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. Author manuscript; available in PMC: 2026 Mar 21.
Published in final edited form as: Psychol Addict Behav. 2026 Mar 19;40(6):683–690. doi: 10.1037/adb0001145

Cannabis Use Frequency and Driving Safety: Associations Between Cannabis Use Frequency and Driving Perceptions and Behaviors

Jenni B Teeters 1, Alejandro B Ramirez 1, Nathan D Meredith 1
PMCID: PMC13004164  NIHMSID: NIHMS2149472  PMID: 41854673

Abstract

Background:

Driving after cannabis use (DACU) represents a growing public health concern among emerging adults as cannabis legalization and rates of daily use continue to increase. More frequent cannabis users may hold more permissive beliefs and engage in more frequent cannabis-impaired driving behaviors, yet few studies have directly tested this assumption.

Methods:

Participants were 149 emerging adult cannabis users (74.1% women; average age = 20.70; 79.2% White) from the southeastern United States who endorsed driving after cannabis use at least 3 times in the past 3 months. Participants completed measures assessing cannabis use frequency, DACU-related behaviors (DACU, riding with a cannabis-impaired driver, using cannabis while driving, and driving after simultaneous alcohol and cannabis use), and DACU-related cognitions (perceived dangerousness, perceived legal consequences, peer approval, descriptive norms, and perceived “safe” amounts of cannabis before driving). Linear and negative binomial regressions were used to assess whether past month frequency of cannabis use is associated with risky driving behaviors and cognitions.

Results:

Cannabis use frequency was significantly associated with greater engagement in DACU, riding with a cannabis impaired driver, using cannabis while driving, driving after simultaneous alcohol and cannabis use, and perceiving larger amounts of cannabis as “safe” prior to driving. No significant associations between frequency of cannabis use and driving-related cognitions were found.

Conclusions:

More frequent cannabis users appear to underestimate impairment and engage in riskier driving behaviors. Research in more representative samples is needed given that this sample was majority female, White, non-Hispanic and college-attending.

Keywords: driving after cannabis use, cannabis use, emerging adults, frequent use, risk perceptions

Driving after cannabis use (DACU) is an increasingly prevalent public health concern. Currently, 40 states in the United States have legalized cannabis for medicinal use, and 24 have legalized it for recreational use, expanding access and raising important questions about road safety. From 2002 to 2022, cannabis use in the United States has increased across most age groups, with the highest rates consistently observed among emerging adults (ages 18–29 years; Caulkins, 2024; Rich et al., 2024). Recent data from the 2024 National Survey for Drug Use and Health (SAMHSA, 2025) show that 22.3% of adults report past year cannabis use (38.1% of emerging adults). Rates of daily cannabis use have also been increasing with 17.7 million cannabis-using emerging adults reporting daily or near-daily use within the past 30 days (Caulkins, 2024). Alongside these increases, concerns have grown regarding cannabis-impaired driving. The number of driving accidents involving cannabis has increased in recent years, with DACU contributing an estimated 6,800 excess roadway deaths per year (Pearlson et al., 2021). As cannabis use becomes more widespread, research has documented increases in both daily cannabis use and DACU - two behaviors associated with heightened crash risk and public safety concerns (SAMHSA, 2025). This study further investigates the role that cannabis use frequency plays in DACU-related cognitions and behavior.

Previous studies provide strong evidence that cannabis use deleteriously impacts a number of driving-related cognitive tasks including lane weaving and maintenance, reaction time and attention, and distraction susceptibility (Arkell et al., 2019; Brooks-Russell et al., 2024; Hartman et al., 2015; Meda et al., 2025). Such tasks are integral to driving behaviors, such as car following and overtaking, both of which Meda and colleagues (2025) found to be impaired by cannabis administration. These impairments on driving ability may last anywhere from 2 to 5 hours after cannabis consumption depending on the amount of THC consumed and the method of delivery (Arkell et al., 2019; Meda et al., 2025). Despite scientific evidence showing that cannabis impairs critical driving abilities, many individuals believe there to be no effect of cannabis on driving ability and some cannabis users perceive that cannabis improves driving abilities (Greene, 2018; Mills & Freeman, 2023).

In contrast to opinions regarding DACU, it is widely supported that driving after drinking alcohol (DAD) impairs driving abilities (Alonso et al., 2015; Martin et al., 2013). This is likely due in part to widespread DAD interventions and public service announcements. By comparison, rates of DACU-involved fatalities have increased in recent years (Lira et al., 2021). Previous research has shown DACU to be viewed more favorably than DAD in studies assessing public opinion related to DACU (Danton et al., 2003; Goodman et al., 2020; Greene, 2018; McCarthy et al., 2007). The persistence of DACU despite evidence of its risks highlights the importance of understanding the cognitive and social factors that influence this behavior.

One useful theory that has been applied to DACU is the Theory of Planned Behavior (TPB), which posits that attitudes, perceived behavioral control, and social norms shape behavioral intentions, which in turn predict future behavior (Bosnjak et al., 2020). The TPB emphasizes the role of social norms, such as injunctive norms (beliefs about what behaviors others think one should engage in) and descriptive norms (beliefs about what behaviors others actually engage in). Applied to DACU, individuals who have more positive attitudes towards DACU who also believe peers approve of, or engage in, DACU are more likely to intend to drive after using cannabis. Consistent with this framework, DACU-related cognitions — including lower perceived dangerousness of DACU, greater perceived peer approval, and lower perceived likelihood of legal consequences — are robust predictors of DACU behaviors (Bordovsky et al., 2019; Cuttler et al., 2018; Scott et al., 2021; Teeters et al., 2021).

Though these cognitive predictors are useful in understanding what makes a person more or less likely to drive after using cannabis, they do not fully account for the observed variability in DACU. There remains substantial variance in DACU attitudes and behaviors among cannabis users. Frequency of cannabis use appears to strongly contribute to this variability. In a systematic review, Boicu and colleagues (2024) found that individuals who use cannabis more frequently report more positive DACU cognitions and higher intention to engage in DACU than those who use less frequently (Boicu et al., 2024).

Although much of the literature on DACU has treated “cannabis users” as a single group, emerging evidence suggests that frequency of cannabis use is an important factor when considering risk. More frequent cannabis users not only consume greater quantities of cannabis but also demonstrate higher rates of co-use with alcohol and tobacco (Patton et al., 2005; Yurasek et al., 2017), poorer respiratory and mental health outcomes (Khoj et al., 2024; van der Pol et al., 2013), and measurable cognitive deficits compared to less frequent users (Lovell et al., 2020). Previous research has shown that frequent cannabis use is associated with a number of deleterious outcomes including early-onset psychosis (Di Forti et al., 2014), hyperalgesia (Zhang-James et al., 2023), severe psychological distress (Weinberger et al., 2020), and psychotic symptoms (Hasin, 2018). Salas-Wright and colleagues (2021) found that daily cannabis users were disproportionately more likely to engage in driving under the influence of cannabis compared to non-daily users (Salas-Wright et al., 2021). This suggests that more frequent cannabis users differ in their driving-related behaviors. However, little to no work has examined whether frequency of cannabis use is significantly associated with a variety of DACU-related cognitions and behaviors in a sample of emerging adults reporting recent DACU. If frequency of cannabis use is linked with lowered perceptions of dangerousness and greater social acceptance—and greater engagement in DACU and related behaviors—then prevention and intervention strategies should take this into account. The purpose of the present study is to examine associations between DACU cognitions and behaviors and past month cannabis use frequency. It is hypothesized that frequency of past month cannabis use will be associated with lower perceptions of dangerousness and legal consequences and greater amounts of cannabis deemed “safe” to consume prior to driving. It is also hypothesized that frequency of cannabis use will be associated with greater perceived peer approval and overestimation of the number of peers driving after use. Lastly, it is hypothesized that that frequency of cannabis use will be associated with more frequent DACU, riding with a cannabis-impaired driver (RWCD), cannabis use while driving, and simultaneous alcohol and cannabis use.

Method

Participants

Participants were 149 emerging adults from the southeastern United States recruited as part of an ongoing randomized controlled trial (RCT) testing the efficacy of a mobile based intervention aimed at reducing cannabis-impaired driving among emerging adults (ClinicalTrials.gov: NCT05537116). In order to be eligible for the RCT, participants had to be ages 18–29, have access to a motor vehicle and a valid driver’s license, and report driving after cannabis use at least 3 times in the past 3 months. Additionally, participants had to have access to a smart phone, willingness to read intervention materials, and agree to exchange a brief series of text messages via a secure mobile app (Text Request) with encryption technology post intervention with a research assistant. Participants were excluded from the study if they were currently in treatment for substance use (assessed via one self-report item on the screening survey asking participants if they were currently receiving treatment for substance use). Participants were 74.1% women, 25.9% men, and 79.2% White, 9.4% Black or African American, 1.3% Asian, 4.7% Hispanic or Latino, .7% selected other, and 4.7% selected multiple races. The average age of the participants was 20.70 years (SD = 2.78). Though college attendance was not a requirement of study participation, most participants were college attending (80.5%).

Measures

Cannabis Use.

Cannabis use in the past 7 days was assessed using a modified, brief computer-delivered Timeline Follow-Back assessment (TLFB; Sobell et al., 1996). Participants were instructed to indicate their daily cannabis use during a typical week in the past month, including method of use (smoking, vaping, or edibles), quantity (hits, edibles, joints, bowls, blunts, or bong rips), estimated hours spent high, and whether cannabis was combined with alcohol or another drug. Participants also reported the number of days they used cannabis in the past month.

Driving After Cannabis Use (DACU).

Driving after cannabis use was assessed with the question, “In the past 3 months, how many times have you driven after using marijuana?” (adapted from Arterberry et al., 2017).

Riding With a Cannabis-Impaired Driver (RWCD).

Participants indicated the number of times in the past 3 months they were passengers in a vehicle with a driver who had been using marijuana.

Cannabis Use While Driving.

Using cannabis while driving was assessed with an item asking, “In the past 3 months, how many times have you used marijuana WHILE driving (using marijuana while operating a motor vehicle).”

Perceptions of Dangerousness of DACU.

Perceived dangerousness of driving after cannabis use was assessed with the item, “How dangerous do you believe it is to drive within 2 hours of using marijuana?” on a 4-point Likert scale from 1 (not at all dangerous) to 4 (very dangerous).

Perceptions of Legal Consequences of DACU.

Perception of likelihood of legal consequences following DACU was assessed with the item, “How likely do you think it would be for a driver your age to experience legal consequences (e.g. being stopped by police, being arrested, being drug tested) for driving after using marijuana?” on a 4-point Likert scale from 1 (not very likely) to 4 (very likely).

Perceptions of “Safe” Amount of Cannabis Prior to Driving.

Participants reported the amount of cannabis they believed they could consume and still drive safely, indicating the amount in “hits.” This question was adapted from Borodovsky et al., 2020.

Injunctive Norms- Perceived Peer Approval.

Perceived peer approval was assessed separately for friends and typical college students using a 7-point Likert scale (1 = strongly disapprove, 7 = strongly approve). Friend approval was assessed with, “How much do you think your closest friends approve of driving a car after using marijuana?” Typical college student approval was assessed with, “How much do you think a typical student at your university approves of driving a car after using marijuana?”

Descriptive Norms.

Participants estimated the percentage of students at their university who reported driving after marijuana use in the past 3 months.

Procedures

All data included in this manuscript comes from the baseline assessment of an ongoing longitudinal randomized controlled trial (ClinicalTrials.gov: NCT05537116). All procedures were approved by the University Institutional Review Board, and participants were assured that all data would be kept confidential. Participants were recruited via a university-wide mass email, the university subject pool, flyers posted in the local community, and online advertisements. Following an eligibility screener survey, eligible participants were contacted by trained research staff and invited to participate in the clinical trial.

Emerging adults who elected to participate in the clinical trial were sent a 30-minute baseline questionnaire via text message, which they completed remotely on their mobile phone through a secure web server. Participants were compensated for completing the baseline questionnaire with either a $25 Amazon gift card or subject pool credits, which are points assigned for participation in research studies within psychology courses. The clinical trial is ongoing and includes a 3- and 6-month follow-up assessment. Outcomes from the full trial will be reported once all data is collected.

Data Analysis Plan

For the randomized clinical trial, sample size was calculated using the simr package from R version 4.0.3 for the Poisson mixed-effects model with random intercepts. A total sample size of 142 was needed to have 97.5% confidence of having at least 80% power. All participants who completed the baseline survey of the randomized controlled trial were included in the present data analyses.

To minimize the impact of outliers, values were checked to determine whether any values were greater than 3.29 SDs above the mean on a given variable (Tabachnick & Fidell, 2013). No outliers were identified. Linear regressions were used to examine associations for continuous driving-related perceptions (perceptions of dangerousness, perceptions of legal consequences, perceptions of friend and student approval, DACU descriptive norms).

For count outcomes (driving after cannabis use, riding with a cannabis-impaired driver, cannabis use while driving, and amount of cannabis perceived safe prior to driving), negative binomial regressions were used. This approach was chosen because the count variables were overdispersed (variance exceeded the mean). Negative binomial regression accounts for overdispersion and estimates incidence rate ratios (IRRs), allowing comparison of the expected counts based on cannabis use frequency. Wald chi-square statistics and 95% confidence intervals were used to evaluate model significance and effect sizes.

Transparency and Openness.

We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study, and we follow JARS (Kazak, 2018). Materials and analysis code for this study are available by emailing the corresponding author. Data were analyzed using SPSS Version 31. This study’s design and its analysis were not pre-registered.

Results

In the past 3 months, participants reported driving after cannabis use an average of 25.02 times (SD = 27.25) and riding with a driver who was under the influence of cannabis an average of 12.15 times (SD = 17.29). During the same period, nearly two-thirds of participants (61.7%) reported using cannabis while driving, with an average of 10.27 occasions (SD = 20.17), and 38.7% reported driving after simultaneous use of alcohol and cannabis, with an average of 5.76 occasions (SD = 16.97). Participants reported using cannabis on an average of 20.47 days (SD = 9.82) in the past month. In an average week, participants estimated being high for around 31.40 hours (SD = 27.95).

Driving After Cannabis Use

A negative binomial regression examined cannabis use frequency as a continuous predictor of driving after cannabis use (DACU). The model was significant, Wald χ2(1) = 112.94, p < .001. Greater cannabis use frequency was associated with more frequent DACU (B = 0.079, 95% CI [0.064, 0.093], p < .001). Exponentiating the coefficient indicated that each additional day of cannabis use was associated with approximately an 8% increase in the expected frequency of DACU (IRR = 1.08), 95% CI [1.07, 1.10]).

Riding with a Cannabis-Impaired Driver

A negative binomial regression predicting riding with a cannabis-impaired driver (RWCD) also indicated significant differences by use frequency. The model was significant, Wald χ2(1) = 32.62, p < .001. Greater cannabis use frequency was associated with more frequent RWCD (B = 0.058, 95% CI [0.038, 0.079], p < .001). Exponentiating the coefficient indicated that each additional day of cannabis use was associated with approximately a 6% increase in the expected frequency of RWCD (IRR = 1.06), 95% CI [1.04, 1.08]).

Using Cannabis While Driving

A negative binomial regression examining days of cannabis use as a continuous predictor indicated that more frequent cannabis use was associated with a higher frequency of using cannabis while driving, Wald χ2(1) = 23.19, p < .001. Each additional day of cannabis use was associated with an 8% increase in the expected rate of using cannabis while driving (IRR = 1.08, 95% CI [1.05, 1.12]).

Driving after Simultaneous Use of Alcohol and Cannabis.

A negative binomial regression examined whether days of cannabis use predicted driving after simultaneous use of alcohol and cannabis. The model was significant, Wald χ2(1) = 8.48, p = .004. Greater cannabis use frequency was associated with more frequent driving after simultaneous alcohol and cannabis use (B = 0.070, 95% CI [0.023, 0.117], p = .004). Exponentiating the coefficient indicated that each additional day of cannabis use was associated with approximately a 7% increase in the expected frequency of driving after simultaneous alcohol and cannabis use (IRR = 1.07, 95% CI [1.02, 1.12]).

Perceived Safe Driving After Cannabis Use

A negative binomial regression examined whether days of cannabis use predicted the amount of cannabis perceived to be safe prior to driving. The model was significant, Wald χ2(1) = 10.28, p = .001. Greater cannabis use frequency was associated with endorsing larger amounts of cannabis as safe to consume before driving (B = 0.024, 95% CI [0.009, 0.038], p = .001). Exponentiating the coefficient indicated that each additional day of cannabis use was associated with approximately a 2% increase in the amount of cannabis “hits” perceived to be safe prior to driving (IRR = 1.02, 95% CI [1.01, 1.04]).

Differences in Driving-Related Perceptions

Linear regression analyses were conducted to examine whether frequency of cannabis use was associated with driving-related perceptions. Frequency of cannabis use was not significantly associated with perceived danger of driving after cannabis use (B = −0.01, SE = 0.01, β = −.16, t = −1.89, p = .061), perceived friend approval (B = 0.02, SE = 0.01, β = .13, p = .126), perceived student approval (B = −0.01, SE = 0.01, β = −.07, p = .436), descriptive norms (B = 0.25, SE = 0.20, β = .10, p = .224), or perceived likelihood of legal consequences (B = 0.01, SE = 0.01, β = .05, p = .567).

All regression results are reported in Table 1.

Table 1.

Associations Between Frequency of Cannabis Use and Driving-Related Behaviors and Perceptions

Outcome Variable Model Type B SE 95% CI Test Statistic p
Driving After Cannabis Use Negative binomial 0.079 0.007 [0.064, 0.093] Wald χ2 = 112.94 < .001
Riding With a Cannabis-Impaired Driver Negative binomial 0.058 0.010 [0.038, 0.079] Wald χ2 = 32.62 < .001
Using Cannabis While Driving Negative binomial 0.078 0.016 [0.046, 0.110] Wald χ2 = 23.19 < .001
Driving After Simultaneous Use Negative binomial 0.070 0.024 [0.023, 0.117] Wald χ2 = 8.48 .004
Amount Safe Before Driving Negative binomial 0.024 0.007 [0.009, 0.038] Wald χ2 = 10.28 .001
Perceived Danger Linear −0.009 0.005 — t = −1.89 .061
Perceived Friend Approval Linear 0.018 0.012 — t = 1.54 .126
Perceived Student Approval Linear −0.010 0.013 — t = −0.78 .436
Descriptive Norms Linear 0.247 0.202 — t = 1.22 .224
Perceived Legal Consequences Linear 0.005 0.008 — t = 0.57 .567
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Note. Negative binomial regression was used for count outcome; linear regression was used for continuous perception variables. B values represent log-count coefficients for negative binomial models. IRRs reported in text are derived from exponentiated B values.

Discussion

Driving after cannabis use (DACU) continues to be a pressing public health issue, especially as cannabis legalization and frequent use increase among emerging adults. The present study aimed to examine whether frequency of cannabis use was associated with perceptions of, and engagement in, cannabis-impaired driving behaviors. In line with our hypotheses, frequency of cannabis use was significantly associated with greater engagement in risky driving behaviors, including DACU, riding with a cannabis-impaired driver (RWCD), using cannabis while driving, and driving after simultaneous use of cannabis and alcohol. Frequency of cannabis use was also associated with the amount of cannabis perceived as “safe” to consume prior to driving. Regarding injunctive norms (peer approval), descriptive norms, perceptions of dangerousness, and perceptions of legal consequences, no associations between cannabis use frequency and these variables was observed. These patterns reflect a growing body of literature that links frequent cannabis use with elevated impaired-driving risk among emerging adults.

Frequency of cannabis use was associated with more frequent engagement in DACU and RWCD, which is consistent with several prior findings. Using data from the National Survey for Drug Use and Health, Salas-Wright and colleagues (2021) found that daily cannabis users were more likely to report DACU, as well as riding with an impaired driver, than occasional users (Salas-Wright et al., 2021). Similar dose-response relationships have been found among Canadian high-school students, where cannabis dependence was the most robust predictor of DACU and RWCD (Cantor et al., 2021). Furthermore, regional studies of young adults in Michigan and Washington state also report consistent findings with daily-users overrepresented among those who drive after cannabis use or ride with impaired drivers (Hicks et al., 2022; Hultgren et al., 2024). While it is not surprising that people who use cannabis more frequently report more frequent DACU and RWCD, the present study confirms these patterns in a heavy using sample of emerging adults reporting recent DACU. The current results replicate and extend this body of literature by showing that frequency of use is also associated with using cannabis while driving and driving after simultaneous alcohol and cannabis use. Both behaviors are of particular concern, given the likelihood to result in increased driving-related impairment (Eichelberger, 2023).

One particularly novel finding of the present study was that frequency of cannabis use was associated with using cannabis while driving (using cannabis while operating a motor vehicle), as this is an underexamined cannabis use behavior. Ortiz-Peregrina and colleagues (2020) reported that in a sample of occasional (≥ 1 time per week but < 4 times per week in the past 3 months) cannabis users aged 19–36, 15% reported smoking while driving, a rate much lower than the 61.7% found in the present sample of cannabis users reporting recent DACU (Ortiz-Peregrina et al., 2020). This risky behavior was especially prevalent among daily/near-daily users (20+ days in the past month) with 65.9% of daily/near-daily users reporting using cannabis while driving 14.08 (SD = 24.25) times in the past 3 months. It is important to note that the act of using cannabis while driving is particularly concerning since it may indicate that frequent users may misperceive their ability to safely multitask (consume cannabis and drive), even though evidence suggests that impairment exists even at low doses of THC (Cantor et al., 2021; Cristiano et al., 2023; Ortiz-Peregrina et al., 2020). While underexamined, the risk of cannabis use while driving should not be underestimated as it is uniquely dangerous and combines physical distraction with continuous ingestion and effects of THC, in turn leading to a progressive escalation of impairment throughout motor vehicle operation and a reduction in the driver’s ability to self-monitor and make safe decisions.

The elevated rates of simultaneous alcohol and cannabis use documented in this study are also particularly concerning, as the risk to road safety is greater than with either substance alone (Fares et al., 2022). In the full sample, 38.7% reported driving after simultaneous use of alcohol and cannabis use and frequency of cannabis use was significantly associated with frequency of driving after simultaneous use. Known as additive impairment, laboratory and roadside studies have found that co-use is associated with additive impairment in lane weaving and divided attention tasks (Fares et al., 2022; Gohari et al., 2024; Gonçalves et al., 2022; Hartman & Huestis, 2013). Relevant to this finding and other risky cannabis behaviors are studies that utilize ecological momentary assessment and indicate that simultaneous alcohol and cannabis use dramatically increases willingness to drive soon after co-consumption (Wycoff et al., 2025). By documenting the link between frequency of use and driving after simultaneous use, these findings highlight a critical subgroup for intervention.

Frequency of cannabis use was associated with perceiving greater amounts of cannabis as “safe” to consume prior to driving. Regarding “safe” amounts, these findings replicate that of McDonald and colleagues (2021), in which frequent cannabis users were less likely to view any level of cannabis consumption as unsafe for driving (McDonald et al., 2021). Similarly, these findings align with those of Borodovsky and colleagues (2020) showing that higher intoxication levels perceived as safe for driving corresponded to more frequent driving under the influence of cannabis. Our finding regarding perceptions of DACU safety replicates a consistent pattern observed in the literature: frequent users consistently report lower perceived dangerousness across adolescent, young-adult, and adult samples (LoParco et al., 2025; McDonald et al., 2021; O’Connell et al., 2022). Similarly, Cuttler and colleagues (2018) found that a higher frequency of cannabis use predicts beliefs that DACU is safe, driving within 1 hour of use, and a greater likelihood of DACU-related incidents (Cuttler et al., 2018). A possible explanation for this finding might be that misperceptions of tolerance and the normalization of cannabis-related impairment within social networks of frequent users underlies these beliefs. Laboratory research findings have even demonstrated that despite a subjective sense of safety and tolerance, even experienced cannabis users show deficits in vigilance, reaction time, and complex driving tasks (Hartman & Huestis, 2013). Therefore, misperceptions of tolerance and perceived dangerousness may lead to a false sense of security that in turn encourages riskier driving practices (Dutra et al., 2022).

Surprisingly, no significant associations relating to perceptions of dangerousness, injunctive norms (peer approval), legal consequences, descriptive norms, and frequency of use were observed even though previous research has identified these cognitive/perceptual differences as important drivers of DACU (Cristiano et al., 2023; McCartney et al., 2025). Additionally, previous research has emphasized the role of injunctive and descriptive norms as important in shaping DACU intentions (McCarthy et al., 2007; Scott et al., 2021; Ward et al., 2017). From a theoretical perspective, these findings partially align with the TPB, which posits that attitudes, perceived behavioral control, and social norms shape behavioral intentions and subsequent behavior. Consistent with TPB, frequent use was associated with greater perceived “safe” amounts of cannabis prior to driving. However, contrary to expectations based on the TPB, no differences were observed in perceptions of dangerousness, injunctive or descriptive norms, or perceived likelihood of legal consequences. This pattern suggests that for frequent users, DACU behavior may be driven more by attitudinal and perceived control factors (e.g., tolerance beliefs, self-perceived competence) than by normative influences. Future research should therefore consider extending TPB models of DACU to incorporate additional constructs such as perceived tolerance or subjective impairment, which may uniquely influence frequent users’ decision-making about DACU. Additionally, Romm and colleagues (2022) suggest that legalization environments may alter perceptions of safety without necessarily shifting perceived norms. Therefore, it could be that more frequent cannabis users internalize personal risk minimization while still perceiving the broader legal environment as unchanged.

Together, the current findings reinforce a growing consensus that cannabis use frequency is a robust predictor of DACU and related behaviors (Cantor et al., 2021; Hicks et al., 2022; Hultgren et al., 2024; Salas-Wright et al., 2021). By assessing multiple outcomes simultaneously, including DACU, RWCD, using cannabis while driving, and driving after simultaneous alcohol and cannabis use, this study extends prior findings and highlights the broad behavioral and perceptual risk profile of frequent cannabis users. Additionally, the identification of differences in perceived “safe” amounts highlights the potential importance of tolerance misperceptions as a mechanism linking frequency of use to impaired driving.

From a public health perspective, frequent cannabis users are at a particularly heightened risk for cannabis-impaired driving. Given the converging evidence that frequency of cannabis use is a predictor for increased impaired driving risk, interventions should prioritize heavy/frequent cannabis users. In an emerging adult sample like this one, brief screening at campus health centers or primary-care visits can identify frequent users for targeted educational interventions focused on objective impairment (e.g., slowed reaction time), rather than subjective tolerance. Specifically, ensuring that frequent users understand the myth that tolerance somehow decreases your risk when driving is especially important, as demonstrated by the literature on tolerance-misperception (Dutra et al., 2022; González-Roz et al., 2025; Huỳnh et al., 2024; McCartney et al., 2025).

Several limitations should be acknowledged. First, the cross-sectional design prevents conclusions about causality. Second, reliance on self-report measures may introduce bias; however, self-report remains the most practical approach for large-scale behavioral data collection and has been shown to correspond with cannabis urinalysis results (Curran et al., 2019). When feasible, future studies should consider incorporating objective indicators of cannabis-related behaviors, including measures of impairment such as THC concentration or performance-based driving tasks. Third, the RCT was conducted with participants in a single region in the Southeastern United States and was not demographically varied with the majority of participants identifying as female, White, non-Hispanic, and college attending, which limits generalizability. Furthermore, future studies should explore potential sex differences and include more demographically representative samples to enhance generalizability and to clarify any potential population-specific risk factors, as prior research highlights significant differences in DACU by gender (Cristiano et al., 2023; O’Malley & Johnston, 2013; Wrathall et al., 2025) and ethnicity (Benedetti et al., 2021; Salas-Wright et al., 2023). It is also important to note that, although medical cannabis was legalized in the state of data collection (Kentucky) in 2022, the first medical cannabis dispensary did not open until December 2025, after data collection for the present study had concluded; recreational use remains illegal.

Future research should employ longitudinal designs to clarify whether frequent cannabis use predicts changes in impaired driving behaviors over time. Additionally, attention should be paid to factors such as THC potency and administration method (e.g., vaping, edibles, smoking) as these may play an important role in risk. It is also important to explore contextual factors such as peer dynamics, enforcement exposure, and accessibility of cannabis, which could provide a more complete understanding of DACU risk. Finally, intervention research should examine strategies to reduce misperceptions of safe amounts of cannabis and risk perceptions among frequent cannabis users, especially targeting beliefs about “safe” amounts. For example, Teeters and colleagues (2021, 2022) showed that a brief, mobile-based intervention combining personalized feedback and motivational interviewing style text-messages significantly increased perceptions of dangerousness and decreased instances of DACU and RWCD among emerging adult cannabis users (Teeters et al., 2021, 2022). However, these studies did not examine whether intervention efficacy differed by frequency of cannabis use. Future work should test cannabis use frequency as a moderator of intervention outcomes to determine whether more frequent cannabis users respond differently to preventive strategies, thereby informing more targeted and effective intervention approaches.

Frequency of cannabis use is associated with greater cannabis-impaired driving behaviors, including DACU, RWCD, using cannabis while driving, and driving after simultaneous alcohol and cannabis use. Frequency of cannabis use is also associated with greater perceived “safe” amounts of cannabis prior to driving, suggesting that tolerance misperceptions may contribute to these elevated risks. These findings highlight the importance of targeted prevention and intervention strategies that address risk perceptions and emphasize objective impairment among frequent cannabis users.

Public Health Significance Statement:

Frequency of cannabis use is associated with riskier cannabis-related driving behaviors and overestimation of safety of driving after cannabis use. Targeted prevention and intervention efforts are critical to reducing cannabis-impaired driving and related injuries among frequent cannabis users.

Acknowledgments:

This project was supported by the National Institute of Drug Abuse (1R15DA051833-01A1).

Footnotes

Declarations of interest: none

References

  1. Alonso F, Pastor JC, Montoro L, & Esteban C (2015). Driving under the influence of alcohol: Frequency, reasons, perceived risk and punishment. Substance Abuse Treatment, Prevention, and Policy, 10(1), 11. 10.1186/s13011-015-0007-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Arkell TR, Lintzeris N, Kevin RC, Ramaekers JG, Vandrey R, Irwin C, Haber PS, & McGregor IS (2019). Cannabidiol (CBD) content in vaporized cannabis does not prevent tetrahydrocannabinol (THC)-induced impairment of driving and cognition. Psychopharmacology, 236(9), 2713–2724. 10.1007/s00213-019-05246-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arterberry BJ, Treloar H, & McCarthy DM (2017). Empirical profiles of alcohol and marijuana use, drugged driving, and risk perceptions. Journal of Studies on Alcohol and Drugs, 78(6), 889–898. 10.15288/jsad.2017.78.889 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Benedetti MH, Li L, Neuroth LM, Humphries KD, Brooks-Russell A, & Zhu M (2021). Demographic and policy-based differences in behaviors and attitudes towards driving after marijuana use: An analysis of the 2013–2017 Traffic Safety Culture Index. BMC Research Notes, 14(1), 226. 10.1186/s13104-021-05643-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Boicu B, Al-Hakim D, Yuan Y, & Brubacher J (2024). Attitudes toward driving after cannabis use: A systematic review. Journal of Cannabis Research, 6(1), 37. 10.1186/s42238-024-00240-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bosnjak M, Ajzen I, & Schmidt P (2020). The theory of planned behavior: Selected recent advances and applications. Europe’s Journal of Psychology, 16(3), 352–356. 10.5964/ejop.v16i3.3107 [DOI] [Google Scholar]
  7. Brooks-Russell A, Wrobel J, Brown T, Bidwell LC, Wang GS, Steinhart B, Dooley G, & Kosnett MJ (2024). Effects of acute cannabis inhalation on reaction time, decision-making, and memory using a tablet-based application. Journal of Cannabis Research, 6(1), 3. 10.1186/s42238-024-00215-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cantor N, Kingsbury M, Hamilton HA, Wild TC, Owusu-Bempah A, & Colman I (2021). Correlates of driving after cannabis use in high school students. Preventive Medicine, 150, 106667. 10.1016/j.ypmed.2021.106667 [DOI] [PubMed] [Google Scholar]
  9. Caulkins JP (2024). Changes in self-reported cannabis use in the United States from 1979 to 2022. Addiction, 119(9), 1648–1652. 10.1111/add.16519 [DOI] [PubMed] [Google Scholar]
  10. Chou SP, Grant BF, Dawson DA, Stinson FS, Saha T, & Pickering RP (2005). Twelve-month prevalence and changes in driving after drinking: United States, 1991–1992 and 2001–2002. Drug and Alcohol Dependence, 80(2), 223–230. 10.1016/j.drugalcdep.2005.03.013 [DOI] [PubMed] [Google Scholar]
  11. Cristiano N, Hathaway A, Cullen G, Wrathall M, & Walters D (2023). High time for the development of gendered interventions to prevent young people driving after cannabis use: Evidence from Canada’s National Cannabis Survey. Drugs: Education, Prevention and Policy, 30(6), 593–602. 10.1080/09687637.2022.2117021 [DOI] [Google Scholar]
  12. Curran HV, Hindocha C, Morgan CJA, Shaban N, Das RK, & Freeman TP (2019). Which biological and self-report measures of cannabis use predict cannabis dependency and acute psychotic-like effects? Psychological Medicine, 49(9), Article 9. 10.1017/S003329171800226X [DOI] [Google Scholar]
  13. Cuttler C, Sexton M, & Mischley L (2018). Driving under the influence of cannabis: An examination of driving beliefs and practices of medical and recreational cannabis users across the United States. Cannabis, 1(2), Article 2. 10.26828/cannabis.2018.02.001 [DOI] [Google Scholar]
  14. Danton K, Misselke L, Bacon R, & Done J (2003). Attitudes of young people toward driving after smoking cannabis or after drinking alcohol. Health Education Journal, 62(1), 50–60. 10.1177/001789690306200106 [DOI] [Google Scholar]
  15. Di Forti M, Sallis H, Allegri F, Trotta A, Ferraro L, Stilo SA, Marconi A, La Cascia C, Reis Marques T, Pariante C, Dazzan P, Mondelli V, Paparelli A, Kolliakou A, Prata D, Gaughran F, David AS, Morgan C, Stahl D, … Murray RM (2014). Daily use, especially of high-potency cannabis, drives the earlier onset of psychosis in cannabis users. Schizophrenia Bulletin, 40(6), 1509–1517. 10.1093/schbul/sbt181 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dutra LM, Farrelly M, Gourdet C, & Bradfield B (2022). Cannabis legalization and driving under the influence of cannabis in a national U.S. sample. Preventive Medicine Reports, 27, 101799. 10.1016/j.pmedr.2022.101799 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Eichelberger A (2023). Prevalence of alcohol, cannabis, and simultaneous use among drivers in six states. Transportation Research Record: Journal of the Transportation Research Board, 2677(11), 237–244. 10.1177/03611981231165024 [DOI] [Google Scholar]
  18. Goodman SE, Leos-Toro C, & Hammond D (2020). Risk perceptions of cannabis- vs. alcohol-impaired driving among Canadian young people. Drugs: Education, Prevention and Policy, 27(3), 205–212. 10.1080/09687637.2019.1611738 [DOI] [Google Scholar]
  19. Fares A, Wright M, Matheson J, Mann RE, Stoduto G, Le Foll B, Wickens CM, Brands B, & Di Ciano P (2022). Effects of combining alcohol and cannabis on driving, breath alcohol level, blood THC, cognition, and subjective effects: A narrative review. Experimental and Clinical Psychopharmacology, 30(6), 1036–1049. 10.1037/pha0000533 [DOI] [PubMed] [Google Scholar]
  20. Gohari MR, Patte KA, Elton-Marshall T, Cole A, Turcotte-Tremblay A-M, Bélanger R, & Leatherdale ST (2024). The association between single and dual use of cannabis and alcohol and driving under the influence and riding with an impaired driver in a large sample of Canadian adolescents. Traffic Injury Prevention, 25(6), 765–773. 10.1080/15389588.2024.2342571 [DOI] [PubMed] [Google Scholar]
  21. Gonçalves PD, Gutkind S, Segura LE, Castaldelli-Maia JM, Martins SS, & Mauro PM (2022). Simultaneous alcohol/cannabis use and driving under the influence in the U.S. American Journal of Preventive Medicine, 62(5), 661–669. 10.1016/j.amepre.2021.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. González-Roz A, Iza-Fernández C, Alemán-Moussa L, & Secades-Villa R (2025). Prevalence and correlates of driving under the influence of alcohol and cannabis among Spanish adolescents. Journal of Studies on Alcohol and Drugs, 86(4), 626–632. 10.15288/jsad.24-00132 [DOI] [PubMed] [Google Scholar]
  23. Greene KM (2018). Perceptions of driving after marijuana use compared to alcohol use among rural American young adults: Perceptions of driving after marijuana use. Drug and Alcohol Review, 37(5), 637–644. 10.1111/dar.12686 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hartman RL, Brown TL, Milavetz G, Spurgin A, Pierce RS, Gorelick DA, Gaffney G, & Huestis MA (2015). Cannabis effects on driving lateral control with and without alcohol. Drug and Alcohol Dependence, 154, 25–37. 10.1016/j.drugalcdep.2015.06.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hartman RL, & Huestis MA (2013). Cannabis effects on driving skills. Clinical Chemistry, 59(3), 478–492. 10.1373/clinchem.2012.194381 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hasin DS (2018). US epidemiology of cannabis use and associated problems. Neuropsychopharmacology, 43(1), 195–212. 10.1038/npp.2017.198 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hicks DL, Resko SM, Ellis JD, Agius E, & Early TJ (2022). Driving after cannabis use among young adults in Michigan. Cannabis and Cannabinoid Research, 7(1), 100–106. 10.1089/can.2020.0096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hultgren BA, Calhoun BH, Fleming CB, Rhew IC, Larimer ME, Kilmer JR, & Guttmannova K (2024). Trends in driving under the influence of alcohol and cannabis among young adults in Washington State from before to during the COVID-19 pandemic. American Journal of Public Health, 114(S8), S698–S701. 10.2105/AJPH.2024.307767 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Huỳnh C, Beaulieu-Thibodeau A, Fallu J-S, Bergeron J, Jacques A, & Brochu S (2024). Factors related to the low-risk perception of driving after cannabis use. Accident Analysis & Prevention, 202, 107584. 10.1016/j.aap.2024.107584 [DOI] [PubMed] [Google Scholar]
  30. Khoj L, Zagà V, Amram DL, Hosein K, Pistone G, Bisconti M, Serafini A, Cammarata LM, Cattaruzza MS, & Mura M (2024). Effects of cannabis smoking on the respiratory system: A state-of-the-art review. Respiratory Medicine, 221, 107494. 10.1016/j.rmed.2023.107494 [DOI] [PubMed] [Google Scholar]
  31. Lira MC, Heeren TC, Buczek M, Blanchette JG, Smart R, Pacula RL, & Naimi TS (2021). Trends in cannabis involvement and risk of alcohol involvement in motor vehicle crash fatalities in the United States, 2000‒2018. American Journal of Public Health, 111(11), 1976–1985. 10.2105/AJPH.2021.306466 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. LoParco CR, Olsson SE, Greene KM, Berg CJ, Walters ST, Zhou Z, & Rossheim ME (2025). Driving under the influence of alcohol, cannabis, and delta-8 THC: Perceived likelihood, risk perceptions, and behaviors. Journal of Psychoactive Drugs, 0(0), 1–10. 10.1080/02791072.2024.2339506 [DOI] [Google Scholar]
  33. Lovell ME, Akhurst J, Padgett C, Garry MI, & Matthews A (2020). Cognitive outcomes associated with long-term, regular, recreational cannabis use in adults: A meta-analysis. Experimental and Clinical Psychopharmacology, 28(4), 471–494. 10.1037/pha0000326 [DOI] [PubMed] [Google Scholar]
  34. Martin TL, Solbeck PAM, Mayers DJ, Langille RM, Buczek Y, & Pelletier MR (2013). A review of alcohol-impaired driving: The role of blood alcohol concentration and complexity of the driving task. Journal of Forensic Sciences, 58(5), 1238–1250. 10.1111/1556-4029.12227 [DOI] [PubMed] [Google Scholar]
  35. McCarthy DM, Lynch AM, & Pedersen SL (2007). Driving after use of alcohol and marijuana in college students. Psychology of Addictive Behaviors, 21(3), 425–430. 10.1037/0893-164X.21.3.425 [DOI] [PubMed] [Google Scholar]
  36. McCartney D, Zhou C, Lavender I, Gordon R, Kevin RC, Bedoya‐Pérez M, & McGregor IS (2025). The driving‐related attitudes, beliefs and behaviours of cannabis users in the Australian Capital Territory following decriminalisation. Drug and Alcohol Review, 44(2), 588–601. 10.1111/dar.13983 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. McDonald AJ, Hamilton HA, Wickens CM, Watson TM, Elton-Marshall T, Wardell JD, Rueda S, Roerecke M, Stoduto G, & Mann RE (2021). Driving under the influence of cannabis risk perceptions and behaviour: A population-based study in Ontario, Canada. Preventive Medicine, 153, 106793. 10.1016/j.ypmed.2021.106793 [DOI] [PubMed] [Google Scholar]
  38. Meda SA, Stevens MC, Boer ER, Pittman B, Gueorguieva R, Huestis MA, & Pearlson GD (2025). A randomized, placebo-controlled, double-blind, pilot study of cannabis-related driving impairment assessed by driving simulator and self-report. Journal of Psychopharmacology (Oxford, England), 39(4), 364–372. 10.1177/02698811251324379 [DOI] [PubMed] [Google Scholar]
  39. Mills L, & Freeman J (2023). Investigating predictors of driving immediately after consuming cannabis: A study of medical and recreational cannabis users in Australia. Transportation Research Part F: Traffic Psychology and Behaviour, 96, 213–221. 10.1016/j.trf.2023.06.015 [DOI] [Google Scholar]
  40. O’Connell M, Levy S, Shrier LA, & Harris SK (2022). Trends in cannabis-related attitudes and behaviors among cannabis-using adolescent and young adult outpatients following medical cannabis legalization in Massachusetts. Substance Abuse, 43(1), 328–335. 10.1080/08897077.2021.1941517 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. O’Malley PM, & Johnston LD (2013). Driving after drug or alcohol use by US high school seniors, 2001–2011. American Journal of Public Health, 103(11), 2027–2034. 10.2105/AJPH.2013.301246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Ortiz-Peregrina S, Ortiz C, Castro-Torres JJ, Jiménez JR, & Anera RG (2020). Effects of smoking cannabis on visual function and driving performance. A driving-simulator based study. International Journal of Environmental Research and Public Health, 17(23), 9033. 10.3390/ijerph17239033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Patton GC, Coffey C, Carlin JB, Sawyer SM, & Lynskey M (2005). Reverse gateways? Frequent cannabis use as a predictor of tobacco initiation and nicotine dependence. Addiction, 100(10), 1518–1525. 10.1111/j.1360-0443.2005.01220.x [DOI] [PubMed] [Google Scholar]
  44. Pearlson GD, Stevens MC, & D’Souza DC (2021). Cannabis and driving. Frontiers in Psychiatry, 12, 689444. 10.3389/fpsyt.2021.689444 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Rich JJ, Back SE, Bui TC, Bernstein EY, & Le P (2024). Trends in marijuana and heavy alcohol use by cigarette smoking status among US adults: An analysis of the 2002–2019 NSDUH. Drug and Alcohol Dependence, 261, 111355. 10.1016/j.drugalcdep.2024.111355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Romm KF, Patterson B, Wang Y, Wysota CN, Bar-Zeev Y, Levine H, & Berg CJ (2022). Drivers and passengers in vehicles driven under the influence of alcohol or marijuana: Behavior profiles and risk factors among young adults in a longitudinal study. Journal of Studies on Alcohol and Drugs, 83(3), 342–351. 10.15288/jsad.2022.83.342 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Salas-Wright CP, Cano M, Hai AH, Oh S, & Vaughn MG (2021). Prevalence and correlates of driving under the influence of cannabis in the U.S. American Journal of Preventive Medicine, 60(6), e251–e260. 10.1016/j.amepre.2021.01.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Salas-Wright CP, Hai AH, Vaughn MG, Hodges JC, & Goings TC (2023). Driving under the influence of cannabis and alcohol: Evidence from a national sample of young drivers. Addictive Behaviors, 147, 107816. 10.1016/j.addbeh.2023.107816 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Scott B, Ward N, Otto J, & Finley K (2021). Modeling the system of beliefs that influence driving under the influence of cannabis (DUIC). Accident Analysis & Prevention, 151, 105988. 10.1016/j.aap.2021.105988 [DOI] [PubMed] [Google Scholar]
  50. Sobell LC, Brown J, Leo GI, & Sobell MB (1996). The reliability of the Alcohol Timeline Followback when administered by telephone and by computer. Drug and Alcohol Dependence, 42(1), 49–54. 10.1016/0376-8716(96)01263-X [DOI] [PubMed] [Google Scholar]
  51. Teeters JB, Armstrong NM, King SA, & Hubbard SM (2022). A randomized pilot trial of a mobile phone–based brief intervention with personalized feedback and interactive text messaging to reduce driving after cannabis use and riding with a cannabis impaired driver. Journal of Substance Abuse Treatment, 142, 108867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Teeters JB, King SA, & Hubbard SM (2021). A mobile phone–based brief intervention with personalized feedback and interactive text messaging is associated with changes in driving after cannabis use cognitions in a proof-of-concept pilot trial. Experimental and Clinical Psychopharmacology, 29(2), 203–209. 10.1037/pha0000442 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. van der Pol P, Liebregts N, de Graaf R, ten Have M, Korf DJ, van den Brink W, & van Laar M (2013). Mental health differences between frequent cannabis users with and without dependence and the general population. Addiction, 108(8), 1459–1469. 10.1111/add.12196 [DOI] [PubMed] [Google Scholar]
  54. Ward NJ, Otto J, Schell W, Finley K, Kelley-Baker T, & Lacey JH (2017). Cultural predictors of future intention to drive under the influence of cannabis (DUIC). Transportation Research Part F: Traffic Psychology and Behaviour, 49, 215–225. 10.1016/j.trf.2017.06.013 [DOI] [Google Scholar]
  55. Weinberger AH, Zhu J, Lee J, Anastasiou E, Copeland J, & Goodwin RD (2020). Cannabis use among youth in the United States, 2004–2016: Faster rate of increase among youth with depression. Drug and Alcohol Dependence, 209, 107894. 10.1016/j.drugalcdep.2020.107894 [DOI] [PubMed] [Google Scholar]
  56. Wrathall M, Cristiano N, Walters D, & Hathaway A (2025). At the crossroads: Examining the role of place and gender as predictors of cannabis use-related driving behaviour. The Journal of Rural and Community Development, 20(4), 135–156. [Google Scholar]
  57. Wycoff AM, Darmour CA, McCarthy DM, & Trull TJ (2025). Event-level influences of alcohol, cannabis, and simultaneous use on perceived driving risk. Experimental and Clinical Psychopharmacology, 33(2), 170–177. 10.1037/pha0000758 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Yurasek AM, Aston ER, & Metrik J (2017). Co-use of alcohol and cannabis: A review. Current Addiction Reports, 4(2), Article 2. 10.1007/s40429-017-0149-8 [DOI] [Google Scholar]
  59. Zhang-James Y, Wyon E, Grapsas D, & Johnson B (2023). Daily cannabis use may cause cannabis-induced hyperalgesia. The American Journal on Addictions, 32(6), 532–538. 10.1111/ajad.13456 [DOI] [PubMed] [Google Scholar]

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