Latent Transition Analysis of Time-Varying Cannabis Use Motives to Inform Adaptive Interventions

What is it about?

The prevalence of daily cannabis use among older adolescents and young adults (AYAs) in the U.S. has increased significantly over the past decade, which has significant public health implications. This rise in cannabis use means that more individuals may be seeking or in need of treatment for adverse outcomes (e.g., cannabis use disorder) arising from excessive cannabis use. A key new treatment approach for cannabis use and related problems could include adaptive interventions. One promising input to the development of such adaptive interventions is self-reported motives for cannabis use, which can be used to develop tailored interventions that help to reduce consumption over time or in certain circumstances. Research has demonstrated that these motives can be collected from individuals engaging in cannabis use on a yearly, monthly, or even daily basis, and serve as strong predictors of both the frequency of cannabis use and associated adverse outcomes. This study presents secondary analyses of data from four longitudinal studies that collected time-varying motives with different frequencies, along with distal measures of cannabis use, from both AYAs and adults. We apply random intercept latent transition analysis (RI-LTA) with distal outcomes to 1) study the frequency of transitions in latent classes of motives for cannabis use across different periods of time, and 2) identify the types of transitions in latent motive classes that are predictive of adverse outcomes in the future. The identification of such transitions has direct implications for the development of adaptive interventions designed to prevent adverse health outcomes related to excessive cannabis use.

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Photo by Grav on Unsplash

Photo by Grav on Unsplash

Why is it important?

This study identifies specific transitional patterns in individuals’ motives for cannabis use, such as stress relief, pain management, or coping with mental health issues, that can predict greater cannabis consumption in the future. By identifying these patterns, we hope to lay an empirical foundation for developing timely, personalized interventions tailored to the changing motives of individuals for cannabis use, potentially preventing the long-term adverse health outcomes associated with frequent use.

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