Personal computing devices, such as smartphones and wearable technology, are transforming care for mental and behavioral health. These devices can track various aspects of daily life, including activity, sleep patterns, location, and physiological signals such as heart rate. This tracking enables the design and development of intervention systems that can deliver timely, personalized support. The systems will help a person to better manage their health outcomes, for example, reducing stress or improving physical activity. However, knowing when a person can receive, process, and use the support in their daily lives to enable long-term sustainable engagement is a major challenge. Factors such as a person's current activity, location, emotions, motivation, and even how much effort they think it will take to engage can influence how willing they are to interact with an intervention. This project aims to address these challenges by creating smart systems that optimize how interventions are delivered and by determining the best time, type, and device for providing support. The project will thus improve engagement and promote sustainable behavior change. The project proposes new approaches to understanding a person's behavioral states using smartphones and wearable technology. The project will also evaluate how these states impact how a person interacts with interventions. The findings and tools from this project will enable behavioral scientists and intervention designers to crea