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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Drug Alcohol Depend. 2022 Dec 20;243:109750. doi: 10.1016/j.drugalcdep.2022.109750

Association Between Affect and Cannabis Use Varies by Social Context

Rebecca K Denson 1, Donald Hedeker 2, Robin J Mermelstein 1,3
PMCID: PMC9884136  NIHMSID: NIHMS1864349  PMID: 36634576

Abstract

Background:

Cannabis use is rising globally, underscoring the importance of understanding contextual factors related to cannabis use. Although much work has retrospectively examined cannabis use patterns and effects, fewer studies have evaluated cannabis use in natural environments.

Methods:

The present study used ecological momentary assessment (EMA) to examine the subjective experience of cannabis use (i.e., positive and negative affect) and how cannabis’ mood effects are modified by the social context, defined as being alone or with others, in which use occurs. Associations between cannabis’ mood effects and cannabis use disorder symptomatology were additionally examined. Participants (N = 200) completed baseline assessments and two 7-day waves of EMA data collection. Mixed-effects models examined between- and within-subject effects for positive and negative affect at cannabis use and nonuse times and interactions between cannabis use and social context.

Results:

Positive affect was elevated at cannabis use times, compared to nonuse times, regardless of social context. The relationship between cannabis use and negative affect was moderated by social context, such that negative affect was elevated at cannabis use times when participants were alone and reduced at cannabis use times when participants were with others. Higher levels of cannabis use disorder symptomatology and cannabis use frequency were both associated with lower negative affect at cannabis use times.

Conclusions:

These results suggest that elevated positive affect is consistent across cannabis use times regardless of social context, but negative affect may vary more by the presence of others.

Keywords: cannabis, affect, social context, cannabis use disorder, positive affect, negative affect, ecological momentary assessment

1. Introduction

Cannabis use is increasing in the United States and globally; an estimated 200 million people worldwide used cannabis in 2019 (World Drug Report, 2021), and in the United States approximately 48.2 million people or 18% of Americans used cannabis at least once in 2019 (SAMHSA, 2020). Cannabis use motives include mood regulation and social conformity (Buckner et al., 2016; Buckner et al., 2018; Haug et al., 2017; Peraza et al., 2019), highlighting the importance of understanding the impact of cannabis on mood and the social context in which cannabis use occurs. Much existing research has used retrospective self-reports (e.g., Spinella et al., 2019) or laboratory studies (e.g., Fogel et al., 2017) to characterize the affective and social context of cannabis use. Less common, however, are studies with methodology allowing examination of naturalistic, momentary contextual factors involved in cannabis use.

Ecological momentary assessment (EMA) is uniquely positioned to examine the context and subjective experience of cannabis use naturalistically. EMA data collection occurs via repeated sampling of participants’ behaviors and emotions in real time and in natural settings, allowing for less recall bias and improved ecological validity compared to other methodologies (Shiffman et al., 2008). Thus, EMA studies are distinctive in their ability to characterize affect, behavior, and substance use in natural environments.

A relatively small body of research has used EMA to examine the naturalistic context of cannabis use. Many of these studies have enrolled undergraduate students (Buckner et al., 2012a; Buckner et al., 2016; Buckner et al., 2015; Phillips et al., 2018; Sznitman et al., 2022) or clinical samples (e.g., Henquet et al., 2010; Tyler et al., 2015), two subpopulations that may be limited in generalizing to broader populations of adults who use cannabis. Among undergraduates, distinct social norms and peer influences affect the context of substance use (Phillips et al., 2018), and associations between affect and cannabis use differ between clinical and community samples (see Wycoff et al., 2018 for a review). Further, in a review of EMA research examining cannabis use and affect, Wycoff et al. (2018) noted that most studies had not considered diagnoses of CUD in analyses. The present study addresses limitations with participant selection and CUD symptomatology in conjunction with the advantage of real-time, ecologically valid self-reports of cannabis use and associated contextual factors.

While individuals who use cannabis commonly report using cannabis to cope with negative emotions (Bonn-Miller et al., 2008; Johnson et al., 2010), research examining momentary associations between affect and cannabis use has been mixed. Negative affect appears to be elevated prior to cannabis use among clinical samples, whereas community samples have shown elevated positive affect prior to cannabis use and mixed results regarding negative affect (Wycoff et al., 2018). Additionally, the relationship between negative affect and cannabis use may relate to CUD. Using cannabis to cope with negative affect has been associated with dependence symptoms in adolescents (Fox et al., 2011) and adults (Johnson et al., 2010). Elevated negative affect prior to cannabis use was seen in several studies in which the majority of the sample met CUD criteria (Buckner et al., 2012a; Buckner et al., 2015; Buckner et al., 2013; Wycoff et al., 2018). However, in a recent study of college students, the relationship between intent to use cannabis and CUD symptoms was not moderated by positive or negative affect (Sznitman et al., 2022). No studies to date have used EMA to evaluate the relationship between affect, cannabis use, and CUD in a community sample.

The social context in which cannabis use occurs has implications for understanding cannabis use broadly and identifying intervention targets. Cannabis use is most likely to occur in social situations and when others are using cannabis, suggesting that social environment is influential in determining the likelihood of use (Buckner et al., 2012a; Buckner et al., 2015; Phillips et al., 2018; Treloar Padovano and Miranda, 2018). The social context of cannabis use may also relate to cannabis-associated problems. Spending time with cannabis-using peers during cannabis use treatment has been associated with greater craving and urges to use cannabis among adolescents and young adults (Meisel et al., 2021). Among college students, cannabis dependence was associated with a higher likelihood of using cannabis in the presence of others (Phillips et al., 2018). However, solitary cannabis use has been associated with more dependence symptoms than cannabis use among others in non-college student adults (Spinella et al., 2019). Thus, although the social context in which cannabis use occurs is clearly important, much remains to be clarified. Given that social context is associated with cannabis use patterns and CUD, it is possible that the mood effects of cannabis may differ based on the social environment in which cannabis use occurs.

The present study used EMA to identify in-the-moment affective and environmental contextual factors related to cannabis use in a community sample of adults. We aimed to characterize how affect at cannabis use times differed from background times when cannabis was not used. We hypothesized that social context would moderate the relationship between cannabis use and affect, such that cannabis use in the presence of others would be characterized by elevated positive affect compared to positive affect at nonuse times, whereas solitary cannabis use would be characterized by elevated negative affect compared to negative affect at nonuse times. We additionally predicted that elevated negative affect at cannabis use times would be associated with greater CUD symptomatology.

2. Material and methods

2.1. Participants

The sample was drawn from an observational longitudinal study of individuals who use both cigarettes and e-cigarettes (Electronic Nicotine Delivery Systems or ENDS). Eligible participants were age 18 or older, Chicago-area residents who reported using cigarettes weekly in the past 30 days and ENDS once or more in the past 14 days. In addition, eligible participants needed to read and speak English and carry a study smartphone programed with EMA interviews. Participants were included in the current study if they reported cannabis use during EMA interviews (N = 200). Data were collected between 2016-2018, prior to legalization of recreational cannabis use in Illinois.

2.2. Procedures

Recruitment occurred via online/print advertisements, local university listservs, word of mouth, and targeted venue recruitment. Interested participants completed a screening survey online and a phone screening survey if eligible. Participants who were eligible after the phone screen attended a baseline visit where they provided informed consent and completed psychosocial, demographic, and substance use questionnaires. Participants completed seven days of EMA at baseline and 4-8 months later. Participants received 5-6 random EMA prompts during waking hours and initiated reports when using tobacco products. Cannabis use data were collected via random sampling and tobacco event-reporting; both assessed whether participants had used cannabis within the last hour. Responses from EMA interviews across both waves of data collection were combined to form the current dataset.

2.3. Measures

2.3.1. Demographics

Demographic information including gender, age, race, ethnicity, and education level was collected via self-report questionnaires at baseline.

2.3.2. Nicotine Use and Dependence

At baseline, participants reported past 30-day cigarette and ENDS use, which was averaged to produce 30-day daily rates of cigarette and ENDS use. Cigarette and ENDS use were also reported in EMA interviews. Participants completed two versions of the Nicotine Dependence Syndrome Scale (NDSS; Rest et al., 2021) at baseline to assess dependence based on cigarette and e-cigarette/vaporizer use. The NDSS is a 19-item assessment that measures nicotine craving, withdrawal avoidance, resistance to behavioral change, smoking priority, and tolerance. Participants responded on a 5-point Likert-scale ranging from 1 (not at all true) to 5 (extremely true). Item responses were summed and averaged to produce an index score, with higher scores suggesting greater dependence.

2.3.3. Alcohol Use

Baseline alcohol use was assessed using an alcohol use frequency question, derived from the NIAAA Task Force list of recommended alcohol questions for researchers. Past-hour alcohol use was reported in EMA interviews.

2.3.4. Cannabis Use Disorder

Cannabis use disorder symptoms were assessed at baseline using the Cannabis Use Disorders Identification Test-Revised (CUDIT-R; Adamson et al., 2010). The CUDIT-R contains 8 items assessing cannabis consumption, problems, dependence, and psychological features. Participants responded on a 4-point Likert scale with anchors varying by item. Item scores were summed to yield scale scores ranging from 0-32, with higher values indicating greater cannabis use and problems. Scores ≥ 13 may indicate cannabis use disorder.

2.3.5. Depressive Symptoms

Depressive symptoms were measured using the Center for Epidemiological Studies Depression inventory (CES-D; Radloff, 1977). The CES-D measures frequency of past-week depressive symptoms including low mood, physiological symptoms, happiness, interpersonal problems, and psychomotor speed, with response items from 0 (rarely or none of the time) to 3 (most or all of the time). Item responses were summed to yield a total score ranging from 0-60; scores ≥ 16 may indicate depression.

2.3.6. EMA Interviews: Cannabis Use Frequency, Affect, and Objective Context

EMA procedures are described in section 2.2. For both random and tobacco event prompts, participants reported whether cannabis, tobacco, and alcohol products were used in the past hour and recorded their social environment (i.e., alone; with a partner/spouse, family, friend(s), coworker(s), children, other(s)). Participants were prompted to indicate their current subjective mood on a series of rating scales from 1 (not at all) to 10 (very much). The positive affect scale included “I feel…” “Happy,” “Relaxed,” “Cheerful,” “Confident.” The negative affect scale included “I feel…” “Sad,” “Stressed,” “Angry,” “Frustrated,” “Nervous/Anxious.” Item scores were averaged to produce a single scale score for positive and negative affect each. Coefficient alphas ranged from .84 to .85 for positive affect and .92 to .93 for negative affect across time points and random or tobacco event reports.

2.4. Data Analysis

Mixed-effects (aka multilevel or hierarchical linear) models were run separately for positive and negative affect to examine associations between cannabis use and affect. The mixed-effects models separated between- and within-subjects effects for affect during cannabis use and nonuse times, as well as examined the effect of social context on affect and the interaction between cannabis use and social context. Background times when no cannabis use was reported served as a consistent measure of baseline mood. Observations were nested within measurement waves nested within individuals. All models included random intercept, wave, and within-subject cannabis use effects. The use of multilevel modeling with EMA data allows for simultaneous examination of within-subject effects (e.g., variations in mood when using or not using cannabis) while controlling for between-subjects effects (e.g., impact of cannabis use level on mood; Hedeker et al., 2008). Models included gender, CES-D and CUDIT-R scores centered around the mean, and concurrent tobacco (cigarette or ENDS) and alcohol use as covariates. Gender was included as a covariate due to gender differences in cannabis use patterns and associated mood (CBHSQ, 2020; Crane et al., 2015; Fogel et al., 2017; Nia et al., 2018). CES-D scores were included given the potential for baseline mood symptoms to impact affect and cannabis use; cannabis use co-occurs with depression at notable rates (Feingold & Weinstein, 2021). CUDIT-R scores were included as a covariate to control for CUD symptoms in analyses of mood. Concurrent tobacco and alcohol use were included in models given that the sample was recruited based on tobacco use, thus we expected high rates of tobacco and cannabis co-use, and due to the mood-altering effects of nicotine and alcohol (Benowitz, 2009; Freed, 1978; Warburton and Mancuso, 1998). Models were run using SAS PROC MIXED.

3. Results

3.1. Participant Characteristics

Participant demographics are presented in Table 1. Participants ranged from 18-57 years old (M = 30.47, SD = 10.38). Approximately 11.5% identified as Hispanic or Latino; 44.5% as Non-Hispanic White; 29.5% as Non-Hispanic Black or African American; 10.0% as Asian or Pacific Islander; 1.0% as American Indian or Alaskan Native; and 3.5% as other race/ethnicity. Regarding educational background, 7% of participants reported completing grades 9-11, 19.5% reported completing grade 12 or GED, 53.5% reported completing 1-3 years of college, and 20% reported completing 4+ years of college.

Table 1.

Demographic Information and Descriptive Statistics (N = 200)

Variable n (%) or Mean (SD)
Demographics
Female 71 (35.5%)
Male 129 (64.5%)
Age (years) 30.47 (10.38)
Race/Ethnicity
 Non-Hispanic White 89 (44.5%)
 Non-Hispanic Black or African American 59 (29.5%)
 Hispanic or Latino 23 (11.5%)
 Asian or Pacific Islander 20 (10.0%)
 American Indian or Alaskan Native 2 (1.0%)
 Other 7 (3.5%)
Highest Education
 Grades 9-11 14 (7.0%)
 Grade 12 or GED 39 (19.5%)
 1-3 years of college 107 (53.5%)
 4+ years of college 40 (20.0%)
Substance Use
Daily cigarette smoking rate (30-day average) 8.1 (7.30)
Daily ENDS use rate (30-day average) 5.3 (8.04)
Cigarette NDSS 2.9 (0.68)
ENDS NDSS 2.4 (0.78)
Cannabis use frequency (past 6 months)
 None 25 (12.5%)
 Monthly or less 21 (10.5%)
 2-4 times/month 28 (14.0%)
 2-3 times/week 30 (15.0%)
 4+ times/week 96 (48.0%)
Alcohol use frequency (past year)
 None 14 (7.0%)
 1-11 times/year 33 (16.5%)
 1-3 times/month 48 (24.0%)
 1-2 times/week 60 (30.0%)
 3-4 times/week 29 (14.5%)
 5-6 times/week 8 (4.0%)
 Daily 8 (4.0%)
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3.2. EMA Interviews

Overall, 14,160 EMA events were recorded across both waves of data collection (M = 70.80, SD = 34.95 per person). Of these, 2,672 were cannabis use events and 11,488 were events with no cannabis use. Participants reported an average of 13.36 cannabis use events each (SD = 14.30). 45.35% of EMA events were randomly prompted and 54.7% were tobacco event reports. Alcohol use was reported in 9.9% of all events. Participants reported being alone in 42.1% of all events, 13.4% of which were cannabis use events and 86.61% nonuse events. 41.1% of cannabis use occurred alone and 58.9% occurred in the presence of others. When using cannabis among others, participants were most likely to report being with a friend(s) or a partner/spouse (48.0% and 43.1% of cannabis use events, respectively).

3.3. Substance Use, Cannabis Use Disorder, and Depressive Symptoms

Descriptive statistics on baseline substance use are presented in Table 1. Almost half of the sample (48%) reported frequent cannabis use of at least 4 times/week. Scores on the CUDIT-R ranged from 0-29 with a mean of 9.90 (SD = 6.57). 28.5% of the sample scored ≥ 13 on the CUDIT-R, a screening cutoff for cannabis use disorder (Adamson et al., 2010). Participants smoked an average of 8.1 cigarettes a day, and used ENDS an average of 5.3 times daily (see Table 1). CES-D scores ranged from 0-58, with a mean of 16.97 (SD = 11.94). On the CES-D, 48.00% of the sample scored ≥ 16, the cutoff indicating depression.

3.4. Positive Affect

Mixed-effects models tested positive and negative affect outcomes separately when cannabis was used or not used and when participants were alone or with others. Average positive and negative affect when cannabis was used or not used and when participants were alone or with others are presented in Table 4. Main effect models are presented in Table 2 and interaction models are presented in Table 3. Across all events, the mean positive affect was approximately 6.215 (SD = 2.244), and slightly higher in wave 2 than wave 1 of data collection (estimate = −0.2408, SE = 0.0977, p = .02). Higher CES-D scores were associated with lower average positive affect (estimate = −0.0513, SE = 0.009, p < .0001). Individuals with higher levels of tobacco use had significantly higher positive affect (between-subjects effect, estimate = 1.3983, SE = 0.3273, p < .0001), and within-subject, positive affect was significantly elevated when individuals had just used tobacco (estimate = 0.374, SE = 0.0310, p <.0001) and during times when they used alcohol (estimate = 0.2873, SE = 0.0474, p < .0001). Positive affect was significantly lower when individuals were alone (estimate = 5.9979, SE = 0.1150) than with others (estimate = 6.4058, SE = 0.1145; within-subject estimate = −0.3149, SE = 0.0283, p < .0001). Positive affect was also significantly higher at cannabis use times (estimate = 6.7052, SE = 0.1177) than no use times (estimate = 6.0613, SE = 0.1147; within-subject estimate = 0.5709, SE = 0.0600, p < .0001). The association between positive affect and social context was modified dependent on level of cannabis use: individuals with heavier cannabis use had greater reductions in positive affect when alone, compared to when they were with others, than individuals with lower levels of cannabis use (within-subject social context x between-subjects cannabis use interaction estimate = −0.6102, SE = 0.2014, p = .003; Table 3, Table 4).

Table 4.

Average Positive and Negative Affect by Cannabis Use and Social Context.

Positive Affect Negative Affect
N # Events Estimate SE Estimate SE
No cannabis, with others 197 4388 6.2543 0.1179 2.5779 0.1026
No cannabis, alone 200 7100 5.9269 0.1169 2.6182 0.1011
Cannabis use, with others 184 1574 6.8738 0.1222 2.4054 0.1071
Cannabis use, alone 153 1098 6.4587 0.145 2.5365 0.1295
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Table 2.

Main Effect Models.

Main Effect Models Positive Affect Negative Affect
Estimate SE p Estimate SE p
Intercept 5.7782 0.3907 <.0001 1.8983 0.3092 <.0001
Wave −0.2408 0.0977 0.0151 0.1176 0.0876 0.182
Gender (female) −0.0863 0.2126 0.6849 −0.047 0.1693 0.7811
CES-D −0.0513 0.009 <.0001 0.0586 0.0071 <.0001
CUDIT-R 0.0279 0.0176 0.1124 −0.0251 0.0143 0.0787
Between-Subjects Effects
 Tobacco use 1.3983 0.3273 <.0001 −0.039 0.2627 0.8818
 Alcohol use −0.0766 0.8766 0.9304 1.4852 0.7093 0.0363
 Alone −0.3928 0.4973 0.4296 1.0219 0.397 0.0101
 Cannabis use −0.0817 0.8184 0.9205 −0.5473 0.65 0.3998
Within-Subjects Effects
 Tobacco use 0.374 0.0310 <.0001 −0.0442 0.0241 0.0667
 Alcohol use 0.2873 0.0474 <.0001 −0.0282 0.037 0.4464
 Alone −0.3149 0.0283 <0001 0.0715 0.022 0.0012
 Cannabis use 0.5709 0.0600 <.0001 −0.0868 0.0587 0.1408
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Table 3.

Interaction Models.

Interaction Models Positive Affect Negative Affect
Estimate SE p Estimate SE p
 Intercept 5.4877 0.5009 <0001 1.8968 0.4019 <.0001
 Wave −0.245 0.098 0.0137 0.1176 0.0877 0.1822
 Gender (female) −0.0801 0.212 0.7057 −0.0442 0.1694 0.7942
 CES-D −0.0514 0.009 <0001 0.0585 0.0071 <.0001
 CUDIT-R 0.0276 0.0175 0.1155 −0.0247 0.0143 0.0836
Between-Subjects Effects
 Tobacco use 1.3984 0.3264 <0001 −0.039 0.2627 0.882
 Alcohol use −0.111 0.875 0.8991 1.4841 0.7101 0.0366
 Cannabis use 1.5515 2.0332 0.4454 −0.3839 1.6256 0.8133
 Alone 0.1452 0.7616 0.8488 1.034 0.6156 0.0931
Within-Subjects Effects
 Tobacco use 0.3728 0.031 <0001 −0.0434 0.0241 0.072
 Alcohol use 0.2867 0.0474 <0001 −0.0234 0.0371 0.5286
 Cannabis use 0.615 0.1773 0.0006 −0.1745 0.1748 0.3192
 Alone −0.1983 0.0478 <.0001 0.0183 0.0372 0.6224
Interactions
 BS Alone x BS Cannabis use −3.1274 3.5181 0.374 −0.3163 2.8087 0.9103
 WS Alone x WS Cannabis use −0.0514 0.0753 0.4949 0.1724 0.059 0.0035
 WS Alone x BS Cannabis use −0.6102 0.2014 0.0025 0.2854 0.1568 0.0687
 BS Alone x WS Cannabis use −0.0848 0.2909 0.7708 0.1927 0.2858 0.5
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3.5. Negative Affect

Across all events, average negative affect was approximately 2.439 (SD = 1.757), and higher CES-D scores were associated with higher negative affect (estimate = 0.0586, SE = 0.0071, p <.0001). Negative affect was significantly elevated for individuals with greater alcohol use (between-subjects estimate = 1.4852, SE = 0.7093, p = .04), although for a given individual, negative affect did not differ during alcohol use compared to other times (within-subject estimate = −0.0282, SE = 0.037, p = .44). Negative affect was also significantly higher for those who spent more time alone (between-subjects estimate = 1.0219, SE = 0.397, p = .01), and within individuals, negative affect was significantly higher during alone times than times with others (estimate = 0.0715, SE = 0.022, p = .001). In the main effects model, negative affect did not significantly vary by cannabis use (within-subjects estimate = −0.0868, SE = 0.0587, p = .14), but this effect was significantly modified by the interaction with social context (within-subject interaction estimate = 0.1724, SE = 0.059, p = .004). Negative affect was elevated when individuals were alone and using cannabis (M = 2.5365, SE = 0.1295) and reduced when individuals were with others and using cannabis (M = 2.4054, SE = 0.1071; Table 4).

3.6. Positive and Negative Affect and Cannabis Use Disorder

Additional models examined interactions between affect at cannabis use times and overall CUD symptomatology (CUDIT-R total score) and baseline cannabis use frequency (CUDIT-R item 1).

For positive affect, neither the interaction between within-subject cannabis use and CUDIT-R total score (estimate = 0.0056, SE = 0.0094, p = .55) nor the interaction between within-subject cannabis use and CUDIT-R cannabis use frequency (estimate = 0.0102, SE = 0.0466, p = .83) was significant.

In the negative affect model that included between-subjects cannabis use, within-subject cannabis use, and CUDIT-R total score, the interaction between within-subject cannabis use and CUDIT-R total score was significant (estimate = −0.0233, SE = 0.0095, p = .01); as cannabis use disorder symptomatology increased, negative affect at cannabis use times decreased. In the negative affect model that included between-subjects cannabis use, within-subject cannabis use, and CUDIT-R cannabis use frequency, the interaction between within-subject cannabis use and CUDIT-R cannabis use frequency was significant; as cannabis use frequency increased, negative affect at cannabis use times decreased (estimate = −0.1900, SE = 0.044, p < .0001).

4. Discussion

This study used ecological momentary assessment to examine the effects of cannabis use and social context, defined as being with others or alone, on mood in a sample of adults. More cannabis use occurred when participants were with others than alone and positive affect was elevated at cannabis use times, consistent with prior research (Buckner et al., 2013; Treloar Padovano and Miranda, 2018). Participants with higher levels of tobacco use during EMA data collection reported higher average positive affect; this is likely due to study design, as positive affect is generally elevated following tobacco use and EMA event reporting occurred directly after tobacco use. Contrary to our hypothesis, cannabis use was associated with increased positive affect regardless of social context, CUD symptoms, or cannabis use frequency. This global association between cannabis use and positive affect aligns with the finding that enhancement motives are a frequently-reported reason for cannabis use (Glodosky and Cuttler, 2020). This is also consistent with recent findings of an association between intention to use cannabis and positive affect that was not moderated by cannabis-related problems (Sznitman et al., 2022).

The relationship between cannabis use and negative affect was moderated by social context; negative affect was elevated when participants were alone and using cannabis and reduced when individuals were with others and using cannabis. Previous work has not examined the moderating role of social context in the relationship between cannabis use and affect, but research on alcohol use has shown similar patterns. Creswell’s (2021) social-contextual framework for alcohol use disorder risk posits that social drinking is associated with enhanced positive emotion, and drinking alone is associated with alleviation of negative emotion. Although in this study positive affect did not differ by social context, using cannabis alone was associated with elevated negative affect compared to nonuse times. Individuals may have engaged in solitary cannabis use to alleviate elevated negative affect, as negative affect coping is a common motive for use (Bonn-Miller et al., 2008; Buckner et al., 2013; Zvolensky et al., 2007). However, the present study did not evaluate cannabis use motives. Further, whether cannabis consistently relieves negative affect is unclear, with some studies reporting reductions (Buckner et al., 2015) and others reporting no reduction (Buckner et al., 2012b; Tournier et al., 2003) in negative mood following cannabis use. The current findings offer a potential explanation for this, suggesting that cannabis use may be differentially effective in improving negative affect when individuals are alone vs. with others.

The relationship between cannabis use and negative affect may also depend on CUD symptomatology and cannabis use frequency. We found that as CUD symptoms increased, negative affect at cannabis use times decreased. In the three-stage cycle of addiction framework, the withdrawal/negative affect stage of the cycle leads to preoccupation/craving and subsequent intoxication in which negative affect is relieved through substance use (Koob and Volkow, 2016). Thus, individuals with more CUD symptoms might experience elevated negative affect during transient abstinence and reduced negative affect after substance use. However, we also found that as baseline cannabis use frequency increased, negative affect at cannabis use times was reduced. The larger magnitude of this effect compared to that of CUD symptoms on negative affect suggests that cannabis use frequency likely drove the latter effect. Following the three-stage cycle, individuals with higher cannabis use frequency may be more chronic in their cannabis use and experience reductions in negative affect following cannabis use, further increasing cannabis use via negative reinforcement. The present study also found elevated positive affect at cannabis use times regardless of CUD symptomatology or use frequency. In contrast to the negative reinforcement model, Snitzman et al. (2022) suggested that cannabis use in their sample of college students with low rates of psychiatric disorders may have been maintained by high positive affect, as opposed to relief of high negative affect. Thus, the present findings regarding positive affect may also reflect maintenance of cannabis use through elevated positive affect, rather than relief of negative affect.

This sample reported high rates of depressive symptoms, consistent with previous research linking cannabis use and depression. Approximately 10% of adults with major depressive disorder use cannabis, although the directionality of this association is currently unknown (Feingold & Weinstein, 2021; Volkow, 2004). Individuals with depression may use cannabis to self-medicate symptoms, or cannabis may play a causal role in the onset or worsening of depression (Volkow, 2004). Shared genetic vulnerabilities may also underlie comorbid cannabis use and depression (Lynskey et al., 2004).

4.1. Limitations

The present study’s sample was drawn from a larger sample recruited based on cigarette and e-cigarette use, which may limit interpretations of the present findings. However, all participants in the present study reported cannabis use during EMA interviews. Given that 42% of those who use cannabis also use nicotine and 50% of those with CUD smoke cigarettes (Agrawal et al., 2012; Akbar et al., 2019), this study is representative of a large proportion of those who use cannabis. Further, nicotine use and dependence in this sample were moderately low. Additionally, cigarettes and e-cigarette use was included as a covariate in analyses to control for potential mood effects of nicotine. An additional limitation is the underrepresentation of cannabis use times, because cannabis event-reporting did not occur. However, a large amount of data was collected for both cannabis and non-cannabis use events via random prompting and tobacco event-reporting. The study design also did not allow the examination of changes in affect from pre- to post-cannabis use. To address this limitation, background times with no cannabis use were used as a measure of baseline mood to compare with cannabis use times.

4.2. Conclusions

This project used ecological momentary assessment to examine relationships between cannabis, social context, and mood. Positive affect was elevated at cannabis use times regardless of social context, whereas the relationship between cannabis use and negative affect was moderated by social context. Negative affect was elevated at cannabis use times when individuals were alone, and reduced at cannabis use times when individuals were with others. Additionally, as baseline cannabis use frequency increased, negative affect at cannabis use times decreased. These results point to several important future directions for research. Future EMA research should aim to collect data before and after cannabis use and require cannabis event-reporting to better examine the time course of mood changes related to cannabis use and context. Further, future studies should evaluate motives for cannabis use in varying social contexts and associations between motives, affect, and CUD.

Highlights.

  • Positive affect is elevated at cannabis use times regardless of social context

  • Negative affect is reduced at cannabis use times when individuals are with others

  • Negative affect is elevated at cannabis use times when individuals are alone

  • Higher cannabis use disorder symptoms related to less negative affect at use times

Funding:

Research reported in this publication was supported by the NCI and FDA Center for Tobacco Products (CTP) grant R01CA184681 and NIDA grant R01DA05117.

Footnotes

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Conflict of Interest

No conflict declared.

Declaration of Competing Interest:

The authors report no declarations of interest.

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