Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards consumption entails risks. To promote digital well-being, most platforms now offer a service that periodically prompts users to take breaks. These, however, must be set up manually, and so may be suboptimal for both users and the system. In this paper, we study the role of breaks in recommendation, and propose a framework for learning optimal breaking policies that promote and sustain long-term engagement. Based on the notion that user-system dynamics incorporate both positive and negative feedback, we cast recommendation as Lotka-Volterra dynamics. We give an efficient learning algorithm, provide theoretical guarantees, and evaluate our approach on semi-synthetic data.