Latent transition analysis of time-varying cannabis use motives to inform adaptive interventions.

Abstract

Objective: The rising prevalence of daily cannabis use among older adolescents and young adults in the United States has significant public health implications. As a result, more individuals may be seeking or in need of treatment for adverse outcomes (e.g., cannabis use disorder) arising from excessive cannabis use. Our objective was to explore the potential of self-reported motives for cannabis use as a foundation for developing adaptive interventions tailored to reduce cannabis consumption over time or in certain circumstances. We aimed to understand how transitions in these motives, which can be collected with varying frequencies (yearly, monthly, daily), predict the frequency and adverse outcomes of cannabis use. Method: We conducted secondary analyses on data collected at different frequencies from four studies: the Medical Cannabis Certification Cohort Study (n = 801, biannually), the Cannabis, Health, and Young Adults Project (n = 359, annually), the Monitoring the Future Panel Study (n = 7,851, biennially), and the Text Messaging Study (n = 87, daily). These studies collected time-varying motives for cannabis use and distal measures of cannabis use from adolescents, young adults, and adults. We applied latent transition analysis with random intercepts to analyze the data. Results: We identified the types of transitions in latent motive classes that are predictive of adverse outcomes in the future, specifically transitions into or staying in classes characterized by multiple motives. Conclusions: The identification of such transitions has direct implications for the development of adaptive interventions designed to prevent adverse health outcomes related to cannabis use.