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. Author manuscript; available in PMC: 2026 Jul 11.
Published before final editing as: Psychol Addict Behav. 2026 Jul 9:10.1037/adb0001173. doi: 10.1037/adb0001173

Day-Level Latent Classes of Alcohol and Cannabis Use Motives: Associations by Combined Use, Level of Use, and Consequences

Ashley N Linden-Carmichael 1, Xiangyu Tao 2, Jennifer L Shipley 1, María E Contreras-Pérez 2, Helene R White 2, Alexander W Sokolovsky 3, Stephanie T Lanza 4, Kristina M Jackson 2
PMCID: PMC13353201  NIHMSID: NIHMS2181173  PMID: 42423690

Abstract

Objective:

Motives for alcohol and cannabis use are highly dynamic and vary across days. Limited prior work has explored endorsement of multiple motives within a day and how select combinations correspond to use patterns among young adults. The current study used a nuanced analytic technique – multilevel latent class analysis (MLCA) – applied to day-level data to characterize motives for alcohol and cannabis use and examine the associations between day-level class membership and same-day alcohol, cannabis, and co-use outcomes.

Method:

Participants (n=328; Mage=19.8 [SD=1.31]; 51.5% female; 75% White) were college students enrolled in a 54-day ecological momentary assessment study, yielding 5,907 daily reports. Each day, participants recorded their motives for using alcohol and/or cannabis as well as their substance use behavior (type and level of use, substance-related consequences).

Results:

Six day-level classes were identified using MLCA: Fun Motives Days (37.5% of days), Social and Fun Motives Days (36.4%), Offered/Availability Motives Days (10.1%), Expand and Fun Motives Days (9.0%), Coping Motives Days (5.0%), and General High Motives Days (2.0%). Classes differed across all substance use outcomes and harms. General High Motives days had the highest proportion of co-use days and the greatest level of alcohol use, negative consequences, and cannabis use occasions, followed by the Social and Fun Motives class.

Conclusions:

Most alcohol and/or cannabis use days were characterized by multiple motives, with the greatest level of substance use and harms associated with days in which multiple motives were endorsed. Findings may help inform just-in-time interventions aimed at reducing substance use and co-use–related consequences.

Keywords: motives, daily diary, alcohol use, cannabis use, co-use, young adults, multilevel latent class analysis

Alcohol and cannabis are among the most commonly used substances by young adults in the United States (US; Substance Abuse and Mental Health Services Administration, 2024). An estimated 27% of young adults who attend college report recent, heavy episodic drinking, that is, consumption of 4+ drinks for females or 5+ drinks for males in one sitting (Patrick et al., 2024). Prevalence of cannabis use has increased over time amidst changes in legalization across the US: in 2024, an estimated 1 in 10 young adult college students reported daily or near-daily cannabis use (Patrick et al., 2024). Many young adults also report co-using alcohol and cannabis, such that the effects of both substances overlap (see Lee et al., 2022 for a review). Co-use is prevalent in young adults: 25% who consume alcohol also report past-year co-use (Terry-McElrath & Patrick, 2018), and 73% of college students who used alcohol and cannabis in the last year reported co-use (White et al., 2019). Importantly, while heavy alcohol and cannabis use are both associated with increased risk for acute negative consequences (e.g., hangover, acute physical effects, social/interpersonal concerns, blackouts; see White & Hingson, 2014), individuals who engage in co-use are at greater risk for heavier use and more harms than those who use only alcohol or cannabis (Lee et al., 2022; Subbaraman & Kerr, 2015). A recent surge of intensive longitudinal (e.g., diary, ambulatory assessment) work has sought to tease apart between-person (i.e., individuals who report co-use vs. only alcohol or cannabis) from within-person associations (i.e., days with co-use vs. days with only alcohol or cannabis), yielding mixed findings (see Farrelly et al., 2025). Some studies support more harms related to co-use occasions than to single-substance use occasions (e.g., Linden-Carmichael et al., 2020; Sokolovsky et al., 2020), and others indicate null differences in harms (e.g., Lee et al., 2020; Mallett et al., 2019).

Alcohol, Cannabis, and Co-Use Motives

Comprehensively understanding key psychosocial predictors, such as motives for engaging in alcohol, cannabis, and co-use, can aid in advancing prevention and intervention programming for young adults at increased risk for harms. The motivational model (Cooper, 1994; Cox & Klinger, 1988) and the socio-ecological theoretical framework (Freisthler et al., 2014) posit that individuals have multiple, distinct internal motives for engaging in substance use and individuals engage in particular substance use patterns (e.g., use only alcohol, use only cannabis, or alcohol and cannabis co-use). Despite experiencing more harms on co-use occasions, co-use is generally linked with greater perceived positive consequences (e.g., Fairlie et al., 2021; Linden-Carmichael et al., 2020), suggesting that young adults combine alcohol and cannabis to achieve specific, desired effects. Accordingly, certain psychosocial risk factors – such as motivations for use – are more potent on occasions individuals engage in co-use, for example, relative to other use occasions (e.g., only alcohol occasions).

Regarding alcohol use motives, the Cooper et al. (1995) model identifies four distinct motives for use: social motives (i.e., to increase sociability), enhancement motives (i.e., to enhance positive affect), conformity motives (i.e., to fit in with others), and coping motives (i.e., to cope with negative affect). The most commonly assessed motives for cannabis use also include social, enhancement, conformity, and coping, plus “expansion motives” (i.e., expanding one’s perceptions and experiences from cannabis use; Simons et al., 1998). More recent work has applied the motivational model to co-use of alcohol and cannabis, revealing four primary motives that correspond most strongly to alcohol and cannabis co-use: conformity reasons, calming/coping reasons, and social reasons, and a motive specific to co-use, positive effects (e.g., to achieve a better high or to achieve cross-fading effects) (Patrick et al., 2018). Importantly, motives for alcohol and cannabis use are dynamic, documented to vary across days (Votaw & Witkiewitz, 2021). Findings from intensive longitudinal designs (e.g., daily diary, ecological momentary assessment data) have found that level and intensity of day-level motives are differentially associated with the type of substance used, level of use, and consequences, in patterns similar to what is observed at the between-person level. For example, level of enhancement and social motives are most positively and strongly linked to alcohol use and alcohol-related consequences relative to coping and conformity motives at the day-level (e.g., Stevenson et al., 2019; Votaw & Witkiewitz, 2021). Similarly, enhancement motives for cannabis use were consistently strongly associated with cannabis use (Bonar et al., 2017; Pearson et al., 2020), while conformity motives were most strongly associated with cannabis-related consequences (Pearson et al., 2020). Enhancement motives (Arterberry et al., 2021; Jackson et al., 2021; Patrick et al., 2019) and co-use-specific motives (e.g., cross-faded; Linden-Carmichael et al., 2025) are among the most critical determinants of co-use outcomes, with additional evidence that elevated social, conformity, and coping motives are linked with co-use use (Lee et al., 2022). Limited research has explored day-level associations between co-use motives and harms, with one study finding that cross-fading motives were unassociated with negative consequences (Patrick et al., 2020).

While most prior work has examined associations between individual motives and use outcomes, motives rarely occur in isolation. Rather, individuals commonly endorse multiple reasons for substance use. In a daily diary study, Stevens et al. (2021) examined associations between daily alcohol-only, cannabis-only, and co-use motives with level of alcohol and cannabis use and found that across all types of use days, individuals endorsed a day-level average of 1.60 alcohol-only motives, 1.54 cannabis-only motives, and 1.98 co-use motives. Moreover, endorsement of more motives corresponded with heavier alcohol and cannabis use. Researchers have not, however, examined whether certain combinations of motives for use (e.g., days with coping motives and social motives) are particularly associated with higher-risk use outcomes. Given findings that experiencing multiple motives is common and linked with heavier use, further research is warranted to uncover common combinations of motives occurring among young adults in daily life and to identify day-level combinations linked to acute substance-related outcomes.

Multilevel Latent Class Analysis for Characterizing Substance Use Motives in Daily Life

Multilevel latent class analysis (MLCA)—as applied to daily or momentary data—is a nuanced approach for comprehensively capturing multiple motives for use on a given day. Traditional latent class analysis (LCA) has been widely used to identify profiles of individuals based on shared characteristics, including latent classes of reasons, intentions, or motives for substance use (e.g., de Jonge et al., 2022; Lake et al., 2020). As applied to intensive longitudinal data, MLCA identifies types of days based on multiple day-level attributes. Recent work has applied MLCA to day-level data to identify substance use behavior patterns (e.g., Evans-Polce et al., 2025; Linden-Carmichael et al., 2022), reasons for abstaining from substance use (Wilkinson & Linden-Carmichael, 2023), and profiles of positive and negative affect (Van Doren et al., 2024).

Findings from limited extant MLCA work have illuminated the complexity of substance use behavior and its predictors: although research has typically examined psychosocial predictors (e.g., social drinking motives) and substance use behavior (e.g., number of alcoholic drinks) as individual constructs, young adults commonly endorse multiple behaviors (e.g., heavy alcohol use and light cannabis use) on a given day. Given the dynamic nature of motives (see Votaw & Witkiewitz, 2021) and differential associations between select motives and substance use outcomes (Bresin & Mekawi, 2021; Patrick et al., 2018; Petrucci et al., 2020), individuals may endorse multiple motives on a given day, and certain combinations of motives may be more strongly associated with likelihood of using certain substance(s), amount of use, and substance use-related consequences.

Current Study

The purpose of the current study was to characterize day-level motives for alcohol, cannabis, and co-use, as well as how same-day substance use outcomes vary by day-level class membership. Toward this goal, Aim 1 was to conduct an MLCA to identify day-level latent classes of alcohol and cannabis use motives (i.e., social, coping, offered/availability, fun, conformity, mind expansion, and enhancement of other drugs). Aim 2 was to compare classes based on same-day substance use outcomes (i.e., odds of co-use, level of alcohol use, level of cannabis use, and number of negative consequences).

Method

Participants and Procedures

The present study was a secondary data analysis of a multi-site longitudinal study investigating combined alcohol and cannabis use among college students in the United States (see Jackson et al., 2021; Stevens et al., 2021; White et al., 2019 for the full protocol). A total of 2,501 full-time students aged 18–24 was recruited from three universities with varying state-level recreational cannabis policy contexts (i.e., prohibition, decriminalization, and legalization); medical cannabis was legal in all three states. Eligible participants endorsed past-year use of both alcohol and cannabis. Those reporting past-month simultaneous use (i.e., use of both substances together so that their effects overlapped; n = 596) were invited to complete a 56-day repeated daily survey (RDS) protocol in two 28-day bursts separated by three months (Fall 2017 and Winter 2018); due to technical issues during the first two days, analyses retained data from 54 days. During the RDS phase, participants completed brief smartphone-based surveys at five fixed times per day, capturing real-time information on substance use motives and outcomes.

The analytic sample included all participants who provided daily data, restricted to days with complete coverage (i.e., when data were collected to cover all 24 hours for that day) (n=328 participants; 96.2% of those who were invited for the RDS)1. There were no additional exclusions. The sample was approximately 51.5% female, and 75% identified as White, 10.1% as Asian, 10.1% as Hispanic/Latinx, and the remainder as Black, multiracial, or other. Descriptive statistics for the analytic sample are presented in Table 1. All procedures were approved by the Institutional Review Board at the coordinating university. Participants provided informed consent prior to study participation.

Table 1.

Participant Characteristics Within Analytic Sample (n=328).

Characteristic M (SD) or n (%)
Demographics
Age 19.80 (SD = 1.3)
Sex (% male) 157 (47.9%)
Gender
 Female 169 (51.5%)
 Male 157 (47.9%)
 Transgender female 0
 Transgender male 2 (0.1%)
 Gender queer 2 (0.1%)
 Other 1 (0.3%)
Race
 American Indian or Alaska Native 3 (0.9%)
 Asian 33 (10.1%)
 Black or African American 10 (3%)
 Native Hawaiian or other Pacific Islander 1 (0.3%)
 White 246 (75%)
 Self-reported “other” 7 (2.1%)
 Multiracial 28 (8.5%)
Ethnicity
 Hispanic or Latine 33 (10.1%)
 Non-Hispanic or Latine 295 (89.9%)
Substance Use Characteristics
Days with alcohol use (past 30 days) 8.32 (SD = 5.6)
Days with cannabis use (past 30 days) 13.56 (SD = 10.7)
Same day alcohol and cannabis use (past 30 days) 3.56 (SD = 8.4)
AUDIT score (total) 9.65 (SD = 5.2)
AUDIT score (consumption subscale) 5.72 (SD = 2.31)
AUDIT score (problems subscale) 3.94 (SD = 3.84)
AUDIT score (% score of 8 or higher) 61.8%
CUDIT score (total) 9.49 (SD = 6.1)
CUDIT score (consumption subscale) 3.98 (SD = 1.91)
CUDIT score (problems subscale) 5.51 (SD = 4.82)
CUDIT score (% score of 8 or higher) 57.9%
Day-level Substance Use Characteristics
Number of standard drinks on alcohol-only use days 2.64 (SD = 4.6)
Number of cannabis sessions on cannabis-only use days 3.42 (SD = 3.6)
Number of standard drinks on co-use days 5.55 (SD = 4.7)
Number of cannabis sessions on co-use days 5.64 (SD = 7.3)
Number of negative substance-related consequences 0.21 (SD = 0.5); Median = 0, IQR = 0–0, Range = 0–4
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Measures

Baseline measures.

At baseline, participants reported demographic characteristics, including age, sex assigned at birth, and gender identity, as well as recent substance use (alcohol use, cannabis use, and same-day use of both substances). To characterize the sample with respect to risk for problematic use, participants completed the Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993) and the Cannabis Use Disorders Identification Test–Revised (CUDIT-R; Adamson et al., 2010); higher scores indicate greater risk for substance-related problems.

Day-level substance use.

At each RDS, participants reported their substance use since the previous assessment via a graphical interface that had time across the bottom. Participants tapped the screen across time to indicate each time they used cannabis. The total number of taps was summed for that survey period (see Jackson et al., 2021 for screenshots). Due to a highly skewed distribution, values were capped at 12 across the day, which corresponded to the 99th percentile of use. Alcohol use was reported separately on another dedicated screen interface. Participants tapped the screen each time they drank a standard drink, which was also summed for descriptive and modeling purposes (range = 0–46 across the day). Each day was categorized into one of three mutually exclusive use types based on participants’ reports: alcohol-only, cannabis-only, or co-use (i.e., use of both alcohol and cannabis). For analysis, alcohol-only and cannabis-only days were combined into a single-substance use category, contrasted with co-use days. This was operationalized as a binary indicator (0 = single use, 1 = co-use), consistent with prior work (e.g., Jackson et al., 2020; Stevens et al., 2021).

Day-level use motives.

At each RDS, if a participant endorsed alcohol-only use, cannabis-only use, or co-use, they were asked, “What motivated you [to drink, to use marijuana, to drink and use marijuana], respectively, between [time X] and [time Y]?” and instructed to select all applicable motives from the following list: to be social (social), to cope (coping), it was offered (offered/availability), to have fun (fun), to fit in (conformity), to expand awareness (expansion), or to get higher from another drug (effect enhancement)2. These categories were adapted from the Drinking Motives Questionnaire Revised (Cooper, 1994), the Marijuana Motives Measure (Simons et al., 1998), and a measure of alcohol and cannabis co-use motives (Patrick et al., 2018), and have been used in prior naturalistic studies of substance co-use (e.g., Jackson et al., 2021; Stevens et al., 2021). Participants could endorse multiple motives per assessment to capture the multidimensional nature of substance use occasions. We included all motives endorsed each day across all types of substance use (i.e., alcohol only, cannabis only, and both alcohol and cannabis); if any motive was selected for any substance, that motive was included as a motive for that day.

Day-level negative consequences.

Participants reported past-day substance use-related negative consequences in the first RDS on days following self-reported substance use. The following consequences were queried (yes/no) “because of yesterday’s [use of alcohol, use of marijuana, or use of alcohol and marijuana together]”: “hangover” (19% of co-use daily observations), “nauseous or vomited” (7%), “injured self” (1%), “drove car high” (7%), “had a blackout” (4%), “rude or aggressive” (1%), and “unwanted sex” (0.5%). Identical consequences were assessed for each type of use occasion: alcohol-only, cannabis-only, and co-use. Note that each day, participants could respond to one, two, or three of these questions depending on their use pattern during the previous day (e.g., use of alcohol only in one survey and combined use in another survey on the same day). Given that the surveys were administered via mobile phones, item wording was brief. This set of acute consequences was selected from several validated measures administered in the parent study, including the Brief Young Adult Alcohol Consequence Questionnaire (Kahler et al., 2005), Brief Marijuana Consequences Questionnaire (Simons et al., 2012), Young Adult Alcohol Consequences Questionnaire (Read et al., 2006), Rutgers Alcohol Problem Index (White & Labouvie, 1989), and the Rutgers Marijuana Problem Index (White et al., 2005). The number of consequences variable was created by adding across all three substance use types (i.e., alcohol, cannabis, and alcohol and cannabis together) and counting each specific consequence (e.g., hangover) only once, even if it was reported across different substance use types.

Data Analysis Strategy

Following procedures for LCA (Collins & Lanza, 2010) and prior work applying LCA to day-level data (e.g., Linden-Carmichael et al., 2022), we used MLCA to identify distinct day-level profiles of motives for alcohol and/or cannabis use. MLCA was conducted on six binary motive indicators using Mplus Version 8.8; we used the most current version of Mplus estimation optimization. Person ID was specified as a clustering variable to account for the nested data structure (daily observations within individuals), and full information maximum likelihood was used to handle missing data under the assumption of missing at random.

Model fit was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), and model entropy, which reflects classification certainty (Celeux & Soromenho, 1996). Given that the bootstrap likelihood ratio test is not available for clustered data, model selection was based on a combination of fit indices, interpretability of latent classes, and parsimony. One- through seven-class models were estimated using 1,000 random sets of starting values to ensure model convergence and identification.

Following class enumeration, we examined distal outcomes using the BCH (Bolck et al., 2004) approach implemented in Mplus (Asparouhov & Muthén, 2021; Bakk & Kuha, 2021). The BCH method uses inverse-probability weighting to preserve latent class measurement error when assessing associations with external variables, providing unbiased estimates compared to traditional modal assignment. Daily outcomes included the type of substance use day (0 = single-substance use, 1 = co-use, treated as dichotomous), number of drinks, number of cannabis use occasions, and number of negative consequences (treated as continuous variables). Pairwise comparisons were tested using Wald chi-square tests, and standard errors were adjusted for clustering at the person level.

Power.

As noted, this study represents a secondary data analysis. Power analyses for the original study as described above were calculated at Level 2 (i.e., person-level) and Level 1 (i.e., day-level). To detect Level 2 effects, power was estimated across a range of small-to-moderate effect sizes (d=.10, .20, .30) using Optimal Design software (Raudenbush et al., 2011). Given 56 observations per person, ICC=.10 and α=.05, power for N=300 (assuming 15% drop-out rate for N=360) was .80 to detect d=.10 and 1.00 for d=.20 and d=.30. To estimate power for Level 1 effects, Snijders and Bosker’s (1993) power program was used. For a model predicting a daily variable (e.g., alcohol use) from variables on that day (e.g., social motives, marijuana use) as well as three Level-2 covariates, assuming random intercept variance=.05, random slope variance=.05, and α=.05, power approached 1.00 to detect even small effects (ES=.20) with 10 time points.

Transparency & Openness

We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study. The data and analytic code are available upon request. Data were analyzed using Mplus 8.8 statistical software. This study was not preregistered.

Results

Descriptive Statistics

Across the 54-day RDS period, there were 11,417 person-days with complete coverage. Almost half (48.3%) of days with complete coverage did not involve alcohol and/or cannabis use and were excluded from the current analysis. The current analysis included only days with complete coverage on which participants indicated alcohol and/or cannabis use, yielding a total of 5,907 person-days of alcohol and/or cannabis use observations (M = 18.0 days per participant, SD = 13.8, range = 1–53). Participants reported alcohol use on 24.8% of those days, cannabis use on 25.9% of those days, and alcohol and cannabis (same-day) co-use on 49.4% of those days. On use days, participants reported consuming both alcohol and cannabis at varying levels. Descriptive statistics across study days are presented in Table 1.

Day-Level Latent Class Solution

We estimated multilevel latent class models specifying 1- through 7-class solutions (see Table 2 for model fit statistics). We ensured the optimal log likelihood estimation was replicated. Model fit improved steadily with additional classes, as reflected in decreasing AIC and BIC values. Entropy values exceeded 0.80 for most models, indicating good classification precision. Although the 7-class model showed the lowest AIC, lowest adjusted BIC, and highest entropy, one of its classes accounted for only 0.8% of days, raising concerns about overfitting and the interpretability of that class. In contrast, the BIC favored the 6-class solution. Moreover, the 6-class solution offered theoretically coherent distinctions between classes by capturing different combinations of cannabis and alcohol use motives. Taken together, based on model fit, interpretability, and theoretical richness, the 6-class model was selected as the optimal solution. See Figure 1 for conditional item-response probabilities by class.

Table 2.

Model Fit Information for 1- to 7-Class Solutions.

Class solution LL AIC BIC Sample Size Adjusted BIC Entropy
1 class −16246.180 32506.36 32553.15 32530.903 --
2 class −16012.670 32055.34 32155.6 32107.932 0.911
3 class −15698.873 31443.75 31597.48 31524.388 0.622
4 class −15477.276 31016.55 31223.75 31125.244 0.868
5 class −15324.878 30727.76 30988.43 30864.497 0.807
6 class −15242.986 30579.97 30894.12 30744.762 0.852
7 class −15220.777 30551.56 30919.17 30744.395 0.928
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Note. LL = Log Likelihood. AIC = Akaike Information Criterion. BIC = Bayesian Information Criterion. Bolded numbers represent the best fit. The model met Mplus’ criterion for maximum likelihood estimate identification.

Figure 1.

Figure 1.

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Six latent classes of day-level motives for alcohol and cannabis use (5,907 days reported by 328 participants).

Class 1 (Social and Fun Motives; 36.4% of days) reflected high endorsement of social and fun motives, with some endorsement of offered/availability. Class 2 (General High Motives; 2.0%) showed elevated endorsement across nearly all motives. Class 3 (Fun Motives; 37.5%) was defined solely by high motives to experience fun, with no or little endorsement of other motives. Class 4 (Expand and Fun Motives; 9.0%) was characterized by strong expansion and moderate fun motives, consistent with cannabis-focused, experiential use. Class 5 (Offered/Availability Motives; 10.1%) showed near-exclusive endorsement of offered/availability. Class 6 (Coping Motives; 5.0%) was marked by exclusive endorsement of coping motives. It should be noted that our use of class labels is intended only as descriptive reflections of the data and is not necessarily canonical.

Day-Level Latent Classes of Substance Use and Consequences

Classes differed significantly across all substance use outcomes and negative consequences (see Table 3). Use-type comparisons revealed that Class 2 (General High Motives) was primarily characterized by co-use of alcohol and cannabis use rather than single use of either substance, followed by Class 1 (Social and Fun Motives). In contrast, Class 3 (Fun Motives), Class 4 (Expand and Fun Motives), and Class 6 (Coping Motives) were less likely to report co-use.

Table 3.

Day-Level Comparisons of Substance Use Behavior Across Six Day-Level Latent Classes

Outcome Overall χ2 Class 1: Social & Fun Motives
(36.4%)		 Class 2: General High Motives
(2.0%)		 Class 3: Fun Motives
(37.5%)		 Class 4: Expand & Fun Motives
(9.0%)		 Class 5: Offered/Availability
(10.1%)		 Class 6: Coping Motives
(5.0%)
Proportion of co-use days 354.15 0.39b 0.99a 0.15c 0.16c 0.23c 0.10c
Number of standard alcoholic drinks 183.91 4.40a 6.31a 1.63b 0.74c 1.83b 1.03c
Number of cannabis uses 51.53 3.60b 7.92a 4.16b 5.15b 4.20b 1.78c
Number of consequences 115.96 0.36b 0.65a 0.13b 0.11b 0.11b 0.07c
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Note. Values are means. Different superscripts (ᵃ, ᵇ, ᶜ) within a row indicate significant pairwise differences at p < .05.

*

Negative value reflects model-based estimates and should be interpreted as 0 in practice.

With respect to level of use, Class 2 (General High Motives) reported the highest levels of alcohol use, significantly exceeding all other classes except Class 1 (Social and Fun Motives). In contrast, Class 4 (Expand and Fun Motives) and Class 6 (Coping Motives) showed the lowest levels of alcohol use. In terms of cannabis use, Class 2 (General High Motives) similarly exhibited the highest average number of cannabis use occasions, followed by Class 4 (Expand and Fun Motives) and Class 5 (Offered/Availability Motives). Class 6 (Coping Motives) reported the lowest cannabis use levels.

A similar pattern emerged for negative consequences: Class 2 (General High Motives) reported the highest mean number of negative consequences, followed by Class 1 (Social and Fun Motives), which was not significantly different from Classes 3 (Fun Motives), 4 (Expand & Fun Motives), or 5 (Offered/Availability). Notably, Class 6 (Coping Motives) reported the fewest negative consequences.

Discussion

The goal of the current study was to characterize day-level motives for alcohol use, cannabis use, and co-use of alcohol and cannabis in daily life, and to identify how day-level profiles correspond to same-day substance use outcomes. We identified six day-level latent classes of motives. Notably, only two classes (Class 6, Coping Motives; Class 3, Fun Motives) endorsed exclusively or almost exclusively one motive; other classes were characterized by multiple motives. Consistent with prior literature demonstrating that social and enhancement reasons are the most commonly endorsed motives among college-attending young adults in general (Cooper et al., 2016) and in daily life (Votaw & Witkiewitz, 2021), the largest day-level classes were characterized by substance use to be sociable and to have fun. With respect to associations between latent class membership and day-level substance use outcomes, there was fairly high consistency across type, level, and consequences of use, with findings suggesting that classes defined by complex and overlapping motives, particularly Class 2 (General High Motives), were associated with more intense substance use patterns (e.g., co-use of alcohol and cannabis) and risk. In contrast, narrower motivational profiles with only one or two motives endorsed, such as Class 6 (Coping Motives), Class 4 (Expand and Fun Motives), and Class 3 (Fun Motives), corresponded to substance use days that were more moderate or more selective (e.g., Expand and Fun Motives were more likely to be a cannabis but not co-use day).

Findings from the current study expand upon existing theoretical models of motives that generally examine each motive in isolation. The current study found that motives commonly co-occur on certain days. For example, some days included social and fun motives (Class 1), and other days included all motives (Class 2). Days with the highest motive endorsement (Class 2) were also linked with the highest levels of alcohol use, cannabis use, negative consequences, and co-use. These findings align with an earlier study with these data, showing a positive association between the absolute number of motives endorsed with the amount of alcohol and cannabis consumed and odds of experiencing alcohol-only consequences and cannabis-only consequences (Stevens et al., 2021). Our six-class solution also provides further evidence that motives are highly dynamic in young adults’ daily lives (Votaw & Witkiewitz, 2021).

Although our findings generally support that number of motives is linked with poorer use outcomes (e.g., General High Motives class exhibited higher proportions of co-use days and cannabis use occasions, heaviest alcohol use, and most negative consequences), we also see consistent evidence for some specific patterns of motives characterized by individual or multiple motives being associated with more consequences and/or heavier use. For instance, the comparison of outcomes for Class 3 (Fun) vs. Class 6 (Coping) suggests that although we often conceptualize coping motives as particularly problematic or indicative of risk for problematic use at the person-level (e.g., Merrill & Read, 2010), at the day-level here we see that fun motives are linked with more negative consequences. This aligns with previous research at the day-level that found that coping motives were not as strongly linked with use and related consequences as other motives (i.e., social and enhancement; Stevenson et al., 2021; Votaw & Witkiewitz, 2021). Overall, our findings suggest that number of motives is not necessarily always the driver of use outcomes, but there are certain combinations of motives or specific motives that are particularly risky.

Our findings that specific motives and combinations of motives are proximally linked with higher-risk substance use patterns may help inform the development and advancement of just-in-time interventions (JITs). Although still in their infancy, JITs have been found to reduce alcohol-related coping motives and alcohol use (Blevins et al., 2021), but far less work has focused on motives in the context of cannabis use and higher-risk substance use, such as co-use of alcohol and cannabis. Days with multiple motives for use may serve as an indicator to trigger intervention content (e.g., protective behavioral strategies) delivery on participants’ mobile devices. To optimize the timing of intervention delivery based on an individual’s motives for use, however, an important next step is to better understand the dynamics of motives for use, and specifically, whether specific motives change leading up to and/or after the initiation of substance use, as this has implications for the timing of the intervention. Moreover, in the case of co-use, future work could identify how specific motives for use change at the point of consuming the second substance, which point to the timing of the intervention (e.g., an intervention might target specific motives prior to use of the second substance). This would build on recent work by our team that motivations for use self-reported in the morning were differentially related to types of use occasions later that day (e.g., morning conformity motives were linked with higher odds of later-day alcohol-only use; morning social motives were linked with higher odds of later-day co-use; Linden-Carmichael et al., 2025). Given that co-use tends to occur after 9 pm (Arterberry et al., 2021), there is a large window in between morning and nighttime during which interventionists could deliver content that is individually tailored to one’s motives for use, and we hope our findings stimulate work that pinpoints the ideal timing within this large window.

Limitations and Future Directions

Several limitations should be noted for the present study. First, there are several limitations with respect to the sample and measurement. Although our sample consisted primarily of white college-attending young adults across three universities with varying regional characteristics, findings may not generalize to young adults who do not attend college or students from minoritized racial-ethnic groups. Findings are also based on self-reported alcohol and cannabis use, which may be subject to recall bias or social desirability influences. Additionally, given the nature of the day-level analyses, findings should be interpreted as same-day associations with respect to motives and substance use behavior. It is unknown, for example, whether certain motives occurred before substance use or after substance use occurred or whether individuals retrospectively endorsed more motives or selected motives as a means of explaining or justifying particular use patterns. Unpacking how motives change throughout an alcohol or cannabis use episode is a relatively unexplored area, but some research has found that other psychosocial predictors, such as affect, change throughout an alcohol use occasion (Russell et al., 2020). Future research should build upon these findings to disentangle temporal associations between motives and substance use behavior. Future work could also build upon our measurement of motives and consequences of use when examining co-use specifically—our study aggregated motives and consequences across substances. It is important to acknowledge that our day-level consequence item was fairly limited in scope (e.g., did not include consequences such as increasing negative affect; Wycoff et al., 2024) and it did not measure the total number of consequences, but rather the total number of types of consequences reported. As one potential result of this, although 61.8% and 57.9% of our sample exhibited higher-risk alcohol and cannabis use (as assessed via the AUDIT and CUDIT), respectively, consequences were only reported on 21% of days. This relatively low rate of consequences may be due to the fact that consequences were assessed across all alcohol, cannabis, and co-use days (including days with only one drink or one hit of cannabis).

There are also a couple of limitations to note regarding our analytic approach. Our 6-class solution yielded a small class, representing 2% of days. Although this class (“General High Motives”) represented a relatively small percentage of days, our fit indices suggested a 6 or 7-class solution given our large sample size of >5,000 alcohol and/or cannabis use days. We also retained this class given that it was conceptually distinct from other classes such that participants endorsed higher overall motivations on these days relative to others, which served as a risk factor for day-level substance use outcomes. Nevertheless, there may be challenges with respect to replicability in future work, particularly in datasets with smaller sample sizes. Finally, our current approach uses a marginal modeling approach that handles day- and person-level covariates of day-level latent class membership but does not explicitly model person-level day-level latent class prevalences. There is promising work underway for determining best practices for a multilevel LCA approach that simultaneously models latent classes of days as well as latent classes of individuals (e.g., Finch & French, 2014; Lyrvall et al., 2024). Given that our primary study goal was to characterize day-level motives and identify day-level associations between latent classes and use outcomes, we opted for a marginal approach; however, future work would benefit from expanding similar models to partition person-level (“Level 2”) and day-level prevalences of latent classes.

Conclusions

Despite limitations, the current study identified multiple distinct day-level configurations of substance use motives among college-attending young adults. Study findings underline the complex nature of motives for substance use, showcasing that individuals commonly endorse multiple motives within the same day and that days with multiple motives are more strongly associated with negative use outcomes relative to single-motive days.

Public Health Significance Statement.

College students use individual substances and combinations of substances for different reasons on different days. Understanding the motives for substance use will enable intervention designers to develop interventions that address specific reasons for substance use, on the specific days when students are at greatest risk.

Acknowledgments

This work is supported by awards R01 DA057588 (PI: Lanza) and R01 DA040880 (MPIs: Jackson & White) from the National Institute on Drug Abuse (NIDA). The NIDA did not have any role in study design, collection, analysis, and interpretation of the data; writing the report; and the decision to submit the report for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no conflicts of interest to report.

Footnotes

1

Participants completed up to five surveys per day with predictable survey times (9:00 am, 2:00 pm, 5:00 pm, 8:00 pm, 11:00 pm). Thus, the daily assessment was designed to span 24 hours a day. Each survey assessed behavior since the completion time of the prior survey. The morning survey assessed the time between the last survey taken the day before and bedtime as well as a summary of the previous day’s behavior. If one survey was missed, the current survey pulled the completion time from the prior completed survey as an anchor point. For example, if the 8:00 pm survey was missed, the 11:00 pm survey would use the time that the 5:00 pm survey was completed (e.g., 5:13 pm) as an anchor. If more than one survey was missed, the current survey used the scheduled time of the prior survey as a time anchor. For example, if the 5:00 pm and 8:00 pm surveys were both missed, then the 11:00 pm survey used 5:00 pm as a time anchor. This design allowed for the collection of a full day’s coverage even if a survey was missed. When any three surveys were missed or two surveys in a row were missed, that day was excluded. Therefore, a “complete coverage” day meant that no two surveys were missed in a row that day (Stevens et al., 2020 Supplemental Material).

2

An eighth motive was included (“too high from another drug.” Because it was endorsed by less than 1% of the participants, it was excluded from the analysis.

CRediT Author Contribution Statement

Ashley Linden-Carmichael: Conceptualization, funding acquisition, supervision, writing-original draft, writing – review and editing

Xiangyu Tao: Formal analysis, writing-original draft, writing – review and editing

Jennifer L. Shipley: Writing-original draft, writing – review and editing

María E. Contreras-Pérez: Writing-original draft, writing – review and editing

Helene R. White: Conceptualization, project administration, funding acquisition, writing-review. and editing

Alexander W. Sokolovsky: Methodology, writing-original draft, writing – review and editing

Kristina M. Jackson: Conceptualization, funding acquisition, writing-review and editing

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