• Journal of Nutrition and Health
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• v.52 no.5
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• pp.488-500
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• 2019

Purpose: This study examined the effects of a mobile app-based program to reduce the dietary sugar intake according to the stages of the behavioral change in dietary sugar reduction in female college students. Methods: The program used in this study can monitor the dietary sugar intake after recording the dietary intake and provide education message for the reduction of dietary sugar intake. In an eight-week pre-post intervention study, 68 female college students were instructed to record all the food they consumed daily and received weekly education information. At pre-post intervention, the subjects were asked to answer the questionnaire about sugar-related nutrition knowledge, sugar-intake behavior, and sugar-intake frequency. For statistical analysis, ANOVA and a paired t-test were used for comparative analysis according Precontemplation (PC), Contemplation Preparation (C P), and A M (Action Maintenance) stage. Results: Significant differences were observed in the frequency of snacking, experience of nutrition education, and preference for sweetness according to the stages of behavior change in dietary sugar reduction. After finishing an intervention, the sugar-related nutrition knowledge score was increased significantly in the stages of Precontemplation (PC) and Contemplation Preparation (C P). The score of the sugar intake behavior increased significantly in all stages. The intake frequency of chocolate, muffins or cakes, and drinking yogurt decreased significantly in the PC stage and the intake frequency of biscuits, carbonated beverages, and fruit juice decreased significantly in the C P stage. Subjects in the PC and C P stages had an undesirable propensity in nutrition knowledge, sugar-intake behavior, and sugar-intake frequency compared to the A M stage, but this intervention improved significantly their nutrition knowledge, sugar-intake behavior, and sugar-intake frequency. Conclusion: This program can be an effective educational tool in the stages of PC and C P, and is expected to further increase the usability and sustainability of mobile application if supplemented appropriately to a health platform program.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.7
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• pp.1054-1064
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• 2013

The purpose of this study was to examine the question of whether there is any difference in dietary habits, climacteric symptoms, and general health characteristics of middle-aged women according to food supplements (FS) use. A total of 745 midlife females participated in a face-to-face interview conducted by qualified interviewers, which guaranteed a higher quality of data collection. Three levels of FS use were defined: None, Single, and Multi for 0, 1, and 2 or more types of FS use, respectively. None, Single, and Multi accounted for 33.56%, 33.29%, and 33.15% of total subjects, respectively. FS users (Single and Multi) exerted more interest in FS and were more likely to believe that FS is helpful for health promotion and amelioration of climacteric symptoms than None (P<0.0001). Self-perceived health status of Multi was lower than that of None, but not different from Single (P<0.05). Factor analysis extracted three factors for dietary habits: regularity, variety and moderation, and four factors for climacteric symptoms: emotional, physical, psycho-somatic, and hot flash. The factor scores for dietary variety as well as emotional, psycho-somatic, and hot flash symptoms were higher for FS user than for None (P<0.01). Single reported more frequent family meals compared to None. Findings of the present study elucidated potential links between the level of FS use, dietary habits, and climacteric symptoms of middle-aged women, suggesting a possible scenario: the greater the climacteric symptoms a woman perceives, the more likely the woman will adopt FS use, the greater the efforts toward dietary improvement, such as dietary variety. Based on that, in this study, more peri-menopausal women belonged to Single and Multi; further investigation on the association between FS use, dietary quality, and climacteric symptoms in conjunction with menopausal status may be needed.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.05a
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• pp.35-81
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• 1998

Distinct Element Method(DEM) has a great advantage to model the discontinuous behaviour of jointed rock masses such as rotation, sliding, and separation of rock blocks. Geometrical data of joints by a field monitoring is not enough to model the jointed rock mass though the results of DE analysis for the jointed rock mass is most sensitive to the distributional properties of joints. Also, it is important to use a properly joint law in evaluating the stability of a jointed rock mass because the joint is considered as the contact between blocks in DEM. In this study, a stochastic modelling technique is developed and the dilatant rock joint is numerically modelled in order to consider th geometrical and mechanical properties of joints in DE analysis. The stochastic modelling technique provides a assemblage of rock blocks by reproducing the joint distribution from insufficient joint data. Numerical Modelling of joint dilatancy in a edge-edge contact of DEM enable to consider not only mechanical properties but also various boundary conditions of joint. Preprocess Procedure for a stochastic DE model is composed of a statistical process of raw data of joints, a joint generation, and a block boundary generation. This stochastic DE model is used to analyze the effect of deviations of geometrical joint parameters on .the behaviour of jointed rock masses. This modelling method may be one tool for the consistency of DE analysis because it keeps the objectivity of the numerical model. In the joint constitutive law with a dilatancy, the normal and shear behaviour of a joint are fully coupled due to dilatation. It is easy to quantify the input Parameters used in the joint law from laboratory tests. The boundary effect on the behaviour of a joint is verified from shear tests under CNL and CNS using the numerical model of a single joint. The numerical model developed is applied to jointed rock masses to evaluate the effect of joint dilation on tunnel stability.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.