Using Functional Regression to Inform Development of Just-in-Time Adaptive Interventions for Cannabis Use

Cannabis is the most commonly used illicit substance. While some behavioral interventions have demonstrated effectiveness in reducing use, they typically consist of several weeks of in-person treatment, and show only modest rates of end of treatment abstinence and minimal long-term abstinence. Delivering interventions via mobile technology creates new opportunities for gathering fine-grained information on response to treatment and providing in-context support during critical windows of time to adapt the intervention to an individual’s needs. These adaptive interventions have been termed just-in-time adaptive interventions (JITAI) because they are delivered in context and in response to user needs. The proposed project aims to inform JITAI for cannabis use by using novel modeling techniques to identify windows of time and also patterns of use within these windows that are most associated with longer-term outcomes.

In the proposed project, we will combine data from 6 (3 adult and 3 adolescent) clinical trials conducted by CTBH faculty evaluating treatment for cannabis use disorder. With the combined data, we propose to apply functional regression models to accomplish the following Specific Aims: 1) identify critical windows of time during which decreased likelihood of use is most strongly related to end-of treatment abstinence and longer-term abstinence, 2) identify interactions between self-report use and use indicated by urinalysis that are associated with non-response to treatment, and 3) determine whether important use patterns differ between adolescent and adult users. This research will inform the development of novel JITAIs by identifying critical windows for intensifying monitoring of use, increasing support, and adapting treatment. It will also provide an initial demonstration of the utility of functional regression to intensively collected data.