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. Author manuscript; available in PMC: 2024 Apr 1.
Published in final edited form as: Mindfulness (N Y). 2023 Mar 27;14(4):797–807. doi: 10.1007/s12671-023-02110-x

Is Mindfulness Associated With Safer Cannabis Use? A Latent Profile Analysis of Dispositional Mindfulness Among College Students Who Use Cannabis

Hannah A Carlon 1, Jennifer Earnest 1, Margo C Hurlocker 1
PMCID: PMC10664800  NIHMSID: NIHMS1943478  PMID: 37997576

Abstract

Objectives

Previous research cites mindfulness as a protective factor against risky substance use, but the specific association between dispositional mindfulness (DM) and cannabis use has been inconsistent. Despite known heterogeneity of DM facets across college students, much of the prior research in this area has relied on variable-centered approaches. Only a handful of prior studies within the cannabis literature have utilized person-centered approaches, and only one has specifically examined unique profiles of dispositional mindfulness in relation to patterns of use among college students.

Method

The present study used latent profile analysis (LPA) to identify subtypes of DM and their relationships with cannabis use behaviors (i.e., hazardous use and consequences of use) in a sample of 683 U.S. college students who endorsed past-month cannabis use and participated in an online survey of substance use behaviors, hypothesizing that a three-profile model would be replicated. We also examined whether age and prior experience with mindfulness predicted DM profile membership (hypothesizing that these variables would differentially predict membership) and explored mean differences in alcohol use across profiles.

Results

LPA results revealed three discrete profiles of DM: non-judgmentally aware, judgmentally observing, and moderate traits. Participants in the non-judgmentally aware profile were less likely to have prior mindfulness experience than the other profiles, but age did not predict profile membership. Judgmentally observing had more hazardous cannabis use and consequences than the other profiles, and no mean differences emerged on alcohol use.

Conclusions

These results build upon the only known study that investigated how DM relates to cannabis use. Further research is needed to elucidate this relationship, which can inform the application of mindfulness interventions for hazardous cannabis use in college students.

Pre-registration

This study was not pre-registered.

Keywords: dispositional mindfulness, cannabis, college students, latent profile analysis

Mindfulness-based interventions (MBIs), or interventions that aim to target non-judgmental awareness of the present moment, have been effective in treating addiction (Goldberg et al., 2021; Korecki et al., 2020), including via mindfulness-based relapse prevention (Bowen et al., 2014; Massaro et al., 2022). Although mindfulness can be targetable through MBIs and other practices (e.g., meditation, yoga), mindfulness also is thought to be dispositional (i.e., existing at the trait level; Rau & Williams, 2016). Extant research shows that mindfulness can be protective against substance use (Black et al., 2012; Bramm et al., 2013; Brewer et al., 2009; Marlatt et al., 2004; Rogojanski et al., 2011), though some cross-cutting limitations emerge. For instance, the construct of mindfulness is thought to be multidimensional, and studies on mindfulness dimensions and substance use behaviors tend to show mixed associations. Further, research in this area has been historically dedicated to alcohol use, often ignoring other types of substance use and their associations with mindfulness dimensions.

Dispositional mindfulness (DM), according to Baer et al. (2006), is theorized to have five related yet distinct “facets”: observing (taking notice of thoughts, feelings, sensations, perceptions), describing (labeling thoughts, feelings, sensations, perceptions), acting with awareness (acknowledging present moment experiences as they unfold), non-judgment (neutrality towards inward experiences, such as thoughts and emotions), and non-reactivity (allowing thoughts to come and go without being carried away by them). The Five Facet Mindfulness Questionnaire (FFMQ) is an extant, commonly-used self-report measure with good psychometric strength that measures these five DM facets (Baer et al., 2006). DM is thought to be heterogeneously-distributed across individuals (Kuyken et al., 2010; Papies et al., 2012), although prior research shows that the five DM facets are idiosyncratic. In fact, among non-meditating samples, DM facets have shown negative intercorrelations with each other (Baer et al., 2006; Brown et al., 2015). This suggests that a composite measure of DM is perhaps meaningless and that DM should be studied from a perspective that recognizes this peculiarity (Pearson et al., 2015).

Similarly, these five facets have differential associations with substance use behaviors. Observing and describing have been unrelated to substance use, while acting with awareness, non-judgment, and non-reactivity have been consistently related to substance use behaviors, such that deficits in these three facets (i.e., acting with awareness, non-judgment, and non-reactivity) would indicate more severe substance use behaviors (Karyadi et al., 2014; Levin et al., 2014). Indeed, a meta-analysis of 39 articles studying the relationship between trait mindfulness and substance use found that only acting with awareness, non-judgment, and non-reactivity were robustly associated with substance use behaviors, but also that the overall association between DM and substance use behaviors is somewhat weak (r = −0.13; Karyadi et al., 2014). This pattern is still observed among clinical samples: among adults seeking outpatient substance use disorder (SUD) treatment, one study found that scores on acting with awareness, non-judgment, and non-reactivity were lower among those with current SUD diagnoses (Levin et al., 2014). Further, these associations seem to differ based on the type of substance. Specifically, alcohol has been examined most commonly in this context; its use tends to be associated with deficits in describing, acting with awareness, and non-judgment, but shows no associations with the observing facet, though most research in this area has been conducted among college-aged and young adults (e.g., Fernandez et al., 2010; Murphy & MacKillop, 2012).

The research on the relationship between cannabis use and DM facets is more scarce and less consistent. For example, one study found a negative association between cannabis and DM (Philip, 2010). This study, conducted among a sample of 428 college students, found that DM (measured using the Mindfulness Attention Awareness Scale [MAAS]; Brown & Ryan, 2003) was negatively associated with cannabis use (measured using modified version of the Daily Drinking Questionnaire; Collins et al., 1985), but found that this relationship did not remain significant when the analysis was restricted to those who used cannabis in the past 28 days (Philip, 2010). Additionally, the study observed that DM was unrelated to cannabis-related problems (measured using the Marijuana Problem Index; Vandrey et al., 2005). Importantly, the MAAS captures DM across four facets (mindful observation, letting go, non-aversion, and non-judgment), but it is recommended that the measure is conceptualized and scored unifactorially (Brown & Ryan, 2003), so specific facets were not compared to cannabis use in this study (Philip, 2010). Another study, conducted among 1,572 college students in France, found a positive association between cannabis use (measured using the Cannabis Use Disorders Identification Test-Revised [CUDIT-R]; Adamson et al., 2010) and the observing facet of the FFMQ, but negative correlations with other facets (Bronchain et al., 2020). Curiously, another study found no significant relationships between DM facets and cannabis use among a sample of 97 adults with PTSD who use cannabis (Bonn-Miller et al., 2010). This study used the Kentucky Inventory of Mindfulness Skills (KIMS; Baer et al., 2004) to measure DM (across four facets: observing, describing, acting with awareness, and non-judgmental acceptance) and the Marijuana Smoking History Questionnaire (MSHQ; Bonn-Miller & Zvolensky, 2009) to measure cannabis use. Given the limited empirical work, variable measures used across studies, and inconsistent associations between DM facets and cannabis use, perhaps different methodology should be applied to probe the unclear relationship between DM and cannabis use.

Cannabis is the most commonly-used illicit substance by college students (Arria et al., 2017). Correlational studies show that risky cannabis use is associated with consequences and challenges for college-aged adults across psychological and emotional (e.g., depression and anxiety; Patton et al., 2002), physical (e.g., impaired lung function; Taylor et al., 2002), and academic performance domains (e.g., lower grades, negative attitudes toward school, lower attendance; Lynskey & Hall, 2000; Roebuck et al., 2004). Adolescents in particular perceive little risk surrounding cannabis use (Johnston et al., 2011), thus suggesting the need for prevention and intervention development for risky cannabis use among adolescent and college-aged individuals. The results of these correlational studies are supported by those of longitudinal studies: cannabis use among young adults is also linked with more distal, long-term consequences, such as not graduating college, not becoming married, and not being steadily employed (Schulenberg et al., 2005; Tucker et al., 2005).

Variable-centered statistical approaches have most commonly been applied when exploring DM and cannabis use (i.e., Bonn-Miller et al., 2010; Philip, 2010), which could help explain the aforementioned inconsistent findings. Variable-centered approaches assume that samples represent a homogenous population, which is often not reflective of complex trait constructs such as DM. For example, factor analytic models (which have been commonly used in analyses of DM facets) find that loading the observing facet onto a mindfulness latent factor fits quite poorly, which is contradictory to the definition of mindfulness altogether (e.g., Baer et al., 2006). There is a clear need to investigate DM in a manner that recognizes the known heterogeneity across DM facets in order to accurately capture its relationship with cannabis use behaviors.

Person-centered approaches (e.g., cluster analysis, latent profile analysis) overcome the limitations of variable-centered approaches by identifying distinct, homogenous subgroups among a heterogeneous sample (Howard & Hoffman, 2018). These approaches assume that a construct such as DM contains heterogeneously-distributed elements among individuals. As such, several studies have utilized person-centered approaches to identify distinct profiles of DM among young adults in particular. Two such profiles are judgmentally observing (JO; high on observing and low on non-judgment and acting with awareness), and non-judgmentally aware (NJA; low on observing and high on non-judgment and acting with awareness; Pearson et al., 2015; Sahdra et al., 2017). People who fall in the NJA profile are more likely to have adaptive emotional outcomes (e.g., lower distress intolerance), while people in the JO profile have more maladaptive ones (e.g., higher depression; Pearson et al., 2015). Another study found that individuals in the JO profile have more psychological distress, but also more life satisfaction (Sahdra et al., 2017).

Only one study to date has examined subgroups of mindfulness in relation to patterns of cannabis use among a sample of college students (Bronchain et al., 2020). The study adopted a cluster analysis approach, and three clusters emerged: high traits (i.e., high on all five facets), NJA, and JO. Individuals in the NJA cluster demonstrated less problematic cannabis use, while individuals in the JO and high traits clusters had more problematic cannabis use. Cluster analysis, however, does not account for error, as it does not consider profile membership to be probabilistic or account for the size of a profile (Romesburg, 2004). Additionally, Bronchain et al. (2020) did not involve any variables that likely covary with DM and cannabis use among college students, such as alcohol use, age, and prior experience with mindfulness practice. Latent profile analysis (LPA) is a person-centered approach that considers profile membership to be probabilistic, and also accounts for size of a profile when assessing probability of membership (Masyn, 2013). Therefore, LPA might represent a more robust person-centered approach than a cluster analysis when examining DM.

The present study offered a replication of the cluster analysis study by Bronchain et al. (2020) by using LPA to identify distinct profiles of DM among a sample of college students in the United States who used cannabis in the past month, and further explored how such profiles were associated with risky cannabis use. Given that the FFMQ’s factor structure had been shown to vary based on past mindfulness experience (Baer et al., 2006) and that mindfulness has shown to have differential effects on positive outcomes by age (Shook et al., 2017), we tested whether prior experience with mindfulness practice and age predicted profile membership. In addition to risky cannabis use, we also examined mean differences across profiles on alcohol use since alcohol is the most prevalent substance used by college students in the United States, with cannabis being second-most prevalent (Schulenberg et al., 2019). We hypothesized that the model by Bronchain et al. (2020) (i.e., three profiles of DM: high traits, NJA, and JO) would replicate in the current sample, that individuals in JO would have the most maladaptive cannabis use, and that prior mindfulness experience and age would differentially predict profile membership. We did not make any formal hypotheses about the exploratory alcohol use analyses, given the scarce prior research in this area.

Method

Participants

College students from seven universities across six states in the United States were recruited through psychology department data collection systems at each site and were deemed eligible to complete the online survey if they were at least 18 years old. The current study included 683 participants who endorsed past-month cannabis use and completed the mindfulness measure. The mean age of the final sample was 20 years (SD = 2.92). Most participants identified as female (466; 68.4%). The racial composition of the sample included White non-Hispanic (322; 47.3%), Multiracial (172; 25.3%), Black (98; 14.4%), Hispanic (51; 7.5%), Asian (23; 3.4%), American Indian or Alaska Native (4; 0.6%), and Other (9; 1.3%).

Procedure

Eligible participants were directed to a secure website (Qualtrics) where they signed an Institutional Review Board-approved informed consent and completed a series of quantitative measures on substance use and risk/protective factors. The title of the study was the Stimulant Norms and Prevalence (SNAP) Study, and the participant-facing name was “Project SNAP.” Participants received course credit in exchange for their participation. A full description for the study procedures is provided elsewhere (Looby et al., 2021).

Measures

Demographics Questionnaire

A brief demographics questionnaire was used to capture age (i.e., “Your age: ___”) and past mindfulness experience (i.e., “Do you have any previous or current experience with mindfulness meditation? [Yes/No]”).

Daily Drinking Questionnaire (DDQ)

The DDQ (Collins et al., 1985) was used to measure quantity and frequency of alcohol use during a typical week in the past 30 days. Participants indicated the number of standard drinks they consumed each day of the week, and we calculated quantity of use by summing the total drinks per week. The DDQ is a previously established measure of alcohol use which has been strongly correlated with other self-report measures of drinking (Kivlahan et al., 1990).

Five Facet Mindfulness Questionnaire (FFMQ)

The FFMQ is a self-report measure of dispositional, or trait, mindfulness. It comprises 39 items across five facets: observing (e.g., “I notice how foods and drinks affect my thoughts, bodily sensations, and emotions.”), describing (e.g., “I can easily put my beliefs, opinions, and expectations into words.”), acting with awareness [e.g., “I am easily distracted.” (reversed)], non-judgment [e.g., “I make judgments about whether my thoughts are good or bad. (reversed)], and non-reactivity (e.g., “In difficult situations, I can pause without immediately reacting.”). Participants respond using a five-point Likert scale (1 = never or very rarely true, 5 = very often or always true), with higher scores indicative of higher mindfulness. The FFMQ has demonstrated acceptable psychometric strength (Baer et al., 2006; Christopher et al., 2012), but more recently, studies have found mixed evidence for the validity of its five-factor structure (Karl & Fischer, 2020; Lecuona et al., 2020), futher supporting the utility of person-centered analyses when examining latent subgroups of DM. Indeed, a confirmatory factor analysis (CFA) of the FFMQ’s five-factor model in the present sample yielded mostly poor model fit (χ2 = 3778.25, p = 0.00; CFI = 0.78; RMSEA = 0.08). However, internal consistency scores across subscales ranged from acceptable to good for the current sample (0.80 < α < 0.90).

Brief Marijuana Consequences Questionnaire (B-MACQ)

The B-MACQ is a 21-item self-report measure that evaluates the frequency of marijuana consequences (e.g., “I have been less physically active because of my marijuana use”). Respondents indicate whether they have experienced each of the 21 consequences in the past 30 days with “yes” or “no,” with more yes responses indicative of more consequences. The B-MACQ has demonstrated good psychometric strength (Simons et al., 2012). The internal consistency for the B-MACQ was good with the current sample (α = 0.89).

Cannabis Use Disorder Identification Test Revised (CUDIT-R)

The CUDIT-R is an 8-item self-report assessment that identifies individuals with risky cannabis use. Participants indicate the frequency with which they have experienced specific situations with their cannabis use in the last six months across 4–5 options. For instance, one question asks, “How many hours were you ‘stoned’ on a typical day when you had been using cannabis?” and respondents select one of the following answers: “Less than 1”, “1 or 2”, “3 or 4”, “5 or 6”, or “7 or more”. The CUDIT-R has demonstrated good psychometric strength (Adamson et al., 2010). The internal consistency for the CUDIT-R was good with the current sample (α = 0.86).

Data Analyses

Prior to conducting analyses, data were screened for outliers in SPSS. Latent profile analysis (LPA) was performed in Mplus to identify distinct profiles of mindfulness using the FFMQ (Muthén and Muthén, 2017). Maximum likelihood with robust standard errors was used to estimate missing data in the LPA. As fit measures for LPA are still debated, it is suggested that considering several fit measures to determine an optimal profile solution is best practice. Some research has found that Sample-Size Adjusted Bayesian Information Criterion (SABIC) and Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (LMRT) are reliable indicators of a best-fitting profile solution (Tein et al., 2013). Lower SABIC values suggest better model fit and a statistically significant difference on the LMRT between class solutions (i.e., k vs. k−1) indicates that k class solution is a better fit than k−1 class solution (Nylund et al., 2007). Also, model entropy values between 0.60 and 0.80 indicate acceptable classification accuracy (Jung & Wickrama, 2008), and profiles that contain less than 5% of the sample should not be considered. Lastly, we used compatibility with prior literature (e.g., Bronchain et al., 2020) as a final tool for profile solution selection. After determining the optimal profile solution, we ran the best-fitting model with the covariates (i.e., hypothesized predictors of profile membership: prior experience with meditation and age) included. Specifically, we used a three-step maximum likelihood method that analyzes predictors of latent profiles while accounting for classification error (Bakk & Vermunt, 2016). Finally, differences across profiles in cannabis use behaviors and alcohol use were examined using an adapted version of Bolck, Croon, and Hagenaars’ (BCH) method, which estimates means of cannabis and alcohol use behaviors across latent profiles, taking into account classification error (Bakk & Vermunt, 2016).

Results

Descriptive Analyses

Table 1 depicts the means, standard deviations, and intercorrelations of study measures. The majority of participants did not have prior experience using mindfulness meditation (n = 435; 63.9%). One-way analysis of variance revealed significant differences between participants who did or did not have prior mindfulness meditation experience on the observing (F(678, 1) = 28.87, p = 0.00, ηp2 = 0.04), describing (F(678, 1) = 8.61, p = 0.00, ηp2 = 0.01), acting with awareness (F(678, 1) = 6.33, p = 0.01, ηp2 = 0.01), and non-judgment (F(678, 1) = 5.49, p = 0.02, ηp2 = 0.01) scales. Specifically, participants who did have prior experience, versus those who did not have prior experience, had higher mean scores on the observing (M = 3.33, SD = 0.77; M = 2.99, SD = 0.82) and describing (M = 3.24, SD = 0.72; M = 3.07, SD = 0.68), and lower scores on the acting with awareness (M = 3.06, SD = 0.80; M = 3.22, SD = 0.86) and non-judgment (M = 3.06, SD = 0.89; M = 3.22, SD = 0.86) subscales.

Table 1.

Means, standard deviations, and intercorrelations of study measures

1 2 3 4 5 6 7 8
1. Observe –
2. Describe 0.27** –
3. Aware −0.43** 0.22** –
4. Non-judge −0.51** 0.10* 0.64** –
5. Non-react 0.61** 0.33** −0.33** −0.38** –
6. Hazardous cannabis use 0.14** −0.00 −0.19** −0.15** 0.11** –
7. Cannabis consequence 0.12** −0.07 −0.18** −0.18** 0.01 0.65** –
8. Alcohol use 0.05 −0.02 −0.05 −0.10* 0.06 0.21** 0.11* –
Mean 3.11 3.13 3.16 3.16 2.89 9.10 4.41 13.18
SD 0.81 0.70 0.81 0.87 0.74 6.75 4.70 11.84
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Note.

***

p < 0.001

**

p < 0.01

*

p < 0.05

Profile Enumeration

Across six tested profile solutions, the three-profile solution was the best fitting model (Table 2). Although the SABIC value decreased and the LMRTs were significant across the six solutions, and although the entropy value was slightly lower than adequate for the three-profile model (0.785), the four-, five-, and six-profile solutions included one profile that comprised less than 5% of the sample, which is below the recommended threshold to suggest a unique profile. As such, the three-profile solution was selected as the best fitting model based on SABIC, LMRT, adequate proportions of the sample in each profile, and finally based on alignment with prior literature which have suggested three unique DM profiles in college students (e.g., Bronchain et al., 2020). The estimated pattern of means on the five FFMQ scales across the three latent profiles are shown in Fig. 1 and reported in Table 3. Profile 1 comprised 59 participants (9%) and was characterized by high levels of non-judgment and acting with awareness (i.e., non-judgmentally aware). Profile 2 comprised 168 participants (25%) and was characterized by high levels of observing and non-reactivity, and low levels of non-judgment (i.e., judgmentally observing). Finally, Profile 3 comprised 456 participants (67%) and was characterized by moderate/average levels of all five facets (i.e., moderate traits), given that scores across the five facets were comparable to the average scores among the sample (Table 1).

Table 2.

Unconditional and conditional models for profile solutions 1 through 6

Profiles (k) LMRT SABIC Entropy
1 8050.811
2 534.03*** 7523.252 0.913
3 232.50** 7304.928 0.785
4 230.57** 7088.576 0.879
5 174.15** 6930.088 0.826
6 89.02* 6858.903 0.835
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LMRT = Lo-Mendell-Rubin Adjusted Likelihood Ratio test

SABIC = Sample-size Adjusted Bayesian Information Criteria

***

p < 0.001

**

p < 0.01

*

p < 0.05

Fig. 1.

Fig. 1

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Dispositional mindfulness facets for the three-profile model

Table 3.

Mean [95% CI] comparisons across latent profiles on mindfulness facets

1
Non-judgmentally aware
(n = 59; 9%)		 2
Judgmentally observing
(n = 168, 25%)		 3
Moderate traits
(n = 456, 67%)
Raw scores
 Observing 1.62 [1.42, 1.83] 3.65 [3.47, 3.83] 3.10 [3.02, 3.19]
 Describing 2.81 [2.65, 2.98] 2.95 [2.83, 3.06] 3.25 [3.17, 3.33]
 Acting with awareness 4.42 [4.21, 4.64] 2.39 [2.21, 2.57] 3.30 [3.18, 3.41]
 Non-judgment 4.58 [4.38, 4.78] 2.22 [1.99, 2.44] 3.34 [3.22, 3.46]
 Non-reactivity 1.61 [1.33, 1.88] 3.17 [3.03, 3.32] 2.95 [2.89, 3.00]
Standardized scores (z-score)
 Observing −2.00a 0.95b 0.05c
 Describing −0.53a −0.38a 0.20b
 Acting with awareness 1.59a −1.38b 0.14c
 Non-judgment 1.72a −1.47b 0.31c
 Non-reactivity −1.95a 0.47b 0.12c
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Shared subscripts in a row indicate means scores are not significantly different from each other

Predictors of Model

Using the three-profile solution, we re-ran the model with the hypothesized covariates. No significant differences emerged on prevalence or means across the profiles compared to the three-profile solution without covariates (Table 4). History of mindful meditation practice significantly predicted profile membership, but age did not. Specifically, participants in the non-judgmentally aware (B = −0.99, p = 0.01) were less likely to have prior experience with mindfulness meditation as compared to the moderate traits profile. Further, compared to the moderate traits profile, individuals in the judgmentally observing profile were more likely to have prior experience with mindfulness meditation (B = 0.49, p = 0.03).

Table 4.

Predictors of profile membership with moderate traits (n = 459; 67%) as reference group

Non-judgmentally aware
(n = 59, 9%)		 Judgmentally observing
(n = 164; 24%)
Variable Unstandardized B SE p Unstandardized B SE p
Age −0.15 0.09 0.10 −0.01 0.03 0.79
Past mindfulness 0.99 0.40 0.01 0.49 0.23 0.03
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SE = Standard Error

Prevalence of participants in each profile differ slightly between the profile model with and without covariates because Mplus reassigns cases to profiles when a new model is specified

Equality of Means

Using the profile model with covariates, significant differences emerged on hazardous cannabis use and cannabis-related consequences, but not on weekly alcohol use. Individuals in the judgmentally observing profile had significantly more hazardous cannabis use (M = 11.08, SE = 0.71) than those in the non-judgmentally aware (M = 7.25, SE = 0.90) and moderate traits (M = 8.59, SE = 0.33) profiles, and these two profiles did not significantly differ from each other. Further, the judgmentally observing profile had significantly more cannabis-related consequences (M = 6.15, SE = 0.49) than the non-judgmentally aware (M = 3.13, SE = 0.62) and moderate traits (M = 3.93, SE = 0.23) profiles.

Discussion

Existing research on the relationship between dispositional mindfulness (DM) and cannabis use has yielded inconsistent results, likely due, in part, to the use of variable-centered statistical approaches to examine this relationship. One study used cluster analysis to examine subgroups of mindfulness in relation to patterns of cannabis use among a sample of college students (Bronchain et al., 2020). The present study sought to replicate the study by Bronchain et al. (2020) by using latent profile analysis (LPA), a person-centered approach which controls for error, to examine profiles of DM and their relationship to cannabis use in a sample of college students who endorsed past-month cannabis use.

Using LPA to evaluate profiles of DM, we identified three unique profiles. Specifically, we found one profile characterized by high non-judgement and acting with awareness (i.e., the non-judgmentally aware profile), one profile characterized by high observing and non-reactivity and low non-judgment (i.e., the judgmentally observing profile), and one characterized by DM traits that were close to the sample’s mean (i.e., the moderate traits profile). Regarding age as a predictor of the three-profile model, no significant differences emerged. However, having prior mindfulness experience predicted profile membership: non-judgmental awareness was less likely to have past mindfulness experience compared to moderate traits, while judgmentally observing was more likely to have past mindfulness experience. After examining the association between DM profiles and cannabis use behaviors, we found that those in the judgmentally observing profile displayed more hazardous use and more consequences of use than each of the other two profiles. Finally, no differences emerged on alcohol use.

The three DM profiles we identified somewhat align with findings by Bronchain et al. (2020), such that two of our profiles (i.e., non-judgmentally aware and judgmentally observing) were akin to two of the clusters identified in this prior study, although we did not identify a high traits profile as Bronchain et al. (2020) identified. Further, our profiles largely fit with other studies that used person-centered approaches with college students and adults regardless of substance use history. For example, a non-judgmentally aware profile has been found among both college student and nationally-representative adult samples (Bravo et al., 2016; Pearson et al., 2015; Sahdra et al., 2017). Prior research has also consistently identified a profile with high scores on observing but low scores on non-judgement (i.e., judgmentally observing; Bravo et al., 2016; Pearson et al., 2015; Sahdra et al., 2017). The present study’s judgmentally observing profile seems to fit within this picture through high levels of observing and low levels of non-judgment, though our profile was also characterized by high levels of non-reactivity. The emergence of a moderate traits profile appears to be in line with prior research which has identified an average mindfulness profile among adults (Sahdra et al., 2017). To our knowledge, this was the first study of DM profiles in college students that was restricted to cannabis users. Despite this restricted sample, there appears to be consistency in profile types across person-centered studies with young adult samples.

We found that mindfulness experience significantly predicted membership in judgmentally observing when compared to both non-judgmentally aware and moderate traits. This finding that the profile with the most mindfulness experience is the same profile with the most maladaptive cannabis outcomes is perhaps surprising. Past research has found that the non-judgmentally aware profile has more adaptive outcomes among those with mindfulness experience versus those without (Bravo et al., 2016), which suggests that more mindfulness experience predicts more adaptive outcomes. However, some prior work has found results similar to ours: Baer et al. (2008) found that high scores on the observing DM facet is correlated with past mindfulness experience. This study also reported that the association between observing and poor psychological outcomes varied by mindfulness experience, such that this relationship was positive among those without past experience and negative among those with past experience (Baer et al., 2008). It is possible that individuals who fit in the judgmentally observing profile have more mindfulness experience because this DM profile causes individuals to seek more solutions to their distress (e.g., through mindfulness practice). Given this possibility, greater cannabis use among judgmentally observing individuals would be expected, cannabis use might be another way of seeking a solution to distress. Future research should aim to explore the moderating effects of past mindfulness experience on the relationship between DM and cannabis use among college students, given these varying results across studies.

Participants in the judgmentally observing profile displayed more hazardous use and more consequences of use than the other two profiles. Related research has found similar outcomes with a judgmentally observing profile (JO), such that those who are high on observing and low on non-judgment exhibit more problematic cannabis use (Bronchain et al., 2020), emotional distress (Pearson et al., 2015), and poor psychological well-being (Bravo et al., 2016; Sahdra et al., 2017). However, as the present study’s judgmentally observing profile was also characterized by high non-reactivity, which is typically associated with less substance use (e.g., Barrington et al., 2019), it may be that the combination of observing and judgment cancels out the positive effects of non-reactivity, such that negative outcomes are seen. It has been posited that the peculiar associations between observing and negative outcomes can be explained by the way the FFMQ explicates observing: mostly focused on physical, visual, and auditory sensations (Bronchain et al., 2020). In fact, there is only one item in the observing FFMQ scale that is explicitly related to emotions or thoughts: “I pay attention to how my emotions affect my thoughts and behavior” (Baer et al., 2006). Therefore, it could be that the FFMQ does not holistically capture mindful observing, as it ignores the observation of emotions and thoughts. It is understood that cannabis can enhance the experience of physical sensations, making them more pleasant (Kesner & Lovinger, 2021), which could help explain the more hazardous cannabis use and consequences seen among the judgmentally observing profile. It seems reasonable that individuals who are dispositionally more likely to judge unpleasant sensations are also more likely to use cannabis problematically, in an effort to make physical sensations more pleasant and therefore less worthy of judgment. Taken together, these results suggest a need to further investigate the effects of observing, specifically in its ability to cancel out positive effects of other DM facets, and the way it is operationalized through measures like the FFMQ.

Moreover, the present study’s moderate traits profile exhibited low hazardous cannabis use and consequences, which is at odds with the finding by Bronchain et al. (2020) that a high traits cluster had more problematic cannabis use, which the authors hypothesized was due to high scores on observing. Expectedly, the present study’s non-judgmentally aware profile yielded the lowest levels of hazardous use and consequences, similar to past research which has found the most adaptive outcomes among this profile when compared to others (e.g., Bravo et al., 2016; Bronchain et al., 2020; Pearson et al., 2015). The effects of non-judgment might therefore be further explored in cannabis research, as individuals with low non-judgment seem to have negative outcomes, while those with high non-judgment show more positive cannabis outcomes.

The finding that profiles did not differ on alcohol use is contrary to research detailing the protective nature of DM against problematic alcohol use in college students (e.g., Barrington et al., 2019). Perhaps the present sample, which was restricted to cannabis users, does not reflect alcohol use patterns generalizable to other college student drinking samples.

Limitations and Future Research Directions

The results of the present study should be interpreted in the context of several limitations. First, these data were derived from a cross-sectional survey study, and therefore causal relationships, and the changes in DM profiles’ relationships with cannabis use over time, cannot be inferred. Future research might employ longitudinal person-centered analyses, such as longitudinal LPA or latent transition analysis, to explore the relationship between DM profiles and cannabis use over time. Also, the survey did not provide a definition of mindfulness meditation in the item that assessed participants’ prior mindfulness experience. It is possible that participants approached this question with differing perceptions of what it means to practice mindfulness meditation. We also separated the sample into two groups based solely on whether they reported having any prior experience with mindfulness meditation, and did not capture the length of time participants had been practicing, which could have varied results. Finally, our sample consisted of college students who simply reported using cannabis in the past month, and therefore our results might not generalize to a clinical sample (e.g., individuals seeking treatment for cannabis use disorder).

Variable-centered statistical techniques assume that trait constructs, such as DM, are convergent and exist on a continuum, but person-centered techniques offer a more rigorous way to view traits that might be divergent and are distributed heterogeneously in a population. This study adds to the existing knowledge base that supports the validity of person-centered techniques to explore DM, and further examines profiles of DM in relation to cannabis use in college students. As such, this study may have implications for precision medicine techniques, in both prevention and intervention, for problematic cannabis use via mindfulness-based interventions (MBI)s for college students. For example, college-aged individuals whose DM scores align with the judgmentally observing profile might receive an MBI that specifically focuses on increasing other protective facets, such as non-judgment, to prevent the negative outcomes seen as a function of high observing. Developers of MBIs for problematic cannabis use might work to precisely target those DM profiles that are associated with less hazardous and less consequential use. Future research should aim to replicate these profiles among a larger, more representative sample of college-aged cannabis users, as well as further explore the peculiarity of high observing scores on cannabis outcomes.

Funding

The preparation of this manuscript was supported in part by grants from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA) of the National Institutes of Health (NIH) under Award Numbers T32AA018108 (PI: Witkiewitz), and K23DA052646 (PI: Hurlocker). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflict of Interest The authors have no conflicts to report.

Declarations

Ethics Statement This study was approved by the University of Wyoming IRB using a single-site IRB model.

Informed Consent All human subjects in this study provided consent via an online survey consent form.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Data Availability

All data are available at the Open Science Framework (https://osf.io/469ts/).

References

  1. Adamson SJ, Kay-Lambkin FJ, Baker AL, Lewin TJ, Thornton L, Kelly BJ, & Sellman JD (2010). An improved brief measure of cannabis misuse: The Cannabis Use Disorders Identification Test-Revised (CUDIT-R). Drug and Alcohol Dependence, 110(1–2), 137–143. 10.1016/j.drugalcdep.2010.02.017 [DOI] [PubMed] [Google Scholar]
  2. Arria AM, Caldeira KM, Allen HK, Bugbee BA, Vincent KB, & O’Grady KE (2017). Prevalence and incidence of drug use among college students: An 8-year longitudinal analysis. The American Journal of Drug and Alcohol Abuse, 43(6), 711–718. 10.1080/00952990.2017.1310219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baer RA, Smith GT, & Allen KB (2004). Assessment of mindfulness by self-report: The Kentucky Inventory of Mindfulness Skills. Assessment, 11(3), 191–203. 10.1177/1073191104268029 [DOI] [PubMed] [Google Scholar]
  4. Baer RA, Smith GT, Hopkins J, Krietemeyer J, & Toney L (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13(1), 27–45. 10.1177/1073191105283504 [DOI] [PubMed] [Google Scholar]
  5. Baer RA, Smith GT, Lykins E, Button D, Krietemeyer J, Sauer S, Walsh E, Duggan D, & Williams JMG (2008). Construct validity of the five facet mindfulness questionnaire in meditating and nonmeditating samples. Assessment, 15(3), 329–342. 10.1177/1073191107313003 [DOI] [PubMed] [Google Scholar]
  6. Bakk Z, & Vermunt JK (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling, 23(1), 20–31. 10.1080/10705511.2014.955104 [DOI] [Google Scholar]
  7. Barrington J, Weaver A, & Brebner K (2019). Exploring mindfulness in relation to alcohol and cannabis use among first year university students. College Student Journal, 53(2), 163–174. https://www.ingentaconnect.com/content/prin/csj/2019/00000053/00000002/art00003 [Google Scholar]
  8. Black DS, Sussman S, Johnson CA, & Milam J (2012). Trait mindfulness helps shield decision-making from translating into health-risk behavior. Journal of Adolescent Health, 51(6), 588–592. 10.1016/j.jadohealth.2012.03.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bonn-Miller MO, & Zvolensky MJ (2009). An evaluation of the nature of marijuana use and its motives among young adult active users. The American Journal on Addictions, 18(5), 409–416. 10.3109/10550490903077705 [DOI] [PubMed] [Google Scholar]
  10. Bonn-Miller MO, Vujanovic AA, Twohig MP, Medina JL, & Huggins JL (2010). Posttraumatic stress symptom severity and marijuana use coping motives: A test of the mediating role of non-judgmental acceptance within a trauma-exposed community sample. Mindfulness, 1(2), 98–106. 10.1007/s12671-010-0013-6 [DOI] [Google Scholar]
  11. Bowen S, Witkiewitz K, Clifasefi SL, Grow J, Chawla N, Hsu SH, Carroll HA, Harrop E, Collins SE, & Lustyk MK (2014). Relative efficacy of mindfulness-based relapse prevention, standard relapse prevention, and treatment as usual for substance use disorders: A randomized clinical trial. JAMA Psychiatry, 71(5), 547–556. 10.1001/jamapsychiatry.2013.4546 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bramm SM, Cohn AM, & Hagman BT (2013). Can preoccupation with alcohol override the protective properties of mindful awareness on problematic drinking? Addictive Disorders & Their Treatment, 12(1), 19–27. 10.1097/ADT.0b013e31824c886b [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Bravo AJ, Boothe LG, & Pearson MR (2016). Getting personal with mindfulness: A latent profile analysis of mindfulness and psychological outcomes. Mindfulness, 7(2), 420–432. 10.1007/s12671-015-0459-7 [DOI] [Google Scholar]
  14. Brewer JA, Sinha R, Chen JA, Michalsen RN, Babuscio TA, Nich C, Grier A, Bergquist KL, Reis DL, & Potenza MN (2009). Mindfulness training and stress reactivity in substance abuse: Results from a randomized, controlled stage I pilot study. Substance Abuse, 30(4), 306–317. 10.1080/08897070903250241 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Bronchain J, Raynal P, & Chabrol H (2020). Dispositional mindfulness profiles and cannabis use in young adults. Journal of Rational-Emotive & Cognitive-Behavior Therapy, 39, 509–521. 10.1007/s10942-020-00382-z [DOI] [Google Scholar]
  16. Brown KW, & Ryan RM (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84(4), 822–848. 10.1037/0022-3514.84.4.822 [DOI] [PubMed] [Google Scholar]
  17. Brown DB, Bravo AJ, Roos CR, & Pearson MR (2015). Five facets of mindfulness and psychological health: Evaluating a psychological model of the mechanisms of mindfulness. Mindfulness, 6(5), 1021–1032. 10.1007/s12671-014-0349-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chiesa A, & Serretti A (2014). Are mindfulness-based interventions effective for substance use disorders? A systematic review of the evidence. Substance Use & Misuse, 49(5), 492–512. 10.3109/10826084.2013.770027 [DOI] [PubMed] [Google Scholar]
  19. Christopher MS, Neuser NJ, Michael PG, & Baitmangalkar A (2012). Exploring the psychometric properties of the Five Facet Mindfulness Questionnaire. Mindfulness, 3(2), 124–131. 10.1007/s12671-011-0086-x [DOI] [Google Scholar]
  20. Collins RL, Parks GA, & Marlatt GA (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53(2), 189–200. 10.1037/0022-006X.53.2.189 [DOI] [PubMed] [Google Scholar]
  21. Fernandez AC, Wood MD, Stein LAR, & Rossi JS (2010). Measuring mindfulness and examining its relationship with alcohol use and negative consequences. Psychology of Addictive Behaviors, 24(4), 608–616. 10.1037/a0021742 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Goldberg SB, Pace B, Griskaitis M, Willutzki R, Skoetz N, Thoenes S, Zgierska AE, & Rösner S (2021). Mindfulness-based interventions for substance use disorders. Cochrane Database of Systematic Reviews, 10(10). 10.1002/14651858.CD011723.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Howard MC, & Hoffman ME (2018). Variable-centered, person-centered, and person-specific approaches: Where theory meets the method. Organizational Research Methods, 21(4), 846–876. 10.1177/1094428117744021 [DOI] [Google Scholar]
  24. Johnson D, Mullen D, Smith ID, & Wilson A (2016). Mindfulness in addictions. BJPsych Advances, 22(6), 412–419. 10.1192/apt.bp.114.014142 [DOI] [Google Scholar]
  25. Johnston LD, O’Malley PM, Bachman JG, & Schulenberg JE (2011). Monitoring the Future National Survey Results on Drug Use, 1975–2010. Volume I, Secondary School Students. Institute for Social Research. https://files.eric.ed.gov/fulltext/ED528081.pdf [Google Scholar]
  26. Jung T, & Wickrama KA (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317. 10.1111/j.1751-9004.2007.00054.x [DOI] [Google Scholar]
  27. Karl JA, & Fischer R (2020). Revisiting the five-facet structure of mindfulness. Measurement Instruments for the Social Sciences, 2(7), 1–16. 10.1186/s42409-020-00014-3 [DOI] [Google Scholar]
  28. Karyadi KA, VanderVeen JD, & Cyders MA (2014). A meta-analysis of the relationship between trait mindfulness and substance use behaviors. Drug and Alcohol Dependence, 143, 1–10. 10.1016/j.drugalcdep.2014.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kesner AJ, & Lovinger DM (2021). Cannabis use, abuse, and withdrawal: Cannabinergic mechanisms, clinical, and preclinical findings. Journal of Neurochemistry, 157(5), 1674–1696. 10.1111/jnc.15369 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kivlahan DR, Coppel DB, Fromme K, Miller E, & Marlatt GA (1990). Secondary prevention of alcohol-related problems in young adults at risk. In Craig KD & Weiss SM (Eds.), Health enhancement, disease prevention, and early intervention: Biobehavioral perspectives (pp. 287–300). Springer. [Google Scholar]
  31. Korecki JR, Schwebel FJ, Votaw VR, & Witkiewitz K (2020). Mindfulness-based programs for substance use disorders: A systematic review of manualized treatments. Substance Abuse Treatment, Prevention, and Policy, 15, 1–37. 10.1186/s13011-020-00293-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kuyken W, Watkins E, Holden E, White K, Taylor RS, Byford S, Evans A, Radford S, Teasdale JD, & Dalgleish T (2010). How does mindfulness-based cognitive therapy work? Behaviour Research and Therapy, 48(11), 1105–1112. 10.1016/j.brat.2010.08.003 [DOI] [PubMed] [Google Scholar]
  33. Lecuona O, García-Garzón E, García-Rubio C, & Rodríguez-Carvajal R (2020). A psychometric review and conceptual replication study of the five facets mindfulness questionnaire latent structure. Assessment, 27(5), 859–872. 10.1177/1073191119873718 [DOI] [PubMed] [Google Scholar]
  34. Levin ME, Dalrymple K, & Zimmerman M (2014). Which facets of mindfulness predict the presence of substance use disorders in an outpatient psychiatric sample? Psychology of Addictive Behaviors, 28(2), 498–506. 10.1037/a0034706 [DOI] [PubMed] [Google Scholar]
  35. Looby A, Prince MA, Villarosa-Hurlocker MC, Conner BT, Schepis TS, & Bravo AJ (2021). Young adult use, dual use, and simultaneous use of alcohol and marijuana: An examination of differences across use status on marijuana use context, rates, and consequences. Psychology of Addictive Behaviors, 35(6), 682–690. 10.1037/adb0000742 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lubke G, & Neale MC (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41(4), 499–532. 10.1207/s15327906mbr4104_4 [DOI] [PubMed] [Google Scholar]
  37. Lynskey M, & Hall W (2000). The effects of adolescent cannabis use on educational attainment: A review. Addiction, 95(11), 1621–1630. 10.1046/j.1360-0443.2000.951116213.x [DOI] [PubMed] [Google Scholar]
  38. Marlatt GA, Witkiewitz K, Dillworth TM, Bowen SW, Parks GA, Macpherson LM, Lonczak HS, Larimer ME, Simpson T, & Blume AW (2004). Vipassana meditation as a treatment for alcohol and drug use disorders. In Hayes SC, Follette VM, & Linehan MM (Eds.), Mindfulness and acceptance: Expanding the cognitive-behavioral tradition (pp. 261–287). Guilford Press. [Google Scholar]
  39. Massaro AF, Lecuona O, García-Rubio C, & Castro-Paredes A (2022). Bringing mindfulness-based relapse prevention for substance use disorders into individual therapy with Spanish population: A feasibility and effectiveness study. Mindfulness, 13(3), 766–785. 10.1007/s12671-022-01835-5 [DOI] [Google Scholar]
  40. Masyn KE (2013). Latent class analysis and finite mixture modeling. In Little TD (Ed.), The Oxford Handbook of Quantitative Methods (pp. 551–611). Oxford University Press. 10.1093/oxfordhb/9780199934898.013.0025 [DOI] [Google Scholar]
  41. Murphy C, & MacKillop J (2012). Living in the here and now: Inter-relationships between impulsivity, mindfulness, and alcohol misuse. Psychopharmacology, 219(2), 527–536. 10.1007/s00213-011-2573-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Muthén LK & Muthén B (2017). Mplus 8, Los Angeles: Muthén& Muthén. [Google Scholar]
  43. Nylund KL, Asparouhov T, & Muthén BO (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. 10.1080/10705510701575396 [DOI] [Google Scholar]
  44. Papies EK, Barsalou LW, & Custers R (2012). Mindful attention prevents mindless impulses. Social Psychological and Personality Science, 3(3), 291–299. [Google Scholar]
  45. Patton GC, Coffey C, Carlin JB, Degenhardt L, Lynskey M, & Hall W (2002). Cannabis use and mental health in young people: Cohort study. BMJ, 325(7374), 1195–1198. 10.1177/1948550611419031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Pearson MR, Lawless AK, Brown DB, & Bravo AJ (2015). Mindfulness and emotional outcomes: Identifying subgroups of college students using latent profile analysis. Personality and Individual Differences, 76, 33–38. 10.1016/j.paid.2014.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Philip A (2010). More than meditation: The role of dispositional mindfulness in alcohol and marijuana-related problems. Thesis, Auburn University. http://etd.auburn.edu/handle/10415/2187 [Google Scholar]
  48. Rau HK, & Williams PG (2016). Dispositional mindfulness: A critical review of construct validation research. Personality and Individual Differences, 93, 32–43. 10.1016/j.paid.2015.09.035 [DOI] [Google Scholar]
  49. Roebuck MC, French MT, & Dennis ML (2004). Adolescent marijuana use and school attendance. Economics of Education Review, 23(2), 133–141. 10.1016/S0272-7757(03)00079-7 [DOI] [Google Scholar]
  50. Rogojanski J, Vettese LC, & Antony MM (2011). Coping with cigarette cravings: Comparison of suppression versus mindfulness-based strategies. Mindfulness, 2(1), 14–26. 10.1007/s12671-010-0038-x [DOI] [Google Scholar]
  51. Romesburg C (2004). Cluster analysis for researchers. Lulu Press. [Google Scholar]
  52. Sahdra BK, Ciarrochi J, Parker PD, Basarkod G, Bradshaw EL, Baer R, & Realo A (2017). Are people mindful in different ways? Disentangling the quantity and quality of mindfulness in latent profiles and exploring their links to mental health and life effectiveness. European Journal of Personality, 31(4), 347–365. 10.1002/per.2108 [DOI] [Google Scholar]
  53. Schulenberg JE, Merline A, Johnston LD, O’Malley PM, Bachman JG, & Laetz VB (2005). Trajectories of marijuana use during the transition to adulthood: The big picture based on national panel data. Journal of Drug Issues, 35(2), 255–280. 10.1177/002204260503500203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Schulenberg JE, Johnston LD, O’Malley PM, Bachman JG, Miech RA, Patrick ME (2018). Monitoring the Future National Survey Results on Drug Use, 1975–2017: Volume II, College Students and Adults Ages 19–55. Ann Arbor, MI: Institute for Social Research, The University of Michigan. [Google Scholar]
  55. Shook NJ, Ford C, Strough J, Delaney R, & Barker D (2017). In the moment and feeling good: Age differences in mindfulness and positive affect. Translational Issues in Psychological Science, 3(4), 338–347. 10.1037/tps0000139 [DOI] [Google Scholar]
  56. Simons JS, Dvorak RD, Merrill JE, & Read JP (2012). Dimensions and severity of marijuana consequences: Development and validation of the Marijuana Consequences Questionnaire (MACQ). Addictive Behaviors, 37(5), 613–621. 10.1016/j.addbeh.2012.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Taylor DR, Fergusson DM, Milne BJ, Horwood LJ, Moffitt TE, Sears MR, & Poulton R (2002). A longitudinal study of the effects of tobacco and cannabis exposure on lung function in young adults. Addiction, 97(8), 1055–1061. 10.1046/j.1360-0443.2002.00169.x [DOI] [PubMed] [Google Scholar]
  58. Tein JY, Coxe S, & Cham H (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. 10.1080/10705511.2013.824781 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Tucker JS, Ellickson PL, Orlando M, Martino SC, & Klein DJ (2005). Substance use trajectories from early adolescence to emerging adulthood: A comparison of smoking, binge drinking, and marijuana use. Journal of Drug Issues, 35(2), 307–332. 10.1177/002204260503500205 [DOI] [Google Scholar]
  60. Vandrey R, Budney AJ, Kamon JL, & Stanger C (2005). Cannabis withdrawal in adolescent treatment seekers. Drug and Alcohol Dependence, 78(2), 205–210. 10.1016/j.drugalcdep.2004.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Vermunt JK (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. 10.1093/pan/mpq025 [DOI] [Google Scholar]

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Data Availability Statement

All data are available at the Open Science Framework (https://osf.io/469ts/).

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