Differences in Health Behavior Profiles of Adolescents in Urban and Rural Areas in a Korean City.

Through a latent class analysis approach, we can classify individuals and identify subgroups according to health behavior patterns, and find evidence for the development of customized intervention programs to target high-risk groups. Our study aimed to explore differences in latent classes of health behaviors in adolescents by region (urban vs. rural areas) in a Korean city. This cross-sectional secondary analysis utilized data collected from all first graders' student health checkups in middle school and high school in a city of the largest island in Korea in 2016 (n = 1807). Health behavior indicators included both healthy (consuming breakfast regularly, consuming vegetables daily, consuming milk daily, consuming fast food on a limited basis, engaging in vigorous physical activities, brushing teeth, and practicing hand hygiene) and unhealthy (drinking, smoking, and overusing the internet) behaviors. Nutritional and diet behaviors were important factors for classifying healthy and unhealthy adolescents in both regions. Approximately 11% of rural students belonged to the risky group, which was characterized by a high level of drinking alcohol and smoking. These results suggest that when developing health policies for adolescents, customized policy-making and education based on the targeted groups' behavioral patterns could be more effective than a uniform approach.

Clustering of South Korean Adolescents' Health-Related Behaviors by Gender: Using a Latent Class Analysis.

BACKGROUND: Health-related behaviors during adolescence could influence adolescents' health outcomes, leading to either advantageous or deteriorative conditions. Clustering of adolescents' health-related behaviors by gender identifies the target groups for intervention and informs the strategies to be implemented for behavioral changes. METHODS: Data from 1807 adolescents in grades 7 and 10 in a city in South Korea were used. Health-related behaviors including eating habits, physical activity, hand washing, brushing teeth, drinking alcohol, smoking, and Internet use were examined. Latent class analysis (LCA) was used to identify subgroups of adolescents with regard to their health-related behaviors. RESULTS: A four-class model was the most adequate grouping classification across genders: adolescents with (1) healthy behaviors, (2) neither health-promoting nor health-risk behaviors, (3) good hygiene behaviors, and (4) unhealthy behaviors. The majority of both male and female adolescents were classified into the healthy group. Male adolescents belonging to the healthy group were more likely to engage in vigorous physical activities, while vigorous physical activity was not important for female adolescents. The smallest group was the unhealthy group, regardless of gender; however, the proportion of boys in the unhealthy group was almost twice that of girls. Only female adolescents engaged in excessive Internet use, especially the group with neither health-promoting nor health-risk behaviors. CONCLUSION: To improve adolescents' health-related behaviors, it would be more effective to develop tailored interventions considering the behavioral profiles of the target groups.

Subjective Health and Factors of Health-related Quality of Life in Elderly Haenyo.

PURPOSE: To investigate the subjective health and health-related quality of life (HRQoL) in Haenyo. METHODS: Subjects were 100 elderly Haenyo in Jeju island who belonged to a fishing-village society. Main variables were activities of daily living (ADL), instrumental ADL (IADL), the HRQoL, subjective health, and depression. Subjective health and differences of HRQoL by variables were analyzed by t-test or ANOVA using IBM SPSS Statistics 23. Hierarchical multiple regression was executed to examine the effects of the major factors on the quality of life. RESULTS: The mean age was 69.9 years, the mean period for diving career was 51.5 years, and work hours per month were 37.8. Comorbidity of diseases was 2.74, and the common health problems were osteoporosis and headache/dizziness. HRQoL was significantly different by age (F=4.52, p=.013), education (F=6.10, p=.003), muljil work years (F=3.93, p=.050), depression (t=-3.04, p=.030), subjective health state (F=30.62, p < .01), and degenerative arthritis (F=-2.38, p=.019). In the final model by hierarchical multiple regression, ADL/IADL (ß=.41, p < .001), depression (ß=-.29, p < .001), and subjective health (ß=.43~.51, p < .001) were significant and explained 63.5% of the total variance of HRQoL. CONCLUSION: Haenyo have specific health problems different from those of elderly women in general. ADL/IADL, depression and subjective health affected their HRQoL. It is clear that Haenyos' health problems need further study to improve their health.