data were investigated with multiple logistic regression using the SAS program (ver 8.1, USA) to identify independent predictors of OSA. RESULTS: OSA was diagnosed in seventy-six patients after NPSG. Univariate analysis showed that the male sex, co-existing diabetes, overweight (BMI>or=25), habitual alcohol drinking, large neck circumference (>or=40 cm), high waist circumference/hip circumference (WC/HC) ratio (>or=0.94), and observed apnea were significantly more frequent in OSA patients (por=40 cm) (adjusted OR=4.43, 95% CI: 1.05~18.61) and high WC/HC ratio (adjusted OR=3.48, 95% CI: 1.12~10.80) were found to be the independent predictors of OSA on the NPSG. CONCLUSIONS: We report the predictors of OSA that could be easily identified by clinical and anthropometric evaluations before performing NPSG. This might aid the clinical decision whether or not to perform NPSG in subjects with clinically suspected OSA syndrome.