Characterization of dynamism is an important issue for utilizing or tailoring of several dynamic multi-objective evolutionary algorithms (DMOEAs). One such characterization is the change detection, which is based on proposing explicit schemes to detect the points in time when a change occurs. Additionally, detecting severity of change and incorporating with the DMOEAs is another attempt of characterization, where there is only a few related works presented in the literature. In this paper, we propose a type-detection mechanism for dynamic multi-objective optimization problems, which is one of the first attempts that investigate the significance of type detection on the performance of DMOEAs. Additionally, a hybrid technique is proposed which incorporates our type detection mechanism with a given DOMEA. We present an empirical evaluation by using seven test problems from all four types and five performance metrics, which clearly validate the motivation of type detection as well as significance of our hybrid technique.