Abstract
Health promoting behaviors of an individual are affected by various variables. Recently, there has been a growing concern over important health problems of the middle aged women. Physiological changes in the middle aged women and their responsibility for family care can result in physical and psychological burden experienced by middle aged women. This study was designed to test Pender's model and thus purpose a model that explains health promoting behaviors among middle-aged women in Korea. 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 863 women living in Seoul, between 20th, April and 15th, July 1995. Data were analyzed using descriptive statistics and correlation analysis. The Linear Structural Relationship (LISREL) modeling process was used to find the best fit model which assumes causal relationships among variables. The results are as follows; 1. The overall fit of the hypothetical model to the data was good expect chi -square value (GFI=.96, AGFI=.91, RMR=.04). 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 expect chi-square value (GFI=. 95, AFGI=.92, RMR=.04). 3. Some of modifying factors, especially age, occupation, educational levels and body mass index (BMI) are revealed significant effects on health promoting behaviors. 4. Some of cognitive -perceptual factors, especially internal health locus of control, self-efficacy and perceptive health status are revealed significant effects on health promoting behaviors. 5. All predictive variables of health promoting behaviors, especially age, occupation, educational levels, body mass index(BMI), internal health locus of control, self-efficacy and perceptive health status are explained 20.0% of the total variance in the model.