• Korean Journal of Psychosomatic Medicine
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• v.32 no.1
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• pp.43-51
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• 2024

Objectives : This study aims to explore the prevalence of depressive symptoms among elderly residents in the relatively stable rural areas of Jeju and to examine the relationships between levels of depression, sociodemographic factors, and health habits. Methods : The study site was within rural Jeju, where elderly individuals aged 65 and older were randomly selected from the 'Agricultural Cohort' registered at the Centers for Farmers' Safety and Health Center. Trained interviewers conducted surveys using the Short Form Geriatric Depression Scale (sGDS-K), defining those with scores of 6 or above as experiencing depressive symptoms for the analysis. Other variables such as sex, age, educational level, marital status, annual income, subjective health status, underlying disease, perceived stress levels, smoking, and drinking status were also recorded Results : Out of 533 subjects, the prevalence of depressive symptoms was 35.3%, with 28.5% in male and 45.6% in female (p<0.001). Factors significantly associated with the prevalence of depressive symptoms included marital status (p=0.014), educational level (p<0.001), annual income (p=0.034), subjective health status (p<0.001), perceived stress level (p<0.001), feeling of despair (p<0.001) and suicidal ideas (p<0.001). Multivariate logistic regression analysis revealed that subjective health status, perceived stress level, and feelings of despair were associated with the prevalence of depressive symptoms. Conclusions : The high prevalence of depressive symptoms among the rural elderly in Jeju highlights the need for targeted mental health interventions. Addressing sociocultural factors and improving early detection and intervention strategies can help reduce the socioeconomic impact of depression in this population.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.173-182
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• 2023

Background: This study aims to investigate the effect of restrictions on oral health-related activities of young adults, middle-aged, and elderly in Korea on the quality of life and contribute to the development of intervention strategies to improve the quality of life by improving the importance of oral health care and healthy lifestyle habits of adults. Methods: The 8th National Health and Nutrition Survey was used, targeting adults categorized into three age groups: young adults aged 19 to 29, middle-aged adults aged 30 to 49, and prime-aged adults aged 50 to 64. Demographic characteristics and EQ-5D, HINT-8 and oral-related toothache experience, chewing problems, speaking problems, and complaint of discomfort to chew analyzed. T-test and one-way ANOVA were performed to find out the difference in quality of life according to the restrictions on oral activities of adults, and linear regression analysis was performed to investigate the factors affecting the quality of life of adults. Results: The differences between EQ-5D and HINT-8 according to the restrictions on oral health-related activities of young, middle-aged, and prime-aged were statistically significant in all oral activity restriction variables(p ≦0.05). Factors affecting EQ-5D of all adults were statistically significant in all variables such as region, gender, household monthly income, education level, basic living status, economic activity, subjective oral health status, toothache experience, chewing problem, speaking problem, and complaint of discomfort to chew(p ≦0.05). Factors affecting HINT-8 of all adults were statistically significant in variables such as gender, household monthly income, education level, basic living status, economic activity, toothache experience, chewing, speaking, and complaint of discomfort to chew(p ≦0.05). Conclusions: Various measures are needed to improve the quality of life in old age by allowing adults to face physically, mentally, and socially prepared old age. Based on the results of this study, an adult oral health program should be developed to improve the oral health and quality of life of adults.

• Journal of agricultural medicine and community health
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• v.49 no.1
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• pp.37-49
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• 2024

Objectives: This study aimed to explore whether there are differences in smoking status between two regions of Wonju-City and Chuncheon-City, Gangwon State, and to determine whether the experience of smoking cessation programs in the region affects quit attempts. Methods: The study design was a cross-sectional study in which adults aged 19 and older living in two cities were surveyed using a pre-developed mobile app to investigate social capital for smoking cessation, and a total of 600 citizens were participated, including 310 in Wonju-City and 290 in Chuncheon-City. The statistical analysis was conducted using chi-square test and logistic regression analysis. Results: Wonju-City had a higher prevalence of current smoking than Chuncheon-City. Among smoking cessation programs operated by local public health centers, Wonju-City had a lower odds ratio for experience with smoking cessation education than Chuncheon-City (OR=0.52, 95% CI=0.33 to 0.81). When examining the effect of smoking cessation program experience on quit attempts, in Wonju-City, citizens who had completed smoking cessation education and used a smoking cessation clinic were more likely to attempt to quit than those who had not (OR=2.31 and OR=2.29, respectively). In Chuncheon-City, citizens who were aware of smoking cessation support services were 2.26 times more likely to attempt to quit smoking than those who were not, but statistical significance was not reached due to the small sample size. Conclusion: Therefore, healthcare organizations in both regions should develop more practical intervention strategies to increase smokers' quit attempts, reduce smoking rates in the community, and address regional disparities.

• Journal of agricultural medicine and community health
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• v.20 no.2
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• pp.175-185
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• 1995

This study is to compare family support and mental health between the rural and the urban elderly. In order to do that I collected the data through questioning 238 people in 3 urban areas in Busan and 201 people in 9 rural areas near Daegu. The degree of their family support is 36.70 on the average in the rural area and 40.77 in the urban area. The degree of family support of urban elderly is a little higher. According to general characters between the differences of family support in both areas, in the rural area there are differences in sex, age, whether they have a spouse or not, education level, financial state, number of children, number of co living, status of co living, subjective health status, amount of pocket money and how much they are participating in leisure activity. In the urban area there are differences in sex, whether they have a spouse or not, religion, financial state, number of co living, status of co living, subjective health status, amount of pocket money, how much they are participating in leisure activity and house pattern. In the stepwise multiple regression analysis the main variables that affect degree of family support in the rural area are age, whether they have a spouse or not and financial state which account for 33% of the total variance and in the urban area are subjective health status, financial state, whether they have a spouse or not and number of co-living which account for 35%. Health status is better in the urban area(average 36.87) than in the rural area(57.42). In each item the people whose mark was more than 75%(low) have Depression 8.4%, Somatization 8.0% in the urban area and Somatization 8.5%, Depression 8.5%, Anxiety 4.0%, Phobic anxiety 4.0%, Obsessive compulsive reaction 2.5%, Hostility 2.0%, Paranoid ideation 2.0%, Psychoticism 1.5% and Interpersonal sensitivity 1.5% in the rural area. In the mental health condition, on the basis of 4 points in both areas, the average is Somatization(rural : 1.69, urban : 1.51), Depression (rural : 1.64, urban : 1.37) and Obsessive compulsive reaction(rural : 1.33, urban : 0.99). According to the differences between mental health conditions by general characters, in the rural area the differences are presented in sex, age, whether they have a spouse or not, religion, education level, financial state, number of children, status of co living, subjective health status, amount of pocket money and how much they are participating in leisure activity, in the urban area the differences are presented in sex, whether they have a spouse or not, religion, financial state, number of co living, status of co living, subjective health status, house pattern, amount of pocket money and how much they are participating in leisure activity. In the stepwise multiple regression analysis the main variables that affect mental health condition in the rural are family support degree subjective health status, religion sex, age and financial state which account for 43% of the total and in the urban area are family support degree, subjective health status and financial state which account for 51%. In the matter of family support degree and mental health condition the rural area was -0.4555, of urban area was -0.6446. The rural area that has a high percentage in family support degree and mental health condition Depression was -0.5036, Psychoticism was -0.4265 in the urban area Psychoticism was -0.6452, Depression was -0.5955. Family support has a great influence on mental health of old people and family support and mental health condition can be different according to living area. So in their problems nursing intervention through family and nursing strategies according to living area should be established.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.