The long-term goal of this component is to improve public health by facilitating the evidence-based construction of effective, individualized mobile substance use prevention and intervention services. This component develops data analytic methods that will enable drug abuse prevention and services scientists to more effectively adapt interventions to individuals’ changing needs over time and more effectively expand the reach of their interventions. The end result of this project is that these mobile phone interventions will not only be accessible around the clock (available whenever, and as long as needed), but also highly responsive to dynamically changing individual needs. Just-in-time adaptive interventions are composed of operationalized decision rules that input dynamic individual information (e.g., current craving, geographical location, substance use) and output, via the mobile device, strategies (e.g., motivational, cognitive, behavioral, social) that support the individual’s longer term goals to remain substance use free. To develop interventions that respond nimbly to dynamically changing individual needs, the intervention designer must address nuanced questions such as, “At what risk level should the mobile device prompt the individual and recommend the use of a particular service?” and “Should the length of time an individual has been abstinent and/or the current location of the individual be used in addition to the current risk level to determine whether to recommend the use of a recovery strategy?” Current scientific behavioral theories are, for the most part, silent on these kinds of questions. The analysis of existing intensive longitudinal intervention data could be very informative, but appropriate data analytic methods do not exist. This state of affairs is preventing mobile interventions from fulfilling their potential to provide effective services toward reducing substance use and HIV. The overarching goal of this component is to integrate ideas from statistics, computer science, and behavioral science to develop data analytic methodologies that will (i) enable scientists to construct more effective mobile interventions for delivery of SUD/HIV prevention and SUD recovery services, and (ii) inform development of more dynamic and nuanced behavioral theories.