Objective

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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一种潜在的课堂方法，用于了解纵向睡眠健康以及青春期后期和成年初期与酒精和大麻使用的关系

客观的

睡眠是一种多维健康行为，与物质使用风险升高相关。这是第一项利用潜在类别方法来描述从青春期后期到成年初期的多个维度的睡眠健康特征的研究，并检查与酒精和大麻使用轨迹的关联。

方法

该样本包括 2995 名新兴成年人（六波数据收集中的平均年龄为 18 至 24 岁；其中 54% 为女性），他们提供了有关睡眠维度（质量、持续时间和社交时差）以及酒精和大麻使用频率和后果的数据。纵向潜在类别分析 (LLCA) 模型根据三个睡眠维度对参与者进行特征描述。潜在增长模型检查了紧急睡眠类别中酒精或大麻使用频率和后果随时间变化的轨迹，无论是否控制协变量。

结果

LLCA 模型确定了四种睡眠类别：睡眠良好的人 (n = 451；15.2%)；睡眠质量不佳的人（n = 1024；34.2%）；有问题、睡眠质量一般的人 (n = 1056; 35.3%)；和睡眠质量不佳的人 (n = 460; 15.4%)。与睡眠不佳的人相比，睡眠良好的人报告的酒精或大麻使用水平和后果明显较低，并且酒精后果的增加较少。

结论

持续不良的睡眠健康状况与较高水平的酒精和大麻使用及其后果有关，并且在从青春期后期到成年初期的过渡期间，与酒精相关的后果的增加更大。研究结果具有重要的临床意义，强调解决多维睡眠健康问题可能是减少物质使用频率和后果的重要、新颖的干预目标。