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1.
Annals of Occupational and Environmental Medicine ; : 35-2013.
Article in English | WPRIM | ID: wpr-84418

ABSTRACT

OBJECTIVES: The purposes of this study are 1) to measure the prevalence of smoking according to weekly work hours by using data from the Korean Labor and Income Panel Study (KLIPS), and 2) to explain the cause of high smoking prevalence among those with short or long work hours by relative explanatory fraction. METHODS: Data from a total of 2,044 male subjects who responded to the questionnaire in the 10th year (2007) and 11th year (2008) of the Korean Labor and Income Panel Study were used for analysis. Current smoking, smoking cessation, continuous smoking, start of smoking, weekly work hours, occupational characteristics, sociodemographic and work-related factors, and health behavior-related variables were analyzed. Log-binomial regression analysis was used to study the relationship between weekly work hours and smoking behaviors in terms of the prevalence ratio. RESULTS: The 2008 age-adjusted smoking prevalence was 64.9% in the short work hours group, 54.7% in the reference work hours group, and 60.6% in the long work hours group. The smoking prevalence of the short work hours group was 1.39 times higher than that of the reference work hours group (95% confidence interval of 1.17-1.65), and this was explained by demographic variables and occupational characteristics. The smoking prevalence of the long work hours group was 1.11 times higher than that of the reference work hours group when the age was standardized (95% confidence interval of 1.03-1.19). This was explained by demographic variables. No independent effects of short or long work hours were found when the variables were adjusted. CONCLUSION: Any intervention program to decrease the smoking prevalence in the short work hours group must take into account employment type, job satisfaction, and work-related factors.


Subject(s)
Humans , Male , Employment , Job Satisfaction , Prevalence , Salaries and Fringe Benefits , Smoke , Smoking Cessation , Smoking , Surveys and Questionnaires
2.
Korean Journal of Occupational and Environmental Medicine ; : 180-188, 2012.
Article in Korean | WPRIM | ID: wpr-171206

ABSTRACT

OBJECTIVES: To reveal the influence of job stress change on body mass index (BMI) and waist circumference in white-collar male workers. METHODS: A total of 277 male workers in a Korean R&D company were enrolled between 2008 and 2010. Baseline and follow-up data were collected with structured self-administered questionnaires and anthropometric measurements by nurses. The questionnaire survey included general and work-related characteristics and the Korean Occupational Stress Scale-Short Form (KOSS-SF). The job stress scores in each examination were dichotomized at the median values for the Korean workers and categorized into four groups as follows: Group I: Both low job stress (2008, 2010), Group II: High job stress (2008) & low job stress (2010), Group III: Low job stress (2008) & high job stress (2010), Group IV: Both high job stress (2008, 2010). Multiple logistic regression modeling was used to determine the influence of job stress change on BMI and waist circumference. RESULTS: The adjusted odds ratio for the change in waist circumference above the 75th percentile for Group IV in 'job demand' increased more than in Group I (OR = 2.54 95% CI=1.06~5.55). Also, Group IV in 'job demand' has higher odds ratio for change in BMI above the 75th percentile than Group I (OR=2.25 95% CI=1.01~5.00). Adjusted odds ratios comparing Group II to Group I for the change in waist circumference above the 75th percentile were 0.36 (95% CI=0.15~0.87) in 'inadequate social support', 0.12 (95% CI=0.02~0.98) in 'lack of reward', 0.25 (95% CI=0.08~0.80) in 'total score', respectively. CONCLUSIONS: These results suggest that sustained high job control is a risk factor for abdominal obesity and weight gain. Also, diminished job stress has a negative influence on change in abdominal obesity. Further studies are required to establish job stress intervention plans.


Subject(s)
Humans , Male , Body Mass Index , Follow-Up Studies , Logistic Models , Longitudinal Studies , Obesity, Abdominal , Odds Ratio , Surveys and Questionnaires , Risk Factors , Waist Circumference , Weight Gain
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