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Based on self-determination theory (SDT), motivation can be understood as existing on a continuum from the most to the least self-determined (autonomous motivation, introjected regulation, external regulation, amotivation) with more self-determined motivations associated with positive health outcomes, and less self-determined motivations associated with negative health outcomes. Using a newly developed measure of motivations to use cannabis responsibly (TRSQ), we sought to identify unique subpopulations of cannabis users based on their motivations to use (or not use) cannabis use responsibly, positing that those with more self-determined motivations would report higher use of cannabis protective behavioral strategies (PBS) and lower cannabis use/problems. A sample of 408 past month cannabis users were recruited from a multisite study of college students (n=1856). We used latent profile analysis to determine the number of unique subpopulations based on these motivations, which supported a 5-profile solution. Consistent with our hypotheses, the class with the highest level of autonomous motivation and lowest level of amotivation (“Self-Determined Class”, n=40, 10.4% of the sample) reported the highest cannabis PBS use (z=.67) and lowest cannabis use (z=.31), and lowest cannabis use disorder symptoms (z=-.22). Further, the class with the highest level of amotivation and lowest level of autonomous motivation (“Amotivated Class”, n=83, 21.5% of the sample) reported the lowest cannabis PBS use (z=-.22) and highest cannabis use (z=-.16), highest consequences (z=.22), and highest cannabis use disorder symptoms (z=.27). Similar to previous research that has demonstrated that latent profile analysis can be used to distinguish individuals based on their motivational profiles for drinking responsibly, our results support subtyping individuals based on their motivations to use cannabis responsibly. Additional research with larger sample sizes is needed to determine the level of generalizability of these motivational profiles, and longitudinal studies are needed to identify if these classes predict outcomes in the long-term.


Poster presented at the Brain & Behavioral Health Research Day 2023

3rd Place Poster prize for Population Health.



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