For the past 30 years, scholars across the fields of epidemiology, education, psychology, and numerous other fields have worked to develop interventions designed to reduce risk and enhance protection to prevent mental, emotional, and behavioral problems across the lifespan. This article presents a series of next steps that leverage this foundational science to inform the development of adaptive preventive interventions. Adaptive preventive interventions (APIs) tailor the intervention to fit the diverse, sometimes changing, needs of participants with the goal of better prevention outcomes for more individuals. Secondary analyses of data from preventive intervention trials to identify moderators, mediators, and antecedents of attrition and intervention failure can be useful for designing effective APIs. Moderators that identify intervention effect heterogeneity can be used within an API to tailor the intervention to meet the unique needs of important participant subgroups. Mediators and predictors of disengagement and attrition can be helpful tailoring variables in an API to trigger change to the intervention. Preventive intervention trials that incorporate frequent assessment of potential mediators, moderators, and antecedents of attrition during the intervention period are needed. Secondary analyses of data from preventive intervention trials provide an important foundation for next-generation APIs.

在过去的 30 年里，流行病学、教育、心理学和许多其他领域的学者一直致力于开发旨在降低风险和加强保护的干预措施，以预防整个生命周期的精神、情绪和行为问题。本文提出了一系列后续步骤，利用这一基础科学为适应性预防干预措施的发展提供信息。适应性预防干预措施 (API) 定制干预措施，以满足参与者多样化的、有时不断变化的需求，目的是为更多的人提供更好的预防结果。对预防性干预试验的数据进行二次分析，以确定调节因素、中介因素以及自然减员和干预失败的前因，有助于设计有效的 API。可以在 API 中使用识别干预效果异质性的调节器来定制干预措施，以满足重要参与者亚组的独特需求。脱离和流失的中介因素和预测因素有助于调整 API 中的变量以触发干预措施的变化。需要进行预防性干预试验，其中包括在干预期间频繁评估潜在调节因素、调节因素和自然减员的前因。对预防性干预试验数据的二次分析为下一代 API 提供了重要基础。