Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits. To promote digital well-being, most platforms now offer a service that periodically prompts users to take a break. These, however, must be set up manually, and so may be suboptimal for both users and the system.In this paper, we propose a framework for optimizing long-term engagement by learning individualized breaking policies. Using Lotka-Volterra dynamics, we model users as acting based on two balancing latent states: drive, and interest---which must be conserved. We then give an efficient learning algorithm, provide theoretical guarantees, and empirically evaluate its performance on semi-synthetic data.