Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia, PA, USA; Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA.
Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, University Park, PA, USA.
Addict Behav. 2023 Oct;145:107780. doi: 10.1016/j.addbeh.2023.107780. Epub 2023 Jun 17.
Simultaneous alcohol and cannabis use (i.e., simultaneous use) is prevalent among young adults and often associated with negative consequences. Understanding reasons for not drinking (RND) may provide insight into a key intervention target for reducing negative consequences associated with simultaneous use. RND may vary on a day-to-day level, and multiple RND may be endorsed on a given day. Latent class analysis (LCA) of daily diary data is a nuanced approach that can identify complex patterns of daily RND as well as its day- and person-level covariates. The current study was a secondary data analysis of daily diary data from young adults who engaged in heavy drinking and recent simultaneous use (n = 154). We aimed to: (1) characterize daily RND, (2) use LCA to classify day-level patterns of RND, and (3) compare latent classes on same-day variables (i.e., positive and negative affect, day of the week), previous-day variables (i.e., substance use, intoxication level, consequences), and person-level characteristics (i.e., age, sex, baseline substance use frequency, simultaneous use motives). Participants completed up to 14 consecutive diaries. Multilevel LCA identified four classes of heterogeneous daily RND profiles. Daily RND classes significantly differed in terms of day of the week, previous day quantity of cannabis use, and several baseline variables (age, typical substance use, simultaneous use motives). Study findings offer preliminary support for heterogeneous RND classes among young adults engaging in simultaneous use and suggest multiple avenues for future research.
同时饮酒和使用大麻（即同时使用）在年轻人中很普遍，并且经常与负面后果相关。了解不饮酒的原因（RND）可能有助于深入了解减少与同时使用相关的负面后果的关键干预目标。RND 可能会在每天的基础上发生变化，并且在特定的一天可能会有多种 RND 得到认可。基于日常日记数据的潜在类别分析（LCA）是一种细致入微的方法，可以识别日常 RND 的复杂模式及其日和人的协变量。本研究是对参与重度饮酒和近期同时使用的年轻人的日常日记数据的二次数据分析（n = 154）。我们的目的是：（1）描述日常 RND，（2）使用 LCA 对 RND 的日模式进行分类，（3）比较同一日变量（即积极和消极情绪、星期几）、前一日变量（即物质使用、醉酒程度、后果）和个人特征（即年龄、性别、基线物质使用频率、同时使用动机）上的潜在类别。参与者完成了多达 14 个连续的日记。多层次的 LCA 确定了四种不同的日常 RND 模式。日常 RND 类在星期几、前一天大麻使用量以及几个基线变量（年龄、典型物质使用、同时使用动机）方面存在显著差异。研究结果初步支持了同时使用的年轻人中存在异质的 RND 类，并为未来的研究提供了多种途径。
