Linear mixed effect (LME) models have become popular in modeling data in a wide variety of fields, particularly in public health. These models are beneficial because they are able to account for both the means as well as the covariance structure of clustered or longitudinal data. However, as studies are able to collect an increasing amount of data for large numbers of predictors, a major challenge has been the selection of only important variables to create a more interpretable, parsimonious model. Previous methods for LME models have been inefficient in variable selection, but three new methods attempt to select and estimate both important fixed and important random effects simultaneously. The models are compared through analysis of simulated longitudinal data. Additionally, as an example of the important applications to public health, the methods are applied to the Trial of Activity in Adolescent Girls (TAAG) study, to determine important predictors for Moderate to Vigorous Physical Activity (MVPA).