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Journal of Concurrent Disorders Vol. (TBD) No. (TBD), 2020 (pp. TBD)
Effects of therapeutic cannabis on simulated driving: A pilot study
Patricia Di Ciano, PhD1,2,4,6*, Ana Matamoros, MSc1,4, Justin Matheson, BSc1,4, Andrew Fares, BSc1,4, Hayley
A. Hamilton, PhD1,5, Christine M. Wickens, PhD1,5, Tara Marie Watson, PhD1, Robert E. Mann, PhD1,5,
Bernard Le Foll, MD, PhD2,4,6,7,8,9, Patrick A. Byrne, PhD10, Bruna Brands, PhD1,3,4
1 Institute for Mental Health Policy Research,
Centre for Addiction and Mental Health
33 Russell Street
Toronto, Ontario
2 Translational Addiction Research Laboratory,
Centre for Addiction and Mental Health
33 Russell Street
Toronto, Ontario
3 Controlled Substances Directorate,
Health Canada,
Ottawa, Ontario,
Canada
4 Department of Pharmacology & Toxicology,
University of Toronto,
27 King's College Circle,
Toronto, Ontario, M5S3H7,
Canada
5 Dalla Lana School of Public Health,
University of Toronto,
155 College Street,
Toronto, Ontario, M5T3M7,
Canada
6Campbell Family Mental Health Research Institute
7Family and Community Medicine,
University of Toronto
500 University Avenue
Toronto, Ontario, M5G 1V7
8Department of Psychiatry,
University of Toronto
250 College Street, 8th floor
Toronto, Ontario, M5T 1R8
9Institute of Medical Sciences,
University of Toronto
1 King’s College Circle
Toronto, Ontario, M5S 1A8
Journal of Concurrent Disorders Vol. (TBD) No. (TBD), 2020 (pp. TBD)
10Road Safety Research Office,
Ministry of Transportation of Ontario
Email: patricia.diciano@camh.ca
Abstract
Background: Although medical cannabis has been available to Canadians since 2001, there is little research on the effects
of cannabis on driving in individuals who use cannabis medically. This pilot study sought to determine the effects of
therapeutic cannabis use on simulated driving. Methods: Eligible participants reported daily use of cannabis for therapeutic
purposes, with a medical authorization. Prior to the test session, participants were asked not to smoke their regular dose.
Participants (n=14) completed self-report questionnaires, including subjective effects questionnaires (visual analog scales),
the Addiction Research Centre Inventory (ARCI), and Profile of Mood States (POMS), and provided blood (for
determination of THC and metabolites). They also drove a simulator both before and after smoking their usual daily dose
of cannabis. Outcome measures on simulated driving consisted of overall mean speed, straightaway mean speed,
straightaway lateral control, and brake latency. Speed and lateral control were also measured under cognitive load. Results:
After smoking cannabis, overall mean speed was reduced. No effects of therapeutic cannabis were found on straightaway
mean speed or straightaway lateral control for either condition (standard or cognitive load) or on brake latency. After
smoking therapeutic cannabis in the lab, changes in speed and lateral control were negatively correlated with the amount of
cannabis smoked per day. Prior to smoking therapeutic cannabis in the lab, under baseline conditions, speed and lateral
control under cognitive load were also correlated with the amount of cannabis used per day. Therapeutic cannabis use
increased subjective reports and blood levels of THC and metabolites. Conclusions: The present study suggests that, even
with repeated daily use, cannabis consumption among therapeutic users may alter driving behavior. This has implications
for road safety and use of cannabis for therapeutic purposes.
Keywords: cannabis; driving; medicinal cannabis; weaving; speed
Trial registration: N/A
Submitted: Mar. 3, 2020 Revised: Mar. 15, 2020 Accepted: Mar. 16, 2020
Introduction
With the growing legalization of cannabis for non-
medical use, assessing the outcomes of cannabis use
in the population is of increasing significance
(Fischer, Murphy, Kurdyak, Goldner, & Rehm,
2015). According to data from the Canadian Cannabis
Survey (CCS) ((HealthCanada, 2018), 13% of
respondents indicated that they used cannabis for
therapeutic purposes. In terms of driving, 39% of CCS
respondents who had used cannabis in the past 12
months also reported driving within two hours of
consumption (HealthCanada, 2018). Although it is
still too early to know whether driving under the
influence of therapeutic cannabis will increase post-
legalization (Hall & Lynskey, 2016), evidence-
informed understanding of the impacts of therapeutic
cannabis use on driving and greater knowledge of the
characteristics of therapeutic users is needed.
Epidemiological studies have found an increased risk of
motor vehicle collisions in motorists that are under the
influence of cannabis (Bondallaz et al., 2016; Sayer et al.,
2014). Driving simulators provide a safe means to study
the effects of drugs on driving. In this regard, consistent
with epidemiological data, some studies with driving
simulators have found an increase in collisions after use of
cannabis (Ogourtsova, Kalaba, Gelinas, Korner-Bitensky,
& Ware, 2018; Ronen et al., 2008). Simulator studies have
looked at a number of variables that may influence driving
after cannabis use, most commonly speed (Anderson,
Rizzo, Block, Pearlson, & O'Leary, 2010; Arkell et al.,
2019; Lenne et al., 2010; Ronen et al., 2010; Ronen et al.,
2008) and ‘weaving’ (i.e. standard deviation of lateral
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position; SDLP) (Arkell et al., 2019; Bosker et al., 2012;
Micallef et al., 2018; Ronen et al., 2010; Ronen et al.,
2008; Veldstra, Bosker, de Waard, Ramaekers, &
Brookhuis, 2015). Where effects have been found, the
most consistent are on SDLP (Arkell et al., 2019; Bosker
et al., 2012; Micallef et al., 2018; Ronen et al., 2010;
Ronen et al., 2008; Veldstra et al., 2015); but see (Micallef
et al., 2018; Ogourtsova et al., 2018), and sometimes speed
(Lenne et al., 2010; Ronen et al., 2008), but not always
(Anderson et al., 2010; Arkell et al., 2019; Ogourtsova et
al., 2018; Ronen et al., 2010). Other studies have also
found decreased steering control (Lenne et al., 2010;
Ronen et al., 2010; Ronen et al., 2008), longer reaction
time (Lenne et al., 2010), and increased headway (Lenne
et al., 2010). No effects were found on brake latency
(Liguori, Gatto, & Jarrett, 2002; Liguori, Gatto, &
Robinson, 1998). In our recent study of the effects of
smoked cannabis on the simulated driving performance of
young recreational cannabis users, we observed that those
driving 30 minutes after smoking cannabis demonstrated
significant reductions in speed, both driving as usual and
also driving under conditions of increased task demands
(Brands et al., 2019).
At present, to the best of our knowledge, there have been
no studies of the effects of therapeutic cannabis use on
driving. Therapeutic users often use cannabis frequently,
or daily (Goulet-Stock et al., 2017), and the few studies
centered on the effects of repeated, or frequent, cannabis
use on simulated driving have yielded equivocal results. In
one study, driving errors following the smoking of
cannabis were worse in regular cannabis users compared
to non-regular users (Downey et al., 2013). This suggests
that repeated users do not habituate to the effects of
cannabis on driving, consistent with evidence for impaired
driving in one study of habitual cannabis users (Tank et al.,
2019). In other studies, weaving was more evident in
occasional users, as compared to regular users, after oral
synthetic THC (dronabinol) (Bosker et al., 2012) or
smoked cannabis (Hartley et al., 2019), suggesting that
regular users may be tolerant to the effects of cannabis.
An interesting observation in this latter study is that
differences between occasional and frequent users of
cannabis were also evident following placebo, suggesting
that frequent therapeutic users of cannabis may
demonstrate different driving abilities even at baseline.
Indeed, in a cross-sectional study, it was found that
participants who used cannabis at least 4 times a week had
slower mean speeds, and were relatively slower than the
car in front of them, as compared to a group of infrequent
cannabis users. (Doroudgar et al., 2018). The frequent
users of cannabis also had mean blood THC levels above
the legal cut-off of 5 nanograms/ml. Thus, detriments in
baseline driving performance may be related to residual
levels of THC, as the authors propose (Doroudgar et al.,
2018).
The above-mentioned studies of the effects of repeated
cannabis use on driving also assayed levels of Δ-9-
tetrahydrocannabinol (THC) in the blood after
administration of cannabinoids. In all studies, levels of
THC were higher in the blood of frequent users of
cannabis, as compared to occasional users (Bosker et al.,
2012; Downey et al., 2013). The findings of increased
levels of THC in frequent cannabis users speaks to the
importance of further delineating the relationship of THC
to changes in driving. Given the mixed findings with
respect to the effects of repeated cannabis on driving, it is
not clear if these increased levels of THC in the blood are
associated with decrements in driving behavior. An
increasing number of jurisdictions worldwide are
considering or implementing non-zero per se limits for
THC detected in drivers, and research to identify
scientifically supported limits is ongoing (Hedlund, 2015;
Watson & Mann, 2016).
The purpose of the present pilot study was to study
simulated driving in therapeutic cannabis users who were
asked to smoke their usual dose of cannabis before driving
the simulator. Driving was measured, and subjective
effects questionnaires were administered both before and
30 minutes after smoking cannabis. Blood was also
collected at these time points for determination of levels of
THC and its metabolites. It was hypothesized that
therapeutic cannabis would affect driving, increase blood
levels of THC, and yield subjective effects.
Materials and Methods
Study participants were recruited through advertisements
in local social media and posters in the community.
Inclusion criteria were: 1) males and females aged 19 years
and older; 2) daily use of cannabis and an authorization to
use cannabis for therapeutic purposes; 3) holds a valid
class G or G2 Ontario driver’s licence (or equivalent from
another jurisdiction); 4) willing to abstain from alcohol and
other drugs (other than nicotine and drugs required for
treatment of a medical condition) for 48 hours prior to
study session; and 5) provides written and informed
consent. Exclusion criteria were: 1) use of psychoactive
medications or drugs other than cannabis, prescription
opioids, or nicotine; and 2) self-reported current alcohol or
other drug dependence.
Participants were screened for eligibility over the
telephone through self-reports. Upon eligibility
confirmation by telephone, participants were asked to
attend the Centre for Addiction and Mental Health
(CAMH), a large addiction and mental health teaching and
research hospital in Toronto, Canada, for one study
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session. Participants were instructed not to smoke their
first daily dose of cannabis prior to coming to CAMH on
the day of testing, and not to use alcohol or drugs other
than cannabis (unless required for treatment of a medical
condition, and excluding nicotine) during the 48 hours
prior to attending their session. This study received ethical
approval from the Research Ethics Boards of both CAMH
and Health Canada. This study was carried out in
accordance with The Code of Ethics of the World Medical
Association (Declaration of Helsinki).
Study session: Upon attending the lab, participants
provided informed consent. Participants provided a urine
sample to screen for recreational drugs and a saliva sample
to screen for use of cannabis. A DrugWipe (Alcohol
Countermeasure Systems) with a 10 ng/ml cut-off was
used to screen for cannabis use. A breathalyzer was also
performed to verify that participants had not consumed
alcohol prior to study participation. Participants were then
asked to provide a blood sample for measurement of Δ-9-
tetrahydrocannabinol (THC) and metabolites (11-nor-9-
carboxy-∆ 9-tetrahydrocannabinol (THC-COOH) and 11-
hydroxy-∆9-tetrahydrocannabinol (11-OH-THC)), both
before smoking their own usual dose of a cannabis
cigarette and 30 minutes after smoking. After completing
an initial practice session on the driving simulator to
mitigate possible practice effects on driving outcomes,
participants were asked to drive the simulator (Virage
model VS500M) before and 30 minutes after smoking the
cigarette.
The Profile of Mood States (POMS) (Pollock, Cho, Reker,
& Volavka, 1979), Addiction Research Centre Inventory
(ARCI, short form 49 item) (Haertzen & Hickey, 1987),
and Visual Analogue Scales (VAS) were administered to
measure subjective drug effects before and 30 minutes
after cannabis exposure. For the ARCI, the subscales were:
amphetamine, morphine-benzedrine, lysergic acid
diethylamine, benzedrine, pentobarbital-chlorpromazine-
alcohol, euphoria, and sedation. For the POMS, the
subscales were: Tension-Anxiety, Anger-Hostility,
Depression-Dejection, Friendliness, Fatigue, Confusion,
Vigor, Elation, Arousal, and Positive Mood. For the VAS,
the measures were: ‘I feel this effect’, ‘I feel this high’, ‘I
feel the good effects’, ‘I feel the bad effects’, ‘I like
cannabis’, ‘This feels like cannabis’, and ‘I feel the rush’.
All subjective effects questionnaires were administered by
computer.
Prior to driving the simulator, participants completed a
questionnaire that assessed their demographics, concurrent
drug use, cannabis use, and therapeutic cannabis use while
driving. Questionnaire data were collected and managed
using REDCap (Research Electronic Data Capture)
electronic data capture tools, hosted at CAMH (Harris et
al., 2009). REDCap is a secure, web-based application
designed to support data capture for research studies,
providing 1) an intuitive interface for validated data entry;
2) audit trails for tracking data manipulation and export
procedures; 3) automated export procedures for seamless
data downloads to common statistical packages; and 4)
procedures for importing data from external sources.
Driving simulator: The CAMH Virage VS500M simulator
consists of the driver’s side instrument cluster, steering
wheel, controls, and center console of a General Motors
compact car. The steering wheel provides dynamic force
feedback, as do the brake and accelerator pedals. The
visual system consists of three 50-inch screens providing a
180° field of view in the front, and two 17-inch side
displays providing visual feedback for the left and right
blind zones.
Two of the driving scenarios (to measure speed and lateral
control) were programmed on the same 9-km stretch of
rural highway with posted speed limits of 80 km/hr, and
included periodic driving interactions (e.g. slowly moving
vehicle, disabled vehicle at roadside) that differed for each
scenario. The road was a single lane, with a lane in the
opposite direction. Participants could change lanes if they
wished. Participants were instructed to drive as they
normally would, allowing for assessment of driver
behavior (i.e. how a driver chooses to operate the vehicle)
as opposed to one’s ability to perform certain driving
skills. For the first rural highway scenario, participants
drove the simulator under standard conditions, and for the
second they drove under conditions of increased cognitive
load for the entire session, where they were instructed to
count backwards by threes from a randomly selected 3-
digit number 21,22. The addition of a counting backward
task has a long history of use to increase the complexity of
cognitive and other tasks 23.
The third driving scenario, designed to measure brake
latency, was programmed on a 4-lane expressway with a
speed limit of 100 km/hr. Prior to the drive, participants
were instructed to drive at the posted speed limit and to
brake in response to stop signs that periodically appeared
at the roadside. Consistent with a choice reaction time task
(Risser et al., 2008; Sommer et al., 2008), participants were
required to brake in response to 7 of the 10 appearing
stimuli (which of the 7 trials requiring the participant to
brake differed for each scenario, as did the timings between
all trials). Participants were instructed to brake as quickly
as possible and come to a complete stop if a stop sign
appeared facing them, on the right side of the road. If a stop
sign appeared at an angle, they were instructed to keep
driving, and not brake at all.
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The first two practice scenarios were the same as the first
two testing scenarios, but with more objects on the road.
The practice scenario for brake latency presented stop
signs in a different order and with different timings than
any of the testing scenarios. The scenarios (including both
standard and cognitive load conditions) were presented in
the same order for all participants.
Outcome measures: Our driving outcome variables of
interest were brake latency, straightaway mean speed,
overall mean speed, and straightaway lateral control; the
latter three measures were most commonly affected by
cannabis in previous research (Anderson et al., 2010;
Downey et al., 2013; Hartman et al., 2015; Lenne et al.,
2010; Ronen et al., 2010). Lateral control was
operationalized as the standard deviation of the absolute
distance between the centre of the simulated vehicle and
the centre of the lane in which the participant was driving,
in meters. Straightaway lateral control and straightaway
mean speed were calculated from a straightaway section
(i.e. a straight stretch of road approximately 1.6 km in
length without any traffic control signals or other moving
vehicles). For the overall mean speed measure, data
collected throughout the entire scenario were included,
where participants had to manoeuver or brake in response
to stimuli. Speed and lateral control variables were
assessed under both single task and cognitive load
scenarios.
Statistical Analyses:
Effects of therapeutic cannabis on driving variables: The
driving variables (straightaway lateral control,
straightaway lateral control under cognitive load,
straightaway mean speed, straightaway mean speed under
cognitive load, overall mean speed, and overall mean
speed under cognitive load) were analysed with two-way
Condition (standard, cognitive load) X Time (pre, post)
repeated-measures ANOVAs. Where a significant effect of
Time was revealed, THC pre-cannabis was entered as a
covariate into an ANCOVA, to determine whether THC at
baseline had an effect on driving. Latency was analysed
with a t-test comparing pre and post cannabis measures.
Levels of THC and metabolites in blood and subjective
scores after therapeutic cannabis: Levels of THC and each
metabolite, as well as each VAS score and ARCI and
POMS subscale, were analysed with paired t-tests
comparing the value before cannabis (pre) to the value
after cannabis (post).
Correlations: Correlations between a number of variables
and driving measures before smoking cannabis were
conducted. The variables entered into the correlation were:
1) number of times per day that cannabis is smoked; 2)
amount of cannabis used per occasion; 3) grams used per
day (calculated as variable 1 multiplied by variable 2); and
4) levels of THC and metabolites before smoking cannabis.
The changes in driving (post cannabis – pre cannabis) were
also correlated with the above-mentioned demographic
variables, the change in weight of the cigarette, and the
time taken to smoke the cigarette. Correlation coefficients
were also conducted between changes in driving measures
after smoking and changes in THC and metabolites (post
cannabis – pre cannabis).
Data were analysed with SPSS version 25. A significance
level of p<0.05 was adopted for all analyses.
Results
A total of 14 participants completed the simulator trials.
Three withdrew due to an experience of simulator sickness,
a vertigo-like reaction to interacting with a driving
simulator. One participant tested positive for use of
recreational drugs, and another experienced negative
effects of having refrained from smoking cannabis that
day; these two participants were also withdrawn from the
study. All participants that completed the study screened
negative for recreational drugs, and 10 screened negative
for THC in saliva.
Demographics: Most participants were male (n=11), with
an average age + SEM of 42 + 9. Seven of the participants
reported using cannabis five or more times per day. All
participants had authorization to use medical cannabis. The
average (+SEM) amount used per occasion was 1.1 + 1.1
grams. The average amount used per day (amount per
occasion X number of occasions per day) was 4.8 + 5.7
grams. Of the 14 participants, 5 reported taking cannabis
for pain, 1 for anxiety, and 8 for multiple conditions.
The mean amount smoked of the cannabis cigarette in the
laboratory was 387 + 56.0 mg (mean + SEM). There was
no correlation between the amount smoked per occasion
and the amount smoked in controlled laboratory conditions
(p=.122). Of the 14 participants in the therapeutic group, 4
reported smoking a strain with over 20% THC, 6 reported
smoking a strain with 15-20% THC, and 2 smoked
cannabis with 10-15% THC. One participant smoked a
high cannabidiol (CBD) strain with less than 1% THC.
Eight reported smoking low CBD strains, but 4 participants
did not provide the CBD content. One participant did not
report what they smoked during the laboratory test.
Self-reported driving under the influence of cannabis: Of
the 14 participants, 8 reported that, within the past year,
they had consumed cannabis within 1 hour of driving. Of
these, 9 reported ‘no risk’ or ‘slight risk’ of driving after
cannabis use. Participants reported that cannabis affected
them in a number of ways. For example, three participants
Journal of Concurrent Disorders Vol. (TBD) No. (TBD), 2020 (pp. TBD)
reported no difference in driving after using cannabis, and
one mentioned that their driving was ‘not the best’ after
driving. Two reported that their driving improved. One
reported being more cautious. One reported being more
focused and drove slower because they were more
paranoid.
Effects of therapeutic cannabis on simulated driving:
Therapeutic cannabis reduced overall mean speed, as
revealed by an effect of Time in a Condition X Time
ANOVA (F(1, 13)=10.395, p=0.007). Entering THC
(ng/ml) at baseline as a covariate in an ANCOVA did not
affect this, as an effect of Time was still revealed (p=0.05;
Figure 1).
Figure 1
Overall Mean Speed (+ SEM) in km/hr before or 30
minutes after smoking therapeutic cannabis
Note. *p<0.05, effect of Time (Time X Condition ANOVA);
open and grey bars represent standard and cognitive load
cognitions, respectively.
For straightaway mean speed and straightaway lateral
control, two-way Condition X Time ANOVAs revealed no
significant effects. Analysis of brake latency also did not
reveal an effect of cannabis (p>0.05; see Figures 2-4).
Figure 2
Straightaway Mean Speed (+ SEM) in km/hr before or 30
minutes after smoking therapeutic cannabis.
Note. Open and grey bars represent standard and cognitive load
conditions, respectively. (Brands et al., 2019).
Figure 3: Straightaway Lateral Control (+ SEM) in meters
before or 30 minutes after smoking therapeutic cannabis.
Note. Open and grey bars represent standard and cognitive load
conditions, respectively (Brands et al., 2019).
Figure 4: Latency (+ SEM) in seconds before or 30
minutes after smoking therapeutic cannabis
Effects of therapeutic cannabis on blood levels of THC and
subjective measures: Analysis of blood levels of THC and
metabolites with t-tests revealed that levels were
significantly higher after smoking therapeutic cannabis
(THC: t(13)=-6.510, p<0.001; 11-OH-THC: t(13)=-5.953,
Journal of Concurrent Disorders Vol. (TBD) No. (TBD), 2020 (pp. TBD)
p<0.001; THC-COOH: t(13)=-4.869, p<0.001). For the
VAS, the difference between pre-cannabis and post-
cannabis was also significant for all measures except for ‘I
feel the bad effects’ (‘I feel this effect’: t(13)=-11.394,
p<0.001; ‘I feel this high’: t(13)=-7.401, p<0.001; ‘I feel
the good effects’: t(13)=-18.101, p<0.001; ‘I like
cannabis’: t(13)=-7.156, p<0.001; ‘This feels like
cannabis’: t(13)=-14.864p<0.001; ‘I feel the rush’: t(13)=-
4.698, p<0.001). See Table 1.
Table 1
Mean + SEM measures of THC and metabolites and scores
on VAS before (pre) and after (post) smoking therapeutic
cannabis
Blood Measure
Pre
Post
THC
3.99 + 0.93
19.56 + 3.01*
THC-COOH
45.04 +
10.52
63.46 + 12.23*
11-OH-THC
1.51 + 0.38
4.78 + 0.74*
VAS
Pre
Post
I feel this effect
4.43 + 3.62
69.00 + 6.01*
I feel this high
4.29 + 3.61
59.79 + 7.34*
I feel the good effects
1.93 + 1.65
75.71 + 4.09*
I feel the bad effects
4.43 + 4.43
11.14 + 4.56
I like cannabis
14.86 + 8.57
81.29 + 5.39*
This feels like
cannabis
5.36 + 4.27
89.57 + 4.23*
I feel the rush
0.86 + 0.72
39.86 + 8.50*
*p<0.05 pre vs post, t-tests.
Analysis of the ARCI and POMS with paired t-tests from
baseline to 30 minutes after smoking cannabis revealed no
significant effects for any of the POMS subscales. For the
ARCI, the subscales of ‘amphetamine’ (t(13)=-2.347,
p=0.035) and ‘euphoria’ (t(13)=-2.188, p=0.047) were
significantly increased after smoking.
Correlations between demographic variables, THC and
metabolites and simulated driving: It was observed that
two participants smoked a great deal of cannabis per day
(15 grams and 20 grams). An outlier test revealed that the
more extreme value was an outlier and this data was
removed from analysis, resulting in a sample size of 13.
Results of the correlational analysis are provided in Table
2. Of note, driving measures under cognitive load before
smoking cannabis were correlated with the amount smoked
per occasion and the number of grams smoked per day.
After smoking cannabis, the change in driving scores was
also correlated with the amount used per occasion/day, and
the time taken to smoke the cannabis in the lab.
Discussion
The purpose of the present pilot study was to investigate
the effects of therapeutic cannabis use on simulated
driving. It was found that therapeutic cannabis reduced
overall mean speed with no effects on straightaway mean
speed, straightaway lateral control, or brake latency. After
smoking therapeutic cannabis, changes in speed and lateral
control were correlated with the amount smoked per day as
well as the amount smoked during the test session.
Therapeutic cannabis users had elevated THC and
metabolite levels at baseline; smoked cannabis increased
these levels. Under conditions of cognitive load prior to
smoking cannabis, speed measures and lateral control were
correlated with the amount of cannabis used per day in
therapeutic users.
The main finding that therapeutic cannabis use decreased
overall mean speed is consistent with some previous
studies, which found that recreational cannabis use
decreased measures of speed (Lenne et al., 2010; Ronen et
al., 2008). As mentioned in the Introduction, not all reports
have found decreased speed in response to recreational
cannabis (Anderson et al., 2010; Arkell et al., 2019;
Ogourtsova et al., 2018; Ronen et al., 2010). Where
decreases have been found, it has been suggested that these
decreases are compensatory (Ward & Dye, 1999)as cited
in Ronen et al., 2008), and this may be the case for those
who use cannabis for therapeutic purposes. The reason for
the discrepancy in findings of the various studies is not
known, but may be related to the driving scenarios used.
Indeed, in the present study, therapeutic cannabis users did
not demonstrate overall effects on straightway mean speed.
Measures of overall mean speed would have included
challenges in the roadside, such as passing a slow-moving
vehicle. Decreases in overall mean speed may therefore
represent an attempt by the driver to compensate for
obstacles on the road, an effect which may be enhanced by
cannabis.
Several previous studies have reported that drivers ‘weave’
more after smoking cannabis (Arkell et al., 2019; Bosker
et al., 2012; Micallef et al., 2018; Ronen et al., 2010;
Ronen et al., 2008; Veldstra et al., 2015), measured as an
increase in SDLP or other measures of lateral control. It
should be mentioned that, in the present study, therapeutic
users of cannabis did not demonstrate changes in lateral
control after smoking therapeutic cannabis. It is possible
that our measures were not as sensitive as those used in
previous studies. As well, drivers in our study were
Journal of Concurrent Disorders Vol. (TBD) No. (TBD), 2020 (pp. TBD)
instructed to drive as they normally would, while in some
other studies instructions emphasized maintaining a
specified speed, meaning that participants in the present
study could have maintained their lane control as a result
of reducing their speed, (Brands et al., 2019).
After smoking therapeutic cannabis, changes in lateral
control were found to be correlated with the amount
smoked during the session and also the amount smoked per
day; lateral control decreased with greater amounts
smoked. Thus, it is possible that heavier use of therapeutic
cannabis may result in greater changes in driving after
smoking therapeutic cannabis. All participants in the
present study were taking cannabis to treat an underlying
condition; 5 of the 14 participants used cannabis mainly to
treat pain and 8 for multiple conditions, including pain in
some cases. It is known that pain can affect driving (Nilsen
et al., 2011), and thus it is possible that use of therapeutic
cannabis may affect performance decrements caused by
pain or other underlying pathology. Indeed, changes in
lateral control after smoking therapeutic cannabis were
correlated with the amount of cannabis smoked per day,
suggesting that those with more acute symptoms who take
more cannabis may demonstrate greater reduction of
symptom-induced changes in driving.
Even before smoking cannabis, changes in speed and
lateral control under cognitive load were correlated with
the amount smoked on a daily basis. Thus, it is possible
that the underlying illness, or residual THC, may affect
driving. Unfortunately, the present study did not collect
data to determine the degree of pain or other malaise
suffered, and therefore changes in baseline performance
could be affected by the degree of symptomology. Further
research is needed to disentangle these various effects on
driving. If residual THC is impairing driving, then this has
consequences for road safety, and suggests that people who
use therapeutic cannabis must be aware of any residual
effects of cannabis.
The finding of residual levels of THC in the blood is an
important observation. The current legal limit of THC in
Canada is 2-5 ng/ml, and on average, participants had 3.99
ng/ml of THC in the blood prior to smoking. This has
important consequences for the use of therapeutic
cannabis, and for establishing safe timelines of driving
after use of therapeutic cannabis. It may be possible that
therapeutic users of cannabis require a longer washout
period before driving. This is especially important given
that the amount of cannabis smoked per day was correlated
with changes in speed and lateral control, findings which
suggest that drivers may be impaired at baseline.
Limitations: Interpretations of this pilot study must be
made in view of several limitations. First, study
participants smoked their own strain of cannabis. This may
have introduced variability in the data, as the potency and
cannabinoid composition of the cannabis were unknown.
A second limitation is the inherent difficulty in definitively
concluding that participants had not smoked cannabis on
the day of testing. This leads to the possibility that the
relationships seen in the baseline data in this study were
related to recently smoked cannabis. The only method to
definitively conclude that participants have not smoked
cannabis on a given day is through inpatient studies, and
these are beyond the scope of many investigations. A third
limitation is related to the fact that there were no
correlations between the amount smoked at home and the
amount of cannabis consumed in the laboratory. Indeed,
participants smoked more at home per occasion than in the
one instance in the laboratory. This suggests that the results
of the present study may have limited ecological validity,
and future studies will need to investigate whether
different doses of cannabis have different effects on
therapeutic cannabis users. A fourth limitation relates to
the use of straightaway road scenarios to measure lateral
control. Straight roadways are less sensitive to changes in
weaving than roads with curvatures. Thus, future studies
will need to investigate the possibility that different road
conditions may have varying effects on driving after the
use of therapeutic cannabis. This last point is particularly
germane given that there is some inconsistency in the
driving simulator literature. For example, as mentioned in
the Introduction, some, but not all, studies have found
effects of cannabis on speed, and most studies have found
effects on SDLP. Thus, differences may be related to the
types of scenarios used. Finally, the sample size in the
present study is limited. With a total sample of 14
participants, this study is nevertheless consistent with
another recently published report (Arkell et al., 2019).
Despite the relatively small sample size, we still saw
effects on mean speed. However, there is a possibility that
the lack of effect on lateral control may be related to a lack
of statistical power. Visual inspection of the lateral control
data suggests that our experimental design may not have
been ideal for detecting changes in lateral control after
cannabis (discussed above). Nevertheless, this study is a
pilot study and suggests that further investigation of the
effects of therapeutic cannabis on driving are warranted.
Author Disclosures
Role of Funding Source: This study was funded by an
RSRPP grant by Ministry of Transportation of Ontario to
BB. The funder had no role in the design, analysis, or
interpretation of the study. The funder was not involved in
the manuscript preparation.
Contributors: PDC oversaw the trial conduct, analyzed the
data, and prepared the first draft of the manuscript; AM
Journal of Concurrent Disorders Vol. (TBD) No. (TBD), 2020 (pp. TBD)
collected the data; JM collected the data; AF collected the
data; HH designed the questionnaire; CMW helped to
design the study; TMW designed the questionnaire; REM
helped to design the study; BLF provided medical
oversight and helped to design the study; BB was the study
PI, designed the study, and provided oversight. All authors
assisted with data interpretation and approve the submitted
manuscript.
Conflict of Interest: BLF has/will receive some in-kind
donation of cannabis product from Canopy and Aurora and
medication donation from Pfizer and Bioprojet, and was
provided a coil for transcranial magnetic stimulation study
from Brainsway. BLF has/will perform research with
industry funding obtained from Canopy, Bioprojet,
Alcohol Countermeasures, and Alkermes. BLF has
received in-kind donations of nabiximols from GW
Pharma for past studies funded by CIHR and NIH.
Figure Captions
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Table 2
Correlations (r2) between driving measures and various demographic characteristics or blood levels of THC and
metabolites of THC
Baseline
ovMS
OvMS_C
StrMS
StrMS_C
StrLatCont
StrLatCon_C
Latency
# Times/Day
0.332
0.341
0.295
0.414
0.246
0.514
0.244
Amt Smoked per
Occasion
0.467
0.779*
0.425
0.673*
0.353
0.732*
-0.137
Grams Used Per Day
0.481
0.749*
0.447
0.692*
0.341
0.747*
-0.086
Baseline THC
0.167
0.229
0.256
0.401
0.484
0.45
0.321
Baseline THC-COOH
0.118
0.46
0.378
0.526
0.503
0.449
0.235
Baseline 11-OH-THC
0.412
0.282
0.449
0.515
0.571*
0.478
0.146
Change After Cannabis
ovMS
OvMS_C
StrMS
StrMS_C
StrLatCont
StrLatCon_C
Latency
# Times/Day
-0.197
0.113
-0.078
0.054
-0.041
-0.32
-0.388
Amt Smoked per
Occasion
-0.564*
-0.578*
-0.691*
-0.734*
-0.589*
-0.835*
-0.049
Grams Used Per Day
-0.555*
-0.491
-0.649*
-0.647*
-0.506
-0.843*
-0.17
Change in Cigarette
weight
-0.73*
-0.082
-0.285
-0.19
-0.523
-0.376
-0.124
Time Taken to Smoke
-0.673*
-0.548
-0.501
-0.742*
-0.546
-0.906*
-0.264
Change in THC
-0.24
0.341
0.175
0.425
0.182
0.255
0.235
Change in THC-COOH
0.029
0.18
-0.044
0.375
-0.057
0.359
0.473
Change in 11-OH-THC
-0.222
0.223
0.12
0.477
-0.054
0.511
0.518
*p<0.05, significant correlations; OvMS: Overal Mean Speed; OvMS_C: Overall Mean Speed Cogitive Load; StrMS: Straightaway
Mean Speed; StrMS_C: Straightaway Mean Speed Cognitive Load; StrLatCont: Straightaway Lateral Control; StrLatCon_C:
Straightaway Lateral Control Cognitive Load; # Times/Day: Number of Smoking Occasions per day
Copyright: ©2020 Patricia Di Ciano, Ana Matamoros, Justin Matheson, Andrew Fares, Hayley A. Hamilton, Christine M. Wickens,
Tara Marie Watson, Robert E. Mann, Bernard Le Foll, Patrick A. Byrne, and Bruna Brands. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are credited.
