A Latent Class Approach to Understanding Longitudinal Sleep Health and the Association with Alcohol and Cannabis Use During Late Adolescence and Emerging Adulthood
Published in: Addictive Behaviors, Volume 134 (November 2022). doi: 10.1016/j.addbeh.2022.107417
Posted on RAND.org on October 12, 2022
Sleep is a multi-dimensional health behavior associated with elevated risk of substance use. This is the first study to utilize a latent class approach to characterize sleep health across multiple dimensions and across time from late adolescence to emerging adulthood, and to examine associations with alcohol and cannabis use trajectories.
The sample included 2995 emerging adults (mean ages = 18 to 24 years across six waves of data collection; 54% female) who provided data on sleep dimensions (quality, duration, and social jetlag) and frequency and consequences of alcohol and cannabis use. Longitudinal latent class analysis (LLCA) models characterized participants according to the three sleep dimensions. Latent growth models examined trajectories of frequency and consequences of alcohol or cannabis use over time among emergent sleep classes, with and without controlling for covariates.
LLCA models identified four sleep classes: good sleepers (n = 451; 15.2%); untroubled poor sleepers (n = 1024; 34.2%); troubled, moderately good sleepers (n = 1056; 35.3%); and suboptimal sleepers (n = 460; 15.4%). Good sleepers reported significantly lower levels of alcohol or cannabis use and consequences, and less of an increase in alcohol consequences as compared to suboptimal sleepers.
Persistent poor sleep health was associated with higher levels of alcohol and cannabis use and consequences, and greater increases in alcohol-related consequences during the transition from late adolescence to emerging adulthood. Findings have important clinical implications, highlighting that addressing multi-dimensional sleep health may be an important, novel target of intervention to reduce substance use frequency and consequences.