Detecting changes in relationships over time is a central problem in longitudinal data analysis. While classical change-point methods focus on mean regression, structural shifts may occur differently across the distribution of the response. We propose a CUSUM-based testing framework for detecting structural changes in Bayesian quantile regression models, allowing quantile-specific shifts to be identified. Our approach leverages Bayesian quantile regression with a plug-in estimator for the regression coefficients and constructs test statistics based on cumulative sums of quantile score processes. We establish theoretical guarantees for validity under parameter stability and consistency under single-change alternatives. Simulation studies demonstrate that the method maintains good size control and strong power, effectively detecting structural changes across the response distribution. The framework opens new possibilities for uncovering nuanced dynamics in longitudinal studies.