Scalability is an important issue for Distributed Interactive Application (DIA) designers. In order to achieve this, it is important to minimise the network traffic required to maintain the DIA. A commonly used technique to reduce network traffic is through short-term entity dynamics extrapolation. However, this technique makes no use of a priori information regarding entity dynamics. We have been developing methods to employ this information through a number of techniques, primarily statistical in nature, which have shown great promise in constrained experimental environments. The main tenet of our approach is that user behaviour in real DIAs follows patterns, and through acquisition, analysis and exploitation of these patterns, a reduction in network traffic can be achieved. In this paper, we report on our development of a realistic DIA based on an industry standard SDK in which we have implemented data acquisition routines that allow us to do this. Results are presented for trial runs using the system. These results clearly exhibit patterns of user behaviour consistent with our previous research and suggest that the exploitation of this knowledge can help reduce network traffic.