The article introduces two new methods for building and evaluating e-health interventions: the Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART). MOST involves three phases—screening, refining, and confirming—to identify and optimize intervention components. In the screening phase, components are tested to determine their effectiveness. The refining phase fine-tunes the intervention, focusing on optimal dosages. The confirming phase evaluates the optimized intervention in a randomized trial. SMART is a design for building time-varying adaptive interventions, allowing for adjustments based on individual characteristics. Both methods use randomized experimentation to ensure valid inferences. MOST helps identify active components and optimal dosages, while SMART identifies decision rules and tailoring variables for adaptive interventions. These methods aim to create more effective e-health interventions with broader public health impact. The article emphasizes the potential of e-health interventions due to their reach and cost-effectiveness. MOST and SMART are practical and promising for e-health research, enabling more potent interventions that can be widely implemented. The study highlights the importance of adaptive interventions and the role of randomized trials in developing evidence-based strategies. The integration of SMART with MOST can enhance the development of time-varying adaptive interventions. Overall, these methods offer a systematic approach to optimize e-health interventions for better public health outcomes.The article introduces two new methods for building and evaluating e-health interventions: the Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART). MOST involves three phases—screening, refining, and confirming—to identify and optimize intervention components. In the screening phase, components are tested to determine their effectiveness. The refining phase fine-tunes the intervention, focusing on optimal dosages. The confirming phase evaluates the optimized intervention in a randomized trial. SMART is a design for building time-varying adaptive interventions, allowing for adjustments based on individual characteristics. Both methods use randomized experimentation to ensure valid inferences. MOST helps identify active components and optimal dosages, while SMART identifies decision rules and tailoring variables for adaptive interventions. These methods aim to create more effective e-health interventions with broader public health impact. The article emphasizes the potential of e-health interventions due to their reach and cost-effectiveness. MOST and SMART are practical and promising for e-health research, enabling more potent interventions that can be widely implemented. The study highlights the importance of adaptive interventions and the role of randomized trials in developing evidence-based strategies. The integration of SMART with MOST can enhance the development of time-varying adaptive interventions. Overall, these methods offer a systematic approach to optimize e-health interventions for better public health outcomes.