This project focuses on finding what defines an appropriate moment to notify in a smart reminder system. Specifically, the goal is to find a way in which smart reminders systems can be extended through the use of user values to ultimately provide more appropriately timed reminders. This is essential in providing software aided support. A system is designed from scratch, combining existing concepts of activity prediction and value-based design. A statistical Markov chain model is made from predictions based on Expectation Maximization and Apriori algorithms. User values are quantified and optimized following the concept of a Socially Adaptive Electronic Partner and added to the model to identify an appropriate moment for a notification. The concept of values is broken down into two aspects. Firstly, the value loss invoked by the nuisance of receiving a notification during a certain activity. Secondly, the expected value gain achieved by actually remembering is simulated through the expected time between the moment of notification and the deadline. The model is implemented in a Node.js web application, following the principles of a RESTful web API. The model is tested for both its success in correct prediction and the moment selection. The basic predictive model shows a 91% success rate but falls short at 73% when considering values. After optimizing the system for user values, up to a 13% improvement in the success rate and an 18% improvement in the score (more appropriate moment) is found for the model considering user values with respect to the basic, predictive model. Overall, a clear and workable approach to value-based smart reminders is shown through a statistical and dynamic approach to incorporate the concept of user values in a smart-reminder system.