Abstract
Background
Given recent historical increases in young adult frequent cannabis use and changes in cannabis policies throughout the United States, there is a need to examine high-risk patterns of use. This paper examined predictors and cannabis-related outcomes of “wake-and-bake” cannabis use, operationalized as use within 30 minutes of waking.
Methods
Participants were 409 young adults (Mage=21.61 years, 50.8% female) enrolled in a longitudinal study on simultaneous alcohol and cannabis use (i.e., using alcohol and cannabis at the same time such that their effects overlap). Eligibility criteria included reporting alcohol use 3+ times and simultaneous alcohol and cannabis use 1+ time in the past month. Participants completed twice-daily surveys for six 14-day bursts across two calendar years. Aims were tested using multilevel models.
Results
Analyses were limited to cannabis use days (9,406 days; 33.3% of all sampled days), and thereby to participants who reported using cannabis (384 participants; 93.9% of the sample). Wake-and-bake use was reported on 11.2% of cannabis use days and at least once by 35.4% of participants who used cannabis. On wake-and-bake use days, participants were high for more hours and had greater odds of driving under the influence of cannabis, but did not experience more negative consequences, relative to non-wake-and-bake cannabis use days. Participants who reported more cannabis use disorder symptoms and those reporting higher average social anxiety motives for cannabis use reported more frequent wake-and-bake use.
Conclusions
Wake-and-bake cannabis use may be a useful marker of high-risk cannabis use, including driving under the influence of cannabis.
Keywords: Cannabis, Marijuana, Morning cannabis use, Wake-and-bake, Cannabis consequences, Driving under the influence of cannabis
1. Introduction
Understanding young adult cannabis use patterns is as important as ever given historical increases in frequent cannabis use among young adults and continued changes in the legalization of cannabis throughout the United States. More than one-quarter of U.S. young adults report past-month cannabis use, and the prevalence of daily or near daily use (20+ past-month occasions) is steadily increasing, with nearly one-in-twelve young adults reporting daily or near daily use (Schulenberg et al., 2021). Additionally, cannabis potency has increased over the past 50 years (Freeman et al., 2021), particularly in the past decade (Chandra et al., 2019). Yet, perceived risk of cannabis use has decreased over the past 20 years, especially among young adults (Waddell, 2022). Although some risk factors for and consequences of cannabis use have been identified (Fischer et al., 2022; Leung et al., 2020), there is a need for deeper examination of specific high-risk patterns of use.
One potentially high-risk cannabis use behavior is using cannabis shortly after waking up (colloquially referred to as “wake-and-bake”). A Google search of the term “wake-and-bake” yields internet articles, edible recipes, and songs, many of which glamorize this behavior as a highly enjoyable and relaxing way to start one’s day. Yet, little empirical research exists on wake-and-bake use among young adults. Research on use of other substances, like tobacco and alcohol, suggests that using substances shortly after waking may be indicative of a problematic pattern of use. For instance, nicotine researchers rely on time from waking in the morning until the first cigarette of the day as an indicator of dependence, and time of first use is strongly associated with problematic nicotine use behaviors (Branstetter et al., 2020; Guertin et al., 2015; Selya et al., 2016). Similarly, morning alcohol use has been used in diagnostic and screening tests (Babor et al., 2001; Cherpitel, 1999) and is a central indicator of alcohol use dependence (York, 1995). Yet for one cannabis-specific diagnostic and screening measure (i.e., Cannabis Use Disorders Identification Test – Revised; Adamson et al., 2010), which was adapted from a diagnostic and screening measure for alcohol use (i.e., Alcohol Use Disorders Identification Test; Saunders et al., 1993), researchers removed the morning use item, as it was thought to be a carryover item from the alcohol measure.
1.1. Potential Risks Associated with Wake-and-Bake Use
An important first step towards furthering our understanding of wake-and-bake cannabis use is determining associated risks. Specifically, to what extent is this behavior associated with using greater quantities of cannabis, experiencing more negative cannabis-related consequences, and engaging in other risky behaviors, like driving under the influence of cannabis? In an ecological momentary assessment study of adolescents and young adults who used cannabis frequently, Shrier and colleagues (2013) found that participants took more hits of cannabis during morning cannabis use events than during events occurring in the afternoon or after midnight. In a cross-sectional study of adults who used cannabis daily, Earleywine and colleagues (2016) found that individuals who used cannabis in the morning reported experiencing greater cannabis-related problems than those who did not use cannabis in the morning when controlling for the quantity of cannabis used on an average occasion. These findings suggest that wake-and-bake use may be associated with greater cannabis use and problems. However, both studies examined general morning use (cannabis use before noon) rather than wake-and-bake use specifically (shortly after waking).
Attention on driving under the influence of cannabis seems to be increasing in the young adult substance use literature (Azofeifa et al., 2019; Patrick et al., 2021) as policies on cannabis legalization continue to evolve in the United States. Despite clear evidence that cannabis use impairs driving ability (Fischer et al., 2022; McCartney et al., 2021) and is associated with increased risk of motor vehicle accidents (Preuss et al., 2021), many young adults perceive driving under the influence of cannabis to be relatively low-risk, especially compared to perceived risks of driving under the influence of alcohol (Greene, 2018). This lower perceived risk could explain why some evidence indicates that young adults are more likely to drive under the influence on cannabis-only occasions relative to alcohol-only occasions (Hultgren et al., 2021). Since wake-and-bake cannabis use occurs at the start of one’s day prior to taking part in any potential commitments and/or responsibilities (e.g., work, school, social obligations, errands), and cannabis use can result in driving impairments for as long as five hours (Fischer et al., 2022; McCartney et al., 2021), it is important to know whether wake-and-bake use is associated with greater odds of driving under the influence of cannabis. This is especially important given the severe or fatal consequences (for the driver, passengers, individuals in other vehicles, and pedestrians) that can result from driving under the influence of cannabis.
1.2. Potential Predictors of Wake-and-Bake Use
Identifying predictors of wake-and-bake use is another important step in understanding this behavior. Regarding differences among cannabis use days, identifying which days of the week wake-and-bake use is more likely to occur would be an appropriate starting point, as prior work has shown weekend versus weekday differences in cannabis use more generally (Patrick et al., 2016). Regarding differences between young adults who use cannabis, the limited evidence indicates that wake-and-bake use may be common among individuals who use cannabis in greater amounts, experience more cannabis-related problems, and may be at elevated risk for cannabis use disorder (CUD; Earleywine et al., 2016; Shrier et al., 2013), similar to nicotine and alcohol research showing morning use as indicative of substance use disorders (Higgins-Biddle & Babor, 2018). Young adults’ propensity to wake-and-bake may also be associated with cannabis use motives, as they may use cannabis in the morning to cope with or manage demands associated with school or work (Shrier et al., 2013).
1.3. Current Study
The current study examined predictors and cannabis-related outcomes of wake-and-bake cannabis use, defined as using cannabis within 30 minutes of waking up. The study had two aims. Aim 1 tested daily-level associations between wake-and-bake use and three cannabis-related outcomes: number of hours high (an index of cannabis use quantity), total number of negative cannabis-related consequences, and driving under the influence of cannabis. We expected wake-and-bake use would be positively associated with each of the three cannabis-related outcomes assessed. Supplemental analyses tested daily-level associations between wake-and-bake use and each of 10 specific negative cannabis consequences. Aim 2 tested person-level (baseline number of CUD symptoms, mean alcohol use, cannabis use motives, and demographic characteristics) and daily-level predictors (previous day alcohol use, weekends versus weekdays) of wake-and-bake use.
2. Material and Methods
2.1. Participants and Procedure
Participants were a community sample of 409 young adults enrolled in a longitudinal study examining daily experiences related to simultaneous alcohol and cannabis use (i.e., use of alcohol and cannabis at the same time so that their effects overlap; Lee et al., 2020; Patrick et al., 2020). In a longitudinal measurement-burst design, participants completed twice-daily surveys (i.e., morning and afternoon) for 14 consecutive days in each of six bursts spaced four months apart (i.e., spanning two years) as well as a baseline survey and three yearly follow-up surveys. Daily morning surveys were completed between 9:00 AM and 12:00 PM, and daily afternoon surveys were completed between 3:00 PM and 6:00 PM. The onset of the COVID-19 pandemic occurred during Burst 6, which may have impacted young adults’ substance use behaviors (Graupensperger et al., 2021). Because the onset of the COVID-19 pandemic occurred before Burst 6 for some, but not all participants, daily surveys from Burst 6 were excluded from analyses.
All procedures were approved by the University of Washington institutional review board. A multimethod sampling approach was used to recruit participants through print and online advertisements; flyers; friend referral; and outreach at two-year colleges and local events and to community agencies involved with young adults. Eligible participants were 18–25 years old, reported alcohol use three or more times in the past month, reported past-month simultaneous alcohol and cannabis use, and lived within 60 miles of the study office in Seattle, WA. Additional details of the study and procedures are described elsewhere (Lee at al., 2020; Patrick et al., 2020). In Bursts 1–5, participants completed at least one daily survey on 88.4% of sampled days and on an average of 12.37 (SD=3.55) out of 14 days per burst. Retention of participants across bursts was high with 89.0% responding to at least one daily survey in each of the first five bursts.
Analyses were limited to the 9,406 cannabis use days (33.3% of sampled days) in Bursts 1–5 (December 2017 to January 2020), and thereby to the 384 participants (93.9% of all participants) who reported cannabis use on at least one sampled day in Bursts 1–5. The analytic sample included days with cannabis use whether or not other substances were used. In the analytic sample, 48.2% of participants identified as White Non-Hispanic/Latinx (NHL), 20.1% Other NHL (e.g., Black or African American, more than one race), 16.7% Hispanic/Latinx, and 15.1% Asian NHL. At baseline, the average age was 21.61 years (SD=2.15); 50.8% reported their biological sex as female, 48.2% were enrolled in a four-year college or university, 39.8% were employed part-time, and 26.0% were employed full-time.
2.2. Daily Measures Asking about the Previous Day
Number of hours high.
Each morning, participants were asked “Did you use marijuana yesterday?” Response options were “No” (0) and “Yes” (1). When participants reported using cannabis, they were asked “How many total hours were you high yesterday?” Response options ranged from “Less than one hour” (0) to “23–24 hours” (23). On days participants did not complete the morning survey (9.6% of days), they were given the opportunity to complete these questions on the afternoon survey later that day. Number of hours high has been shown to be a reasonable and more parsimonious proxy for cannabis use quantity in daily designs (Calhoun et al., 2022).
Negative cannabis consequences.
Each morning, if participants reported cannabis use, they were asked “Did any of the following happen to you as a result of your marijuana use yesterday?” Participants were presented a list of 10 negative cannabis consequences (e.g., had low motivation, felt anxious or worried) and selected which they experienced because of their cannabis use. Items were adapted from several sources, including a validated daily alcohol-related consequences measure (Lee et al., 2017) and a validated past-month cannabis-related consequences measure (Lee et al. 2021). Items were selected based on their relevance to cannabis use and appropriateness for daily assessment. Counts representing the number of negative consequences reported each day were calculated by summing across the 10 items.
Driving under the influence of cannabis.
Each morning, participants were asked, “Yesterday, did you drive a car/motor vehicle within three hours after using marijuana?” Response options were “No” (0) and “Yes” (1).
Morning cannabis use.
Each morning, if participants reported using cannabis the previous day, they were asked, “When did you use marijuana yesterday? Check all that apply.” Participants could select which times of day they used cannabis from eight 3-hour windows (e.g., 6:00 AM to 9:00 AM, 9:00 AM to 12:00 PM). For sensitivity analyses that examined morning cannabis use instead of wake-and-bake use, morning use was operationalized as days participants reported using cannabis between 6:00 AM and 12:00 PM.
Alcohol use.
Each morning, participants were asked, “Did you drink alcohol yesterday?”. When participants reported using alcohol, they were asked, “How many total drinks did you have yesterday?”. Response options ranged from “1 drink” (1) to “25 or more drinks” (25) in one drink intervals. On days participants did not complete the morning survey (9.6% of days), they were given the opportunity to complete these questions on the afternoon survey later that day. On days participants reported not using alcohol, the number of drinks variable was coded as 0.
2.3. Daily Measures Asking about the Current Day
Wake-and-bake cannabis use.
Each afternoon, in reference to the current day, participants were asked “Did you use marijuana within 30 minutes of waking up today?” Response options were “No” (0) and “Yes” (1). This variable was lagged to align with other daily-level measures asking about cannabis use and related variables on the previous day.
2.4. Baseline Measures
CUD symptoms.
Participants completed the Cannabis Use Disorder Identification Test – Revised (CUDIT-R; Adamson et al., 2010), a scale designed to screen for cannabis misuse during the previous six months and indicate risk for possible CUD. The CUDIT-R contained nine items with varying response options. Scores were calculated by summing the last eight items.
Cannabis use motives.
Participants completed the 36-item Comprehensive Marijuana Motives Questionnaire (Lee et al., 2009). Participants were given the prompt “Thinking of all the times you have used marijuana, how often would you say that you use for each of the following reasons?” They then responded to 36 items such as “because you were depressed,” “to celebrate,” and “because you had trouble sleeping.” Response options were “Almost never/never” (0), “Some of the time” (1), “Half of the time” (2), “Most of the time” (3), and “Almost always/always” (4). Items were grouped into 12 three-item subscales: enjoyment, conformity, coping, experimentation, boredom, alcohol-related, celebration, altered perceptions, social anxiety, perceived low risk, sleep, and availability. Subscale scores were computed by calculating the mean of the three items in each subscale (range of alphas: 0.60–0.93).
2.5. Statistical Analyses
Logistic and Poisson multilevel models were used to test all aims. All models were estimated using maximum likelihood estimation based on the Laplace approximation in the glmmTMB package (Brooks et al., 2017) in R 4.2.2 (R Core Team, 2022). Random effects for daily-level variables were specified to the extent that doing so improved model fit based on likelihood ratio tests. A daily-level random effect was included to account for overdispersion in Poisson models, when necessary (Harrison, 2014). Two steps were taken to fully disentangle within- and between-person associations at the daily (Level 1) and person (Level 2) levels, respectively. First, burst number (coded as 1–5) and day number within burst (coded as 1–14) variables, both of which were centered at their midpoints, were included at Level 1 to account for any potential trends over time in predictor or outcome variables (Bolger & Laurenceau, 2013; Wang & Maxwell, 2015). Second, all other Level 1 variables were person-mean-centered (Hamaker & Muthén, 2020). All Level 2 variables were grand-mean-centered. All models controlled for biological sex, age at baseline, and race/ethnicity at Level 2 and weekend (Saturday-Sunday) versus weekdays (Monday-Friday) at Level 1. Sensitivity analyses were conducted examining whether a similar pattern of findings was observed when morning cannabis use was used in place of wake-and-bake cannabis use in the models used to test Aims 1 and 2.
3. Results
3.1. Descriptive Statistics
Descriptive statistics are presented in Table 1. In this sample of young adults who used cannabis, wake-and-bake use was reported on 11.2% of all cannabis use days and was reported at least once by 35.4% of participants. Among participants who reported wake-and-bake use at least once, the median number of wake-and-bake use days was 2.00 (M=6.96, SD=10.62, range=1–58). The median number of hours high was 3.00 (M=3.09, SD=2.41, range=0–21) across all cannabis use days and 5.00 (M=5.16, SD=3.10, range=0–21) on wake-and-bake use days. The median number of negative cannabis consequences was 1.00 (M=1.21, SD=1.53, range=0–10) across all cannabis use days and 1.00 (M=1.26, SD=1.55, range=0–9) on wake-and-bake use days. Driving under the influence of cannabis was reported on 15.7% of all cannabis use days and on 35.8% of wake-and-bake use days. Alcohol use occurred on 42.2% of all cannabis use days and on 42.2% of wake-and-bake use days.
Table 1.
Descriptive Statistics
| Daily Level (N = 9,406) | ||||
|---|---|---|---|---|
| Variable | n | M (SD) or % | Min. | Max. |
| Wake-and-bake use | 8,453 | 11.2 | 0 | 1 |
| No. of hours high | 9,357 | 3.09 (2.41) | 0 | 21 |
| No. of negative cannabis consequences | 8,335 | 1.21 (1.53) | 0 | 10 |
| Drove under the influence of cannabis | 8,282 | 15.7 | 0 | 1 |
| No. of drinks | 9,405 | 1.57 (2.61) | 0 | 25 |
| Weekend days | 9,406 | 29.8 | 0 | 1 |
| Person Level (N = 384) | ||||
| Variable | n | M (SD) or % | Min. | Max. |
| Cannabis use disorder symptoms (CUDIT-R) | 384 | 11.55 (6.40) | 0 | 31 |
| Cannabis use motives (CMMQ) | ||||
| Alcohol-related | 384 | 0.87 (0.80) | 0 | 4 |
| Altered perceptions | 384 | 1.63 (1.20) | 0 | 4 |
| Availability | 384 | 1.74 (0.98) | 0 | 4 |
| Boredom | 384 | 1.61 (1.06) | 0 | 4 |
| Celebration | 384 | 1.52 (0.98) | 0 | 4 |
| Conformity | 384 | 0.45 (0.70) | 0 | 4 |
| Coping | 384 | 1.01 (1.06) | 0 | 4 |
| Enjoyment | 384 | 2.82 (1.01) | 0 | 4 |
| Experimentation | 383 | 0.84 (0.84) | 0 | 4 |
| Perceived low risk | 384 | 1.47 (1.14) | 0 | 4 |
| Sleep | 384 | 1.46 (1.24) | 0 | 4 |
| Social anxiety | 384 | 0.85 (0.99) | 0 | 4 |
| Male biological sex | 384 | 49.2 | 0 | 1 |
| Age at baseline | 384 | 21.61 (2.15) | 18 | 26 |
| Race/ethnicity | ||||
| Asian NHL | 384 | 15.1 | 0 | 1 |
| Hispanic/Latinx | 384 | 16.7 | 0 | 1 |
| Other NHL | 384 | 20.1 | 0 | 1 |
| White NHL | 384 | 48.2 | 0 | 1 |
Note. CUDIT-R = Cannabis Use Disorder Identification Test – Revised (Adamson et al., 2010); CMMQ = Comprehensive Marijuana Motives Questionnaire (Lee et al., 2009); NHL = Non-Hispanic/Latinx. Weekend days: Monday-Friday = 0; Saturday-Sunday = 1. Response options for CMMQ items: 0 = Almost never/never, 1 = Some of the time, 2 = Half of the time, 3 = Most of the time, 4 = Almost always/always.
3.2. Aim 1: Wake-and-Bake Use as a Predictor of Cannabis-Related Outcomes
3.2.1. Number of Hours High
Results of a Poisson multilevel model testing associations between wake-and-bake use and number of hours high are presented in Table 2. At the daily level, participants reported being high for 23% more hours, on average, on wake-and-bake use days compared to non-wake-and-bake cannabis use days. That is, participants were high for an average of 2.65 hours (95% CI: 2.44, 2.88) on wake-and-bake use days and an average of 2.15 hours (95% CI: 2.04, 2.26) on non-wake-and-bake cannabis use days. At the person level, the proportion of cannabis use days that participants reported wake-and-bake use was positively associated with participants’ average number of hours high on cannabis use days.
Table 2.
Poisson Multilevel Model Testing Associations between Wake-and-Bake Cannabis Use and Number of Hours High
| Fixed Effects | Outcome: Number of Hours High | |
|---|---|---|
| Rate Ratio | 95% CI | |
| Level 2: Person Level | ||
| Intercept | 2.18*** | 2.07, 2.29 |
| Proportion of cannabis use days that wake-and-bake use was reported | 4.40*** | 3.21, 6.03 |
| Mean number of drinks | 0.93*** | 0.90, 0.96 |
| Male biological sex | 1.29*** | 1.17, 1.42 |
| Age at baseline | 0.98* | 0.95, 1.00 |
| Race/ethnicity (Ref.: White NHL) | ||
| Asian NHL | 0.84* | 0.73, 0.97 |
| Other NHL | 1.00 | 0.88, 1.14 |
| Hispanic/Latinx | 0.90 | 0.79, 1.03 |
| Level 1: Day Level | ||
| Wake-and-bake use day | 1.23*** | 1.16, 1.32 |
| Number of drinks | 1.01 | 1.00, 1.02 |
| Weekend day | 1.09*** | 1.06, 1.12 |
| Day number within burst | 1.00 | 0.99, 1.00 |
| Burst number | 1.00 | 0.98, 1.02 |
| Random Effects | SD | |
| Intercept | 0.41 | |
| Wake-and-bake use day | 0.16 | |
| Number of drinks | 0.03 | |
| Burst number | 0.12 | |
Note. NDays = 7,597, NPersons = 376. NHL = Non-Hispanic/Latinx. Weekend day: 0 = Monday-Friday, 1 = Saturday-Sunday.
p < .05,
p < .01,
p < .001
3.2.2. Negative Cannabis Consequences
Results of a Poisson multilevel model testing associations between wake-and-bake use and negative cannabis consequences are presented in Table 3. This model controlled for number of same-day hours high and number of same-day drinks at the daily and person levels. Wake-and-bake use was not associated with number of negative cannabis consequences at the daily or person levels. That is, the number of negative cannabis consequences participants experienced did not differ significantly between wake-and-bake use days and non-wake-and-bake cannabis use days, and the proportion of cannabis use days that participants reported wake-and-bake use was not associated with participants’ average number of negative consequences reported on cannabis use days. Participants experienced 14% more negative consequences, on average, for each additional hour they reported being high on any given cannabis use day.
Table 3.
Poisson Multilevel Model Testing Associations between Wake-and-Bake Cannabis Use and Number of Negative Cannabis Consequences
| Fixed Effects | Outcome: Number of Negative Cannabis Consequences | |
|---|---|---|
| Rate Ratio | 95% CI | |
| Level 2: Person Level | ||
| Intercept | 0.85*** | 0.78, 0.94 |
| Proportion of cannabis use days that wake-and-bake use was reported | 1.11 | 0.56, 2.23 |
| Mean number of hours high | 0.98 | 0.91, 1.06 |
| Mean number of drinks | 0.98 | 0.92, 1.04 |
| Male biological sex | 0.81* | 0.68, 0.97 |
| Age at baseline | 0.99 | 0.95, 1.03 |
| Race/ethnicity (Ref.: White NHL) | ||
| Asian NHL | 1.27 | 0.98, 1.66 |
| Other NHL | 0.78* | 0.62, 0.98 |
| Hispanic/Latinx | 0.86 | 0.67, 1.11 |
| Level 1: Day Level | ||
| Wake-and-bake use day | 0.99 | 0.90, 1.09 |
| Number of hours high | 1.14*** | 1.11, 1.16 |
| Number of drinks | 0.97*** | 0.95, 0.99 |
| Weekend day | 0.96 | 0.90, 1.01 |
| Day number within burst | 0.98*** | 0.98, 0.99 |
| Burst number | 0.97** | 0.95, 0.99 |
| Random Effects | SD | |
| Intercept | 0.77 | |
| Number of hours high | 0.08 | |
| Number of drinks | 0.05 | |
| Overdispersion parameter | 0.35 | |
Note. NDays = 6,878, NPersons = 374. NHL = Non-Hispanic/Latinx. Weekend day: 0 = Monday-Friday, 1 = Saturday-Sunday.
p < .05,
p < .01,
p < .001
Results of supplemental analyses testing associations between wake-and-bake use and 10 specific negative cannabis consequences are presented in Supplemental Tables A through E. These models controlled for number of same-day hours high and number of same-day drinks at the daily and person levels. At the daily level, participants had 1.45 times greater odds, on average, of feeling antisocial or intentionally avoiding others on wake-and-bake use days than on non-wake-and-bake cannabis use days. Participants had 0.42 times lesser odds of feeling dizzy on wake-and-bake use days than on non-wake-and-bake cannabis use days. There were no statistically significant associations between wake-and-bake cannabis use days and any of the other eight negative cannabis consequences. At the person level, there were no significant associations between wake-and-bake cannabis use and any of the 10 negative cannabis consequences.
3.2.3. Driving Under the Influence of Cannabis
Results of a logistic multilevel model testing associations between wake-and-bake use and driving under the influence of cannabis are presented in Table 4. This model controlled for number of same-day hours high and number of same-day drinks at the daily and person levels. At the daily level, participants had 1.70 times greater odds, on average, of driving a car or motor vehicle under the influence of cannabis on wake-and-bake use days than on non-wake-and-bake cannabis use days. At the person level, the proportion of cannabis use days that participants reported wake-and-bake use was positively associated with the proportion of cannabis use days participants drove under the influence of cannabis.
Table 4.
Logistic Multilevel Model Testing Associations between Wake-and-Bake Cannabis Use and Driving Under the Influence of Cannabis
| Fixed Effects | Outcome: Driving Under the Influence of Cannabis (0 = No, 1 = Yes) | |
|---|---|---|
| Odds Ratio | 95% CI | |
| Level 2: Person Level | ||
| Intercept | 0.04*** | 0.03, 0.05 |
| Proportion of cannabis use days that wake-and-bake use was reported | 69.69*** | 9.89, 491.12 |
| Mean number of hours high | 1.18 | 0.96, 1.47 |
| Mean number of drinks | 1.01 | 0.83, 1.22 |
| Male biological sex | 0.64 | 0.37, 1.12 |
| Age at baseline | 1.03 | 0.91, 1.17 |
| Race/ethnicity (Ref.: White NHL) | ||
| Asian NHL | 1.06 | 0.47, 2.40 |
| Other NHL | 0.85 | 0.42, 1.70 |
| Hispanic/Latinx | 0.47 | 0.22, 1.02 |
| Level 1: Day Level | ||
| Wake-and-bake use day | 1.70*** | 1.29, 2.24 |
| Number of hours high | 1.13** | 1.03, 1.23 |
| Number of drinks | 0.98 | 0.95, 1.02 |
| Weekend day | 0.94 | 0.79, 1.13 |
| Day number within burst | 0.99 | 0.97, 1.02 |
| Burst number | 1.03 | 0.97, 1.10 |
| Random Effects | SD | |
| Intercept | 1.99 | |
| Number of hours high | 0.21 | |
Note. NDays = 6,839, NPersons = 374. NHL = Non-Hispanic/Latinx. Weekend day: 0 = Monday-Friday, 1 = Saturday-Sunday.
p < .05,
p < .01,
p < .001
3.3. Aim 2: Predictors of Wake-and-Bake Use
To determine which of the 12 cannabis use motives subscales, measured at baseline, should be included in the model used to test Aim 2, separate logistic multilevel models (each containing one motives subscale) were used to test bivariate associations between baseline cannabis use motives and the proportion of cannabis use days that participants reported wake-and-bake use (Supplemental Table F). To make tests more conservative for these preliminary analyses, p-values were adjusted using the Bonferroni correction. Cannabis use motives related to altering perceptions, boredom, coping, sleep, and social anxiety were positively associated with the proportion of cannabis use days that participants reported wake-and-bake use. These five motives subscales were included in the model used to test Aim 1.
Results of a logistic multilevel model testing person-level (number of CUD symptoms at baseline, baseline cannabis use motives, demographic characteristics) and daily-level (i.e., number of drinks on previous day, weekend versus weekdays) predictors of wake-and-bake use are shown in Table 5. In contrast to previous analyses in which same-day number of drinks was used to quantify alcohol use, this model used a lagged version of the number of drinks variable so that the association between number of drinks on the previous day could be tested as a predictor of wake-and-bake cannabis use the following morning. At the person level, number of CUD symptoms, social anxiety cannabis use motives, and age were positively associated with the proportion of cannabis use days that participants reported wake-and-bake use. Specifically, for each one-unit increase in baseline CUD symptoms (CUDIT-R scores), participants had 1.11 times greater odds of wake-and-bake use on any given cannabis use day. For each one-unit increase in baseline social anxiety motives, participants had 1.53 times greater odds of wake-and-bake use on any given cannabis use day. For each one-year increase in age at baseline, participants had 1.16 times greater odds of wake-and-bake use on a given cannabis use day. Cannabis use motives pertaining to altered perceptions, boredom, coping, and sleep were not significantly associated with odds of wake-and-bake cannabis use on a given cannabis use day. Biological sex and race/ethnicity were also not significantly associated with odds of wake-and-bake use on a given cannabis use day. At the daily level, number of drinks on the preceding day was positively associated with the probability of wake-and-bake use on cannabis use days, such that participants had 1.13 times greater odds of wake-and-bake use for each additional drink they consumed the day before. Participants had 2.15 times greater odds of wake-and-bake use on weekends versus weekdays.
Table 5.
Logistic Multilevel Model Testing Predictors of Wake-and-Bake Cannabis Use
| Fixed Effects | Outcome: Wake-and-Bake Cannabis Use (0 = No, 1 = Yes) | |
|---|---|---|
| Odds Ratio | 95% CI | |
| Level 2: Person Level | ||
| Intercept | 0.01*** | 0.01, 0.02 |
| Cannabis use disorder symptoms (CUDIT-R) | 1.11*** | 1.06, 1.17 |
| Cannabis use motives (CMMQ) | ||
| Altered perceptions | 0.91 | 0.71, 1.16 |
| Boredom | 0.87 | 0.66, 1.15 |
| Coping | 0.99 | 0.75, 1.30 |
| Sleep | 1.18 | 0.92, 1.50 |
| Social anxiety | 1.53** | 1.14, 2.06 |
| Male biological sex | 1.48 | 0.87, 2.52 |
| Age at baseline | 1.16* | 1.03, 1.31 |
| Race/ethnicity (Ref.: White NHL) | ||
| Asian NHL | 0.73 | 0.32, 1.68 |
| Other NHL | 1.02 | 0.52, 2.02 |
| Hispanic/Latinx | 1.49 | 0.75, 2.96 |
| Level 1: Daily Level | ||
| Number of drinks on previous day | 1.13** | 1.05, 1.22 |
| Weekend day | 2.15*** | 1.44, 3.21 |
| Day number within burst | 0.97* | 0.95, 0.99 |
| Burst number | 0.89 | 0.77, 1.05 |
| Random Effects | SD | |
| Intercept | 1.89 | |
| Number of drinks on previous day | 0.17 | |
| Weekend day | 0.82 | |
| Burst number | 0.44 | |
Note. NDays = 7,621, NPersons = 377. CUDIT-R = Cannabis Use Disorder Identification Test – Revised (Adamson et al., 2010); CMMQ = Comprehensive Marijuana Motives Questionnaire (Lee et al., 2009); NHL = Non-Hispanic/Latinx. Weekend day: 0 = Monday-Friday, 1 = Saturday-Sunday.
p < .05,
p < .01,
p < .001
3.4. Sensitivity Analyses: Wake-and-Bake Use Versus Morning Use
Sensitivity analyses that used morning cannabis use, operationalized as cannabis use between 6:00 AM and 12:00 PM, instead of wake-and-bake use in the models testing Aims 1 and 2 are presented in Supplemental Tables G through J. Although the magnitudes of the associations varied, the direction and statistical significance of the associations were generally the same between models using wake-and-bake use and those using morning cannabis use. The main difference was that morning use was inversely associated with number of cannabis consequences at the person level, whereas wake-and-bake use was not associated with total negative cannabis consequences at either level.
4. Discussion
This study tested predictors and cannabis-related outcomes of wake-and-bake cannabis use (i.e., using within 30 minutes of waking up) in a young adult community sample. Wake-and-bake use was reported on approximately one-tenth of cannabis use days and by one-third of participants who reported using cannabis. On wake-and-bake use days relative to non-wake-and-bake cannabis use days, participants reported being high for more hours and were more likely to drive under the influence of cannabis. Similarly, participants who reported wake-and-bake use more often reported greater average numbers of hours high and reported driving under the influence more frequently throughout the study period. However, we found no evidence of daily- or person-level associations between wake-and-bake use and total number of negative cannabis consequences. Wake-and-bake use was more common among participants who reported greater CUD symptoms at baseline and among those who reported using cannabis to manage social anxiety more frequently. Wake-and-bake use was also more common on weekends than on weekdays and when greater amounts of alcohol were consumed the previous day.
4.1. A Correlate of CUD Symptoms but Not Consequences
Wake-and-bake cannabis use was positively associated with number of hours high and was more common among those reporting greater CUD symptoms. These findings are consistent with research indicating morning cannabis use may be associated with using greater amounts of cannabis (Shrier et al., 2013) and with nicotine and alcohol research showing that nicotine use soon after waking and morning alcohol use are associated with riskier patterns of use (Babor et al., 2001; Branstetter et al., 2020; Guertin et al., 2015; Selya et al., 2016). The association between CUD symptoms and wake-and-bake use is especially interesting given that the revised gold-standard assessment of CUD symptoms (CUDIT-R) removed the item assessing morning cannabis use, as researchers thought it was a carryover item from alcohol measures assessing use to manage withdrawal and was more suggestive of more time spent using cannabis than a unique risk behavior associated with CUD symptoms (Adamson et al., 2010). Future work may want to determine whether wake-and-bake use is a unique factor associated with CUD symptoms and/or clinical diagnosis of CUD, or whether it is a byproduct of tending to actively use for longer periods of time within a day.
Wake-and-bake cannabis use was not directly associated with the number of acute negative cannabis consequences participants experienced; however, considered alongside the significant association between wake-and-bake use and greater hours high and the positive association between hours high and negative consequences, there may be an indirect effect of wake-and-bake use on greater negative consequences that our data and models were not well-suited to test (i.e., a within-person mediation effect). Future studies could better examine potential indirect effects of wake-and-bake use on negative consequences and could focus on specific consequences relevant to using cannabis early in the day (e.g., lack of motivation, school/work performance).
4.2. A Potential Risk Factor for Driving Under the Influence
Participants were more likely to drive under the influence of cannabis on wake-and-bake use days than on non-wake-and-bake cannabis use days. This association may be due to cannabis use occurring prior to any commitments or responsibilities (e.g., work, school, social obligations, errands) on wake-and-bake use days, rather than use occurring after finishing some or all of these commitments and responsibilities on non-wake-and-bake use days. However, some young adults report perceptions that driving under the influence of cannabis is less dangerous than driving under the influence of alcohol (Greene, 2018). Therefore, they may be less concerned about driving after morning cannabis use. Nonetheless, recent meta-analytic findings conclude that THC (Δ-9-tetrahydrocannabinol, the main psychoactive ingredient in cannabis) consumption is associated with impaired driving performance and driving-related cognitions and that these impairments can persist for five or more hours (Fischer et al., 2022; McCartney et al., 2021). Further, acute cannabis use prior to driving appears to increase risk for motor vehicle accidents (Preuss et al., 2021).
4.3. Wake-and-Bake Use and Coping with Social Anxiety
Findings indicated that young adults who reported higher social anxiety coping motives at baseline reported wake-and-bake use on a greater proportion of cannabis use days. Supplementary analyses showed that participants had greater odds of feeling antisocial or intentionally avoiding others on wake-and-bake use days compared to non-wake-and-bake cannabis use days. Experimental studies suggest that cannabis craving is highest during, rather than prior to, stressful social interactions among those with clinically elevated symptoms of social anxiety (Buckner et al., 2011). Taken together with the findings from the present study, it may be that individuals who use cannabis to cope with social anxiety do so in the morning to cope with anxiety that occurs in expectation of a social situation and/or in anticipation of craving that could occur during upcoming daily social interactions. Consistent with this hypothesis, young adults with higher levels of social anxiety symptoms are more likely to report using substances to cope with anticipatory anxiety prior to social events than those with lower social anxiety (Buckner et al., 2020). As social anxiety symptoms are related to more cannabis-related problems among young adults (Single et al., 2022), determining whether wake-and-bake use confers greater risk of negative consequences among those who use cannabis to cope with social anxiety will be an important next step in this line of research.
4.4. Is Wake-and-Bake Cannabis Use Distinct from Morning Use?
Sensitivity analyses that replaced the wake-and-bake use indicator with a morning use (i.e., cannabis use between 6:00 AM and 12:00 PM) indicator demonstrated that these operationalizations were very similar in their associations with cannabis-related outcomes. The primary difference was that wake-and-bake use was not significantly associated with number of negative cannabis consequences, whereas morning use was inversely associated with consequences at the person level. For many applications, wake-and-bake use and morning use may mostly be overlapping operationalizations of the same behavior (e.g., coding for the two indicators was the same on 87% of days in this study). For other applications, the difference between using within 30 minutes of waking and using anytime in the morning may be important, such as intervention studies designed to reduce cannabis use among individuals who use cannabis daily or near daily. Differences between these two operationalizations is an area for future research.
4.5. Strength and Limitations
This study had several strengths. First, the longitudinal measurement-burst design resulted in a relatively large analytic sample and allowed for a modeling approach that tested both within- and between-person associations. Second, wake-and-bake use was operationalized specifically as using cannabis within 30 minutes of waking up, rather than as morning cannabis use more generally irrespective of when participants woke up (e.g., Earleywine et al., 2016, Shrier et al., 2013). There were also several limitations. First, although operationalizing wake-and-bake cannabis use as use within 30 minutes of waking up in the morning was preferable to morning use more generally, the 30-minute time interval was arbitrary. Future research could examine whether findings vary if other time intervals (e.g., within one hour of waking up) were used. Second, although more than one-third of participants who used cannabis reported wake-and-bake use, wake-and-bake use was relatively infrequent for most participants. Findings may not generalize to individuals who engage in this behavior more regularly. Third, eligibility criteria for study enrollment included reporting alcohol use at least three times in the past month and simultaneous alcohol and cannabis use at least once in the past month. Therefore, this was a heavier substance-using sample than the general young adult population. Fourth, this sample was recruited in a state in which the sale of cannabis for non-medical purposes is legal for individuals age 21 or older. Findings may not generalize to young adults in states in which non-medical cannabis cannot be legally purchased.
5. Conclusion
This paper builds upon the sparse literature on wake-and-bake cannabis use by showing that this behavior was not uncommon in a community sample of young adults using both alcohol and cannabis and that wake-and-bake use was associated with greater cannabis use and greater odds of driving under the influence of cannabis. Wake-and-bake use may be an indicator of harmful patterns of cannabis use and/or risk for CUD. Given recent increases in frequent cannabis use and as changes in the legalization of non-medical cannabis continue to occur, more research is needed on contextual and person-level predictors and longer-term outcomes of wake-and-bake cannabis use.
Supplementary Material
Highlights.
Wake-and-bake cannabis use was reported by 35.4% of participants.
Social anxiety cannabis motives were positively associated with wake-and-bake use.
Wake-and-bake use was associated with being high for more hours.
Wake-and-bake use was linked with driving under the influence of cannabis.
Role of Funding Source
Funding for this study was provided by NIAAA Grant R01AA025037. NIAAA did have any 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 research was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R01AA025037, MPIs Lee and Patrick). Manuscript preparation was also supported by F32AA029589 (PI: Walukevich-Dienst) from NIAAA. The content of this manuscript is solely the responsibility of the author(s) and does not necessarily represent the official views of the NIAAA or the National Institutes of Health. The authors have no conflicts of interest to report.
Footnotes
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Conflict of Interest
All authors declare that they have no conflicts of interest.
Declarations of interest: none
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