Aging biomarkers that predict age given methylomic data are referred to as epigenetic aging clocks. While the earliest, first-generation clocks were exclusively trained to predict chronological age, more recent next-generation models have been explicitly trained to associate with health, lifestyle, and/or age-related outcomes. Although these next-generation models have been trained using distinct approaches and techniques, existing evidence indicates that they associate with a greater number of health and disease signals than first-generation clocks. Moreover, they are often more predictive of age-related outcomes and appear more responsive to interventions. In this work, we provide definitions for first- versus next-generation clocks and discuss the potential merits of further dividing next-generation clocks into sub-categories. In addition, we summarize existing next-generation epigenetic aging clocks, including how they were trained and how they can be accessed. Given the relative value of interventional data over observational data, we comprehensively tabulate existing literature documenting the ability of an intervention to influence at least one epigenetic aging clock. While we acknowledge that the decision to a use a specific clock is ultimately dependent on the research application and goal, current evidence suggests that next-generation models should be generally prioritized for health-oriented association and interventional studies.