TY - JOUR
T1 - Reasons for not drinking among young adults with simultaneous alcohol and cannabis use
T2 - A latent class analysis applied to daily diary data
AU - Wilkinson, M. L.
AU - Linden-Carmichael, A. N.
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
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U2 - 10.1016/j.addbeh.2023.107780
DO - 10.1016/j.addbeh.2023.107780
M3 - Article
C2 - 37354848
AN - SCOPUS:85164210926
SN - 0306-4603
VL - 145
JO - Addictive Behaviors
JF - Addictive Behaviors
M1 - 107780
ER -