• Korean Journal of Health Education and Promotion
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• v.33 no.1
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• pp.1-12
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• 2016

Objectives: This study was to examine by gender the clustering patterns and correlates of healthy lifestyle clusters and the relationships between healthy lifestyle clusters and depressive symptoms in middle-aged and older adults. Methods: The observed/expected ratio of physical activity, smoking, and alcohol consumption were calculated to analyze clustering effects. The correlates of those healthy lifestyle clusters were evaluated using logistic regression models, and the relationship between those healthy lifestyle clusters and depressive symptoms was investigated using multiple regressions by gender. Results: Based on the guidelines this study adopted, we obtained three healthy lifestyle clusters: active healthy lifestyle; passive healthy lifestyle; and unhealthy lifestyle. All three clusters were found in men, but two in women, who did not have an unhealthy lifestyle cluster. High socio-economic status was positively related to healthy lifestyle clusters. Social participation and residence location (in men) and marital status (in women) were significant factors. Having an active or a passive healthy lifestyle was negatively associated with depressive symptoms in women, but such a relationship was not observed in men. Conclusions: The study findings imply that health promotion programs for middle-aged and older adults in Korea should be comprehensive and integrated, considering healthy lifestyle clusters and gender differences.

• Journal of Preventive Medicine and Public Health
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• v.43 no.1
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• pp.73-83
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• 2010

Objectives: We investigated the clustering of selected lifestyle factors (cigarette smoking, heavy alcohol consumption, lack of physical exercise) and identified the population characteristics associated with increasing lifestyle risks. Methods: Data on lifestyle risk factors, sociodemographic characteristics, and history of chronic diseases were obtained from 7,694 individuals ${\geq}20$ years of age who participated in the 2005 Korea National Health and Nutrition Examination Survey (KNHANES). Clustering of lifestyle risks involved the observed prevalence of multiple risks and those expected from marginal exposure prevalence of the three selected risk factors. Prevalence odds ratio was adopted as a measurement of clustering. Multiple correspondence analysis, Kendall tau correlation, Man-Whitney analysis, and ordinal logistic regression analysis were conducted to identify variables increasing lifestyle risks. Results: In both men and women, increased lifestyle risks were associated with clustering of: (1) cigarette smoking and excessive alcohol consumption, and (2) smoking, excessive alcohol consumption, and lack of physical exercise. Patterns of clustering for physical exercise were different from those for cigarette smoking and alcohol consumption. The increased unhealthy clustering was found among men 20-64 years of age with mild or moderate stress, and among women 35-49 years of age who were never-married, with mild stress, and increased body mass index (>$30\;kg/m^2$). Conclusions: Addressing a lack of physical exercise considering individual characteristics including gender, age, employment activity, and stress levels should be a focus of health promotion efforts.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.74-84
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• 2009

GIS based new postal marketing method is presented in this paper with spatiotemporal mining to cope with domestic mail volume decline and to strengthening competitiveness of postal business. Market segmentation technique for socialogy of population and spatiotemporal prediction of consumer propensity to purchase through spatiotemporal multi-dimensional analysis are suggested to provide meaningful and accurate marketing information with customers. Internal postal acceptance & external statistical data of local districts in the Seoul Metropolis are used for the evaluation of geo-lifestyle clustering and spatiotemporal cube mining. Successfully optimal 14 maketing clusters and spatiotemporal patterns are extracted for the prediction of consumer propensity to purchase.

• Korean Journal of Health Education and Promotion
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• v.22 no.4
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• pp.167-177
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• 2005

Objectives: This study was performed to examine the clustering of lifestyle risk factors for chronic diseases in urban poor and rural adults. Methods: As a cross-sectional study, a questionnaire survey was conducted in 2003. Data was collected from 468 urban poor adults and 385 rural adults. And 848 persons data was used for final analysis. We surveyed their smoking habit alcohol consumption, exercise habit education and disease histories. Result: In mea about 25% of the urban poor subjects and about 20% of the rural subjects had three lifestyle risk factors(Prevalence ratio was 1.29). And, in women, about 1.5% of the urban poor subjects and about 0.5% of the rural subjects had three lifestyle risk factors(Prevalence ratio was 4.00). Especially in men, clustering of smoking and excessive alcohol consumption was strongest both the urban poor and rural subjects(Observed/Expected ratio(O/E): 1.4 in the urban poor subjects, 1.3 in the rural subjects). Conclusions: These findings show that the lifestyle risk factors cluster among the urban poor and rural adults. And the clustering is stronger in the urban poor adults than the rural adults. This tendency was important for health education and health promotion. We suggest that more intensive health promotion strategies for the urban poor adults are needed.

• Journal of Sasang Constitutional Medicine
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• v.36 no.2
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• pp.12-26
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• 2024

Objectives We aimed to identify the incidence of metabolic syndrome (MetS) and its clustering components according to constitution type and lifestyle risk factors in Korean adults. Methods This study included 1,978 adults aged 30-55 years from the Korean Medicine Daejeon Citizen Cohort (KDCC) study. We defined lifestyle factors including smoking, alcohol consumption, physical activity, sleep, dietary quality, and weight status. Total lifestyle scores were created based on the six lifestyle factors (ranging from 0 to 5 factors) and classified into two groups: unhealthy (0-2 factors), or healthy (3-5 factors). Cox proportional hazard regression was used to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) of primary endpoints: MetS events and their clustering components. Results During a median follow-up of 2.2 years, we documented 125 new onsets of MetS. Compared with participants with healthy, the HR of unhealthy participants was 2.401 (95% CI: 1.497-3.851) for MetS incidence. After adjusting for covariates, TE type with unhealthy was higher HR values of abdominal obesity (HRs: 1.499, 95%CI: 1.061-2.117) and hypertension (HRs: 1.840, 95%CI: 1.032-3.277), respectively. Conclusion Unfavorable lifestyle factors were highly associated with the prevalence of MetS and its clustering such as abdominal obesity and hypertension in Korean adults with TE. Tailored health management is needed to consider individual traits and healthy lifestyles to prevent cardiometabolic diseases.

• Journal of Preventive Medicine and Public Health
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• v.31 no.1 s.60
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• pp.72-81
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• 1998

The purpose of this study was to investigate the relation between health behavior patterns and demographic, socio-economic characteristics, health status, health information in Korea. The quantification method through canonical correlation analysis was conducted to the data from Korea National Health Survey in 1995, which consisted of 5,805 persons. The health lifestyle patterns were quantified as good diet lifestyle, passive lifestyle to the negative direction and drinker lifestyle, smoker lifestyle, hedonic lifestyle and fitness lifestyle to the positive direction. The covariate were related to health lifestyle patterns in the order of sex, age, marital status, occupation, health information, economic status, level of physical labour and health status. Characteristics of male, age below 50, married, blue colored worker, no health information, low in economic status, heavy level of physical labour, and poor in health status were positively related to drinker lifestyle, smoker lifestyle, hedonic lifestyle, fitness lifestyle sequentially.

• Journal of Korean Clinical Health Science
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• v.4 no.2
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• pp.556-565
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• 2016

Purpose. This study pourpose to fashion lifestyle and color interests in accordance with group university students' lifestyle focused on students in health and nursing fields. Methods. This study administered a structured questionnaire to 321 random subjects who currently major in health and nursing fields and who reside in Daegu city. For the collected data, using the SPSS 18.0, the following analyses were implemented: frequency analysis, factor analysis, K-means clustering analysis, t-test, and ${\chi}^2$-test. Result. In terms of lifestyle, seniors had shown more active groups than passive groups in comparison to their juniors. The active group in terms of lifestyle has shown higher interest in the importance of apparel and fashion leadership in comparison to the passive group. The active group in terms of lifestyle has also shown higher interest in color in comparison to the passive group. Conclusion. A fashion leader leading by examining the fashion life style and color interest in accordance with the lifestyle to target college students to investigate a variety of consumption patterns made according to personal preference consists of a smooth communication between businesses and consumers needed for product development.

• Annual Conference of KIPS
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• 2010.11a
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• pp.1365-1368
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• 2010

이 논문에서는 무분별한 홍보우편물의 발송 및 낭비의 최소화를 위해서 반응율이 가장 높은 고객만을 타겟팅 하기 위한 방법으로, 읍/면/동 보다 더 작은 단위인 소지역을 대상으로 인구 사회 경제학적 특성을 고려한 Geo-Lifestyle 군집화를 수행하였다. 서울지역 16,357개 소지역 중 마케팅에 의미 있는 15,986개 지역을 대상으로 최종 36개의 소지역단위 군집을 구성하였다.

• Journal of Preventive Medicine and Public Health
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• v.39 no.3
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• pp.235-242
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• 2006

Objectives: We wanted to determine the distribution of the clustering of the metabolic risk factors and we wanted to evaluate the related factors in young schoolchildren. Methods: A cross-sectional study of metabolic syndrome was conducted in an elementary school in Seoul, Korea. We evaluated fasting glucose, triglyceride, HDL cholesterol, blood pressures and the body mass index, and we used parent-reported questionnaires to assess the potential risk factors in 261 children (136 boys, 125 girls). We defined the metabolic risk factors as obesity or at risk for obesity ($\geqq$ 85th percentile for age and gender), a systolic or diastolic blood pressure at $\geqq90th$ percentile for age and gender, fasting glucose at $\geqq110mg/dl$, triglyceride at $\geqq110mg/dl$ and HDL cholesterol at $\leqq40mg/dl$. Results: There were 15.7% of the subjects who showed clustering of two or more metabolic risk factors, 2.3% of the subjects who showed clustering for three or more risk factors, and 0.8% of the subjects who showed clustering for four or more risk factors. A multivariate analysis revealed that a father smoking more than 20 cigarettes per day, a mother with a body mass index of = $25kg/m^2$, and the child eating precooked or frozen food more than once per day were associated with clustering of two or more components, with the odds ratios of 3.61 (95% CI=1.24-10.48), 5.50 (95% CI=1.39-21.73) and 8.04 (95% CI=1.67-38.81), respectively. Conclusions: This study shows that clustering of the metabolic risk factors is present in young schoolchildren in Korea, with the clustering being associated with parental smoking and obesity as well as the child's eating behavior. These results suggest that evaluation of metabolic risk factors and intervention for lifestyle factors may be needed in both young Korean children and their parents.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.1-6
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• 2024

In this paper, we develop an AI-based recommendation system that matches the specifications of smartphones from company 'S'. The system aims to simplify the complex decision-making process of consumers and guide them to choose the smartphone that best suits their daily needs. The recommendation system analyzes five specifications of smartphones (price, battery capacity, weight, camera quality, capacity) to help users make informed decisions without searching for extensive information. This approach not only saves time but also improves user satisfaction by ensuring that the selected smartphone closely matches the user's lifestyle and needs. The system utilizes unsupervised learning, i.e. clustering (K-MEANS, DBSCAN, Hierarchical Clustering), and provides personalized recommendations by evaluating them with silhouette scores, ensuring accurate and reliable grouping of similar smartphone models. By leveraging advanced data analysis techniques, the system can identify subtle patterns and preferences that might not be immediately apparent to consumers, enhancing the overall user experience. The ultimate goal of this AI recommendation system is to simplify the smartphone selection process, making it more accessible and user-friendly for all consumers. This paper discusses the data collection, preprocessing, development, implementation, and potential impact of the system using Pandas, crawling, scikit-learn, etc., and highlights the benefits of helping consumers explore the various options available and confidently choose the smartphone that best suits their daily lives.