Wide area data delivery requires timely propagation of up-to-date information to thousands of clients over a wide area network. Applications include web caching, RSS source monitoring, and email access via a mobile network. Data sources vary widely in their update patterns and may experience different update rates at different times or unexpected changes to update patterns. Traditional data delivery solutions are either push-based, which requires servers to push updates to clients, or pull-based, which require clients to check for updates at servers. While push-based solutions ensure timely data delivery, they are not always feasible to implement and may not scale to a large number of clients. In this paper we present adaptive pull-based policies that can reduce the overhead of contacting remote servers compared to existing pull-based policies. We model updates to data sources using update histories, and present novel history-based policies to estimate when updates occur. We present a set of architectures to enable rapid deployment of the proposed policies. We develop adaptive policies to handle changes in update patterns, and present two examples of such policies. Extensive experimental evaluation using three data traces from diverse applications shows that history-based policies can reduce contact between clients and servers by up to 60% compared to existing pull-based policies while providing a comparable level of data freshness. Our adaptive policies are further shown to dominate individual history based policies.