• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of Nutrition and Health
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• v.48 no.1
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• pp.46-57
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• 2015

Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M), ${\geq}30%$ (sufficient, S). All data were analyzed on the metabolic risk and nutritional state after application of weighted value and adjustment of sex, age, residential area, income, education, job or jobless, and energy intake using a general linear model or logistic regression. Results: The subjects of group S were 16.9% of total subjects, group M 39.2%, group L 37.6%, and group VL 6.3%. The VL group included more male subjects, younger-aged (19 to 40 years), urban residents, higher income, higher education, and fewer breakfasts eaters together with family members. Among the 4 groups, the VL group showed the highest waist circumference, while the S group showed the lowest waist circumference, body mass index, and serum total cholesterol. The groups of VL and L with lower intake of breakfast energy showed high percent of energy from protein and fat, and low percent of energy from carbohydrate. With the increase of breakfast energy level, intake of energy, most nutrients and food groups increased, and the percentage of subjects consuming nutrients below EAR decreased. The VL group showed relatively higher intake of snacks, sugar, meat and eggs, oil, and seasonings, and the lowest intake of vegetable. Risk of obesity by waist circumference was highest in the VL group by 1.90 times of the S group and the same trend was shown in obesity by BMI. Risk of dyslipidemia by serum total cholesterol was 1.84 times higher in the VL group compared to the S group. Risk of diabetes by Glu-FBS (fasting blood sugar) was 1.57 times higher in the VL group compared to the S group. Conclusion: The results indicate that higher breakfast energy level is positively related to lower metabolic risk and more desirable nutritional state in Korean adults. Therefore, breakfast energy intake more than 30% of their own EER would be highly recommended for Korean adults.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.7 no.1
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• pp.5-28
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• 2001

Objectives: The objective of this study was to investigate analysis of bone mineral density according to Women with low back pain women. Methods: The data were collected from women who visited Physical Examination Center of a Catholic university hospital located in Daegu. Questionnaires were completed by 50 women during the period from July 20, 2000 to January 12, 2001. The sample was divided into three groups(the normal group of 16 cases and the osteopenia group of 12cases and the osteoporosis group of 22 cases). Bone mineral density(BMD) of lumbar spine was measured using energy absorptiometry. Results: The bone mineral density of the lumbar spine decreased with aging. The bone mineral density of the lumbar spine decreased with the serum Calcium and Phosphorus and Alkaline phosphatase increased. The mean bone mineral density of the lumbar spine of healthy women in age(50~59) was 0.87g/$cm^2$, the lumbar spine of women with low back pain in age(50~59) was 0.77g/$cm^2$. In the multiple regression of risk factors to bone mineral density(BMD) of lumbar spine were correlated with age, marriage existence, exercise time, the loving food of taste, calcium, bone mineral density standard T scores(p<0.05). The experience for LBP increased as weight increased(Odds ratio=999.000). The experience for LBP increased as number of Exercise decreased(Odds ratio=999.000). The experience for LBP increased as menopause existence increased(Odds ratio=999.000). The experience for LBP increased as serum Calcium and Phosphorus increased (Odds ratio=999.000). however all four variables had significant no relationship. The correlation in variables in relation to low back pain and bone mineral density, age showed contra-correlation with low back pain existence, Alkaline phosphatase(p<0.01). Weight showed contra-correlation with body mass index(BMI)(p<0.01). Exercise time showed correlation with number of exercise(p<0.01). The loving food of taste showed contra-correlation with Alkaline phosphatase(p<0.05). Bone mineral density showed correlation with menopause existence(p<0.05). Conclusions: Results from this study indicated that a statistically significant association between bone mineral density of the lumbar spin and age, marriage existence, exercise time, the loving food of taste, calcium, bone mineral density standard T scores. In logistic regression test, there were no related variables. The combination of bone mineral density measurement and assessment of the bone turnover rate by measuring biochemical would be helpful for the treatment of patients with risks of osteoporosis. The more precise study for risk factors to osteoporosis is essential.