Objective of this study was to look into sleep patterns of university students in association with their lifestyle, specifically to examine mean sleep duration, prevalence of sleep disturbances and their correlates. This study also aimed to examine a possible association of sleep patterns of young adults with new media like computers and videos, which were supposed to have a great influence on their lifestyle. Self-reported sleep data were derived from questionnaires administered to a total of 1,414 students of one university located in Chullabuk-do, Korea. Statistical methods such as t-test, analyses of variance, chi-square test and multivariate logistic regression were used for analysis. The mean sleep duration of the respondents was 6.7+/-1.3 hr. Of the respondents, 30.2% reported having insufficient sleep. About one third of them pointed to visual media including computers as the primary reason. The proportion of those having some types of sleep disturbances was 36.2%. The risk of subjective sleep disturbances was significantly lower among those perceiving themselves healthy than among those perceiving themselves unhealthy (OR=0.44; 95% CI: 0.34-0.57).
Adult ; Female ; Human ; Logistic Models ; Male ; Prevalence ; *Sleep ; Sleep Disorders/*epidemiology ; Students ; Time Factors ; Universities

To determine bowel patterns and the prevalence of defecation difficulty in young university students, we administered a self-reported questionnaire to 1,617 college students about their bowel habits and eating patterns and obtained the following: 83.7% showed defecation frequency between 2 times per day and 3 times per week, and 33.4% reported difficulty in defecation. Among the subjects with defecation difficulty, 69% complained of constipation and 31% of diarrhea. It was also shown that the prevalence of self-reported defecation difficulty varied by sex. Women were more likely to have defecation difficulty than men (OR=2.5; 95% CI: 2.005-3.149). There were also differences between men and women in respect to the bowel habits (frequency, regularity, thickness, volume, form and time required) and food preferences. Men reported a higher frequency of defecation than women (p<.001). The dietary fiber intake volume of the subjects with defecation difficulty was smaller than subjects without the problem (OR=0.83, CI; 0.706-0.978). Moreover, those whose favorite food was meat were more likely to have defecation difficulty than those preferred vegetables (OR=1.39; 95% CI: 1.058-1.820). Irregular defecation was reported in 44.5% of the students, especially non-residents of Cheolla province (OR=1.2; 95% CI: 1.007-1.480). Non-residents ate dietary fiber significantly less than residents and there were some differences in diet habits and also in bowel habits.
Constipation ; Defecation* ; Diarrhea ; Dietary Fiber ; Eating ; Female ; Food Habits ; Food Preferences ; Humans ; Male ; Meat ; Prevalence* ; Surveys and Questionnaires ; Vegetables

There are several hurdles to overcome before implementing pharmacogenomics (PGx) in precision medicine. One of the hurdles is unawareness of PGx by clinicians due to insufficient pharmacogenomic information on drug labels. Therefore, it might be important to implement PGx that reflects pharmacogenomic information on drug labels, standard of prescription for clinicians. This study aimed to evaluate the level at which PGx was being used in clinical practice by comparing the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines and drug labels of the US Food and Drug Administration (FDA) and the Korea Ministry of Food and Drug Safety (MFDS). Two PGx guidelines and drugs labels were scrutinized, and the concordance of the pharmacogenomic information between guidelines and drug labels was confirmed. The concordance of the label between FDA and MFDS was analyzed. In FDA labels, the number of concordant drug with guidelines was 24, while 13 drugs were concordant with MFDS labels. The number of drugs categorized as contraindication, change dose, and biomarker testing required was 7, 12 and 12 for the FDA and 8, 5 and 4 for the MFDS, respectively. The pharmacogenomic information of 9 drugs approved by both FDA and MFDS was identical. In conclusion, pharmacogenomic information on clinical implementation guidelines was limited on both FDA and MFDS labels because of various reasons including the characteristics of the guidelines and the drug labels. Therefore, more effort from pharmaceutical companies, academia and regulatory affairs needs to be made to implement pharmacogenomic information on drug labels.

There are several hurdles to overcome before implementing pharmacogenomics (PGx) in precision medicine. One of the hurdles is unawareness of PGx by clinicians due to insufficient pharmacogenomic information on drug labels. Therefore, it might be important to implement PGx that reflects pharmacogenomic information on drug labels, standard of prescription for clinicians. This study aimed to evaluate the level at which PGx was being used in clinical practice by comparing the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines and drug labels of the US Food and Drug Administration (FDA) and the Korea Ministry of Food and Drug Safety (MFDS). Two PGx guidelines and drugs labels were scrutinized, and the concordance of the pharmacogenomic information between guidelines and drug labels was confirmed. The concordance of the label between FDA and MFDS was analyzed. In FDA labels, the number of concordant drug with guidelines was 24, while 13 drugs were concordant with MFDS labels. The number of drugs categorized as contraindication, change dose, and biomarker testing required was 7, 12 and 12 for the FDA and 8, 5 and 4 for the MFDS, respectively. The pharmacogenomic information of 9 drugs approved by both FDA and MFDS was identical. In conclusion, pharmacogenomic information on clinical implementation guidelines was limited on both FDA and MFDS labels because of various reasons including the characteristics of the guidelines and the drug labels. Therefore, more effort from pharmaceutical companies, academia and regulatory affairs needs to be made to implement pharmacogenomic information on drug labels.