The treatment of drug use and HIV often requires sequential, individualized decision-making concerning the type or delivery of treatment. An adaptive intervention is a treatment design that uses ongoing information from the patient to guide whether, and how to modify the treatment over time. By providing the appropriate treatment to those who need it, when they need it, adaptive interventions hold the promise of improving long-term outcomes for greater numbers of people, thereby increasing the reach and impact of drug use and HIV treatments in real-world settings. The sequential multiple assignment randomized trial (SMART) is a powerful experimental design that is used for constructing efficacious AIs. However, in order to make the SMART maximally useful to drug use and HIV intervention scientists, methodological work is needed to expand the options for analyzing data that arise from a SMART and to expand the kinds of SMARTs that are currently being designs, which are limited in important ways. This proposal is a competing renewal application of R01(DA039901; ending 07/31/20), in which our multidisciplinary team of behavior intervention and data scientists has successfully developed and tested a suite of methods for the design and longitudinal analysis of SMARTs. In this renewal we embark in new methodological directions related to use of novel treatment technologies and resulting intensive longitudinal data (ILD) to inform the development of effective AIs. A growing number of drug abuse and HIV interventions capitalize on modern technologies such as mobile and web-based applications, and wearable devices to deliver and augment treatments. These technologies enable the collection of frequent measures of an individual?s behavior and experiences. Such data have great potential for addressing new research questions about the dynamic effects of adaptive interventions and for constructing more sophisticated, yet less burdensome tailoring variables. However, existing longitudinal methods for the analysis of SMARTs do not currently accommodate ILD. Further, existing SMART designs do not currently take full advantage of the clinical potential of ILD in terms of how ILD can be employed as tailoring variables. Our objective is to bridge these critical gaps by developing and evaluating new methods to enable drug use/HIV scientists to analyze multiple types of ILD in a SMART; developing and evaluating new methods to analyze existing ILD in order to design more effective SMARTs; applying and illustrating these methods using data from three SMART studies and two observational studies in drug abuse and HIV that employ an ILD collection protocol; and developing free, user-friendly resources to implement these methods. The methods developed in this project will improve clinical and public health outcomes by enabling drug use and HIV scientists to develop more potent adaptive interventions to guide the individualization of drug use and HIV treatments.