Measurement and Modeling of Within-Person Variability in Cannabis Protective Behavioral Strategies: A Novel Approach Using Scale Development, Daily Data, and Machine Learning Methods

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PROJECT SUMMARY/ABSTRACT Cannabis is the most commonly used controlled substance in the US.1,2 Young adults (YA; ages 18-25) report highest rates of use, and recent epidemiological surveys show an increase in both proportion of YA using cannabis and frequency of use among those that use.3,4 As frequent and heavy cannabis use is associated with a variety of short- and long-term unwanted physical and psychosocial outcomes (e.g., altered brain development, impaired judgement and memory, poor educational outcomes),5–7 there is a need for approaches to help individuals reduce use and/or use-related harms. One approach to mitigating substance- related harm is using protective behavioral strategies (PBS). PBS for cannabis consist of strategies an individual can use before, during, after, or instead of using to reduce use or consequence.8,9 In retrospective assessments, frequency of PBS use is associated with lower past 30-day cannabis use and consequences and mediates the relation between various risk factors and cannabis outcomes,8,10–15 highlighting PBS as a promising means of reducing cannabis use and harms. However, research on PBS-focused interventions is mixed9,16,17; this mixed support may be due to gaps in the PBS literature. Specifically, the majority of PBS research has consisted of cross-sectional, between-person retrospective designs, thus we lack understanding about which strategies work for whom under what circumstances. Emerging PBS research suggests both between- and within-person (i.e., daily) variability in whether an individual uses any PBS, and if so, which strategies they use. This suggests a need for a daily measure of cannabis PBS to increase understanding of how and when individuals utilize PBS and under what circumstances PBS are or are not effective. To address these gaps, the proposed F31 will take a novel, multimethod approach incorporating scale development work, a daily data study design, and machine learning methods. Specific Aims include (1) developing and validating a daily measure of cannabis PBS; and (2) utilizing a daily data design and machine learning techniques to develop models predicting PBS efficacy (reductions in use/consequences) for each strategy for a given individual. To complement these aims, the applicant will receive training in (1) etiology, prevention, intervention, and harm reduction methods for substance use, with a focus on cannabis PBS; (2) psychometric development and quantitative methods including multilevel modeling and machine learning; (3) daily data study design and methodology; and (4) research dissemination, including manuscript/grant writing and conference presentations. Study findings will have important implications for future PBS intervention research. Specifically, results can be used to better understand cannabis PBS on a daily level and improve future technology-based PBS interventions to reduce cannabis-related harms.