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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: Psychol Addict Behav. 2022 Nov 10;37(4):616–625. doi: 10.1037/adb0000891

Ratings of Executive Function as a Risk Factor for Adolescents' Frequent Cannabis Use: A Prospective Longitudinal Study

Jessica M Cavalli 1, Anita Cservenka 1, David C R Kerr 1,2, Stacey S Tiberio 2, Lee D Owen 2
PMCID: PMC10169534  NIHMSID: NIHMS1843865  PMID: 36355660

Abstract

Objective:

We examined the extent to which behavioral ratings of children’s executive function (EF) in early adolescence predicted adolescents’ cannabis use, and whether associations were independent of parents’ cannabis and alcohol use and adolescents’ alcohol use.

Method:

Participants were 198 offspring (44% boys) of 127 mothers and 106 fathers. Parents and teachers completed the Behavior Rating Inventory of Executive Function (BRIEF) at ages 11–14 years. Youth were interviewed repeatedly from ages 14 to 20 years regarding frequency of cannabis and alcohol use. Two-level models regressed dichotomous cannabis outcomes (annual, weekly, or daily use) on age at the within-person level and the random intercept of cannabis use on EF, parent substance use, and covariates (age 7 IQ indicators, child gender, parent education, and mean of ages assessed) at the between-person level.

Results:

Poorer child EF predicted significantly (p<.05) higher likelihood of weekly (b[SE]=.64[.24]) and daily (b[SE]=.65[.25]), but not annual (b[SE]=.38[.22]), cannabis use. Parent cannabis use (b[SE]=.53[.25] to .81[.39], p<.05) independently predicted all three outcomes, and effects were distinct from those explained by parent alcohol use (b[SE]=.66[.29] to .81[.35], p<.05). EF remained a significant predictor of weekly and daily cannabis use after adjusting for parental alcohol and cannabis use, and adolescents’ alcohol use.

Conclusions:

Children exhibiting poorer EF were more likely to use cannabis weekly and daily in later adolescence. Whereas literature suggests poorer EF may be a consequence of cannabis use, these findings suggest EF should be considered prior to cannabis use initiation. EF during childhood may be a fruitful prevention target.

Keywords: executive function, cannabis use, adolescence, parental substance use, longitudinal studies

Global increases in the prevalence of cannabis use (United Nations Office of Drugs and Crime, 2015), shifts in public perceptions of the risk of cannabis use (Levy et al., 2021), the rapid expansion of the cannabis industry, and the changing cannabis policy landscape (National Conference of State Legislatures, 2022) make it imperative that research is conducted to better understand risk factors for and potential consequences of cannabis use. Past-month prevalence of daily cannabis use has reached a 40-year high for college students ages 19–22 years (7.9% in 2020) and noncollege peers (13% in 2019; Schulenberg et al., 2021). Because cannabis use typically begins during adolescence and prevalence peaks in early adulthood (Chen et al., 2017; Johnston et al., 2021; Schulenberg et al., 2021), researchers have focused on the relation between cannabis use and processes that continue developing during these life stages, such as cognitive functioning.

The association between cannabis use and cognitive functioning has been a topic of substantial interest for decades. Despite numerous studies (for recent reviews, see Duperrouzel et al., 2020; Ellingson et al., 2021; Scott et al., 2018), there is ongoing debate on the extent to which cannabis use leads to poorer cognitive functioning, poorer cognitive functioning is a risk factor for subsequent cannabis use, or the relationship is bidirectional. Of particular interest are relations between cannabis use and executive functions (EFs), such as inhibitory control, working memory, and cognitive flexibility. EFs are a set of top-down, higher-order cognitive processes critical for cognitive, social, and psychological development (Diamond, 2013) and adaptive functioning (Gilbert & Burgess, 2008). Deficits in EFs and underlying prefrontal cortex functioning play a crucial role in the development and maintenance of substance use disorders (Goldstein & Volkow, 2002; Hester et al., 2010; Luciana, 2020). Additionally, individual differences in the vulnerability to substance use and the transition to addiction may emerge from differential impairments in one or more EFs (George & Koob, 2010). Thus, examining the link between EFs and cannabis use is particularly pertinent and may provide valuable information for prevention efforts.

However, conclusions from cross-sectional studies focused on EFs in adolescent and young-adult cannabis users remain equivocal. Although some find that relative to nonusers, adolescent and young-adult cannabis users exhibit worse performance on lab-based neurocognitive tasks assessing EFs (Behan et al., 2014; Lahanas & Cservenka, 2019; Selamoglu et al., 2021), others find they perform similarly (Cavalli & Cservenka, 2020; Costa Porfirio et al., 2020; Hooper et al., 2014). One possible explanation for these mixed results may be the lack of adjustment for relevant confounds and for EF prior to cannabis use, in particular. For instance, longitudinal studies using task-based measures of EF have not found cannabis use and concurrent EF to be related after adjusting for pre-substance use EF (Fried et al., 2005; Nguyen-Louie et al., 2015).

Although longitudinal studies may shed light on the temporal nature of the associations between EF and cannabis use, such studies have been limited. For example, several longitudinal studies have considered the relationship between EF task performance and cannabis use; however, their analytic approaches precluded the ability to establish whether changes in cannabis use impact change in EF across time or vice versa (Becker et al., 2018; Infante et al., 2020; Jacobus et al., 2015). Other studies have typically addressed temporality using an exposure model framework, investigating weaker EF performance as a potential consequence of cannabis use. However, not all studies assess EF prior to cannabis use onset (Meier et al., 2012), leaving open the possibility that poorer EF predated cannabis dependence. Among those that do adjust for pre-cannabis EF, cannabis use was not a significant predictor of later inhibitory control (Paige & Colder, 2020) or working memory (Willford et al., 2021). These latter two studies highlight the importance of accounting for pre-cannabis use EF when examining the link between cannabis use and EF, and that doing so may lead to different conclusions about the nature of the association.

In contrast to the exposure model framework, fewer longitudinal studies have used a risk-model framework to examine lower EF as a risk factor for subsequent cannabis use. Research suggests that prior to cannabis use (Tervo-Clemmens et al., 2018) and after cannabis use initiation (Cousijn et al., 2014), alterations in brain function subserving EFs and weaker executive planning performance (Tervo-Clemmens et al., 2018) are risk factors for later cannabis use. Furthermore, a recent study by Guo and colleagues (2022) found that better cognitive flexibility task performance was associated with a 32% lower odds of initiating adolescent cannabis use, whereas poorer self-reported inhibitory control was associated with a 92% greater odds of adolescent cannabis initiation. Thus, several studies lend support for examining EF as a risk factor for cannabis use.

Moreover, the relationship between EF and cannabis use may be bidirectional, such that lower preexisting EF performance may confer risk for cannabis use, and cannabis use during adolescence and young adulthood may interrupt the normative development of EFs. Specifically, research finds lower working memory performance prior to cannabis use initiation is associated with an earlier age of cannabis use onset and that, in turn, cannabis use onset and frequency of use across adolescence is related to declines in EF performance by early adulthood (Castellanos-Ryan et al., 2017). Similarly, Wilson and colleagues (2021) report that individuals who subsequently develop a cannabis use disorder (CUD) by age 24 years show poorer working memory performance before cannabis use initiation. Additionally, participants in this sample who developed a CUD did not exhibit developmentally normative changes in inhibitory control from ages 11 to 24 years relative to those who did not develop a CUD. Thus, poor EF may increase vulnerability for developing a CUD, and the trajectory of EF maturation may be altered for individuals who develop a CUD during adolescence and young adulthood. Due to the limited number of longitudinal studies assessing EF and cannabis use, additional prospective longitudinal research is needed, especially studies examining the link from the risk model perspective. As such, the present study prospectively investigates behavioral ratings of EF in early adolescence as a risk factor for later cannabis use frequency.

Several other gaps are present in the current longitudinal literature, which the present study sought to address. First, research on cannabis use risk factors typically considers relatively low threshold outcomes, such as any use. Yet, findings from cross-sectional research suggest that poorer EF task performance is related to more frequent and sustained cannabis use, not just any use (Block et al., 2022; Castellanos-Ryan et al., 2017; Dahlgren et al., 2016; Gruber et al., 2012; Lahanas & Cservenka, 2019; Lisdahl & Price, 2012). Therefore, the present study considered behavioral ratings of EF as a predictor of later cannabis use at three different frequency levels in the past year: any, weekly, or daily use.

Second, the present study also adds to the existing literature by assessing EFs via the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000) a multi-informant, comprehensive, multidimensional behavioral rating scale of EF problems in school and home contexts. The BRIEF yields eight subscales that fall under two composite subdomains. The metacognition regulation subdomain includes the initiate, working memory, plan/organize, monitor, and organization of materials subscales. The behavioral regulation subdomain comprises inhibit, shift, and emotional control subscales. As individual differences in substance use and addiction may derive from differential problems in one or more EFs (George & Koob, 2010), these subdomains offer the ability to thoroughly examine the link between behavioral ratings of EF and cannabis use and explore whether one confers greater risk for cannabis use.

Third, prior research suggests that neurocognitive tests, like those typically used in cannabis research, may provide limited information about EF problems in daily life (Clark et al., 2017). As the BRIEF measures constructs distinct from those assessed through neurocognitive tests (Toplak et al., 2013), it may be a useful tool for providing novel insight into how poorer EF may be a risk factor for cannabis use. Although one study used the self-report version of the BRIEF to examine risk for cannabis use initiation (Guo et al., 2022), to our knowledge, no studies have examined early adolescents’ EF from a multi-informant perspective (e.g., parent, teacher) at multiple developmental timepoints and examined its role in predicting later cannabis use. As such, the results from the present study may provide complimentary information to the majority of studies that use neurocognitive tests to assess EFs among individuals who use cannabis.

Fourth, longitudinal research should account for family and other factors that may be shared risks for EF and cannabis use. In particular, parents’ substance use may contribute to their child’s cannabis use through a number of mechanisms (Kerr et al., 2015; Tiberio et al., 2020), including that parents’ substance use may be a cause or consequence of their own EF. However, past longitudinal research on EF and cannabis use has not adjusted for parental substance use (Becker et al., 2018; Castellanos-Ryan et al., 2017; Cousijn et al., 2014; Infante et al., 2020; Meier et al., 2012; Morin et al., 2019; Paige & Colder, 2020; Tervo-Clemmens et al., 2018; Wilson et al., 2021), except recently (Guo et al., 2022). For example, Morin and Colleagues (2019) found that increases in adolescent cannabis use over 4 years were associated with decreases in performance across several domains of EF, but there was no adjustment for parental substance use; this leaves in question whether parental substance use may have increased the risk for their children’s cannabis use or decreases in EF. As such, the present study investigates whether parent cannabis and alcohol use are associated with children’s weaker EF and later cannabis use frequency, and whether the latter association is independent of child EF. A final essential factor to consider is co-occurring alcohol use, which is common among adolescents who use cannabis (Norton & Colliver, 1988; O’Hara et al., 2016) and may explain apparent associations between EF and cannabis use. Thus, the present study examines whether behavioral ratings of EF in early adolescence increases risk for cannabis use in later adolescence after accounting for alcohol use.

The Present Study

The present study considered a multi-informant measure of EF during late childhood to early adolescence as a predictor of cannabis use frequency in the past year, collected at multiple points across adolescence. The central hypothesis was that lower levels of EF would be associated with an elevated risk of cannabis use after adjusting for child gender and childhood factors (i.e., child cognitive ability, parents’ education, and substance use). We were primarily interested in the extent to which poorer EF would predict weekly or daily cannabis use.

Second, we hypothesized that parents’ cannabis use would be a risk factor for adolescents’ later frequent cannabis use; prior research, including our own, has examined parent use in relation to child onset of any substance use but not the more frequent levels examined here. In these tests, we also considered the degree to which associations between parents’ and adolescents’ cannabis use would be independent of the prediction from child EF. Additionally, these models adjusted for parent alcohol use to establish whether risk from parents’ cannabis use to adolescents’ cannabis use was present when controlling for other parental substance use. Third, to determine whether poorer EF confers risk for cannabis use independent of co-occurring alcohol use, rather than shared with more general substance use risks, we adjusted for adolescents’ alcohol use across the same developmental period. Finally, returning to the central model, we explored whether either of two subdomains of EF—metacognition or behavioral regulation—were stronger predictors of adolescents’ cannabis use.

Method

Transparency and Openness

This study’s design and analyses were not preregistered. Data collection is ongoing and data are not currently available in a data archive; materials and analysis code are available by emailing the corresponding author. Herein, we report study procedures, how we determined sample size, all data exclusions, and all measures. There were no study manipulations. All measures and procedures were approved by the Oregon Social Learning Center Institutional Review Board.

Participants

Participants were 198 children (88 boys) of 106 fathers and 127 mothers. Fathers were originally recruited into the Oregon Youth Study (OYS) as boys aged 9–10 years in the mid-1980s. OYS was a community-based sample of boys in Grade 4 (n = 206 participants, 74% of those eligible were recruited) from schools in neighborhoods with higher rates of delinquency in a medium-sized metropolitan area in the Pacific Northwest. The OYS boys were representative of the area at the time, with 90% White non-Latino, and most from families of lower socioeconomic status (Capaldi & Patterson, 1989; Hollingshead, 1975). The OYS had high retention; 89% or more of those who are living (six have died) participated at each of the yearly assessments from ages 9–10 to 40–42 years. Offspring (n = 338 as of 2021) of OYS men and the mothers of these children were recruited to the Three Generation Study. Initially all offspring were invited to participate, but later budget constraints limited recruitment to the men’s first two biological offspring per partner.

Child assessments were timed to occur at ages 21 months, and 3, 5, 7, 9–10, 11–12, 13–14, 15–16, 17–18, and 19–201 years. Included in the current subsample are children with at least one assessment of EF at ages 11–12 or 13–14 years, and at least one assessment of substance use that occurred on or later than the last date of any EF measure. Of the 198 children in the subsample, 4.5% were African American/Black, 1% Asian American, 8.6% Native American, and 75% White non-Hispanic, 4% White Hispanic, with 6.6% identifying as mixed race/ethnicity; 196 (99.0%) were biological offspring of the OYS men. Of the 106 fathers, 37 had 1 child, 48 had 2, 19 had 3, and 2 had 4 children in the subsample. Median (interquartile range) annual family income was $44,844 ($22,600–68,000). Given the wide range of ages at which OYS men became fathers, child assessments occurred between 2006 and 2021. The median (interquartile range) date of assessment was 2016 (2013–2019). For reference, non-medical cannabis use became legal for adults in Oregon in 2015.

Measures

Executive function.

The primary predictor was assessed using the BRIEF (Gioia et al., 2000) completed by mothers, fathers, and teachers. The BRIEF yields the Global Executive Composite, eight subscales, and two composite subdomains: metacognition (e.g., “Needs help from an adult to stay on task”, “Has trouble getting started on homework or chores”, “Does not plan ahead for school assignments”) and behavioral regulation (e.g., “Smaller events trigger big reactions”, “Has trouble putting the brakes on his/her actions”, “Mood changes frequently"). Subscales for the metacognition subdomain includes the initiate, working memory, plan/organize, monitor, and organization of materials subscales. The behavioral regulation subdomain comprises the inhibit, shift, and emotional control subscales.

Data were available from all three informants (for n = 129 children), two informants (for n = 56), or one informant (for n = 13). Items (72 items for parents; 73 for teachers) were summed to create a Global Executive Composite from each informant (Cronbach alpha, α =.98), with higher scores representing poorer EF. Within each informant, a mean of reports at ages 11–12 and 13–14 years was then calculated; scores were correlated over time at r = .81 for mothers, r = .81 for father, and r = .41 for teachers (two different individuals; all p ≤ .01). Cross-age mother-, father-, and teacher-reported EF scores also were correlated (r = .46-.53, p <. 001), supporting the approach of creating an aggregate measure from the mean of standardized mother, father, and teacher cross-age scores (α = .77). Behavioral Regulation Index scores based on 28 or 29 items and Metacognition Index scores based on 44 items were constructed similarly.

Adolescents’ cannabis use.

For the primary cannabis use outcomes, children were interviewed in person or by phone at ages 11–12, 13–14, 15–16, 17–18, and 19–20 years regarding their substance use (Child Interview; Capaldi et al., 2001). They were asked if they had ever tried cannabis, and those who responded yes were then asked if they had used cannabis in the past year. Those responding yes reported on frequency of use in the past year on an 8-point scale (1 = just once or twice, 2 = once every 2–3 months, 3 = once a month, 4 = once every 2–3 weeks, 5 = once a week, 6 = 2–3 times a week, 7 = once a day, 8 = 2–3 times a day or more). Three related binary outcomes were calculated from this set of variables: yearly or more frequent use (coded 1) versus no use (coded 0); weekly or more frequent use (1) versus less frequent and no use (0); and daily or more frequent use (1) versus less frequent and no use (0). Most youth participated in multiple follow-up years; n = 24 at one assessment, n = 63 at two assessments, n = 99 at three assessments, n = 12 at four assessments, for a total of 495 observations across ages 11 to 20 years.

Adolescents’ alcohol use.

The Child Interview also queried use of beer, wine, coolers, and liquor. For each beverage type, children were asked if they had ever consumed it; if they had done so in the past year; and if so, the number of times they had used (frequency) and the usual amount consumed (volume). Frequency was capped at daily use (365 times per year) and volume ranged from < 1 unit to 6 units, which were placed on an equivalent alcohol content scale (Capaldi et al., 2015). For each beverage type, frequency and volume were multiplied and then these products were summed across types for total yearly alcohol volume, which was log transformed to reduce skew. Finally, a single cross-year alcohol use score was formed from the mean of scores from ages 11–12 to 19–20 years.

Child cognitive ability.

Block Design and Vocabulary subtests of the Wechsler Intelligence Scale for Children (Wechsler, 1991) were administered at ages 11–12 years. The mean of these scaled scores (r = .46, p <.01) was calculated; this measure ranged from 4 to 15 with a mean of 9.75 and standard deviation of 2.20.

Parent education.

From a demographics questionnaire, mothers and fathers answered “What is the last grade in school that you’ve completed?” The variable was the highest degree completed by either the mother or father, collected at child ages 21 months, 3, 5, and 7 years using the following seven response options; 1 = Less than 7th grade (0% of the sample), 2 = 7–9th grade (0%), 3 = (3.6% of sample) 10–11th grade (partial High School), 4 = (28.9%) High school graduate (completed 12thgrade), 5 = (49.2%) 1–3 years college, 6 = (7.1%) 4-year college graduation, and 7 = (11.2%) Graduate professional training or degree (Masters, MD, or PhD).

Parent cannabis use.

Mothers and fathers answered the interview questions: “How many times have you used marijuana in the last year?” and “When using marijuana, how much do you usually use?” Response options for volume included a variety of units (e.g., joints, grams, ounces, hits from bong, joint, liquid extract). As per Washburn and Capaldi (2015), volume was converted into grams, multiplied by the number of times used, and the product was log transformed. The mean of each parents’ reports across child ages 21 months and 3, 5, and 7 years (α = .91 for mothers and .93 for fathers) was calculated, standardized, and then mother and father reports were averaged (r = .39, p < = .01).

Parent alcohol use.

Similar to the child alcohol use measure, mothers’ and fathers’ alcohol use each were calculated from frequency and volume measures on use of beer, wine, and liquor (wine coolers not assessed) at assessment across child ages 1.5 to 7 years. The mean of standardized mother and father scores (r = .38, p <= .01) was the final parent alcohol use variable.

Missing Data

Given the range of birth years, some children are not yet old enough to have participated in later assessments and therefore should not be considered missing. Retention of eligible participants from assessment wave to wave averaged 86% across the study, with lower average retention (80%) in adolescence. Poorer executive function scores were associated with missing outcome data at the first follow-up timepoint (r = .18, p < .01), but not at the subsequent three (r = .04–.10, p = .14–.56) when cannabis use became increasingly prevalent; therefore, attrition was not considered a serious threat. Additionally, each repeatedly measured cannabis use outcome was regressed on age at the within-person level to identify the random threshold on the between-person level. Thus, cases with partially missing outcome data could still be included in the model. Still, we used maximum likelihood with robust standard errors (MLR) as the missing data estimator.

Analysis Plan

All analyses were conducted in Mplus 8.5 (Muthén & Muthén, 1998–2017) and accounted for clustering of multiple adolescents within families using the complex analysis option. In an initial linear regression model, child EF was regressed on child cognitive ability, sex, parents’ education, and parents’ cannabis and alcohol use.

Next, a series of two-level models were run separately for each of the three repeatedly assessed cannabis use outcomes (yearly, weekly, and daily). For each model, dichotomous cannabis use was regressed on individually grand-mean centered age at the within-person level to generate a random threshold of cannabis use based on repeated measures that was used as the dependent variable in the between-person analyses. Significant within-person age effects were assumed (i.e., that cannabis use becomes more likely with age) but were not of interest and are not discussed herein. The inclusion of quadratic age and random slope effects did not significantly improve model fit and thus were not included.

In the first model of interest, we regressed adolescents’ cannabis use on child EF, adjusting for child cognitive ability, sex, parent education, and mean age of observations. Second, to this model we added parents’ cannabis and alcohol use as predictors of adolescents’ cannabis use. In a third model, we further adjusted for adolescents’ alcohol use to identify independent effects of EF on cannabis use. Finally, we explored models using the behavioral regulation and metacognition subscales of EF alone and in combination as independent predictors of the cannabis use outcomes in the covariate adjusted models.

Results

Descriptive Statistics

Raw and t-scores (standardized scores based on age and gender) on the measure of EF (the Global Executive Composite) are reported in Supplementary Table 1. T-scores of 50 represent the mean, whereas scores ≥ 65 are considered in the clinical range. T-scores ranged from 35–92 for maternal reports, 35–87 for paternal reports, and 42–120 for teacher reports. According to mothers, fathers, teacher, or any informant, 24.7%, 11.9%, 42.2%, and 47.5%, respectively, of children were in the clinical range at one or both ages.

Given that predictors and covariates generally were the mean of standardized scores across multiple assessments or informants, descriptive statistics are less informative (see Supplementary Table 2). However, we noted that 33.7% of mothers and 51.3% of fathers reported cannabis use at one or more of the parent cannabis use assessment waves considered presently (child ages 21 months to 7 years).

Proportions of adolescents who endorsed each level of cannabis use at each age assessment from ages 13–20 years are included in Table 1; not shown (to avoid confusion) are eight cases included in the model who had an EF measure only at ages 11–12 years and who reported on their cannabis nonuse at this age. Of note in Table 1, across the ages considered here, 54.0% reported cannabis use in the past year at least once, 39.8% used weekly or more at least once, and 18.2% used daily or more at least once.

Table 1.

Adolescents’ Cannabis Use Rates by Assessment Year

Assessment Year Cannabis use in the past year
Proportion (%)
Age (years) ≥ Once ≥ Weekly ≥ Daily n
 13–14 8.9 0.0 0.0 45
 15–16 32.8 9.6 4.0 177
 17–18 38.5 15.4 7.7 156
 19–20 51.4 33.9 24.8 109
Cumulative prevalence 54.0 29.8 18.2 198
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Note. n’s vary given that some participants were not yet old enough to participate at the older assessments, and given the analytic inclusion criterion that a cannabis use observation was only considered if it was collected concurrent with or after the executive function measures at ages 11–12 and/or 13–14 years.

Prediction of Children’s Executive Function

Results of linear regression (intercept β [SE] = 1.06 [.46]) indicated EF scores were higher (meaning poorer functioning) among boys, β (SE) = .25 (.07), p < .001, and children with lower scores on general cognitive ability indicators, β (SE) = -.25 (.08), p = .001, after adjusting for nonsignificant effects of parent education, cannabis, and alcohol use, β (SE) = -.03 (.07), .02 (.08), and .05 (.07), respectively (p = .50–.81).

Prediction of Adolescents’ Cannabis Use from Executive Function

Summaries of the three sets of two-level regression models predicting each level (annual vs. no use; weekly vs. less than weekly or no use; daily vs. less than daily or no use) of cannabis use from EF are reported in Table 2. Beta coefficients are in the logit scale and represent the increase in the log odds of cannabis use associated with a one unit increase on the predictor. The standardized cross-informant EF score was the primary predictor of interest; thus, one unit was one standard deviation on this predictor.

Table 2.

Summary of Regression Models Predicting Past-Year, Weekly, or Daily Cannabis Use in Adolescence from Child Executive Function, Parental Cannabis Use, and Child-, Family-, and Parent-Level Covariates

Model 1: Adjusting for
basic covariates		 Model 2: Adjusting for
parent substance use		 Model 3: Adjusting for
adolescents’ alcohol use
Past-year1 cannabis use outcome:
b (SE) 95% CI b (SE) 95% CI b (SE) 95% CI
Executive function score2 0.45 (0.22)* 0.02, 0.87 0.38 (0.22) −0.05, 0.80 0.18 (0.16) −0.13, 0.50
Male −0.57 (0.38) −1.32, 0.18 −0.70 (0.37) −1.43, 0.02 −0.83 (0.31)** −1.45, −0.22
Cognitive ability −0.01 (0.09) −0.18, 0.17 −0.02 (0.08) −0.19, 0.14 −0.04 (0.08) −0.18, 0.11
Parent education 0.05 (0.19) −0.33, 0.43 0.18 (0.17) −0.15, 0.52 0.15 (0.16) −0.16, 0.46
Parent cannabis use - - 0.53 (0.25)* 0.03, 1.03 0.41 (0.21)* 0.01, 0.82
Parent alcohol use - - 0.69 (0.26)** 0.19, 1.19 0.37 (0.22) −0.06, 0.80
Adolescents’ alcohol use - - - - 2.35 (0.43)*** 1.50, 3.20
Weekly cannabis use outcome:
b (SE) 95% CI b (SE) 95% CI b (SE) 95% CI
Executive function score2 0.69 (0.23)** 0.24, 1.15 0.64 (0.24)** 0.17, 1.12 0.51 (0.20)* 0.12, 0.89
Male 0.01 (0.43) −0.84, 0.86 −0.12 (0.43) −0.97, 0.73 −0.35 (0.40) −1.15, 0.44
Cognitive ability −0.01 (0.11) −0.21, 0.20 −0.03 (0.11) −0.24, 0.19 −0.04 (0.10) −0.24, 0.16
Parent education −0.33 (0.24) −0.80, 0.14 −0.20 (0.23) −0.66, 0.26 −0.24 (0.23) −0.70, 0.21
Parent cannabis use - - 0.66 (0.30)* 0.07, 1.25 0.52 (0.27) −0.01, 1.05
Parent alcohol use - - 0.66 (0.29)* 0.09, 1.24 0.35 (0.29) −0.21, 0.91
Adolescents’ alcohol use - - - - 2.03 (0.46)*** 1.12, 2.93
Daily cannabis use outcome:
b (SE) 95% CI b (SE) 95% CI b (SE) 95% CI
Executive function score2 0.76 (0.28)** 0.20, 1.31 0.65 (0.25)* 0.15, 1.15 0.59 (0.25)* 0.11, 1.08
Male −0.16 (0.54) −1.22, 0.90 −0.30 (0.54) −1.36, 0.75 −0.59 (0.54) −1.64, 0.46
Cognitive ability −0.20 (0.13) −0.47, 0.06 −0.22 (0.13) −0.47, 0.03 −0.22 (0.13) −0.47, 0.03
Parent education −0.39 (0.30) −0.99, 0.21 −0.12 (0.28) −0.66, 0.43 −0.14 (0.28) −0.69, 0.42
Parent cannabis use - - 0.81 (0.39)* 0.05, 1.56 0.75 (0.39) −0.01, 1.51
Parent alcohol use - - 0.81 (0.35)* 0.13, 1.50 0.57 (0.35) −0.12, 1.26
Adolescents’ alcohol use - - - - 1.60 (0.49)** 0.64, 2.56
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Notes. b (SE) = unstandardized beta (standard error); betas are in the logit scale and represent the increase in the log odds of cannabis use associated with a one unit increase on the predictor. CI = confidence interval. In two-level models, the dichotomous cannabis use outcome was regressed on individually grand-mean centered age at the within-person level. In Models 1–3, within-person effects of age were all b (SE) = .32 (.08) for past-year cannabis use, and ranged from b (SE) = .57 (.12) to .60 (.13) for weekly cannabis use, and from b (SE) = .72 (.14) to .75 (.16) for daily cannabis use, all p < .001. Results above denote the regressions of the random threshold of cannabis use on predictors and covariates at the between-person level. The threshold is interpretable as the expected value of the latent response variable at which an adolescent transitions to cannabis use at age 17.62 years (the mean age of the sample across the waves in adolescence). Models adjusted for mean age of observation at the between-person level (betas not shown).

1

Any use in the past year measured repeatedly across adolescence.

2

Higher scores indicate poorer executive function.

*

p < .05.

**

p < .01.

***

p < .001.

Models adjusting for basic covariates.

Table 2, Model 1 shows models that were adjusted for child gender, cognitive ability, parent education, and mean age of observations. In these models, poorer EF predicted higher likelihood of adolescents’ annual, weekly, and daily use. None of the covariates were associated with the outcomes.

Models adjusting for parent substance use.

In Table 2, Model 2 parents’ cannabis and alcohol use were added to the covariates in the models summarized in Model 1. Parents’ cannabis and alcohol use each separately predicted a higher likelihood of adolescents’ annual, weekly, and daily cannabis use. Poorer EF remained a significant predictor of the likelihood of weekly or daily, but not annual, cannabis use. The effect sizes for the prediction of cannabis use from EF were small for annual (d = .21), weekly (d = .35), and daily (d = .36) cannabis use. A one standard deviation increase in the EF score (indicating poorer EF) was associated with a 46%, 90%, and 92% greater odds of annual, weekly, and daily cannabis use, respectively.

Models adjusting for adolescents’ alcohol use.

Model 3 of Table 2 builds on the prior models but further adjusts for the significant positive association that adolescents’ alcohol use had with their annual, weekly, and daily cannabis use. In these models, poorer EF persisted as a predictor of a higher likelihood of adolescents’ weekly and daily cannabis use. Parents’ cannabis use remained a significant predictor of adolescents’ annual cannabis use, but the associations that parent alcohol and cannabis use had with weekly and daily adolescent use were not significant.

Sensitivity analyses.

We noted there were 15 cases who reported cannabis use at or before their EF measure, although there were none who reported prior weekly or daily use. Given the potential for temporal confounding, we excluded these cases and reran the models based on 183 children of 102 fathers and 458 person-year observations. The patterns of findings and significance for EF was the same in Models 1–3 for all three outcomes with the exception of Model 1 for “any use” where the path strength was nearly identical but no longer significant; specifically, the beta (SE) for EF in the original versus alternative subsample models were as follows for Model 1: any use: 0.45 (0.22)* versus. 0.43 (0.22); weekly use: 0.69 (0.23)** versus. 0.72 (0.26)**; and daily use: 0.76 (0.28)** versus 0.76 (0.33)*. Thus, temporal confounding did not threaten the validity of these findings.

Exploratory Analyses Predicting Adolescents’ Cannabis Use from Executive Function Subscales

The models described in Table 2, Model 3 were rerun using the behavioral regulation and metacognition subscales of the EF measure as predictors of adolescents’ cannabis use. Separate models for each subscale followed the same pattern as for the global scale: significant prediction of weekly and daily but not annual cannabis use. When the subscales were included in the same model, neither significantly predicted any of the three outcomes. Given these patterns, the results are not reported in greater detail herein.

Discussion

Previous studies examining the link between EF and cannabis use typically have not used prospective assessments to determine whether EF problems may have predated cannabis use (Becker et al., 2018; Cousijn et al., 2014; Infante et al., 2020; Jacobus et al., 2015; Meier et al., 2012). Thus, the present study evaluated behavioral ratings of EF during late childhood to early adolescence as a predictor of cannabis use into late adolescence. Furthermore, we used a multi-informant measure of EF collected from mothers, fathers, and teachers, and we assessed cannabis use at three frequency levels—any use, weekly use, or daily use in the past year—at multiple points across adolescence (age 13 to as late as age 20 years). Child participants showed a range of EF, and significant numbers of youth scored in the clinically significant range according to one or more informants. The main findings indicate that—after adjusting for child sex, child general cognitive ability, and parent education—lower EF ratings predicted a greater likelihood of cannabis use in the past year, as well as weekly and daily cannabis use (vs. less frequent or no use) during adolescence.

The current findings are in line with prior research examining EF as a risk factor for cannabis use. Past longitudinal studies have found that prior to cannabis use (Tervo-Clemmens et al., 2018) and after cannabis use initiation (Cousijn et al., 2014), alterations in brain function subserving EFs are risk factors for subsequent cannabis use. Moreover, weaker working memory (Castellanos-Ryan et al., 2017; Wilson et al., 2021) and executive planning performance (Tervo-Clemmens et al., 2018) assessed before cannabis use initiation is associated with an earlier age of cannabis use onset and an increased likelihood of developing a CUD. Finally, better cognitive flexibility task performance may protect against, whereas self-reported impairments in inhibitory control may increase risk for adolescent cannabis use initiation (Guo et al., 2022). Having measured EF with parent and teacher reports via the BRIEF, the present study contributes to the evidence that regardless of whether child EF is measured via brain functioning, neurocognitive tasks, self-report, or multi-informant reports of child behavior, EF may be a risk factor for cannabis use.

Across all models, lower ratings of childhood EF predicted greater likelihood of adolescents’ endorsement of weekly or daily cannabis use (vs. less frequent use), but was not related to annual use after parents’ substance use was controlled. Although one cannot interpret null effects, it is possible that lower EF is a risk factor specifically for more frequent cannabis use, rather than infrequent use or “experimentation.” Furthermore, this finding mirrors prior cross-sectional studies that have found lower EF to be related to greater cannabis use frequency in adolescents, young adults, and adults (Block et al., 2022; Castellanos-Ryan et al., 2017; Dahlgren et al., 2016; Gruber et al., 2012; Lahanas & Cservenka, 2019; Lisdahl & Price, 2012). It is worth noting that frequent cannabis use in adolescence and young adulthood have been associated with various negative outcomes, including poorer cognitive functioning (Cyrus et al., 2021; Scott et al., 2018), decreased academic performance (Cyrus et al., 2021), higher levels of other substance use and substance use-related problems (Chan et al., 2021; Foster et al., 2018), and alterations in brain function and development (Blest-Hopley, Colizzi, et al., 2020; Blest-Hopley, Giampietro, et al., 2020). As such, child EF may serve as an important target for interventions aimed at preventing, delaying, or decreasing frequent cannabis use.

Importantly, the current findings also support parental cannabis and alcohol use as risk factors for adolescents’ annual, weekly, and daily cannabis use (vs. less frequent or no use), and these predictions were independent of one another and of paths involving EF. Importantly, these results support previous research examining the intergenerational transmission of substance use (Kerr et al., 2015; Madras et al., 2019; O’Loughlin et al., 2019; Tiberio et al., 2020). For example, our prior research using this and related samples has found that parents’ substance use during their own adolescence may contribute to their child’s cannabis use (Kerr et al., 2015; Tiberio et al., 2020). Furthermore, when adjusting for parent cannabis and alcohol use and adolescent alcohol use, lower ratings of childhood EF remained a significant predictor of weekly and daily cannabis use. Here we have highlighted the need for future research to include family-risk factors for cannabis use when assessing the link between EF and cannabis use, and to test whether these associations remain significant when controlling for familial-risk factors.

Given that lower ratings of childhood EF still predicted adolescents’ weekly and daily cannabis use when parent cannabis use was controlled, lower childhood EF appears to confer independent risk for cannabis use, above and beyond parental history of cannabis use. Also relevant to this discussion of independent risk factors, adolescent alcohol use was significantly associated with adolescents’ annual, weekly, and daily cannabis use, but did not account for the associations between childhood EF and frequent cannabis use in adolescence. Taken together, the association between child EF and frequent cannabis use in adolescence was not better explained by more general risk factors for substance use (parent substance use) or by adolescents’ general propensities to use substances (as indicated by their alcohol use).

Strengths, Limitations, and Future Directions

The present study had numerous strengths, including measuring prospective assessments of EF and substance use, examining different levels of cannabis use frequency (annual, weekly, and daily, each relative to less frequent or no use) as outcomes of interest, being the first multi-informant measure of EF collected at multiple points across late childhood to early adolescence, and adjusting for relevant confounds not normally assessed in studies examining the link between EF and cannabis use (e.g., parental substance use). The present study also had some limitations worth noting.

First, as a measure of EF, the BRIEF only used parent and teacher report of the child’s EF, whereas other versions of the BRIEF use self-report (Clark et al., 2017). The present study found that EF was lower among boys, which may result from using parent and teacher reports only, as boys typically display more externalizing behaviors than girls (Rescorla et al., 2007). Thus, the measure may be subject to response bias (although we note that our models adjusted for child sex). Second, the BRIEF includes two composite scores measuring metacognition and behavioral regulation, but it does not measure other aspects of EF (e.g., reasoning, decision making, problem solving) that may be predictive of future cannabis use. Furthermore, the BRIEF is not a neurocognitive task of EF, so it does not assess behavioral performance of participants. As the BRIEF and neurocognitive EF tasks may reflect different constructs (Clark et al., 2017; Toplak et al., 2013), future research may consider using both neurocognitive tasks and the BRIEF for a more thorough assessment of EF as a risk factor for later cannabis use.

Third, cannabis use was not measured after age 20 years. Given that prevalence rates of annual, monthly, and daily cannabis use increase through adolescence and peak in the early to mid-20s (Schulenberg et al., 2021), it would be informative to examine EF as a predictor of cannabis use across this higher-risk period. Fourth, there is a significant need for more refined ways to quantify cannabis use in future research, including quantity, form (e.g., edibles), method (e.g., vaporizers), and cannabinoid content. Fifth, participants resided primarily in Oregon—which has liberal cannabis policies—and youth may have been exposed to more cannabis products, liberal use norms, and parental cannabis use than youth in other regions. For example, in 2016–2017 past-month cannabis use prevalence among adults ages ≥ 26 years was 18% in Oregon compared to 8% in the U.S. overall (Oregon Public Health Division, 2019). Furthermore, although we lack a reference group of repeatedly assessed Oregon parents, parents in the present sample used cannabis at high rates (34% of mothers and 51% of fathers used at one or more childhood assessments). The sample also was predominantly White. Thus, future research with regionally and ethnically diverse children may enhance generalizability. Finally, it was beyond our present scope to query the mechanisms by which risk factors conferred risk for frequent cannabis use. Findings suggest that in addition to the conditions associated with parental alcohol and cannabis use (e.g., modeling, drug availability, poorer monitoring, genetic risk), and adolescents’ alcohol use (e.g., deviant peer influence), poorer EF independently predicts adolescents’ frequent cannabis use. Future research should identify these mechanisms and explore, for example, whether youth with poorer EF may be particularly drawn to cannabis use for its subjective effects or to a peer subculture that in turn explains risk for frequent cannabis use.

Conclusions

Findings from this prospective longitudinal study indicated that behavioral ratings of late childhood EF was a significant risk factor for subsequent cannabis use during adolescence, even when controlling for parental substance use and co-occurring adolescent alcohol use. Specifically, lower childhood EF may increase the risk for weekly and daily cannabis use versus lower levels of use. As frequent cannabis use in adolescence and young adulthood is associated with negative health and life outcomes (Blest-Hopley, Giampietro, et al., 2020; Chan et al., 2021; Cyrus et al., 2021; Foster et al., 2018; Scott et al., 2018) and may disrupt normative brain development (Blest-Hopley, Colizzi, et al., 2020), it is critical to identify mechanisms that can be targeted for prevention and intervention efforts.

Interventions may consider targeting children’s EF skills, including behavioral regulation and metacognition, particularly for those with poorer EFs, given they may benefit the most from EF skills training (Diamond & Lee, 2011). Previous research has shown that EFs can be improved in younger and older children (Diamond & Lee, 2011; Thorell et al, 2009) and lead to significant gains in academic and classroom performance (Holmes & Gathercole, 2014; Loosli et al., 2012; St Clair- Thompson et al., 2010). Therefore, childhood EF—i.e., prior to cannabis use initiation—may be a worthy target for interventions aimed at decreasing the risk for frequent cannabis use during adolescence.

Supplementary Material

Supplemental tables

Public Health Significance:

This study shows that older children with poorer executive function—a subset of cognitive abilities that continue developing into early adulthood—are more likely to use cannabis during adolescence and to do so weekly or daily. This is important because prior research has suggested poorer executive function is a consequence of cannabis use, whereas this study indicates poorer executive function may occur before youth have even tried cannabis.

Funding

Funding for this work was supported by the National Institutes of Health (NIH) from the National Institute of Drug Abuse (NIDA) grant number R01 DA015485 awarded to Drs. Deborah Capaldi and David Kerr. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or NIDA. NIH or NIDA had no further role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Declarations

Compliance with Ethical Standards. Jessica Cavalli, Anita Cservenka, David Kerr, Stacey Tiberio, and Lee Owen each declare that they have complied with APA ethical standards in the treatment of human participants.

Conflict of Interest. Jessica Cavalli, Anita Cservenka, David Kerr, Stacey Tiberio, and Lee Owen each declare they have no conflicts of interest.

Ethical Approval. All study procedures were reviewed and approved by the Oregon Social Learning Center Institutional Review Board. The study was performed following the ethical principles regarding all research involving humans as subjects as set forth in the Declaration of Helsinki, the Nuremburg Code, and the National Commission for the protection of Human Subjects of Biomedical and Behavioral Research entitled Ethical Principles and Guidelines for the Protection of Human Subjects of Research: The Belmont Report. In addition, the requirements set forth in Title 45, Part 46 of the Code of Federal Regulations were followed.

Informed Consent. Participants and their parents/guardians gave their informed assent and consent, respectively, for all study activities.

1

Most assessments occurred within these windows, but particularly the later ages were sometimes delayed; for example, 21.1% of the “19–20-year” assessments occurred at ages 21–22 years, and one occurred at age 23 years. However, we prioritized inclusion of late assessments and the analyses, which were based on actual age rather than target age for the outcomes, accommodated this approach.

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