We focus on the problem of identifying significant variables in high-dimensional repeated measures and longitudinal data. In this article, we introduce a Bayesian approach to select associated fixed and random variables in generalized linear regression models. The Gaussian and diffused-gamma prior for fixed effects and other default priors for random effects are proposed, and an iterated conditional modes algorithm and Markov chain Monte Carlo algorithm are developed in this article. To demonstrate the applicability of the proposed method, we provide simulation studies for different types of responses following Gaussian, binomial, and Poisson distributions. The proposed methodology is further applied to a breastfeeding dataset with the analysis of nipple and breast pain severity and exclusive breastfeeding status.