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Research Article 79
Ved
ABSTRACT
Background
: Understanding, predicting, and reducing the harms associated with cannabis use is an
important field of study. Timing (i.e., hour of day and day of week) of substance use is an established risk
factor of severity of dependence. However, there has been little attention paid to morning use of cannabis
and its associations with negative consequences.
Objectives:
The goal of the present study was to examine
whether distinct classifications of cannabis use habits exist based on timing, and whether these
classifications differ on cannabis use indicators, motives for using cannabis, use of protective behavioral
strategies, and cannabis-related negative outcomes.
Methods
: Latent class analyses were conducted on four
independent samples of college student cannabis users (Project MOST 1, N=2,056; Project MOST 2,
N=1846; Project PSST, N=1,971; Project CABS, N=1,122).
Results
: Results determined that a 5-class
solution best fit the data within each independent sample consisting of the classes: (1) "Daily-morning use”,
(2) “Daily-non-morning use”, (3) “Weekend-morning use”, (4) “Weekend-night use”, and (5) “Weekend-
evening use.” Classes endorsing daily and/or morning use reported greater use, negative consequences and
motives, while those endorsing weekend and/or non-morning use reported the most adaptive outcomes (i.e.,
reduced frequency/quantity of use, fewer consequences experienced, and fewer cannabis use disorder
symptoms endorsed).
Conclusions
: Recreational daily use as well as morning use may be associated with
greater negative consequences, and there is evidence that most college students who use cannabis do avoid
these types of use. The results of the present study offer evidence that timing of cannabis use may be a
pertinent factor in determining harms associated with use.
Key words
: = cannabis use; weekend; weekday; morning; night; time of use; cannabis motives
Negative consequences associated with
cannabis misuse are of primary concern to
cannabis researchers (Pearson, 2019). There is
evidence that cannabis-related negative
consequences may include mental health concerns
such as psychosis (D’Souza et al., 2016; McHugh et
al., 2017), depression and suicidality (Kimbrel et
al., 2018; Roberts, 2019), impacts on cognitive
function and educational achievement (Arria et al.,
2015; Homel et al., 2014; Meier et al., 2012), motor
vehicle accidents (National Academies of Sciences,
Engineering, and Medicine, 2017), and increased
Eleftherios Hetelekides1, Verlin W. Joseph2, Matthew R. Pearson2,
Adrian J. Bravo1, Mark A. Prince3, Bradley T. Conner3, Cross-
Cultural Addictions Study Team**, Protective Strategies Study
Team***, and Marijuana Outcomes Study Team****
1Department of Psychological Sciences, College of William & Mary
2Center on Alcohol, Substance use, And Addictions, University of New Mexico
3Department of Psychology, Colorado State University
Cannabis
2023, Volume 6 (1)
© Author(s) 2023
researchmj.org
DOI: 10.26828/cannabis/2023.01.007
Early Birds and Night Owls:
Distinguishing Profiles of
Cannabis Use Habits by Use
Times with Latent Class
Analysis
Corresponding Author: Adrian J. Bravo, PhD, Department of Psychological Sciences, College of William &
Mary, 540 Landrum Drive, Williamsburg Virginia 23815 USA, Integrated Science Center, Room 1081. Work
Phone: (757) 221-3881. Email: ajbravo@wm.edu
Latent Class Analyses Based on Timing of Cannabis Use
80
risk for developing prescription opioid use
disorders (Olfson et al., 2018), among others. A
variety of risk and protective factors are associated
with cannabis-related consequences, including
characteristics of the individual using cannabis,
the products they are using, and patterns of
consumption. These may include an individual’s
use of protective behavioral strategies (Bravo,
Anthenien et al., 2017), the frequency (Looby &
Earleywine, 2007), quantity (Walden &
Earleywine, 2008; Zeisser et al., 2012), and potency
(Prince & Conner, 2019) of cannabis consumed, as
well as the timing of use, both over the course of a
week (i.e., weekend vs. weekday use; Bravo,
Pearson et al., 2017; Buckner et al., 2019) and over
the course of a single day (Earleywine et al., 2016).
Of these constructs, timing of use has received
little empirical attention as a factor associated
with cannabis use and related consequences,
despite research that supports its relevance. With
regard to timing of use over the course of a week,
one daily diary study conducted among cannabis
using college students found that they reported
significantly greater cannabis use on weekend days
compared to weekdays (Bravo, Pearson et al.,
2017). Another study found that the influence of
cannabis motives on use and related problems were
distinct based on whether the motives were related
to weekend or weekday use (Buckner et al., 2019).
Specifically, all 5 motives (social, coping,
enhancement, conformity, and expansion; Simons
et al., 1998) for using cannabis on the weekdays, as
well as enhancement and conformity motives for
weekend use, were significantly positively
associated with greater cannabis use frequency.
With regard to cannabis related problems, all 5
motives for using cannabis on weekends, as well as
expansion motives for weekday use, were
associated with more problems experienced
(Buckner et al., 2019).
Timing of use over the hours of a single day has
received even less research attention than
weekend vs. weekday use. It is plausible that the
acute subjective and cognitive effects of any
intoxicant, when consumed earlier in the day, may
alter mood, judgment, and decision making such
that the experience of negative consequences
becomes more likely. In the alcohol literature, an
“eye-opener” is an alcoholic drink consumed early
in the day and is often interpreted as an attempt to
relieve withdrawal (Earleywine et al., 2016).
Questions on consumption of “eye-openers” are
commonly deployed in clinical instruments
designed to identify problem drinking (e.g., CAGE,
Beresford et al., 1990; TWEAK, Cherpitel, 1999; T-
ACE, Sokol et al., 1989). Similarly, the first item on
the most used measure for nicotine dependence
asks, “How soon after you wake up do you smoke
your first cigarette?” (Fagerstrom Test for Nicotine
Dependence, FTND; Heatherton et al., 1991).
These examples clearly highlight that morning use
of alcohol and cigarettes is associated with clinical
problems, though the processes by which this
occurs are thought to be complex (Epler et al.,
2014).
Alternatively, “wake and bake” is a colloquial
term that refers to morning cannabis use and is
integrated in the cannabis subculture, though
“wake and bake” is discouraged by individuals who
have been using cannabis for a long time (Lau et
al., 2015). Morning cannabis use may be attributed
to a number of psychological factors, including
mood. Testa and colleagues (2019) identified
increased daily cannabis use among participants
reporting lower positive affect (relative to their own
norms) in the morning time. Additionally, in one of
the few studies to directly examine morning use of
cannabis, Earleywine and colleagues (2016)
compared 257 college students who reported using
daily before noon on all 7 days of a week to 76
participants who also used daily but reported never
using before noon. The researchers found that
morning use accounted for a unique portion of the
variance in cannabis-associated problems when
controlling for quantity of cannabis consumed, age,
and gender, thus supporting morning use as an
indicator of more problematic use. These findings
suggest time of use may be associated with
increased cannabis-related impairment, problems,
and dependence.
While current evidence on the relationship
between morning cannabis use and related
problems is not causal, previous studies indicate
heavier cannabis use may be associated with more
problems associated with dependence. For
example, participants in a clinical trial reported
decreases in subjective intoxication ratings after
using cannabis on 4 consecutive days in accordance
with patterns of increased tolerance (Gorelick et al,
2013). Understanding the association between
timing of cannabis use patterns, dependence, and
related consequences may yield insight toward
preventing harmful use.
Cannabis, A Publication of the Research Society on Marijuana
81
Purpose of the Present Study
The present study aimed to examine whether
distinct patterns of cannabis use exist based on
timing of use (i.e., hour of day and day of week)
using latent class analysis (LCA). We also sought
to examine whether latent classes differed on
cannabis use indicators, motives for using
cannabis, and cannabis-related negative outcomes.
Given the exploratory nature of LCA, we conducted
our analyses across four independent samples of
young adult cannabis users to examine
replicability. We hypothesized that a LCA based on
timing of cannabis use would produce groups that
differ on weekend vs. weekday and morning vs.
non-morning use. We also expected that classes
characterized by morning use and more frequent
use (i.e., number of days) would report overall
greater levels of cannabis use, motives for using
cannabis, and number of negative cannabis-related
consequences.
METHODS
Participants and Procedures
The present study is a secondary data analysis
of four independent studies (Projects MOST 1,
MOST 2, PSST, and CABS) focused on substance
use and mental health among college students.
Detailed descriptions of study participants and
procedures for the parent studies are found in
prior published studies (Project MOST
[Marijuana Outcomes Study Team] 1, Pearson,
Liese, et al., 2017; Project MOST 2, Richards et
al., 2021; Project PSST [Protective Strategies
Study Team], Bravo et al., 2018; Project CABS
[Cross-Cultural Addictive Behaviors Study],
Bravo et al., 2021). All data were collected cross-
sectionally among college students recruited from
participating institution’s Psychology department
participant pools, based on retrospective self-
report surveys. The analytic samples of the
present study were limited to U.S. college
students who reported past month cannabis use
and completed our primary measure of cannabis
use (Project MOST 1, N = 2,056, 59.5% female;
Project MOST 2, N = 1,846, 60.8% female; Project
PSST, N = 1,971, 68.2% female; Project CABS, N
= 1,122, 66.3% female).
Measures
Cannabis Use Time Indicators for LCA
Across all four samples, cannabis use times
was assessed using the Marijuana Use Grid
(MUG; Pearson, Marijuana Outcomes Study
Team, & Protective Strategies Study Team, 2022).
As done in prior studies utilizing the MUG (e.g.,
Bravo et al., 2021; Pearson, Kholodkov, et al.,
2017), a table was created such that each day of
the week (columns) was broken down into six 4-
hour time blocks (rows; 12a-4a, 4a-8a, 8a-12p,
etc.), and participants were asked “During a week
of typical marijuana use in the past 30 days,
please indicate times, days, and approximate
number of grams of marijuana that you used”.
Participants were provided with images of
varying amounts of cannabis to facilitate accurate
estimates of their quantity of use in terms of
grams of flower. Participants wrote into each cell
of the table approximately how many grams of
cannabis they used (if applicable). For the present
study, we coded whether each participant
endorsed using cannabis on a specific day
regardless of time block (e.g., if a participant
endorsed use on Monday [at any time block] they
were coded as a “1” for Monday use) and specific
time block regardless of day of use (e.g., if a
student endorsed use during 4a – 8a time block
[regardless of what day of the week] they were
coded as a “1” for 4a – 8a use). Taken together, 13
(7 days of week and 6 time blocks) dichotomous
variables (0 = no use, 1 = use) were utilized as
indicators in the LCAs.
Auxiliary Outcome Variables
All measures used have been validated among
college student samples and prior published
studies using these datasets have found good
internal consistency for each measure among
marijuana users within each dataset.
Cannabis use.
Typical use frequency and
quantity were assessed using the MUG (Pearson,
Marijuana Outcomes Study Team, & Protective
Strategies Study Team 2022). In addition to
asking which times participants used, they were
also asked to report the quantity of grams of
flower consumed during each time block they had
used within. We calculated typical frequency of
cannabis use by summing the total number of
Latent Class Analyses Based on Timing of Cannabis Use
82
time blocks for which participants reported using
during the typical week (possible range = 0-42).
Typical quantity of cannabis use was calculated
by summing the total number of grams consumed
across time blocks during the typical week. This
measure was collected in all datasets.
Cannabis motives.
Cannabis use motives were
measured with the Marijuana Motives
Questionnaire Short Form (MMQ-SF; Simons et
al., 1998). This 24-item scale uses a 5-factor model
for measuring motives for using cannabis on the
dimensions of enhancement (3 items), conformity
(3 items), expansion (3 items), coping (3 items)
and social (3 items) motives. Participants respond
on a 5-point scale from 1 =
Almost never/never
to
5 =
Almost always/alway
s. For each motive, items
were averaged such that higher scores are
associated with higher endorsement of that
motive. This measure was collected in all
datasets.
Cannabis-related problems and misuse.
Past
30-day cannabis-related problems were assessed
using the 21-item Brief Marijuana Consequences
Questionnaire (B-MACQ; Simons et al., 2012) in
MOST2, PSST, and CABS datasets, and the
longer 50-item version was used in the MOST1
dataset. We summed all items to create a
cannabis-problems composite score characterized
by the number of distinct problems experienced in
the past 30 days. Cannabis use disorder (CUD)
symptoms were assessed using the 8-item
Cannabis Use Disorders Identification Test-
Revised (CUDIT-R; Adamson et al., 2010). Items
were summed to create a total score with greater
scores indicating greater misuse of cannabis. This
measure was collected in all datasets except
Project MOST 1.
Cannabis use norms.
A 9-item scale for
assessing injunctive norms related to cannabis
use (Montes et al., 2021) was employed to examine
participants’ perceptions of others’ approval of
behaviors related to use (i.e., using cannabis,
using to get high, using daily). Participants
responded on a 7-point scale (1 =
Strongly
disapproving
to 7 =
Strongly approving
) and were
asked about three different groups: their best
friends, the typical college student, and their
parents. This measure was only collected in
Projects MOST 1 and MOST 2.
Cannabis internalized norms.
Internalized
norms related to college cannabis use was
assessed using the Perceived Importance of
Marijuana to the College Experience (PIMCES;
Pearson, Kholodkov, et al., 2017). This scale
measures internalized norms related to college
cannabis use and has been validated in college
student populations. The measure includes 13
items (e.g., “
To get high on marijuana is a college
rite of passage
”) and participants respond on a 5-
point scale from 1 =
Strongly disagree
to 5 =
Strongly agree
. This measure was only collected
for Projects MOST 1 and PSST.
Cannabis Protective Behavioral Strategies.
Cannabis protective behavioral strategies were
assessed using the Protective Behavioral
Strategies for Marijuana (PBSM; Pedersen et al.,
2016; 2017). Two versions of this measure exist,
the 50-item version (used in MOST 1 dataset;
Pedersen et al., 2016) and the 17-item version
(used in MOST 2 and PSST datasets; Pedersen et
al., 2017). This scale measures participant’s use of
behavioral strategies for mitigating the negative
impacts of cannabis use. These strategies include
things like limiting use, reducing the likelihood
that others would know they used, and reducing
the likelihood of experiencing legal problems.
Participants were asked to report how often they
used specific strategies on a scale from 1 =
Never
to 6 =
Always
. This measure was collected in all
datasets except Project CABS.
Cannabis Identity
. Identification with being
an individual who uses cannabis was examined
with a 5-item scale modified from the Smoker Self
Concept Scale (Shadel & Mermelstein, 1996).
Participants rated each item from 1 =
Strongly
disagree
to 7 =
Strongly agree
on statements
about how much cannabis plays a role in their life
and personality, as well as others’ perceptions
about the role of cannabis in their life (for
example, “
Marijuana is a part of ‘who I am’
”). This
measure was only collected in Projects MOST 1
and MOST 2.
Statistical Analyses
To test study aims, we conducted independent
LCAs based on cannabis use timing indicators on
the four independent samples using
Mplus
8.3
(Muthén & Muthén 1998 - 2019). In all four
datasets, to determine the optimal class solution,
we examined goodness-of-fit indices (e.g., sample
adjusted Bayesian Information Criterion; Sclove,
1987; Akaike Information Criterion; Akaike 1973,
1974), classification diagnostics (e.g., relative
Cannabis, A Publication of the Research Society on Marijuana
83
entropy), and the Lo-Mendell-Rubin Adjusted
Likelihood Ratio Test (LRT; Lo et al., 2001;
Vuong, 1989). Moreover, we substantively
interpreted the class solutions and adopted advice
from Nagin (2005) suggesting that if it is difficult
to identify the optimal number of latent classes
(for example, if the LRT, goodness-of-fit indices
and classification diagnostics provide an
ambiguous optimal class solution), the most
parsimonious class solution that contains a
smallest class greater than 5% of the total
analytic sample should be selected. After
determining the optimal number of latent classes,
equality of weighted means on the auxiliary
outcome variables were tested across classes
using the automatic BCH method (Asparouhov &
Muthén 2015; Bakk & Vermunt 2016), which
utilizes posterior probability-based multiple
imputations (Asparouhov & Muthén 2007).
RESULTS
Table 1 reports commonly utilized fit statistics
for each sample on 1 through 7 class solutions.
Across each sample, the LRT suggests that a
higher class solution fit better than the previous
class solution (e.g., 5-class solution fit significantly
better than a 4-class solution). Although the 6- and
7-class solutions did fit significantly better than
their k-1 class comparisons on the LRT, AIC, BIC,
and adjusted BIC, the smallest class sizes for 2 of
4 datasets fell below 5% of the total analytic sample
for each class solution above 5. For the 5-class
solution found in the CABS dataset, though the
smallest class size was 4.3% of the total analytic
sample from that dataset, in each other sample the
smallest class size remained above 5%. Further,
the relative entropy for the 5-class solutions across
samples was above 0.85 (above 0.90 in 2 of 4
datasets), which is considered excellent
classification quality (>0.80 is considered ‘high’;
Clark & Muthén, 2009). Therefore, after
examining each of these results in concert with
substantive theoretical interpretation of the
classes (Marsh et al. 2009; Nylund et al. 2007), we
selected the 5-class solutions as best fitting the
data across samples.
The overall pattern of cannabis use endorsed
was generally consistent across all four
independent samples. This occurred such that,
across all samples and classes, participants
endorsed using: 1) at similar levels from Sunday-
Thursday (range across datasets = 36.6-46.3%), 2)
more on Fridays and Saturdays (range across
datasets = 70.4-79.5%), 3) the least between 4am-
8am (range across datasets = 6.8-8.0%), and 4) at
progressively greater rates as the typical day
progressed (8am-12pm range across datasets =
17.6-21.5%; 12pm-4pm range across datasets =
25.2-26.2%; 4pm-8pm range across datasets = 39.9-
48.1%; 8pm-12am range across datasets = 77.1-
82.4%), until the 12am-4am time block where use
endorsement dropped (range across datasets =
22.0-31.6%; see Figure 1). The 5 classes (see Figure
2 and Table 2) identified were also similar across
the four independent samples and were
characterized by 2 daily use classes (classes 1 and
2) and 3 weekend use classes (classes 3-5) of
varying qualities. Class 1 is referred to as the
daily-
morning class
because individuals in this class
were characterized by daily and common morning
use (i.e., 8am-12pm). Class 2 is referred to as the
daily-non-morning class
as individuals in this class
were characterized by daily and uncommon use
between 8am-12pm. Class 3 is referred to as the
weekend-morning class
as individuals in this class
were characterized by mostly weekend (i.e., Friday
and Saturday) and common morning use. Class 4
is referred to as the
weekend-night class
because
individuals in this class were characterized by
weekend use, uncommon morning use, and
common nighttime use (i.e., 8pm-12am). Class 5 is
referred to as the
weekend-evening class
as
individuals in this class were characterized by
weekend use, and common use from 4-8pm but
were the only class to endorse a decrease in use
from 8pm-12am (in 3 of 4 datasets, use from 8pm-
12am was zero for this class). Among the MOST 1,
MOST 2, and PSST samples, class 4 was the
largest class, while in the CABS sample class 5 was
the largest class.
The daily use classes 1 and 2 similarly endorsed
using around 90% of the time or greater on every
day of the week (
daily-morning
, class 1: range
across datasets = 96-100%, mean across datasets =
99.0%;
daily-non-morning
, class 2: range across
datasets = 89.1-100%; mean across datasets =
96.5%) but diverged in their endorsement of
morning use between 8am-12pm (
daily-morning
,
class 1: range across datasets = 78.6-84.2%, mean
across datasets = 81.6%;
daily-non-morning
, class
2: range across datasets = 13.3-18.5%; mean across
datasets = 16.1%). For the three weekend classes,
they endorsed using around 40-90% of the time on
Latent Class Analyses Based on Timing of Cannabis Use
84
Table 1.
Fit Statistics for 1 Through 7 Class Solutions for Latent Class Analysis (LCA) Across Four Independent Samples
Number of Classes
MOST1
1
2
3
4
5
6
7
AIC
30797.245
25081.738
24572.775
24871.021
23834.567
23589.674
23370.512
BIC
30870.416
25233.708
24803.545
24491.589
24222.935
24056.841
23916.478
Sample-Size Adjusted BIC
30829.144
25147.927
24673.284
24316.850
24003.716
23793.143
23608.302
Lo-Mendell Rubin LRT p-value
---
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Relative Entropy
---
0.953
.822
.924
.910
.900
.851
Smallest n (% of total sample)
2056
608.6 (29.6%)
576.6 (28.0%)
227.7. (11.1%)
207.0 (10.1%)
40.6 (2.0%)
41.1 (2.0%)
MOST2
1
2
3
4
5
6
7
AIC
28454.593
22947.702
22516.415
22184.800
21878.992
21672.366
21559.231
BIC
28526.363
23096.763
22742.767
22488.443
22259.926
22130.590
22094.746
Sample-Size Adjusted BIC
28485.063
23010.984
22612.511
22313.709
22040.715
21866.901
21786.580
Lo-Mendell Rubin LRT p-value
---
<.0001
<.0001
0.0489
<.0001
<.0001
.0008
Relative Entropy
---
.963
.836
.915
.903
.916
.863
Smallest n (% of total sample)
1846
557.8 (30.2%)
541.9 (29.4%)
230.0 (12.5%)
206.5 (11.2%)
151.9 (8.2%)
152.5 (8.3%)
PSST
1
2
3
4
5
6
7
AIC
29860.983
23345.254
22888.611
22502.494
22236.757
22029.845
21870.633
BIC
29933.605
23496.084
23117.649
22809.741
22622.212
22493.507
22412.504
Sample-Size Adjusted BIC
29892.303
23410.304
22987.390
22635.004
22402.996
22229.813
22104.331
Lo-Mendell Rubin LRT p-value
---
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Relative Entropy
---
.971
.930
.853
.884
.890
.906
Smallest n (% of total sample)
1971
629.0 (31.9%)
317.3 (16.1%)
289.1 (14.7%)
292.5 (14.8%)
142.9 (7.3%)
132.6 (6.7%)
CABS
1
2
3
4
5
6
7
AIC
16960.277
13585.165
13263.116
13039.313
12861.730
12737.292
13794.691
BIC
17025.574
13720.782
13469.054
13315.571
13208.308
13154.191
14296.193
Sample-Size Adjusted BIC
16984.283
13635.023
13338.827
13140.76
12989.145
12890.560
13988.071
Lo-Mendell Rubin LRT p-value
-----
< .0001
0.0037
.0048
< .0001
<
0.0024
0.0006
Relative Entropy
-----
.953
.854
.905
.853
.845
.870
Smallest n (% of total sample)
1122
384.4 (34.3%)
209.5 (18.7%)
72.2 (6.4%)
50.1 (4.3%)
34.3 (3.1%)
34.1 (2.6%)
Note
. AIC = Akaike Information Criterion, BIC = Bayesian Information Criterion. LRT = Lo-Mendell-Rubin Adjusted Likelihood Ratio Test.
Cannabis, A Publication of the Research Society on Marijuana
85
Table 2.
Description of the 5 Classes Found in LCA Analyses Across 4 Independent Samples
Class #
Class Title
% of total sample for each dataset
Class Description
1
Daily,
morning
use
MOST 1: 10.4% of the total sample
MOST 2: 14.5% of the total sample
PSST: 16.4% of the total sample
CABS: 12.5% of total sample
Across each of the four samples, these classes displayed 96% or greater endorsement of use on
each day of the week, as well as 78.6% or greater endorsement of use between the times of 8am-
12pm. These classes showed the lowest endorsement of use during the time period of 4am-8am
(32.9% or lower) and the greatest endorsement of use between the times of 4pm-8pm and 8pm-
12am (93.4% or greater).
2
Daily, non-
morning
use
MOST 1: 16.1% of the total sample
MOST 2: 13.1% of the total sample
PSST: 14.4% of the total sample
CABS: 22.7% of total sample
Across each of the four samples, these classes exhibited 88.3% or greater endorsement of use on
each day of the week, and 18.5% or less endorsement of use between the times of 8am-12pm.
These classes showed the lowest endorsement of use during the period of 4am-8am (3.7% or
lower) and the greatest endorsement of use during the period of 8pm-12am (76.3% or greater).
3
Weekend,
morning
use
MOST 1: 9.2% of the total sample
MOST 2: 10.0% of the total sample
PSST: 13.2% of the total sample
CABS: 4.5% of total sample
Across each of the four samples, these classes displayed 69.2% or greater endorsement of use on
both Friday and Saturday, while on the remaining days of the week endorsed 55.4% or less use.
In 3 out of 4 samples, this class showed 40.1% or greater endorsement of use between the times
of 8am-12pm (for the PSST sample, 24.9% endorsed use from 8am-12pm). These classes showed
the lowest endorsement of use during the time period of 4am-8am (66.9% for CABS sample,
24.2% or lower for all other samples) and the greatest endorsement of use during the period of
8pm-12am (78.2% or greater).
4
Weekend,
night use
MOST 1: 49.7% of the total sample
MOST 2: 44.2% of the total sample
PSST: 40.7% of the total sample
CABS: 28.2% of total sample
Across each of the four samples, these classes showed 56.7% or greater endorsement of use on
both Friday and Saturday, while on the remaining days of the week endorsed 19.9% or less use.
These class 4 or “Weekend, night use” classes endorsed using less than all other classes in their
respective samples for the time blocks 12am-4am, 4am-8am, 8am-12pm, and 4pm-8pm (17.5%
or less for all samples), while also showing the greatest endorsement of use compared to all other
classes in their respective samples in the time block 8pm-12am (100% endorsement for all
samples). The lowest endorsement of use within this class was during the time period 4am-8am
(0% in all samples).
5
Weekend,
evening
use
MOST 1: 14.6% of the total sample
MOST 2: 18.1% of the total sample
PSST: 15.3% of the total sample
CABS: 34.0% of total sample
Finally, in 3 out of 4 (MOST 1, MOST 2, and PSST) samples, these classes exhibited 41.7% or
greater endorsement of use on both Friday and Saturday, while on the remaining days of the
week endorsed 16.7% or less use. Still in 3 out of 4 samples, these “Weekend, evening use” classes
endorsed a marked decrease in use in the time block 8pm-12am (0% endorsed use in the 3
samples) when compared to 4pm-8pm (between 57.2% and 35.8% endorsed use in the 3 samples).
In the CABS sample, this class displayed similar patterns when compared to the other 3
samples, with some key differences. These are, for each day of the week, class 5 in the CABS
sample showed greater levels of use than class 4, and the decrease in endorsed use from 4pm-
8pm (60.2%) to 8pm-12am (55.2%) was far less pronounced in contrast to the other samples.
Though these differences existed among the CABS sample compared to the other 3, the overall
pattern observed is the same. The class 5 or “Weekend, evening use” class in CABS also endorsed
using on Friday and Saturday (65.3%) greater than the remaining days of the week (30.8%), and
showed a slight decrease in use endorsed between the evening (4pm-8pm) and nighttime (8pm-
12am) time blocks.
Latent Class Analyses Based on Timing of Cannabis Use
86
Figure 1.
Depiction of endorsement rates of cannabis use times across the four independent samples. Note that days of the week and times of
day are dichotomized variables, thus values are interpreted as the percentage of the sample that endorsed using marijuana on a particular day
and at specific times over the course of a day (in terms of six 4-hour blocks of time).
Project MOST 1
Figure 2.
Depiction of the five latent classes defined by the percent likelihood that a participant assigned to a class endorsed using cannabis
on each day of a typical week and at specific times over the course of a day (in terms of six 4-hour blocks of time) across four independent
samples. Note that days of the week and times of day are dichotomized variables, thus values are interpreted as the percentage of each class
that endorsed using marijuana on a particular day and at specific times over the course of a day. Class 1 =
Daily, morning use
; Class 2 =
Daily,
non-morning use
; Class 3 =
Weekend, morning use
; Class 4 =
Weekend, evening use
; Class 5 =
Weekend, afternoon use
.
Cannabis, A Publication of the Research Society on Marijuana
87
Project MOST 2
Project PSST
Project CABS
Figure 2, continued
Latent Class Analyses Based on Timing of Cannabis Use
88
Fridays and Saturdays (
weekend-morning
, class
3: range across datasets = 69.2-93.7%, mean
across datasets = 80.4%;
weekend-night
, class 4:
range across datasets = 62.0-75.4%, mean across
datasets = 66.9%;
Weekend-evening
, class 5:
range across datasets = 41.7-59.5%, mean across
datasets = 51.5%) and less than 56% of the time
on all other days of the week (
weekend-morning
,
class 3: range across datasets = 18.7-55.4%, mean
across datasets = 37.1%;
weekend-night
, class 4:
range across datasets = 7.9-30.8%, mean across
datasets = 14.25%;
weekend-evening
, class 5:
range across datasets = 4.7-16.7%, mean across
datasets = 11.0%). The
weekend-morning
class 3
endorsed greater morning cannabis use (between
8am-12pm) compared to the other weekend
classes (
weekend-morning
, class 3: range across
datasets = 24.9-100%, mean across datasets =
51.5%;
weekend-night
, class 4: range across
datasets = 0.5-4.4%, mean across datasets = 1.6%;
weekend-evening
, class 5: range across datasets =
0.0-13.5%, mean across datasets = 9.15%). The
weekend-night
(class 4) endorsed nighttime
cannabis use (between 8pm-12am) 100% of the
time in all datasets (
weekend-morning
, class 3:
range across datasets = 78.2-93.7%, mean across
datasets = 86.1%;
weekend-night
, class 4: range
across datasets = 0 [all 100%], mean across
datasets = 100%;
weekend-evening
, class 5: range
across datasets = 0.0-55.2%, mean across datasets
= 13.8%). The
weekend-evening
(class 5) endorsed
greater cannabis use in the evening (between 4-
8pm: range across datasets = 35.8-60.2%, mean
across datasets = 41.54%) than they did at night
(between 8pm-12am: range across datasets = 0.0-
55.2%, mean across datasets = 13.8%), whereas no
other class showed decreased endorsement in
these time periods. Thus, the classes are labeled
to describe their defining characteristics relative
to the other classes. The classes are not labeled to
provide a holistic description of their
characteristics and should not be interpreted as
such.
Auxiliary Tests Comparing Latent Classes on
Outcomes
Equality of mean comparisons using the BCH
method across classes and within specific samples
are reported in Table 3. For brevity, we provide
overall summaries of findings across datasets as
opposed to specific findings within each dataset
(see Table 3 for those specific findings). Further,
we only discuss differences that were statistically
significant. All other findings were inconclusive as
to whether or not a mean difference was present
across specific classes.
Cannabis Use Disorder Symptoms and Negative
Consequences
The
daily-morning
class (class 1) tended to
show significantly higher scores compared to
other classes on the CUDIT-R. The
daily-non-
morning
class (class 2) tended to score
significantly higher on the CUDIT-R than all the
weekend classes. The
weekend-morning
class
(class 3) tended to score significantly higher on the
CUDIT-R than
weekend-night
(class 4) and
weekend-evening
(class 5) classes. Regarding
cannabis-related negative consequences, the
daily-morning
class (class 1) tended to show
significantly higher negative consequences on the
Marijuana Consequences Questionnaire
compared to other classes. The
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes
reported higher negative consequences than the
weekend-night
(class 4) and
weekend-evening
(class 5) classes.
Cannabis Use
For typical frequency of cannabis use, the
daily-morning
class (class 1) tended to report
significantly higher frequency of use than all
other classes. The
daily-non-morning
class (class
2) tended to endorse significantly higher
frequency of use than all the weekend classes. The
weekend-morning
class (class 3) tended to endorse
significantly higher frequency of use than the
weekend-night
(class 4) and
weekend-evening
(class 5) classes. The
weekend-night
class (class 4)
tended to endorse significantly higher frequency
of use than the
weekend-evening
class (class 5).
For typical quantity of cannabis use, measured by
the MUG, the
daily-morning
class (class 1) tended
to report significantly higher quantity of use than
all other classes. Further, the
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes
endorsed significantly higher quantity of use than
the
weekend-night
(class 4) and
weekend-evening
(class 5) classes.
Cannabis, A Publication of the Research Society on Marijuana
89
Table 3.
Auxiliary outcome variable means compared within datasets
Dataset
Daily
Morning
(Class 1)
Daily Non-
Morning
(Class 2)
Weekend
Morning
(Class 3)
Weekend
Night
(Class 4)
Weekend
Evening
(Class 5)
Summary of Significant
Differences
Cannabis Use,
Negative
Consequences,
Cannabis Use
Disorder
Symptoms
[Brief] Marijuana
Consequences
Questionnaire
(MACQ)
MOST 1
14.416a
10.686b
12.355ab
5.993c
5.919c
1 > 2, 4, 5
2, 3 > 4, 5
MOST 2
6.654a
5.605b
4.72b
2.547c
2.582c
1 > 2, 3, 4, 5
2, 3 > 4, 5
PSST
6.781a
4.939b
4.68b
1.966c
2.147c
1 > 2, 3, 4, 5
2, 3 > 4, 5
CABS
7.639a
5.331b
4.455b
1.612c
2.857d
1 > 2, 3, 4, 5
2, 3 > 4, 5
5 > 4
Cannabis Use
Disorder
Identification Test-
Revised (CUDIT-R)
MOST 1
MOST 2
13.892a
12.285ab
10.742b
5.971c
5.321c
1 > 3, 4, 5
2, 3 > 4, 5
PSST
15.416a
11.864b
9.824c
5.204d
5.764d
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
CABS
15.793a
11.421b
9.197c
4.435d
6.604e
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
5 > 4
Typical Frequency of
Cannabis
MOST 1
22.621a
9.563b
7.196c
2.301d
1.478e
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
4 > 5
MOST 2
23.873a
9.627b
6.86c
2.202d
1.509e
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
4 > 5
PSST
24.803a
9.459b
7.517c
1.859d
1.569e
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
4 > 5
CABS
26.265a
9.057b
9.768b
1.269c
3.18d
1 > 2, 3, 4, 5
2, 3 > 4, 5
5 > 4
Typical Quantity of
Cannabis
MOST 1
MOST 2
25.037a
9.209b
7.635b
2.135c
1.728d
1 > 2, 3, 4, 5
2, 3 > 4, 5
4 > 5
PSST
26.189a
9.258b
7.719b
2.097c
1.639c
1 > 2, 3, 4, 5
(Table continues)
Latent Class Analyses Based on Timing of Cannabis Use
90
2, 3 > 4, 5
CABS
20.993a
8.242b
10.935c
1.073d
3.347e
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
5 > 4
Cannabis Use
Motives
Social Motives
MOST 1
3.027a
2.725b
3.01a
2.611b
2.39c
1, 3 > 2, 4, 5
2, 4 > 5
MOST 2
2.815a
2.446b
2.462b
2.262c
2.136c
1 > 2, 3, 4, 5
2, 3 > 4, 5
PSST
2.745a
2.416b
2.579ab
2.236c
2.151c
1 > 2, 4, 5
2, 3 > 4, 5
CABS
2.767a
2.564a
2.619ac
2.134b
2.304bc
1, 2 > 4, 5
3 > 4
Coping Motives
MOST 1
2.749a
2.506b
2.588ab
1.98d
2.022d
1 > 2, 4, 5
2, 3 > 4, 5
MOST 2
2.611a
2.572a
2.172b
1.909c
1.836c
1, 2 > 3, 4, 5
3 > 4, 5
PSST
2.53a
2.397a
2.412a
1.853b
1.861b
1, 2, 3 > 4, 5
CABS
3.204a
2.841b
2.409c
1.787d
2.165c
1 > 2, 3, 4, 5
2 > 3, 4, 5
3, 5 > 4
Enhancement
Motives
MOST 1
4.071a
3.898b
3.813b
3.61c
3.31d
1 > 2, 3, 4, 5
2, 3 > 4, 5
4 > 5
MOST 2
3.896a
3.721a
3.438b
3.292b
3.058c
1, 2 > 3, 4, 5
3, 4 > 5
PSST
4.012a
3.719b
3.778b
3.299c
3.208c
1 > 2, 3, 4, 5
2, 3 > 4, 5
CABS
4.115a
4.144a
3.906ac
3.304b
3.676c
1, 2 > 4, 5
3 > 4
5 > 4
Conformity Motives
MOST 1
1.489ab
1.382b
1.663a
1.447b
1.559a
2, 4 > 3, 5
MOST 2
1.461a
1.383a
1.469a
1.357a
1.411a
None
PSST
1.363ab
1.325ab
1.459a
1.307b
1.39ab
3 > 4
CABS
1.263a
1.388ab
1.451ab
1.315ab
1.425b
1 > 5
Expansion Motives
MOST 1
3.129a
2.902ab
2.816b
2.189c
2.122c
1 > 3, 4, 5
2, 3 > 4, 5
MOST 2
3.17a
2.734b
2.556b
2.044c
2.065c
1 > 2, 3, 4, 5
2, 3 > 4, 5
PSST
3.004a
2.751b
2.847ab
2.036c
2.098c
1 > 2, 4, 5
2, 3 > 4, 5
CABS
2.976a
2.787a
2.646a
1.697b
2.203c
1, 2, 3 > 4, 5
5 > 4
(Table continues)
Cannabis, A Publication of the Research Society on Marijuana
91
Cannabis Use
Norms
Injunctive Norms:
Best Friends
MOST 1
6.112a
5.857b
5.387c
5.101d
5.041d
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
MOST 2
6.116a
5.819a
5.385b
5.122bc
4.949c
1, 2 > 3, 4, 5
3 > 5
PSST
CABS
Injunctive Norms:
Typical College
Student
MOST 1
5.335a
5.321a
5.085a
5.205a
5.154a
None
MOST 2
5.264a
5.357a
5.171a
5.128a
5.197a
None
PSST
CABS
Injunctive Norms:
Parents
MOST 1
3.228a
2.726b
2.845b
2.189c
2.187c
1 > 2, 3, 4, 5
2, 3 > 4, 5
MOST 2
3.343a
3.152a
3.067a
2.392b
2.147b
1, 2, 3 > 4, 5
PSST
CABS
Perceived
Importance of
Marijuana to the
College Experience
Scale (PIMCES)
MOST 1
2.931a
2.797b
2.759b
2.476c
2.446c
1 > 2, 3, 4, 5
2, 3 > 4, 5
MOST 2
PSST
2.773a
2.488bc
2.62b
2.374c
2.296c
1 > 2, 3, 4, 5
3 > 4, 5
CABS
Other
Constructs
Protective
Behavioral
Strategies for
Marijuana (PBSM)
MOST 1
3.125a
3.679b
3.757b
4.502c
4.396c
1 < 2, 3, 4, 5
2, 3 < 4, 5
MOST 2
3.272a
3.767b
4.222c
4.748d
4.718d
1 < 2, 3, 4, 5
2 < 3, 4, 5
3 < 4, 5
PSST
3.265a
3.821b
4.217c
4.844d
4.753d
1 < 2, 3, 4, 5
2 < 3, 4, 5
3 < 4, 5
CABS
Marijuana Identity
MOST 1
3.956a
3.231b
2.532c
1.704d
1.691d
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
MOST 2
3.793a
3.27b
2.717c
1.814d
1.774d
1 > 2, 3, 4, 5
2 > 3, 4, 5
3 > 4, 5
PSST
CABS
Latent Class Analyses Based on Timing of Cannabis Use
92
Cannabis Use Motives
For social motives, the
daily-morning
class
(class 1) tended to score significantly higher than
all other classes, except the
weekend-morning
class (class 3) where differences were
inconclusive. The
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes tended to score
significantly higher than the
weekend-night
(class
4) and
weekend-evening
(class 5) classes.
For coping motives, the
daily-morning
(class 1)
classes showed significantly higher scores
compared to the
daily-non-morning
(class 2)
classes and the
weekend-morning
(class 3) classes
in 2 out of 4 datasets. The
daily-non-morning
(class 2) classes showed significantly higher
scores than the
weekend-morning
(class 3) classes
on 2 out of 4 datasets. The
weekend-morning
(class 3) classes tended to show significantly
higher scores than the
weekend-night
(class 4)
and
weekend-evening
(class 5) classes.
For enhancement motives, the
daily-morning
class (class 1) tended to score significantly higher
than other classes (differences were inconclusive
compared to
daily-non-morning
class [class 2] in 2
out of 4 datasets). The
daily-non-morning
class
(class 2) tended to score significantly higher than
the
weekend-night
(class 4) and
weekend-evening
(class 5) classes. In 2 out of 4 datasets, the
weekend-morning
(class 3) class was significantly
higher than both the
weekend-night
(class 4) and
weekend-evening
(class 5) classes; the
weekend-
night
(class 4) class scored significantly higher
than the
weekend-evening
class (class 5).
For expansion motives, the
daily-morning
class (class 1) scored significantly higher than the
weekend-night
(class 4) and
weekend-evening
(class 5) classes, and scored significantly higher
than the
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes in 2 out of 4
datasets. The
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes scored
significantly higher than the
weekend-night
(class
4) and
weekend-evening
(class 5) classes. Finally,
for conformity motives, all classes across datasets
showed inconclusive differences.
Cannabis Use Norms
Regarding participant’s best friends, the daily
classes were significantly different in one (MOST
1) out of two available datasets (MOST 1 and 2),
and each reported higher injunctive norms (i.e.,
higher approval by best friends) than all weekend
classes. The
weekend-morning
class (class 3)
showed significantly higher injunctive norms
scores compared to the
weekend-evening
class
(class 5). For injunctive norms regarding the
typical college student, differences were
inconclusive among classes on both of the two
available datasets (MOST 1 and 2). For injunctive
norms regarding parents, in one (MOST 1) out of
two datasets (MOST 1 and 2) the
daily-morning
class (class 1) showed significantly higher scores
than all other classes, and the
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes
scored significantly greater than the
weekend-
night
(class 4) and
weekend-evening
(class 5)
classes. In the MOST 2 dataset, the daily classes
and the
weekend-morning
class (class 3) scored
significantly higher than the
weekend-night
(class
4) and
weekend-evening
(class 5) classes. For
marijuana internalized norms, the
daily-morning
class (class 1) scored significantly higher than the
other classes, indicating that they perceived
marijuana use to be more integral to the college
experience (i.e., internalized norms). The
weekend-morning
class (class 3) scored
significantly higher than the
weekend-night
(class
4) and
weekend-evening
(class 5) classes.
Cannabis Identity and Protective Behavioral
Strategies
For the cannabis user identity scale (Shadel &
Mermelstein, 1996), assessing the extent to which
individuals identify as a cannabis user, the
daily-
morning
class (class 1) scored significantly higher
than the other classes. The
daily-non-morning
class (class 2) scored significantly higher than the
weekend classes. Finally, the
weekend-morning
class (class 3) scored significantly higher than the
weekend-night
(class 4) and
weekend-evening
(class 5) classes. Regarding marijuana protective
behavioral strategies, the
daily-morning
class
(class 1) reported scores significantly lower than
the other classes (i.e., engaged in fewer harm
reduction strategies). The
daily-non-morning
class (class 2) scored significantly lower than the
weekend classes. The
weekend-morning
(class 3)
classes scored significantly lower than the
weekend-night
(class 4) and
weekend-evening
(class 5) classes.
Cannabis, A Publication of the Research Society on Marijuana
93
DISCUSSION
The present study identified five distinct
latent classes of cannabis use patterns across four
independent samples based on timing of use (i.e.,
day of week and hour of day). The classes were
compared on indicators of cannabis use, use-
related negative outcomes, motives for using, use
of protective behavioral strategies, perceptions,
and norms associated with use. Visually (see
Figure 2), the classes emerged intuitively and
mostly in line with our primary hypothesis, such
that they differed on morning vs. non-morning use
and weekend vs. weekday use. It is important to
note that the classes are labeled to describe their
defining characteristics relative to the other
classes, and not to provide a holistic description of
their characteristics. For example, there were
some individuals in the
daily-morning
class (class
1) who did not endorse use in the mornings
between 8am-12pm (i.e., averaged across
datasets, 18.4% assigned to this class did not
endorse use during this time). We labeled it
daily-
morning
because it showed far greater
endorsement of morning use relative to the other
daily use classes, and this should not be
interpreted to mean all individuals
probabilistically assigned to this class endorsed
morning use. What the label
daily-morning
is
referring to is that individuals assigned to that
class were more likely to endorse morning use
relative to daily-using individuals not assigned to
that class. In other words, any of the classes
labeled as ‘morning’ classes should not be
interpreted to be assessing the ‘effects of morning
use,’ as those classes also endorsed use
throughout the day. Rather, differences between
classes on the auxiliary variables may be partially
explained by these differences in timing of use
that are being highlighted. With this in mind, it is
possible that, for example, some individuals in the
weekend-morning
class (class 3) are primarily
using in the mornings on weekdays and not
weekends, despite endorsing more use on the
weekends and between 8am-12pm compared to
the other classes.
With regard to the auxiliary variables,
morning and daily cannabis use classes reported
lower use of protective behavioral strategies and
greater scores on indicators of use, motives,
related negative consequences, and
perceptions/norms compared to weekend non-
morning use classes. In summary, all classes in all
datasets that used in the morning or daily tended
to report significantly higher scores (and lower
use of protective behavioral strategies) than
classes that did not use daily or in the morning.
The
weekend-night
(class 4) and
weekend-evening
(class 5) classes (i.e., classes that did not use daily
or in the morning) were routinely the lowest
scoring classes (highest scoring for protective
behavioral strategies). These results make sense
intuitively and support the hypothesis that daily
and morning use of cannabis are both associated
with greater risks related to the auxiliary
variables.
It is theoretically coherent, and evident in the
results of the present study, that classes
characterized by both risk-associated factors
(morning and daily use) generally report higher
risk related to cannabis use than other classes.
Further, classes characterized by one risk-
associated factor only (
daily-non-morning
[class 2]
and
weekend-morning
[class 3]) scored similarly,
but still greater than classes characterized by
neither risk-associated factor (
weekend-night
[class 4] and
weekend-evening
[class 5]). Though
causal claims cannot be made due to the cross-
sectional nature of the study, differences in
classes on morning vs. nighttime use in these
analyses appear to be comparable to (though less
impactful in magnitude) daily vs. weekend use as
a risk-associated factor for intensifying cannabis
use, variables related to use, and the experience
of negative consequences. Theoretically, this may
be because morning use makes additional use
later in the day more likely to combat the ‘come
down’. Further, consumption in the morning may
cloud judgment or decision-making and increase
the likelihood of using, or generally behaving, in
riskier ways. It could also be the case that
morning use/timing of use is associated with other
indicators related to outcomes, for example social
use vs. use while alone. It may be that individuals
using more often in the mornings are using more
often on their own, and this may partially account
for the generally more severe consequences
observed. Future research should explicitly
examine via longitudinal and experimental
analyses if and how it may be the case that
morning vs. nighttime use confers unique risks
not explained by daily vs. weekend use or
frequency of use more generally.
Latent Class Analyses Based on Timing of Cannabis Use
94
Clinical Implications
Importantly, in two of three datasets that
contained data on individuals’ experience of
cannabis use disorder (CUD) symptoms, there
were significant differences in the experience of
these symptoms by class. Specifically, the pattern
of symptoms from highest to lowest was as
follows:
daily-morning
(class 1) à
daily-non-
morning
(class 2) à
weekend-morning
(class 3) à
weekend-night
(class 4) and
weekend evening
(class 5). Broadly, for the other auxiliary variables
associated with use (i.e., not the CUDIT-R), the
same pattern was found, except differences
between
daily-non-morning
(class 2) and
weekend-morning
(class 3) classes were
inconclusive. These findings may reflect the
relative strength of association between daily vs.
non-daily and common vs. uncommon morning
use with cannabis-related outcomes. The present
study supports the idea that daily use (i.e., more
frequent use) may be a key risk factor compared
to morning use, given that both daily use classes
displayed generally higher risk of negative
consequences regardless of morning use habits.
After frequency of use is accounted for, morning
vs. uncommon morning use remains a useful
indicator for predicting the experience of CUD
symptoms and other outcomes. This
interpretation is consistent with the results of
Earleywine and colleagues (2016), who found that
morning use accounted for unique variance in
cannabis-associated problems. The results of the
present study indicate that it may be useful for
clinicians to consider emphasizing reducing
both
daily and morning recreational cannabis use,
especially given that harm reduction
interventions on these specific types of use habits
can be relatively straightforward (Earleywine et
al., 2016).
A recent meta-analysis found that, compared
to other motives, coping motives’ relations with
negative outcomes were the strongest and most
reliable, and coping motives were the only factor
to emerge as a significant positive predictor of
cannabis use frequency as well as problems
(Bresin & Mekawi, 2019). In the present study,
both daily use classes reported significantly
greater coping motives for using cannabis
compared to the weekend use classes (for 2/4
datasets; in the other 2/4 datasets,
daily-non-
morning
(class 2) and
weekend-morning
(class 3)
did not differ significantly). These results are
consistent with the findings of Buckner and
colleagues (2019), such that weekday, but not
weekend, coping motives significantly predicted
frequency of cannabis use and associated
problems. The present study implies that
individuals using cannabis daily and in the
morning are using to cope more often than those
who do not use daily or in the morning, and this
corresponds with greater frequency, more
problems experienced, and greater risk of
developing CUD symptoms. Additionally, the
results of two recent meta-analyses found a
medium sized correlation (14% shared variance)
between cannabis use frequency and related
problems (Bresin & Mekawi, 2019; Pearson,
2019). The authors suggest that additional risk
factors need be identified to explain how
(processes) and when (diagnostic criteria)
cannabis use becomes problematic (Bresin &
Mekawi, 2019). Given the results of the present
study, examining timing of use may be promising
for predicting cannabis related problems
(including CUD symptoms) as the legal status of
cannabis, and the corresponding number of
individuals who choose to use, continues to
change.
Limitations and Future Research
This research has several limitations. First,
given the cross-sectional approach, causal claims
cannot be made about any of the classes and
related auxiliary variables. In other words, daily
and morning use may be a correlate rather than a
cause of the differences among classes on
indicators and outcomes measured. Future
research should employ longitudinal and
experimental designs to examine whether the
effects of morning use of cannabis on negative
consequences is a proxy for more frequent use, or
whether it accounts for unique variance in
consequences, as previous studies have suggested.
Also, given the retrospective self-report nature of
the data on a ‘typical week’ of cannabis use over
the past 30-days, precise levels of use are not
accounted for as they would be in a more intensive
design, for example a daily diary. Thus, artefacts
like potential use-sessions that crossover from a
late night into an early morning (i.e., 8pm-12am
à 12am-4am) may not be sufficiently accounted
for and may present a limitation of the current
Cannabis, A Publication of the Research Society on Marijuana
95
study. Additionally, the sample includes only
college students and the number of latent classes
found in the present study may not be
generalizable to other populations. Relatedly,
there were important differences between the
CABS dataset and the other 3 datasets; the
weekend-morning
class (class 3) in CABS
endorsed the greatest use between the times of 12-
4am, 4-8am, and 8am-12pm compared to all other
classes, and maintained similar endorsement of
use from 4-8pm and 8pm-12am. These patterns
were not observed in the other datasets, which
highlights the need for future research to examine
whether these classes replicate robustly in other
diverse datasets. It is also important to note that
the analytic samples included relatively few
individuals with medical cannabis cards,
therefore this work may not generalize to
individuals who use cannabis for medical reasons.
Finally, the cannabis use measures employed in
this study inquired about grams of flower used by
participants, and therefore these results may not
be generalizable to other forms of use (i.e., edibles,
concentrates).
Conclusions
These preliminary results indicate that timing
of use is associated with indicators of cannabis use
and are related to the experience of negative
consequences, including CUD symptoms.
Recreational daily use as well as morning use may
be associated with greater negative consequences,
and there is evidence that most college students
who use cannabis do avoid these types of use.
Probing the relations between timing of cannabis
use and the experience of negative consequences
further and in different populations may prove a
fruitful line of research for understanding the
consequences of cannabis use, as well as
developing effective harm reduction techniques.
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Funding and Acknowledgements:
For all
projects, Dr. Bravo was supported by a training
grant (T32-AA018108) from the National
Institute on Alcohol Abuse and Alcoholism
Latent Class Analyses Based on Timing of Cannabis Use
98
(NIAAA) in the United States during the
duration of data collection. Data collection for
Project CABS was supported, in part, by grant
T32-AA018108. Dr. Joseph is currently funded
by grant T32-AA018108. For all projects, Dr.
Pearson was funded by a career development
grant (K01-AA023233) from the NIAAA. NIAAA
had no role in the study design, collection,
analysis or interpretation of the data, writing the
manuscript, or the decision to submit the paper
for publication.
**This project was completed by the Cross-
cultural Addictions Study Team (CAST), which
includes the following investigators (in
alphabetical order): Adrian J. Bravo, William &
Mary (Coordinating PI); Christopher C. Conway,
Fordham University; James M. Henson, Old
Dominion University; Lee Hogarth, University of
Exeter; Manuel I. Ibáñez, Universitat Jaume I de
Castelló; Debra Kaminer, University of Cape
Town; Matthew Keough, York University; Laura
Mezquita, Universitat Jaume I de Castelló;
Generós Ortet, Universitat Jaume I de Castelló;
Matthew R. Pearson, University of New Mexico;
Angelina Pilatti, National University of Cordoba;
Mark A. Prince, Colorado State University;
Jennifer P. Read, University of Buffalo; Hendrik
G. Roozen, University of New Mexico; Paul Ruiz,
Universidad de la República.
***This project was completed by the Protective
Strategies Study Team (PSST), which includes
the following investigators: Matthew R. Pearson,
University of New Mexico (Coordinating PI);
Adrian J. Bravo, William & Mary (Co-PI); Mark
A. Prince, Colorado State University (site PI);
Michael B. Madson, University of Southern
Mississippi (site PI); James M. Henson, Old
Dominion University (site PI); Alison Looby,
University of Wyoming (site PI); Vivian M.
Gonzalez, University of Alaska-Anchorage (site
PI); Amber M. Henslee, Missouri Science &
Technology (site PI); Carrie Cuttler, Washington
State University (site PI), Maria M. Wong, Idaho
State University (site PI), Dennis E. McChargue,
University of Nebraska-Lincoln (site PI).
****This project was completed by the Marijuana
Outcomes Study Team (MOST), which includes
the following investigators (in alphabetical
order): Amber M. Anthenien, University of
Houston (MOST 1 & 2); Adrian J. Bravo, William
& Mary (MOST 1 & 2); Bradley T. Conner,
Colorado State University (MOST 1 & 2);
Christopher J. Correia, Auburn University
(MOST 1); Robert D. Dvorak, University of
Central Florida (MOST 1 & 2); Gregory A.
Egerton, University at Buffalo (MOST 1 & 2);
John T. P. Hustad, Pennsylvania State
University College of Medicine (MOST 1 & 2);
Tatyana Kholodkov, University of Wyoming
(MOST 1); Kevin M. King, University of
Washington (MOST 1 & 2); Bruce S. Liese,
University of Kansas (MOST 1 & 2); Bryan G.
Messina, Auburn University (MOST 1); James G.
Murphy, The University of Memphis (MOST 1 &
2); Clayton Neighbors, University of Houston
(MOST 1 & 2); Xuan-Thanh Nguyen, University
of California, Los Angeles (MOST 1 & 2); Jamie
E. Parnes, Colorado State University (MOST 1 &
2); Matthew R. Pearson, University of New
Mexico (MOST 1 & 2); Eric R. Pedersen, RAND
(MOST 1 & 2); Mark A. Prince, Colorado State
University (MOST 1 & 2); Sharon A. Radomski,
University at Buffalo (MOST 1 & 2); Lara A.
Ray, University of California, Los Angeles
(MOST 1 & 2); Jennifer P. Read, University at
Buffalo (MOST 1 & 2).
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