STUDYING PHYSICAL ACTIVITY DECLINE FROM ADOLESCENCE TO ADULTHOOD USING LATENT GROWTH CURVE AND RANDOM COEFFICIENT MODELS

The level of physical activity is important for maintenance of good health. Research has demonstrated that virtually all individuals can benefit from physical activities which have been shown to reduce the morbidity from many chronic diseases, like cardiovascular disease and diabetes. Therefore, understanding the trend in activity level from adolescence to young adulthood is very important for public health study. The purpose of this thesis is to describe the natural history of participation in leisure time physical activity from adolescence to young adulthood. The study data are from the University of Pittsburgh Physical Activity Study (PittPAS), which recorded physical activities of 1245 high school students over a period of 14 years. Two longitudinal growth models, a latent growth curve (LGC) model and a random coefficient model are applied to characterize the changes in activity hours per week (HRWK) as well as the effects of sex, race, and grade on these changes. Our analysis results show: Male students are more physically active and have the larger decline rate than Female students; White students are more active, and also have the larger decline rate than Black students; Students from the lower grades spend more time in physical activity and also have the larger decline rate than students in the higher grades. Through analyzing the above physical activity, we also investigate the similarities and differences of LGC models and random coefficient models, such as both models share the same objectives. The LGC model is a multivariate approach, while random coefficient model is a univariate one in terms of the dependent variables. Random coefficient model does not require time-structure data and allows the explanatory variable 'time' to take on different values for each subject. Thus, the random coefficient model has the advantage to handle large amount of missing and irregular data acquired in non-uniform time occasions. Since our study data have a large amount of missing observations and are non-uniformly acquired, random coefficient model is more appropriate in characterizing the changes of HRWK