• Investigative Magnetic Resonance Imaging
• /
• v.14 no.1
• /
• pp.47-55
• /
• 2010

Purpose : To evaluate the geometry of carotid artery by assessing the images of contrast-enhanced MR angiography (CE-MRA) and interrelationships between the geometry of carotid artery and clinical factors. Materials and Methods : 216 consecutive patients who performed supraaortic CE-MRA with fast spoiled gradient-echo imaging were included. Their medical records were reviewed for variable information including risk factors predictive of generalized atherosclerotic disease (age, hypertension (HTN), diabetes mellitus, hyperlipidema, and smoking), sex, body weight, height, and body mass index (BMI). We reviewed the CE-MRA with carotid origin (3 types), carotid artery tortuosity, angle of internal carotid artery bifurcation, the type of aortic arch branching, and the presence of the coiling of carotid artery. Results : Multinomial logistic regression analysis showed that significantly contributed clinical backgrounds for carotid origin were the age and the BMI. With an increase of age at 1, the probability that the type of carotid origin become from type 1 to type 2 was 0.9 times (p=0.004) in right carotid artery (RCA), 0.9 times (p = 0.031) in left carotid artery (LCA), 0.9 times that are likely to be type3 from type 2 (p<0.001) in RCA and 0.9 times in LCA (p=0.009). Increase in BMI at 1 increased odds of becoming type 2 as 1.1 times (p = 0.067) in RCA, 1.1 times (p=0.009) in LCA and increased chance of becoming type 3 as 1.2 times (p = 0.001) in RCA, 1.2 times (p=0.003) in LCA. Mean value of right and left carotid tortuosity were $240.9{\pm}69.0^{\circ}$and $154.4{\pm}55.0^{\circ}$, respectively. Conclusion : The BMI, age, sex and presence of HTN affects the geometry of carotid arteries, the site of origin and tortuosity of carotid artery specifically.

• Tuberculosis and Respiratory Diseases
• /
• v.43 no.3
• /
• pp.377-387
• /
• 1996

Background : In 1980, WHO made a definition in which the term "impairment" as applied to the respiratory system is used to describe loss of lung function, "disability" the resulting diminution in exercise capacity. The measurement of pulmonary function during exercise would give us information about overall functional capacity and respiratory performance that would be lacking in tests performed at rest. We conducted this study to investigate the role of resting pulmonary function test and exercise test for assessing impairment/disability in patients with chronic airflow obstruction(CAO). Method : We studied 19 patients with CAO. The spirometry and body plethysmograph were performed in stable condition. And then patients performed a progressive incremental exercise test to a symptom-limited maximum using cycle ergometer. Patients were divided in two groups, severe and non-severe impairment, according to the resting PFTs and compaired each other. A patient was considered to be severely impaired if FVC < 50 %, FEV1 < 40 % or FEV1/FVC < 40 %. Results : 1) The airway obstruction and hypoxemia of severe impairment group were more severe and exercise performance was markedly reduced compairing to non-severe impairment group. 2) The severe impairment group showed ventilatory limitation during exercise test and the limiting symptomes ware dyspnea in 9/10 patients. 3) The impairment and disability of the patients with tuberculous destructed lung were most marked in patients with CAO. 4) The FEV1 was the most prevalent criterion for the determination of severe impairment based on resting PFTs and was the valuable best correlated to V02max(r=0.81, p < 0.001). 5) The sensitivity of exercise limits for predicting severe disability according to resting PFTs was 80 % and specificity 89 %. Conclusion : In patients with severe CAO, FEV1 is a good predictive of exercise performance and impairment measured by resting PFTs can predict a disability by exercise test.

• Genomics & Informatics
• /
• v.9 no.2
• /
• pp.52-58
• /
• 2011

Osteoporotic fracture (OF), along with bone mineral density (BMD), is an important diagnostic parameter and a clinical predictive risk factor in the assessment of osteoporosis in the elderly population. However, a genome-wide association study (GWAS) on OF has not yet been clarified sufficiently. To identify OF-associated genetic variants and candidate genes, we conducted a GWAS in a population-based cohort (Korean Association Resource [KARE], n=1,427 [case: 288 and control: 1139]) and performed a de novo replication study in hospital-based individuals (Asan and Catholic Medical Center [ACMC], n=1,082 [case: 272 and control: 810]). In a combined meta-analysis, a newly identified genetic locus in an intergenic region at 10p11.2 (near genes FZD8 and ANKRD30A ) showed the most significant association (odd ratio [OR] = 2.00, 95% confidence interval [CI] = 1.47~2.74, p=$1.27{\times}10^{-6}$) in the same direction. We provide the first evidence for a common genetic variant influencing OF and genetic information for further investigation in bone metabolism.

• Korean Journal of Psychosomatic Medicine
• /
• v.27 no.1
• /
• pp.42-49
• /
• 2019

Objectives : Living donor liver transplantation (LDLT) is a life-saving therapy for patients with terminal liver disease. Many studies have focused on recipients rather than donors. The aim of this study was to assess the emotional status and personality characteristics of LDLT donors. Methods : We evaluated 218 subjects (126 male, 92 female) who visited Daegu Catholic University Medical Center from August 2012 to July 2018. A retrospective review of their preoperative psychological evaluation was done. We investigated epidemiological data and the Minnesota Multiphasic Personality Inventory-2 questionnaire. Subanalysis was done depending on whether subjects actually underwent surgery, relationship with the recipient, and their gender. Results : Mean age of subjects was $32.19{\pm}10.91years$. 187 subjects received LDLT surgery (actual donors) while 31 subjects didn't (potential donors). Donor-recipient relationship included husband-wife, parent-children, brother-sister etc. Subjects had statistical significance on validity scale L, F, K and all clinical scales compared to the control group. Potential donors had significant difference in F(b), F(p), K, S, Pa, AGGR, PSYC, DISC and NEGE scales compared to actual donors. F, D and NEGE scales were found to be predictive for actual donation. Subanalysis on donor-recipient relationship and gender also showed significant difference in certain scales. Conclusions : Under-reporting of psychological problems should be considered when evaluating living-liver donors. Information about the donor's overall psychosocial background, mental status and donation process should also be acquired.

• The Journal of Engineering Geology
• /
• v.32 no.4
• /
• pp.697-723
• /
• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Korean Journal of Culture and Social Issue
• /
• v.15 no.3
• /
• pp.399-429
• /
• 2009

The purpose of this research examines the factors that influence happiness among Korean adolescents by focusing on psychological resource (as measured by self-efficacy), relational resource (as measured by social support) and financial resource (as measured by family's monthly income). In addition, the influence of academic achievement on happiness is examined. To examine the influence of socio-economic status and family's monthly income, adolescents living in three different districts in Seoul (from working to middle to upper class districts) were randomly selected and interviewed in their home. A total of 190 elementary school, middle school, high school and university students (male=83, female=107) completed the resiliency of efficacy scale developed by Bandura (1995) and emotional support and happiness scale developed by the present researchers, in addition to background information. The results of the path analysis are as follows. First, the most important predictor of happiness among Korean adolescents is relational resources. In other words, emotional support received from significant others was most predictive of happiness; more than 60 times the effect of family's monthly income, three times the effect of academic achievement, and two times the effect of resiliency of efficacy. The second most important factor that predicted the happiness of Korean adolescents was psychological resource (i.e., resiliency of efficacy), which had 30 times the effect of family's monthly income. In addition resiliency of efficacy played a mediating role between emotional support on one hand and happiness on the other. Third, those respondents who had higher academic achievement reported higher levels of happiness, which had 20 times the effect of family's monthly income. Fourth, family monthly income did not predict happiness among Korean adolescents. Fifth, socio-economic status and school level did not have direct influence on happiness but had mediating influence through their influence on emotional support. In other words, those respondents with higher socio-economic status and elementary school students were more likely to receive social support from significant others, which in turn increased their happiness. These results indicate that the most important predictor of happiness among Korean adolescents is emotional support, followed by resiliency of effic acy and academic achievement, indicating that those adolescents from wealthy families are not necessarily happier.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Asia pacific journal of information systems
• /
• v.24 no.4
• /
• pp.443-472
• /
• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Women's Health Nursing
• /
• v.2 no.2
• /
• pp.298-331
• /
• 1996

Recent improvements in living standard and development in medical care led to an increased interest in life expectancy and personal health, and also led to a more demand for higher quality of life. Thus, the problem of women's health draw a fresh interest nowadays. Since late-middle aged women experience various physical and socio-psychological changes and tend to have chronic illnesses, these women have to take initiatives for their health control by realizing their own responsibility. The basic elements for a healthy life of these women are understanding of their physical and psychological changes and acceptance of these changes. Health promoting behaviors of an individual or a group are actions toward increasing the level of well-being and self-actualization, and are affected by various variables. In Pender's health promoting model, variables are categorized into cognitive factors(individual perceptions), modifying factors, and variables affecting the likelihood for actions, and the model assumes the health promoting behaviors are affected by cognitive factors which are again affected by demographic factors. Since Pender's model was proposed based on a tool broad conceptual frame, many studies done afterwards have included only a limited number of variables of Pender's model. Furthermore, Pender's model did not precisely explain the possibilities of direct and indirect paths effects. The objectives of this study are to evaluate Pender's model and thus propose a model that explains health promoting behaviors among late-middle aged women in order to facilitate nursing intervention for this group of population. The hypothetical model was developed based on the Pender's health promoting model and the findings from past studies on women's health. Data were collected by self-reported questionnaires from 417 women living in Seoul, between July and November 1994. Questionnaires were developed based on instruments of Walker and others' health promotion lifestyle profile, Wallston and others' multidimensional health locus of control, Maoz's menopausal symptom check list and Speake and others' health self-rating scale. IN addition, items measuring self-efficacy were made by the present author based on past studies. In a pretest, the questionnaire items were reliable with Cronbach's alpha ranging from .786 to .934. The models for health promoting behaviors were tested by using structural equation modelling technique with LISREL 7.20. The results were summarized as follows : 1. The overall fit of the hypothetical model to the data was good (chi-square=4.42, df=5, p=.490, GFI=.995, AGFI=.962, RMSR=.024). 2. Paths of the model were modified by considering both its theoretical implication and statistical significance of the parameter estimates. Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data (chi-square =4.55, df=6, p=.602, GFI=.995, AGFI=.967, RMSR=.024). 3. The results of statistical testing were as follows : 1) Family function internal health locus of control, self-efficacy, and education level exerted significant effects on health promoting behaviors(${\gamma}_{43}$=.272, T=3.714; ${\beta}_[41}$=.211, T=2.797; ${\beta}_{42}$=.199, T=2.717; ${\gamma}_{41}$=.136, T=1.986). The effect of economic status, physical menopausal symptoms, and perceived health status on health promoting behavior were insignificant(${\gamma}_{42}$=.095, T=1.456; ${\gamma}_{44}$=.101, T=1.143; ${\gamma}_{43}$=.082, T=.967). 2) Family function had a significance direct effect on internal health locus of control (${\gamma}_{13}$=.307, T=3.784). The direct effect of education level on internal health locus of control was insignificant(${\gamma}_{11}$=-.006, T=-.081). 3) The directs effects of family functions & internal health locus of control on self-efficacy were significant(${\gamma}_{23}$=.208, T=2.607; ${\beta}_{21}$=.191, T=2.2693). But education level and economic status did not exert a significant effect on self-efficacy(${\gamma}_{21}$=.137, T=1.814; ${\beta}_{22}$=.137, T=1.814; ${\gamma}_{22}$=.112, T=1.499). 4) Education level had a direct and positive effect on perceived health status, but physical menopausal symptoms had a negative effect on perceived health status and these effects were all significant(${\gamma}_{31}$=.171, T=2.496; ${\gamma}_{34}$=.524, T=-7.120). Internal health locus and self-efficacy had an insignificant direct effect on perceived health status(${\beta}_{31}$=.028, T=.363; ${\beta}_{32}$=.041, T=.557). 5) All predictive variables of health promoting behaviors explained 51.8% of the total variance in the model. The above findings show that health promoting behaviors are explained by personal, environmental and perceptual factors : family function, internal health locus of control, self-efficacy, and education level had stronger effects on health promoting behaviors than predictors in the model. A significant effect of family function on health promoting behaviors reflects an important role of the Korean late-middle aged women in family relationships. Therefore, health professionals first need to have a proper evaluation of family function in order to reflect the family function style into nursing interventions and development of strategies. These interventions and strategies will enhance internal health locus of control and self-efficacy for promoting health behaviors. Possible strategies include management of health promoting programs, use of a health information booklets, and individual health counseling, which will enhance internal health locus of control and self-efficacy of the late-middle aged women by making them aware of health responsibilities and value for oneself. In this study, an insignificant effect of physical menopausal symptoms and perceived health status on health promoting behaviors implies that they are not motive factors for health promoting behaviors. Further analytic researches are required to clarify the influence of physical menopausal symptoms and perceived health status on health promoting behaviors with-middle aged women.