Topics covered: The effective treatment and management of a wide variety of health disorders often requires individualized, sequential decision making whereby treatment is adapted over time based on the changing disease state or specific circumstances of the patient. Adaptive interventions (also known as dynamic treatment regimens) operationalize this type of individualized treatment decision making using a sequence of decision rules that specify whether, how, for whom, or when to alter the intensity, type, or delivery of pharmacological, behavioral, and/or psychosocial treatments. There has been a huge surge of scientific interest in constructing adaptive interventions via the sequential multiple assignment randomized trial (SMART) design. SMART is a type of multi-stage randomized trial design, developed specifically for the purpose of collecting high-quality data for building optimal adaptive interventions. SMARTs are still new to the great majority of behavioral and social science investigators. In this course, we will introduce adaptive interventions, SMART (including simple design principle, cutting-edge analytic methods (e.g., Q-Learning) for SMART data, and discuss how these ideas can guide responsive and adaptive survey designs.