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. Author manuscript; available in PMC: 2025 Mar 3.
Published in final edited form as: Behav Sleep Med. 2023 May 31;22(2):150–167. doi: 10.1080/15402002.2023.2217969

Daily associations with cannabis use and sleep quality in anxious cannabis users

LC Bidwell 1,2, SR Sznitman 3, R Martin-Willett 2, LH Hitchcock 1
PMCID: PMC10687319  NIHMSID: NIHMS1905106  PMID: 37255232

Abstract

Introduction:

Cannabis is increasingly used to self-treat anxiety and related-sleep problems, without clear evidence either supporting or refuting its anxiolytic or sleep aid effects. In addition, different forms of cannabis and primary cannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) have differing pharmacological effects.

Methods:

Thirty days of daily data on sleep quality and cannabis use were collected in individuals who use cannabis for mild to moderate anxiety (n=347; 36% male, 64% female; mean age=33 years). Participants self-reported both the form (flower or edible) and the ratio of THC to CBD in the cannabis used during the observation period.

Results:

Individuals who reported cannabis use on a particular day also reported better sleep quality the following night. Moderation analyses showed that better perceived sleep after cannabis use days was stronger for respondents with higher baseline affective symptoms. Further, respondents who used cannabis edibles with high CBD concentration reported the highest perceived quality of sleep.

Conclusions:

Among individuals with affective symptoms, naturalistic use of cannabis was associated with better sleep quality, particularly for those using edible and CBD dominant products.

Introduction

Sleep is a critical biological process and poor sleep has been associated with chronic illness (Cappuccio et al., 2010a, 2010b; Shan et al., 2015), obesity (Magee & Hale, 2012), psychological distress (Tsuno et al., 2005), and higher mortality risk (Gallicchio & Kalesan, 2009). Sleep disturbance is a core feature of anxiety (Ahinkorah et al., 2021; Cox & Olatunji, 2016), with even subthreshold anxiety and depression being associated with worse sleep (Alvaro et al., 2013). Meanwhile, cannabis is increasingly being used by the public for addressing medical symptoms, with anxiety and sleep disturbances being two of the most frequently cited therapeutic motivations for cannabis use (Choi et al., 2018; Ilgen et al., 2013; Reynolds et al., 2018).

Current evidence for the usefulness of cannabis, and its primary constituent cannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), in the treatment of sleep disturbance is complex; despite a recent proliferation of reviews there are few empirical studies on the topic (Abrams, 2018; Diep et al., 2022; Kaul et al., 2021; Mondino et al., 2021; Spanagel & Bilbao, 2021; Suraev et al., 2020). It is hypothesized that the cannabinoid type 1 receptor (CB1), which is the primary binding target for THC, is involved in the modulation of light-induced phase shifts, with CB1 receptors active in brain regions central to circadian rhythms (Pacher et al., 2006; Sanford et al., 2008). Some studies suggest a benefit from cannabis use to improve poor sleep in the short term (Abrams, 2018; Babson et al., 2017; Gates et al., 2014) while other work has shown that more frequent use of cannabis is associated with sleep deficits (Conroy et al., 2016; Fakier & Wild, 2011; McKnight-Eily et al., 2011; Mednick et al., 2010; Troxel et al., 2015; Winiger et al., 2021). This may be due in part to the development of tolerance to positive outcomes in heavy chronic users, as well as broader dependence processes linked with abstinence-related sleep distrubances and affective symptoms (Angarita et al., 2016; Babson et al., 2017; Conroy & Arnedt, 2014). Examining the effects of cannabis on sleep in lighter, non-dependent users is an important step to extending the literature.

The mixed relationship between cannabis and sleep is likely further impacted by the differential effects of THC and CBD, as well as the form of administration. Some work suggests that THC diminishes circadian rhythms in humans (Nicholson et al., 2004; Perron et al., 2001), while CBD, a non-intoxicating and weak CB1 agonist appears to be highly dose dependent such that low doses have a stimulating effect and higher doses have a sedative effect (Conroy & Arnedt, 2014; Kaul et al., 2021; Nicholson et al., 2004; Russo et al., 2007). One recent review concluded that CBD was associated with more sleep and sleepiness, more REM at higher doses, and that CBD attenuated THC’s sedative effect (Kaul et al., 2021). In contrast, a systematic review of randomized controlled trials examining the effects of either CBD or synthetic THC formulations on sleep showed no significant effect of either cannabinoid (Spanagel & Bilbao, 2021). However, these studies were clinical samples (epilepsy, schizophrenia, substance use disorder, and chronic pain) and it was not reported whether sleep improved in quality or increased in duration. Further, dose and administration methods across these studies generally lacked external validity, with doses that ranged widely (4 RCTs; 2 mg per day to 1000 mg per day) and forms of administration (e.g. sublingual spray or synthetic oral solutions) that are not consistent with what is typically used and available on legal markets.

Cannabis can be administered in a variety of formulations with varying effects, with whole plant flower and edible forms being the most common (Light et al., 2014; Orens et al., 2018). Smoking or inhalation rapidly delivers cannabinoids from the lungs to the brain, resulting in almost instantaneous effects (5–10 minutes) of usually shorter duration, though the latter can vary widely because smokers can self-titrate doses to their desired effect levels. Comparatively, absorption is 30–90 minutes slower and effects more prolonged (6–8 hours) when cannabinoids are processed in the digestive system (Huestis, 2007). Thus, any effects of cannabis on sleep may differ in individuals who use longer lasting edible forms. Further, very little is known about the effects of cannabis on sleep in the context of affective symptoms such as anxiety. Cannabis use has been associated with both anxiogenic and anxiolytic effects (Botsford et al., 2020; Lee et al., 2021; Stanciu et al., 2021). Research supports that THC use is associated with long-term anxiety, negative affect, depressed mood, and anger or hostility (Cuttler et al., 2018; Fairman & Anthony, 2012; Pahl et al., 2011). In contrast, CBD may have anxiolytic and mood enhancing effects, as well as potentially mitigate the anxiogenic effects of THC (Calapai et al., 2019; Chye et al., 2019; García-Gutiérrez et al., 2020; Neumeister et al., 2015). For example, a recent clinical trial found that compared to placebo, CBD attenuated anxiety symptoms in the context of opiate use reduction (Hurd et al., 2019). Given this lack of research and the key role of sleep in the etiology of anxiety, it is critical to consider both together in the context of cannabinoid use.

Though the research on the secondary effects on anxiety-related sleep problems is equivocal (Bahji et al., 2020; Sharpe et al., 2020; Spanagel & Bilbao, 2021), it is clear that self-medication with cannabis is increasing (Hasin et al., 2015), with adults reporting higher levels of cannabis use, and CBD in particular, to specifically address disordered sleep (Belendiuk et al., 2015; Gutiérrez et al., 2021; Moltke & Hindocha, 2021). Like the relationship between cannabis and sleep in general, the relative ratio of different cannabinoids is pharmacologically relevant to anxiety-related sleep problems, with promise for CBD as an aid to improving outcomes. For example, a retrospective case series study reported improved anxiety and sleep outcomes when CBD was used as an adjunct to usual treatment (Shannon et al., 2019). Unfortunately though, the study of CBD, anxiety, and sleep is nascent, complicated not only by cannabinoid ratios, but also by the different forms of cannabis administration and the many ways that sleep can be studied.

While a handful of studies on substance use and sleep make use of biometric monitoring devices such as actigraphy (Brower et al., 2011; Hartwell et al., 2014), most studies on cannabis and sleep rely on global or retrospective self-report measures of sleep quality. Increasingly though, investigations are making use of daily reporting methods such as questionnaires or diaries (Gorelick et al., 2013; Sznitman et al., 2020; Vandrey et al., 2013), lending a more granular view than global or retrospective measures may allow. Most importantly, daily reporting enables a naturalistic investigation of within-person changes. This is crucial because between-person associations can differ substantially and even contradict within-person associations (Kievit et al., 2013). In fact, consumers with certain baseline characteristics or those who use certain forms of cannabis or use more often may sleep better or worse overall, but their individual sleep patterns may differ on days that they do or do not use cannabis.

We are aware of two prior daily diary studies examining associations among cannabis use and sleep. In the first, cannabis smoked proximal to bedtime was associated with shorter sleep onset latency, but not continuous sleep in a web-based sample of long term daily cannabis users (Sznitman et al., 2020). The second daily study focused on lighter-using college students who specifically used cannabis as a sleep aid and found that cannabis use was associated with improved subsequent night’s sleep on many self-report metrics, but increased next day fatigue (Goodhines et al., 2019). Neither study examined specific formulations of cannabis products. Together this suggests that additional naturalistic within person studies of cannabis use and sleep would extend the literature; particularly studies clearly distinguishing within- and between-person associations, among varying forms of cannabis use, and using methods that include lengthy durations of daily reporting (Bei et al., 2016; Molzof et al., 2018).

This observational study examines the association between cannabis use, negative affect, and sleep quality in a community sample of individuals who report using cannabis for anxiety. More specifically, this study examines within-person variation in cannabis use (daily presence or absence of cannabis use) and its association with daily sleep quality ratings over 30 days. We expected there to be a within-person sleep promoting association with cannabis use in that when an individual uses cannabis they will report better sleep quality the following night as compared to when the same individual did not use cannabis. We were also interested in testing whether the severity of baseline affective symptoms, the use of longer lasting orally administered cannabis products (vs. flower), and use of cannabis products with higher levels of CBD would moderate the daily associations between cannabis use and sleep.

Methods

Participants

This paper reports on daily data from a larger ongoing longitudinal study of cannabis use, affective symptoms, and inflammation among individuals with mild to moderate generalized anxiety [NCT03491384]. This study was approved by the University of Colorado Boulder Institutional Review Board (IRB) and follows all ethical standards from relevant national and institutional committees on human experimentation and the Helsinki Declaration of 1975, as revised in 2008. Participants were recruited through social media, postal mail, and posters within and near the Denver-metro area. Professional research assistants screened potential participants for eligibility based on the IRB-approved inclusion criteria (Figure 1).

Figure 1.

Figure 1.

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Study recruitment flow (Panel A) and inclusion criteria (Panel B).

Timeline and compensation

After screening, participants completed three in-person visits over one month concurrently with 30-days of online surveys. This paper is a secondary analysis of the daily measures of sleep and cannabis use collected via the larger study and thus focuses on baseline and daily assessments only.

Baseline visit (campus).

Participants completed the informed consent process and a battery of assessments (e.g., blood draw, physiological, cognitive, and behavioral tests, and survey questionnaires) in our research lab on the University of Colorado campus. This visit took approximately 2.5 hours to complete and participants received $80 in cash for their participation.

Observational cannabis use (quasi-experimental design).

At the baseline visit, participants self-selected the form of cannabis that they would use during the study (e.g., smoked flower or an edible formulation) based on their typical use patterns. After participants self-selected their form of cannabis, they were randomly assigned to a cannabinoid ratio group within their selected form, based on a random number table generated by the study statistician. In line with the naturalistic design of the study, the products were selected to reflect common cannabinoid profiles found in flower and edible products on the Colorado market. Given that THC and CBD are the primary cannabinoids of interest for cannabis effects on sleep, we selected products that reflected the range of THC and CBD profiles available across the legal market (THC-dominant, CBD-dominant, and balanced THC+CBD). Flower users were randomized to purchase either Strain A (THC dominant: 24% THC; <1% CBD), Strain B (THC+CBD: 14% THC; 9% CBD), or Strain C (CBD dominant: 24% CBD; <1% THC). Edible users were randomized to purchase either Edible A (THC dominant: 10 mg THC; 0 mg CBD), Edible B (THC+CBD: 10 mg THC; 10 mg CBD), or Edible C (CBD dominant: 10 mg CBD; .17 mg THC). We note that the amount of THC and CBD varied across the flower and edible products and thus were not directly compared, but broadly used to assess the impact of cannabinoid profiles commonly found on the legal market (i.e. THC-dominant, CBD-dominant, THC+CBD). Consistent with State of Colorado requirements, the THC and CBD content of each product was labeled following testing in an International Organization of Standards (ISO) 17025 accredited laboratory.

Participants then purchased the assigned product at a local cooperating dispensary. The participating dispensary had no role in study design, data collection, analysis, interpretation, or writing of the report. Participants verified their purchase by uploading a photo of the packaging of their product to a database separate from study records (to maintain blinding with research staff).

Participants were asked to use only the study product during the 30-day study period. They were free to purchase as much or as little of their cannabis product as they wished according to their personal use patterns, and naturalistically administered their cannabis product ad-libitum (in timing, frequency, and amount used). While not the primary aim of the study, given the promising preclinical research on the sedating and anxiolytic effects of high dose CBD, we sought to address the significant question of whether higher levels of CBD would moderate the daily associations between cannabis use and sleep. In order to maximize statistical power to detect small effects in multi-level modeling approaches (Mathieu et al., 2012; Mathieu & Chen, 2010), the three verified product groups were combined for analysis of CBD:THC ratio as follows: CBD dominant vs. THC based products (THC dominant and THC+CBD).

Baseline study assessments

A summary of assessments included in the current analyses are described below. For additional details of assessments included in the larger study, reference the study clinicaltrials.gov record, NCT03491384.

Demographics.

A demographic survey included gender identity (Woman, Man, or Transgender), age, race and ethnicity, education, and employment status. Consistent with prior research, we controlled for these individual differences and affective variables as they may influence sleep (Chattu et al., 2019; Khubchandani & Price, 2019; Peltz et al., 2020) and current cannabis use patterns (Hasin et al., 2019).

Affective symptoms.

Negative affective symptoms were assessed via the Depression, Anxiety, and Stress Scale (DASS-21; Henry & Crawford, 2005; Lovibond & Lovibond, 1996). The DASS-21 contains three subscales, each with seven items and a severity response scale of 0–3 (0-Did not apply to me at all, up to 3-Applied to me very much, or most of the time). Scores on the Depression subscale range from 0–9 for Normal, 10–13 for Mild, 14–20 for Moderate, and 21–27 for Severe depression. Scores on the Anxiety subscale range from 0–7 for Normal, 8–9 for Mild, 10–14 for Moderate, and 15–19 for Severe anxiety. Scores on the Stress subscale range from 0–14 for Normal, 15–18 for Mild, 19–25 for Moderate, and 26–33 for Severe stress. The composite DASS-21 Total Score was used as our measure of negative affect in the current analysis.

Cannabis and other substance use.

The Marijuana Dependence Scale (MDS) is an 11-item survey that is widely used for assessing problematic cannabis use in a variety of populations (Callahan et al., 2013; Lozano et al., 2006; Stephens et al., 2000) We used the sum score for the analyses. The 14-day online Timeline Followback (O-TLFB; Martin-Willett et al., 2019, 2020) assessed typical cannabis use over the two-weeks prior to the baseline visit.

Typical sleep quality.

The Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989) is a 19-item questionnaire measuring sleep quality and disturbance, including: (1) sleep duration, (2) sleep disturbance, (3) sleep latency, (4) daytime dysfunction due to sleepiness, (5) sleep efficiency, (6) overall sleep quality, and (7) sleep medication use over the past two weeks before baseline. Each of these yields a score ranging from 0 to 3 which are then summed to yield a total score ranging from 0 to 21 (a higher total score indicating worse sleep quality). The composite PSQI scale has been tested for internal consistency and demonstrated acceptability from fair to good (Mollayeva et al., 2016).

Daily survey assessments

Daily surveys (remote and online).

At the end of the baseline visit participants completed a practice daily survey (Daily 0). Beginning on the following morning (Daily 1) and for up to 30 days after (Daily 30) surveys were administered by scheduled Research Electronic Data Capture (REDCap) deployment at 12:45AM and expired by midnight that day. The survey was designed to take 1–2 minutes to complete. Participants received $1 in cash for each completed survey and were eligible for a $10 bonus if at least 26 out of 30 surveys were completed, for a possible total of up to $40.

Daily sleep quality - dependent variable.

Daily surveys queried participants’ sleep quality over the previous night (past 24 hours). A single-item sleep measure was selected to support retention over the 30-day period by minimizing the length of the daily survey. Respondents were asked to rate the quality of their sleep compared to their normal sleep patterns, with 0 representing the worst sleep they have ever gotten and 10 representing the best sleep they have ever gotten. Preliminary data analyses showed that the variable met requirements of normal distribution and that no transformation was needed. Single-item measures have been used extensively in sleep research in an effort to increase reporting and decrease burden across diverse samples (Atroszko et al., 2015; Burkhalter et al., 2013; Hughes et al., 2018), and it is generally accepted that while individual variation will be present in terms of rankings and experiences, a common set of components represent overall sleep quality (Cappelleri et al., 2009; Harvey et al., 2008).

Daily cannabis and alcohol use – independent variables.

Daily surveys queried if participants had used their verified cannabis product (yes/no) and whether they had used alcohol (yes/no) during the previous day (past 24 hours). Respondents were also asked whether they had used other (non-study, non-verified) cannabis in the last 24 hours.

Other daily covariates.

The submission timestamp for each respondent’s daily survey was used to calculate and account for weekday versus weekend responses in the models as this may relate to both cannabis use and sleep patterns.

Statistical methods

When dealing with daily data and within-person statistical designs, a person’s standing on a given variable at a given time is a function of three sources of variance: (1) between-person traits that do not change over time, (2) within-person states that change with circumstances, and (3) random error (Kenny & Zautra, 1995). Thus, to examine relationships among phenomena like sleep and cannabis use, one needs to calculate a model that decomposes the variance into between-person trait variables (e.g., gender, age) and within-person state variables (e.g., daily sleep quality, daily cannabis use). We used a multi-level approach to test models where individual time point assessment data were nested within the individual cannabis users. Specifically, a mixed effects model tested the direct associations between daily cannabis use on sleep quality the following night, while holding constant the following variables: gender, age, race and ethnicity, education, employment, baseline negative affective symptoms (DASS), baseline cannabis dependence (MDS), baseline sleep quality (PSQI), cannabis form (flower vs. edible) and CBD:THC ratio (CBD dominant vs. THC-based products) used for the duration of the study, daily alcohol use, day of daily survey (days 1–30), number of days of cannabis use during the study, and whether assessment was completed on a weekend or weekday (Model 1).

To test potential moderation of affective symptoms, cannabis form, and CBD:THC ratio, we included the following interactions in the mixed effects model described above: (1) daily cannabis use * cannabis form (flower vs. edible), (2) daily cannabis use * CBD:THC ratio, (3) cannabis form * CBD:THC ratio, (4) daily cannabis use * cannabis form * CBD:THC ratio, and (5) daily cannabis use * affective symptoms (DASS) (Model 2). Only the most parsimonious models, excluding insignificant interactions, are presented.

Furthermore, to confirm the directionality of the associations, the main effects model was tested in the reverse direction (e.g., the associations of daily sleep quality rating and next-day (lagged) cannabis use) controlling for the same covariates (Model 3).

Mixed effects models take the interdependence that occurs with multiple repeated measures within the same individual into account. We estimated random intercepts and slopes using a first-order autoregressive covariance model structure to account for autocorrelation in the repeated measures. Linear (for daily sleep quality as outcome variable) and logistic (for cannabis use vs. no cannabis use outcome variable in lagged model) mixed effects models were estimated using the xtmixed and xtmelogit Stata commands respectively (StataCorp, 2011). We measured effect sizes of the within-person level associations of daily cannabis use and sleep quality (e.g. local effect sizes) (Raudenbush & Bryk, 2002), by calculating the difference in within-person intercept variance between models with and without daily cannabis use predictor, with the inclusion of all covariates. The difference was then divided by the within-person intercept variance of the models without the within-person cannabis use measure.

Results

Data from time points when respondents answered that they had used cannabis other than that assigned for the study observation period (n=802 assessment points, 7%) were excluded from analysis because of lack of verified data on CBD:THC ratio of the cannabis used. 347 participants were analyzed in total. Study recruitment flow and experimental groups are described in Figure 1. Included participants completed a mean of 23.9 out of 30 possible daily assessments (range: 1–30; S.D.=5.51). Non-response was more prevalent in the latter part of the study, akin to response fatigue.

Sample descriptive

Baseline sample demographics, substance use, mental health, and sleep characteristics are reported in Table 1. The sample reported an average of 5.40 days (S.D.=5.11) of cannabis use in the past two weeks and an average of 1.7 (S.D.=0.11) symptoms of Cannabis Use Disorder at baseline. The average PSQI was 7.34 (S.D. 0.17; range 1–18), indicating that a substantial proportion of the sample exceeded the cutoff (>5) for poor sleep quality (Buysse et al., 1989). 58 participants reported using anti-depressant medication and 12 participants reported using sleep aids at baseline.

Table 1.

Sample demographics, baseline characteristics, and daily attributes (N=348)

Demographics
 Gender, Women, n (%)a 221 (63.5)
 Age, mean (S.D.)b 33.19 (13.24)
 Ethnicity, n (%)
  American Indian or Alaska Native 1 (0.4)
  Asian 12 (3.4)
  African American or Black 6 (1.7)
  Hispanic or Latino 17 (4.9)
  Two or more races/ethnicities 21 (6.0)
  White 279 (80.2)
  Prefer not to answer 12 (3.4)
 Education, n (%)d
  Less than high school 1 (0.4)
  High school diploma 13 (3.7)
  Some college 100 (28.7)
  Associates degree 28 (8.0)
  Bachelors degree 143 (41.1)
  Masters degree 57 (16.4)
  Doctoral degree 6 (1.7)
 Work full- or part-time, n (%)e 285 (81.9)
Baseline survey characteristics
 Cannabis use days in last 2 weeks (O-TLFB), mean (S.D.)f 5.49 (5.11)
 Alcohol use days in last 2 weeks (O-TLFB), mean (S.D.)g 3.36 (3.23)
 Depression, Anxiety, Stress (DASS), mean (S.D.)h 42.9 (20.7)
 Sleep quality (PSQI), mean (S.D.)i 7.68 (3.32)
Daily survey attributes j
 Rating of sleep quality (0–10) in last 24 hours, mean (S.D.) 5.65 (1.82)
 Mean number of days used cannabis during 30-day study period, mean (S.D.) 14.3 (7.46)
 Cannabis use events in entire sample, n (%) 4,972 (54.1)
 Alcohol use events in sample, n (%) 2,244 (20.8)
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a

Gender response options included Man, Woman, and Transgender

b

Age (continuous in years) at the time of baseline.

c

Race and Ethnicity that participant considers themselves to be.

d

Highest level of education obtained.

e

Employment status (Full- or Part-time employment vs. all others).

f

Online Timeline (14-day) Followback (O-TLFB; Martin-Willett et al., 2019; Martin‐Willett et al., 2020; Sobell & Sobell, 1992) assessed cannabis use over the two-weeks prior to the baseline visit.

g

Composite score of Cannabis Use Disorder Symptoms from the DSM5 modified Marijuana Dependence Scale (MDS; Lozano, Stephens, & Roffman, 2006; Stephens, Roffman, & Curtin, 2000) assessing the presence or absence of 11 Cannabis Use Disorder symptoms.

h

Composite score of the Depression, Anxiety, and Stress Scale (DASS-21; Henry & Crawford, 2005; Lovibond & Lovibond, 1996) contains three subscales, each with seven items and a severity response scale of 0–3 (0-Did not apply to me at all, up to 3-Applied to me very much, or most of the time) assessing baseline negative affect.

i

Composite score of the Pittsburgh Sleep Quality Inventory (PSQI) assessing baseline sleep quality.

j

Average daily online survey diary sleep quality rating, percent of days study cannabis was endorsed, total and mean cannabis use events assessed, and total alcohol use events endorsed over each 24-hour period for 30 days.

Daily sleep quality outcomes

On average, participants reported using cannabis 53.96% of the 30-day study period (range: 3%−100% of days). The average sleep quality reported over the study period was 5.65 (out of 10; S.D.=1.82). Alcohol use was reported on 23.7% of all daily reports (Table 1).

Model 1 (Main effects - daily cannabis use).

The within-subject measure of cannabis use was significant (B: 0.456, p<0.001). This indicates that when an individual reported cannabis use on a given day, they also reported better sleep quality the subsequent night compared to when the same individual did not report cannabis use. While the local effect size for the within-person variation in daily cannabis use was small (fixed effect R2=0.04), the standardized measures show that compared to the other predictors in the model, the magnitude of the association between within-person variation in daily cannabis use and sleep quality was large. Furthermore, the unstandardized estimate of 0.456 means that on average, after cannabis use days, the quality of sleep is almost half a level better (on the 10 point sleep quality scale) compared to after non-cannabis use days. The model also shows that worse average baseline sleep quality was related to worse daily sleep quality (B: −0.115, p<0.001) and that as the study progressed participants reported better quality sleep (B: 0.010, p<0.001). Model 1 results are summarized in Table 2.

Table 2.

Mixed effects of daily cannabis use predicting daily sleep quality rating (N=347)

Model 1 (Daily cannabis use): Sleep quality Unstandardized Estimate SE Standardized Estimate SE t p
Fixed effects
 Within person predictors
Daily cannabis use (Within-person) a 0.400 0.039 19.003 1.833 10.37 <.0001
Daily alcohol use (Within-person)b −0.077 0.045 −3.150 1.828 −1.720 0.085
 Between person predictors
Number of days cannabis used during the study 0.047 0.074 3.369 5.333 0.630 0.528
Cannabis form (Flower)c −0.129 0.121 −5.915 5.517 −1.070 0.284
CBD:THC ratio (CBD)d 0.139 0.110 6.284 4.966 1.270 0.206
Men vs.Womene −0.064 0.113 −2.925 5.185 −0.560 0.573
Agef 0.001 0.004 1.051 5.614 0.190 0.852
Race/Ethnicity (White)g 0.082 0.145 2.816 4.964 0.570 0.571
Education (University degree)h 0.183 0.115 8.523 5.364 1.590 0.112
Employment (Work full-time or part-time)i 0.046 0.145 1.720 5.371 0.320 0.749
Baseline affective symptoms (DASS)j 0.001 0.003 2.247 5.603 0.400 0.688
Baseline Cannabis Use Disorder symptomsk −0.011 0.027 −2.323 5.361 −0.430 0.665
Baseline sleep quality (PSQI) l −0.117 0.019 −35.338 5.678 −6.220 <.0001
Survey day (1–30) m 0.011 0.002 9.016 1.566 5.790 <.0001
Survey day of the week (Weekend)n 0.020 0.044 0.729 1.631 0.450 0.655
Intercept 5.874 0.271 5.635 0.052 109.4 <.0001
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Note. Model 1 results reveal that cannabis use predicts participants same-night daily sleep quality rating over a 24-hour period for the 30-day study period (p < 0.001), while accounting for multiple potential covariates described below.

a

Study cannabis use in the last 24-hour daily survey (use vs. no use).

b

Alcohol use endorsement in the last 24-hour daily survey (use vs. no use).

Number of days of cannabis use during the 30 day study

c

Participant’s selected cannabis form [Flower vs. Edible].

d

Participant’s assigned cannabinoid (CBD:THC) ratio of study cannabis [CBD dominant (referent) vs THC dominant or THC+CBD].

e

Men vs. Women.

f

Age (continuous in years) at the time of baseline.

g

Race and Ethnicity that participant considers themselves to be (White vs. all others listed).

h

Highest level of education obtained (University degree vs. all others).

i

Employment status (Full- or Part-time employment vs. all others).

j

Composite score of the Depression, Anxiety, and Stress Scale (DASS-21) assessing negative affect at baseline.

k

Composite score of Cannabis Use Disorder Symptoms from the DSM5 modified Marijuana Dependence Scale (MDS) assessing the presence/absence of 11 Cannabis Use Disorder symptoms at baseline.

l

Composite score of the Pittsburgh Sleep Quality Inventory (PSQI) assessing sleep quality at baseline.

m

Daily diary survey responses over 30-days.

n

Day of the week each survey was completed (Weekend [Fri/Sat] vs. Weekday [Mon-Fri]).

Model 2 (Moderation).

Model 2 results are summarized in Table 3 and depicted in Figures 2A and 2B. The cross- level interaction with affective symptoms (DASS) was significant (B: 0.007, p<0.001). The results (plotted in Figure 2A) show that after cannabis use days for those with higher DASS scores, the daily sleep quality was higher. This is in contrast to after non-use days, the quality of sleep is lower in those with higher DASS scores.

Table 3.

Mixed effects of daily cannabis use predicting daily sleep quality rating with interactions (N=347)

Model 2 (Daily cannabis use): Sleep quality Unstandardized Estimate SE Standardized Estimate SE t p
Fixed effects
 Within person predictors
Daily cannabis use a 0.178 0.087 8.480 4.134 2.050 0.040
Daily alcohol useb −0.079 0.045 −3.230 1.827 −1.770 0.077
 Between person predictors
Number of days cannabis used during the study 0.066 0.074 4.785 5.333 0.900 0.370
Cannabis form (Flower) c −0.315 0.145 −14.43 6.654 −2.170 0.030
CBD:THC ratio (CBD)d −0.184 0.180 −8.313 8.125 −1.020 0.306
Men vs. Womene −0.076 0.113 −3.474 5.169 −0.670 0.502
Agef 0.001 0.004 1.712 5.597 0.310 0.760
Race/Ethnicity (White)g 0.052 0.145 1.797 4.957 0.360 0.717
Education (University degree)h 0.171 0.115 7.987 5.347 1.490 0.135
Employment (Work full-time or part-time)i 0.043 0.144 1.597 5.345 0.300 0.765
Baseline affect (DASS)j −0.002 0.003 −3.811 5.906 −0.650 0.519
Baseline Cannabis Use Disorder symptomsk −0.009 0.026 −1.889 5.338 −0.350 0.724
Baseline sleep quality (PSQI) l −0.116 0.019 −35.01 5.657 −6.190 <.0001
Survey day (1–30) m 0.011 0.002 9.082 1.565 5.800 <.0001
Survey day of the week (Weekend)n 0.020 0.044 0.740 1.630 0.450 0.650
Interactions
CBD:THC ratio*Form (CBD*Flower) 0.515 0.228 19.93 8.807 2.260 0.024
Daily cannabis use*Baseline Affect (DASS) 0.005 0.002 13.05 4.587 2.850 0.004
Intercept 6.117 0.278 5.636 0.051 110.0 <.0001
Open in a new tab

Note. Model 2 results reveal significant moderators of the cannabis use and sleep quality associations reported in Model 1, while accounting for multiple potential covariates described below.

a

Study cannabis use in the last 24-hour daily survey (use vs. no use).

b

Alcohol use endorsement in the last 24-hour daily survey (use vs. no use).

Number of days of cannabis use during the 30 day study

c

Participant’s selected cannabis form [Flower vs. Edible].

d

Participant’s assigned cannabinoid (CBD:THC) ratio of study cannabis [CBD dominant (referent) vs THC dominant or THC+CBD].

e

Men vs. Women.

f

Age (continuous in years) at the time of baseline.

g

Race and Ethnicity that participant considers themselves to be (White vs. all others listed).

h

Highest level of education obtained (University degree vs. all others).

i

Employment status (Full- or Part-time employment vs. all others).

j

Composite score of the Depression, Anxiety, and Stress Scale (DASS-21) assessing negative affect at baseline.

k

Composite score of Cannabis Use Disorder Symptoms from the DSM5 modified Marijuana Dependence Scale (MDS) assessing the presence/absence of 11 Cannabis Use Disorder symptoms at baseline.

l

Composite score of the Pittsburgh Sleep Quality Inventory (PSQI) assessing sleep quality at baseline.

m

Daily diary survey responses over 30-days.

n

Day of the week each survey was completed (Weekend [Fri/Sat] vs. Weekday [Mon-Fri]).

Figure 2: Interaction plots for predicted daily sleep quality.

Figure 2:

Open in a new tab

A) Line graph displaying predicted daily sleep quality (y-axis) as a function of daily cannabis use (lines) and baseline DASS scores (x-axis) in the sample (n=347). Dotted line indicates non-cannabis use days and black line indicates cannabis use days.

B) Bar graph displaying predicted daily sleep quality (y-axis) as a function of cannabinoid ratio (high CBD vs THC+CBD) and form (flower vs edibles). White bars indicate use of high CBD dominant products (high CBD) and grey bars indicate products that contain average to high levels of THC and lower levels of CBD (THC+CBD); left bars indicate flower, right bars indicate edibles. Data points account for mixed effects model covariates (daily alcohol use, total number of cannabis use days, gender, age, race/ethnicity, education, employment, baseline cannabis dependence, baseline sleep quality, survey day, weekday vs weekend).

Furthermore, the interaction between CBD:THC ratio (cannabis concentration) and form was also significant, showing that the respondents using cannabis flower reported poorer quality sleep compared to those using edible cannabis (B: −0.293, p=0.049), and that this association was strongest in those using CBD dominant edible forms (B: 0.549, p=0.017). Specifically, the results visualized in Figure 2B show that individuals using the CBD dominant strain in edible form reported the highest sleep quality.

Model 3 (Reverse).

When tested in reverse, within-person findings indicated that cannabis use was not associated with subsequent (lagged) sleep quality (B: 0.018, p=0.337). Thus, the reversed models confirmed that cannabis use was associated with subsequent night sleep quality rating but not vice versa.

Discussion

This study examined the within-person associations between daily self-reported cannabis use and subsequent night’s sleep quality among individuals who use cannabis for mild to moderate anxiety. The associations found here were not accounted for by gender, age, use of alcohol, presence of cannabis use disorder, average baseline sleep quality, or weekend cannabis use, and the reverse association, previous night’s sleep predicting cannabis use on the following day, was not present. These findings are reported in a community-based sample that spans from relatively light (less than weekly) to heavier (daily) cannabis use, and that is more balanced in gender and more varied in age than most cannabis use studies. The findings that better sleep quality is associated with same-day cannabis use echoes two previous daily dairy studies that suggested cannabis use is associated with improved sleep (Goodhines et al., 2019; Sznitman et al., 2020). Our study extends the literature by using a large sample that enables assessment of cross level planned interactions, examining moderation by baseline levels of negative affect and the type and formulation of cannabis product used. Our results that the association between cannabis use and subsequent better night’s sleep was stronger in respondents with higher baseline affective symptoms supports the importance of determining how sleep and mental health may be interacting in response to cannabis, and whether effects differ in populations with other serious medical conditions (Black et al., 2020; Kleckner et al., 2019; Sarris et al., 2020).

In addition, the association of better sleep after cannabis use days than after days of no use was particularly strong among users of edibles with a comparatively high CBD content. It is possible that the longer lasting pharmacological effects of edibles may enable CBD to maintain better sleep throughout the night (Poyatos et al., 2020), which is consistent with previous work (Hurd et al., 2015; Kaul et al., 2021; Shannon et al., 2019). Importantly, individuals self-selected the form of cannabis they used (flower or edible) so this finding should be interpreted as naturalistic and preliminary. Future work should prioritize investigating differences between inhaled versus orally administered cannabis forms that are associated with varying metabolisms, dosing, and duration effects (Huestis, 2007; Ramesh et al., 2014) that in turn likely modify the impact on sleep. Finally, few if any published studies on cannabis use and sleep have collected daily sleep quality measures for a full 30 days. The extended reporting period captures fluctuations in daily behavior and sleep quality that may occur between weekdays and weekends or week to week. This duration, for a within-subjects study design, allows for analyses to be substantially powered with over 7,000 total observations across 347 participants.

Several methodological features and limitations should be considered in interpreting our results. These data are observational and unmeasured variables are important to the relationships examined. The possibility of a direct effect of cannabis on anxiety symptoms that subsequently improves sleep (Patel et al., 2017; Sarris et al., 2020) was also not examined in this study. While the current data included information on cannabis use and sleep quality within a given day, we did not have data on the precise timing of use (e.g., morning, midday, or evening). This study examined a community sample who were using legal market cannabis ad libitum, allowing for a naturalistic observation of how participants choose to use cannabis in their daily lives. Given individual differences in cannabis metabolism and tolerance, ad libitum dosing procedures that allow participants to use their preferred forms and their preferred levels of cannabis provides important information that is consistent with real world use. Thus, external validity is a strength of these findings in individuals using products that have THC, CBD, or THC+CBD ratios common on the legal market and that are generally more potent than what has been available to researchers for laboratory-based studies (Vergara et al., 2017). This approach, however, results in lack of placebo- and dose-control, therefore limiting the inferences that can be made about direct cannabinoid exposure and dosing across study participants. Further, while research staff were blind to the cannabinoid content of the assigned product, participants were not blinded and thus their expectancies about how different forms of cannabis influence sleep may have influenced the results. Additionally, participant’s motives for selecting edible versus smoked forms of cannabis were not coded. Results should therefore be extended with controlled dosing studies that also incorporate and compare cannabis formulations consistent with those that are accessible on legal markets.

Finally, single-item measures of daily sleep quality and daily cannabis use were selected in order to minimize participant attrition and daily levels of fatigue or sleep aid use or energy were not assessed. While there is debate in the literature as to whether the benefits of objective, laboratory-controlled physiological measures of sleep quality outweigh the more externally valid, but wholly subjective self-report measures (Krystal & Edinger, 2008), combining surveys with less invasive quantitative tools such as actigraphy is increasingly feasible and has the potential to improve the validity of sleep research (Landry et al., 2015; Lockley et al., 1999; Zhang & Zhao, 2007). Further, objective measures of sleep that accompany participants’ subjective daily quality ratings could inform whether different types of cannabis affect sleep latency, continuity, or other factors in unique ways. In addition, given the potential importance of assessing the frequency and quantity of cannabis use in relation to effects on sleep, future studies should seek to include more fine grained cannabis use measures. There is ongoing methodological work in the field seeking to develop brief and meaningful frequency and quantity cannabis use measures that reflect real world products and potencies (Borodovsky et al., 2022; Budney et al., 2022; Martin-Willett et al., 2020). Overall, future laboratory-controlled sleep studies that balance external validity with testing of products and administration methods that are accessible in the real world are needed to help to elucidate what exactly are the mechanisms of sleep effects from these various formulations (e.g., THC versus CBD) and administration modes (e.g., smoked versus edible) in a way that naturalistic studies with brief measures such as ours cannot achieve.

Conclusions

We report on 30 days of daily cannabis use and sleep quality data among a community sample with mild to moderate anxiety. Our results suggest that cannabis use on a particular day is associated with better perceived sleep quality during the night, and that these associations are stronger among those with higher negative affective symptoms and those using CBD dominant edible forms of cannabis.

Acknowledgments:

Authors would like to thank Paige Phillips, Alexander Napolitan, Leigha Larsen, and Marco Ortiz Torres for their contributions and the participants for taking part in the study.

Funding:

Funding for this study was provided by R01DA04413 (PI: Bidwell) and F31AA029632 (PI: Martin-Willett)

Footnotes

Conflict of Interest: The authors declare that they have no conflict of interest.

Data Availability:

The data that support the findings of this study are available on request from the corresponding author, CB. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

References

  1. Abrams DI (2018). The therapeutic effects of Cannabis and cannabinoids: An update from the National Academies of Sciences, Engineering and Medicine report. In European Journal of Internal Medicine (Vol. 49, pp. 7–11). Elsevier B.V. 10.1016/j.ejim.2018.01.003 [DOI] [PubMed] [Google Scholar]
  2. Ahinkorah BO, Aboagye RG, Arthur-Holmes F, Hagan JE, Okyere J, Budu E, Dowou RK, Adu C, & Seidu A-A (2021). A Multi-Country Analysis of Prevalence of Anxiety-Induced Sleep Disturbance and Its Associated Factors among In-School Adolescents in Sub-Saharan Africa Using the Global School-Based Health Survey. Healthcare, 9(2), 234. 10.3390/healthcare9020234 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alvaro PK, Roberts RM, & Harris JK (2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. In Sleep. 10.5665/sleep.2810 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Angarita GA, Emadi N, Hodges S, & Morgan PT (2016). Sleep abnormalities associated with alcohol, cannabis, cocaine, and opiate use: A comprehensive review. In Addiction Science and Clinical Practice (Vol. 11, Issue 1, pp. 1–17). BioMed Central Ltd. 10.1186/s13722-016-0056-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Atroszko PA, Bagińska P, Mokosińska M, Sawicki A, & Atroszko B (2015). Validity and reliability of single-item self-report measures of general quality of life, general health and sleep quality. Comparative European Research, 2, 207–211. [Google Scholar]
  6. Babson KA, Sottile J, & Morabito D (2017). Cannabis, cannabinoids, and sleep: A review of the literature. Current Psychiatry Reports, 19(23), 1–12. 10.1007/s11920-017-0775-9 [DOI] [PubMed] [Google Scholar]
  7. Bahji A, Meyyappan AC, & Hawken ER (2020). Efficacy and acceptability of cannabinoids for anxiety disorders in adults: A systematic review & meta-analysis. In Journal of Psychiatric Research (Vol. 129, pp. 257–264). Elsevier Ltd. 10.1016/j.jpsychires.2020.07.030 [DOI] [PubMed] [Google Scholar]
  8. Bei B, Wiley JF, Trinder J, & Manber R (2016). Beyond the mean: A systematic review on the correlates of daily intraindividual variability of sleep/wake patterns. Sleep Medicine Reviews, 28, 108–124. 10.1016/j.smrv.2015.06.003 [DOI] [PubMed] [Google Scholar]
  9. Belendiuk KA, Babson KA, Vandrey R, & Bonn-Miller MO (2015). Cannabis species and cannabinoid concentration preference among sleep-disturbed medicinal cannabis users. Addictive Behaviors, 50, 178–181. 10.1016/j.addbeh.2015.06.032 [DOI] [PubMed] [Google Scholar]
  10. Black N, Stockings E, Campbell G, Tran LT, Zagic D, Hall D, Farrell M, & Degenhardt L (2020). Cannabinoids for the treatment of mental disorders and symptoms of mental disorders: A systematic review and meta- analysis. Lancet Psychiatry, 6(12), 995–1010. 10.1016/S2215-0366(19)30401-8.Cannabinoids [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Borodovsky JT, Hasin DS, Shmulewitz D, Walsh C, Livne O, Aharonovich E, Struble CA, Habib MI, & Budney AJ (2022). Typical Hits, Grams, or Joints: Evaluating Cannabis Survey Measurement Strategies for Quantifying Consumption. Cannabis and Cannabinoid Research. 10.1089/CAN.2022.0237 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Botsford SL, Yang S, & George TP (2020). Cannabis and Cannabinoids in Mood and Anxiety Disorders: Impact on Illness Onset and Course, and Assessment of Therapeutic Potential. In American Journal on Addictions (Vol. 29, Issue 1, pp. 9–26). Wiley Blackwell. 10.1111/ajad.12963 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brower KJ, Conroy DA, Kurth ME, Anderson BJ, & Stein MD (2011). Ramelteon and improved insomnia in alcohol-dependent patients: A case series. Journal of Clinical Sleep Medicine, 7(3), 274–275. 10.5664/JCSM.1070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Budney A, Borodovsky J, Struble CA, Habib M, Shmulewitz D, Livne O, Aharonovich E, Walsh C, & Hasin DS (2022). Estimating THC consumption from smoked and vaped cannabis products in an online survey of adults who use cannabis. Cannabis and Cannabinoid Research. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Burkhalter H, Wirz-Justice A, Cajochen C, Weaver T, Steiger J, Fehr T, Venzin R, & De Geest S (2013). Validation of a single item to assess daytime sleepiness for the Swiss Transplant Cohort Study. Progress in Transplantation, 23(3), 220–228. 10.7182/pit2013788 [DOI] [PubMed] [Google Scholar]
  16. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ, R. CF III, Monk TH, Berman SR, & Kupfer DJ (1989a). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. 10.1016/0165-1781(89)90047-4 [DOI] [PubMed] [Google Scholar]
  17. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ, R. CF III, Monk TH, Berman SR, & Kupfer DJ (1989b). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. 10.1016/0165-1781(89)90047-4 [DOI] [PubMed] [Google Scholar]
  18. Calapai G, Mannucci C, Chinou I, Cardia L, Calapai F, Sorbara EE, Firenzuoli B, Ricca V, Gensini GF, & Firenzuoli F (2019). Preclinical and Clinical Evidence Supporting Use of Cannabidiol in Psychiatry. In Evidence-based Complementary and Alternative Medicine. 10.1155/2019/2509129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Callahan TJ, Caldwell Hooper AE, Thayer RE, Magnan RE, & Bryan AD (2013). Relationships between marijuana dependence and condom use intentions and behavior among justice-involved adolescents. AIDS and Behavior, 17(8), 2715–2724. 10.1007/s10461-013-0417-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Cappelleri JC, Bushmakin AG, McDermott AM, Sadosky AB, Petrie CD, & Martin S (2009). Psychometric properties of a single-item scale to assess sleep quality among individuals with fibromyalgia. Health and Quality of Life Outcomes, 7(1), 1–7. 10.1186/1477-7525-7-54 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Cappuccio FP, D’Elia L, Strazzullo P, & Miller MA (2010a). Quantity and quality of sleep and incidence of type 2 diabetes: A systematic review and meta-analysis. Diabetes Care, 33(2), 414–420. 10.2337/dc09-1124 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Cappuccio FP, D’Elia L, Strazzullo P, & Miller MA (2010b). Sleep duration and all-cause mortality: A systematic review and meta-analysis of prospective studies. Sleep, 33(5), 585–592. 10.1093/sleep/33.5.585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Chattu VK, Chattu SK, Spence DW, Manzar MD, Burman D, & Pandi-Perumal SR (2019). Do Disparities in Sleep Duration Among Racial and Ethnic Minorities Contribute to Differences in Disease Prevalence? Journal of Racial and Ethnic Health Disparities. 10.1007/s40615-019-00607-7 [DOI] [PubMed] [Google Scholar]
  24. Choi NG, Dinitto DM, & Marti CN (2018). Older marijuana users’ marijuana risk perceptions: Associations with marijuana use patterns and marijuana and other substance use disorders. International Psychogeriatrics, 30(9), 1311–1322. 10.1017/S1041610217002794 [DOI] [PubMed] [Google Scholar]
  25. Chye Y, Christensen E, Solowij N, & Yücel M (2019). The endocannabinoid system and cannabidiol’s promise for the treatment of substance use disorder. Frontiers in Psychiatry, 10(FEB), 63. 10.3389/fpsyt.2019.00063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Conroy DA, & Arnedt JT (2014). Sleep and Substance Use Disorders: An Update. In Current Psychiatry Reports (Vol. 16, Issue 10, pp. 1–9). Current Medicine Group LLC 1. 10.1007/s11920-014-0487-3 [DOI] [PubMed] [Google Scholar]
  27. Conroy DA, Kurth ME, Strong DR, Brower KJ, & Stein MD (2016). Marijuana use patterns and sleep among community-based young adults. Journal of Addictive Diseases, 35(2), 135–143. 10.1080/10550887.2015.1132986 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Cox RC, & Olatunji BO (2016). A systematic review of sleep disturbance in anxiety and related disorders. Journal of Anxiety Disorders, 37, 104–129. 10.1016/j.janxdis.2015.12.001 [DOI] [PubMed] [Google Scholar]
  29. Cuttler C, Spradlin A, & McLaughlin RJ (2018). A naturalistic examination of the perceived effects of cannabis on negative affect. Journal of Affective Disorders, 235, 198–205. 10.1016/j.jad.2018.04.054 [DOI] [PubMed] [Google Scholar]
  30. Diep C, Tian C, Vachhani K, Won C, Wijeysundera DN, Clarke H, Singh M, & Ladha KS (2022). Recent cannabis use and nightly sleep duration in adults: a population analysis of the NHANES from 2005 to 2018. Regional Anesthesia & Pain Medicine, 47(2), 100–104. 10.1136/RAPM-2021-103161 [DOI] [PubMed] [Google Scholar]
  31. Fairman BJ, & Anthony JC (2012). Are early-onset cannabis smokers at an increased risk of depression spells? Journal of Affective Disorders, 138(1–2), 54–62. 10.1016/j.jad.2011.12.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Fakier N, & Wild LG (2011). Associations among sleep problems, learning difficulties and substance use in adolescence. Journal of Adolescence. 10.1016/j.adolescence.2010.09.010 [DOI] [PubMed] [Google Scholar]
  33. Gallicchio L, & Kalesan B (2009). Sleep duration and mortality: a systematic review and meta-analysis. Journal of Sleep Research, 18(2), 148–158. 10.1111/j.1365-2869.2008.00732.x [DOI] [PubMed] [Google Scholar]
  34. García-Gutiérrez MS, Navarrete F, Gasparyan A, Austrich-Olivares A, Sala F, & Manzanares J (2020). Cannabidiol: A potential new alternative for the treatment of anxiety, depression, and psychotic disorders. Biomolecules, 10(11), 1–34. 10.3390/biom10111575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Gates PJ, Albertella L, & Copeland J (2014). The effects of cannabinoid administration on sleep: A systematic review of human studies. In Sleep Medicine Reviews (Vol. 18, Issue 6, pp. 477–487). W.B. Saunders Ltd. 10.1016/j.smrv.2014.02.005 [DOI] [PubMed] [Google Scholar]
  36. Goodhines PA, Gellis LA, Ansell EB, & Park A (2019). Cannabis and Alcohol Use for Sleep Aid: A Daily Diary Investigation. Health Psychology, 38(11). 10.1037/hea0000765 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Gorelick DA, Goodwin RS, Schwilke E, Schroeder JR, Schwope DM, Kelly DL, Ortemann-Renon C, Bonnet D, & Huestis MA (2013). Around-the-clock oral THC effects on sleep in male chronic daily cannabis smokers. American Journal on Addictions, 22(5), 510–514. 10.1111/j.1521-0391.2013.12003.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Gutiérrez R, Vega L, & Dirección LV (2021). Self-medication with cannabidiol (CBD) and associated treatments in the self-care of anxiety in women from Mexico City: A qualitative study. Salud Mental, 44(6), 295–306. 10.17711/SM.0185-3325.2021.038 [DOI] [Google Scholar]
  39. Hartwell EE, Pfeifer JG, McCauley JL, Moran-Santa Maria M, & Back SE (2014). Sleep disturbances and pain among individuals with prescription opioid dependence. Addictive Behaviors, 39(10), 1537–1542. 10.1016/j.addbeh.2014.05.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Harvey AG, Stinson K, Whitaker KL, Moskovitz D, & Virk H (2008). The subjective meaning of sleep quality: A comparison of individuals with and without insomnia. Sleep, 31(3), 383–393. 10.1093/sleep/31.3.383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hasin DS, Saha TD, Kerridge BT, Goldstein RB, Chou SP, Zhang H, Jung J, Pickering RP, Ruan J, Smith SM, Huang B, & Grant BF (2015). Prevalence of marijuana use disorders in the United States between 2001–2002 and 2012–2013. JAMA Psychiatry, 72(12), 1235–1242. 10.1001/jamapsychiatry.2015.1858 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Hasin DS, Shmulewitz D, & Sarvet AL (2019). Time trends in US cannabis use and cannabis use disorders overall and by sociodemographic subgroups: a narrative review and new findings. American Journal of Drug and Alcohol Abuse, 45(6), 623–643. 10.1080/00952990.2019.1569668 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Henry JD, & Crawford JR (2005). The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44(2), 227–239. 10.1348/014466505X29657 [DOI] [PubMed] [Google Scholar]
  44. Huestis MA (2007). Human Cannabinoid Pharmacokinetics. Chemistry & Biodiversity, 4(8), 1770. 10.1002/CBDV.200790152 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Hughes JM, Ulmer CS, Gierisch JM, & Howard MO (2018). Single-Item Measures for Detecting Sleep Problems in United States Military Veterans. Journal of General Internal Medicine, 33(5), 698–704. 10.1007/s11606-017-4250-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Hurd YL, Spriggs S, Alishayev J, Winkel G, Gurgov K, Kudrich C, Oprescu AM, & Salsitz E (2019). Cannabidiol for the reduction of cue-induced craving and anxiety in drug-abstinent individuals with heroin use disorder: A double-blind randomized placebo-controlled trial. American Journal of Psychiatry. 10.1176/appi.ajp.2019.18101191 [DOI] [PubMed] [Google Scholar]
  47. Hurd YL, Yoon M, Manini AF, Hernandez S, Olmedo R, Ostman M, & Jutras-Aswad D (2015). Early Phase in the Development of Cannabidiol as a Treatment for Addiction: Opioid Relapse Takes Initial Center Stage. In Neurotherapeutics (Vol. 12, Issue 4, pp. 807–815). Springer New York LLC. 10.1007/s13311-015-0373-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ilgen MA, Bohnert K, Kleinberg F, Jannausch M, Bohnert ASB, Walton M, & Blow FC (2013). Characteristics of adults seeking medical marijuana certification. Drug and Alcohol Dependence, 132(3), 654–659. 10.1016/j.drugalcdep.2013.04.019 [DOI] [PubMed] [Google Scholar]
  49. Kaul M, Zee PC, & Sahni AS (2021). Effects of Cannabinoids on Sleep and their Therapeutic Potential for Sleep Disorders. Neurotherapeutics, 18(1), 217–227. 10.1007/S13311-021-01013-W/FIGURES/2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Kenny DA, & Zautra A (1995). The Trait-State-Error Model for Multiwave Data. Journal of Consulting and Clinical Psychology, 63(1), 52–59. 10.1037/0022-006X.63.1.52 [DOI] [PubMed] [Google Scholar]
  51. Khubchandani J, & Price JH (2019). Short Sleep Duration in Working American Adults, 2010–2018. Journal of Community Health, 0123456789, 2010–2018. 10.1007/s10900-019-00731-9 [DOI] [PubMed] [Google Scholar]
  52. Kievit RA, Frankenhuis WE, Waldorp LJ, & Borsboom D (2013). Simpson’s paradox in psychological science: a practical guide. Frontiers in Psychology, 4(AUG), 513. 10.3389/fpsyg.2013.00513 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Kleckner AS, Kleckner IR, Kamen CS, Tejani MA, Janelsins MC, Morrow GR, & Peppone LJ (2019). Opportunities for cannabis in supportive care in cancer. Therapeutic Advances in Medical Oncology, 11, 175883591986636. 10.1177/1758835919866362 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Krystal AD, & Edinger JD (2008). Measuring sleep quality. Sleep Medicine, 9(SUPPL. 1), S10–S17. 10.1016/S1389-9457(08)70011-X [DOI] [PubMed] [Google Scholar]
  55. Landry GJ, Best JR, & Liu-Ambrose T (2015). Measuring sleep quality in older adults: a comparison using subjective and objective methods. Frontiers in Aging Neuroscience, 7(SEP), 166. 10.3389/fnagi.2015.00166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Lee C, Round JM, Hanlon JG, Hyshka E, Dyck JRB, & Eurich DT (2021). Generalized Anxiety Disorder 7-Item (GAD-7) Scores in Medically Authorized Cannabis Patients—Ontario and Alberta, Canada: 10.1177/07067437211043393 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Light MK, Orens A, Lewandowski B, & Pickton T (2014). Market size and demand for marijuana in Colorado. Colorado Department of Revenue. http://files/6434/Light et al. - 2014 - Market size and demand for marijuana in Colorado.pdf [Google Scholar]
  58. Lockley SW, Skene DJ, & Arendt J (1999). Comparison between subjective and actigraphic measurement of sleep and sleep rhythms. Journal of Sleep Research, 8(3), 175–183. 10.1046/j.1365-2869.1999.00155.x [DOI] [PubMed] [Google Scholar]
  59. Lovibond SH, & Lovibond PF (1996). Manual for the depression anxiety stress scales.
  60. Lozano BE, Stephens RS, & Roffman RA (2006). Abstinence and moderate use goals in the treatment of marijuana dependence. Addiction (Abingdon, England), 101(11), 1589–1597. 10.1111/j.1360-0443.2006.01609.x [DOI] [PubMed] [Google Scholar]
  61. Magee L, & Hale L (2012). Longitudinal associations between sleep duration and subsequent weight gain: A systematic review. In Sleep Medicine Reviews (Vol. 16, Issue 3, pp. 231–241). W.B. Saunders. 10.1016/j.smrv.2011.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Martin-Willett R, Helmuth T, Abraha M, Bryan AD, Hitchcock L, Lee K, & Bidwell LC (2020a). Validation of a multisubstance online Timeline Followback assessment. Brain and Behavior, 10(1). 10.1002/brb3.1486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Martin-Willett R, Helmuth T, Abraha M, Bryan AD, Hitchcock L, Lee K, & Bidwell LC (2020b). Validation of a multisubstance online Timeline Followback assessment. Brain and Behavior, 10(1). 10.1002/brb3.1486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Martin-Willett R, Helmuth T, Abraha M, Bryan AD, Hitchcock L, Lee K, & Bidwell LC (2020). Validation of a multisubstance online Timeline Followback assessment. Brain and Behavior, 10(1). 10.1002/brb3.1486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Martin-Willett R, McCormick Z, Newman W, Larsen LD, Ortiz Torres MA, & Bidwell LC (2019a). The transformation of a gold standard in-person substance use assessment to a web-based, REDCap integrated data capture tool. Journal of Biomedical Informatics, 94, 103186. 10.1016/J.JBI.2019.103186 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Martin-Willett R, McCormick Z, Newman W, Larsen LD, Ortiz Torres MA, & Bidwell LC (2019b). The transformation of a gold standard in-person substance use assessment to a web-based, REDCap integrated data capture tool. Journal of Biomedical Informatics, 94, 103186. 10.1016/J.JBI.2019.103186 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Mathieu JE, Aguinis H, Culpepper SA, & Chen G (2012). Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. Journal of Applied Psychology, 97(5), 951–966. 10.1037/A0028380 [DOI] [PubMed] [Google Scholar]
  68. Mathieu JE, & Chen G (2010). The Etiology of the Multilevel Paradigm in Management Research. 37(2), 610–641. 10.1177/0149206310364663 [DOI] [Google Scholar]
  69. McKnight-Eily LR, Eaton DK, Lowry R, Croft JB, Presley-Cantrell L, & Perry GS (2011). Relationships between hours of sleep and health-risk behaviors in US adolescent students. Preventive Medicine. 10.1016/j.ypmed.2011.06.020 [DOI] [PubMed] [Google Scholar]
  70. Mednick SC, Christakis NA, & Fowler JH (2010). The spread of sleep loss influences drug use in adolescent social networks. PLoS ONE. 10.1371/journal.pone.0009775 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, & Colantonio A (2016). The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis. Sleep Medicine Reviews, 25, 52–73. 10.1016/j.smrv.2015.01.009 [DOI] [PubMed] [Google Scholar]
  72. Moltke J, & Hindocha C (2021). Reasons for cannabidiol use: a cross-sectional study of CBD users, focusing on self-perceived stress, anxiety, and sleep problems. Journal of Cannabis Research, 3(1), 1–12. 10.1186/S42238-021-00061-5/FIGURES/3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Molzof HE, Emert SE, Tutek J, Mulla MM, Lichstein KL, Taylor DJ, & Riedel BW (2018). Intraindividual sleep variability and its association with insomnia identity and poor sleep. Sleep Medicine, 52, 58–66. 10.1016/j.sleep.2018.08.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Mondino A, Cavelli M, González J, Murillo-Rodriguez E, Torterolo P, & Falconi A (2021). Effects of Cannabis Consumption on Sleep. Advances in Experimental Medicine and Biology, 1297, 147–162. 10.1007/978-3-030-61663-2_11 [DOI] [PubMed] [Google Scholar]
  75. Neumeister A, Seidel J, Ragen BJ, & Pietrzak RH (2015). Translational evidence for a role of endocannabinoids in the etiology and treatment of posttraumatic stress disorder. Psychoneuroendocrinology, 51, 577–584. 10.1016/j.psyneuen.2014.10.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Nicholson AN, Turner C, Stone BM, & Robson PJ (2004). Effect of Δ−9-tetrahydrocannabinol and cannabidiol on nocturnal sleep and early-morning behavior in young adults. Journal of Clinical Psychopharmacology, 24(3), 305–313. 10.1097/01.jcp.0000125688.05091.8f [DOI] [PubMed] [Google Scholar]
  77. Orens A, Light M, Lewandowski B, Rowberry J, & Saloga C (2018). 2017 Market Update. In Market Size and Demand for Marijuana in Colorado. [Google Scholar]
  78. Pacher P, Bátkai S, & Kunos G (2006). The endocannabinoid system as an emerging target of pharmacotherapy. In Pharmacological Reviews (Vol. 58, Issue 3, pp. 389–462). NIH Public Access. 10.1124/pr.58.3.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Pahl K, Brook JS, & Koppel J (2011). Trajectories of marijuana use and psychological adjustment among urban African American and Puerto Rican women. Psychological Medicine, 41(08), 1775–1783. 10.1017/S0033291710002345 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Patel S, Hill MN, Cheer JF, Wotjak CT, & Holmes A (2017). The endocannabinoid system as a target for novel anxiolytic drugs. Neuroscience and Biobehavioral Reviews, 76, 56–66. 10.1016/j.neubiorev.2016.12.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Peltz JS, Bodenlos JS, Kingery JN, & Rogge RD (2020). The role of financial strain in college students’ work hours, sleep, and mental health. Journal of American College Health, 0(0), 1–8. 10.1080/07448481.2019.1705306 [DOI] [PubMed] [Google Scholar]
  82. Perron RR, Tyson RL, & Sutherland GR (2001). Δ9-Tetrahydrocannabinol increases brain temperature and inverts circadian rhythms. NeuroReport, 12(17), 3791–3794. 10.1097/00001756-200112040-00038 [DOI] [PubMed] [Google Scholar]
  83. Poyatos L, Pérez-Acevedo AP, Papaseit E, Pérez-Mañá C, Martin S, Hladun O, Siles A, Torrens M, Busardo FP, & Farré M (2020). Oral administration of cannabis and Δ−9-tetrahydrocannabinol (Thc) preparations: A systematic review. Medicina (Lithuania), 56(6), 1–28. 10.3390/medicina56060309 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Ramesh D, Haney M, & Cooper ZD (2014). Dose-dependent physiological and subjective effects of marijuana in daily marijuana smokers. Drug and Alcohol Dependence, 140, e181–e182. 10.1016/J.DRUGALCDEP.2014.02.509 [DOI] [Google Scholar]
  85. Raudenbush SW, & Bryk AS (2002). Hierarchical linear models: Applications and data analysis methods. SAGE Publications. [Google Scholar]
  86. Reynolds IR, Fixen DR, Parnes BL, Lum HD, Shanbhag P, Church S, Linnebur SA, & Orosz G (2018). Characteristics and Patterns of Marijuana Use in Community-Dwelling Older Adults. Journal of the American Geriatrics Society, 66(11), 2167–2171. 10.1111/jgs.15507 [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Russo EB, Guy GW, & Robson PJ (2007). Cannabis, pain, and sleep: Lessons from therapeutic clinical trials of sativexρ, a cannabis-based medicine. In Chemistry and Biodiversity (Vol. 4, Issue 8, pp. 1729–1743). 10.1002/cbdv.200790150 [DOI] [PubMed] [Google Scholar]
  88. Sanford AE, Castillo E, & Gannon RL (2008). Cannabinoids and hamster circadian activity rhythms. Brain Research, 1222, 141–148. 10.1016/j.brainres.2008.05.048 [DOI] [PubMed] [Google Scholar]
  89. Sarris J, Sinclair J, Karamacoska D, Davidson M, & Firth J (2020a). Medicinal cannabis for psychiatric disorders: A clinically-focused systematic review. BMC Psychiatry, 20(1), 1–14. 10.1186/s12888-019-2409-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Sarris J, Sinclair J, Karamacoska D, Davidson M, & Firth J (2020b). Medicinal cannabis for psychiatric disorders: A clinically-focused systematic review. BMC Psychiatry, 20(1), 1–14. 10.1186/s12888-019-2409-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Shan Z, Ma H, Xie M, Yan P, Guo Y, Bao W, Rong Y, Jackson CL, Hu FB, & Liu L (2015). Sleep duration and risk of type 2 diabetes: A meta-analysis of prospective studies. Diabetes Care, 38(3), 529–537. 10.2337/dc14-2073 [DOI] [PubMed] [Google Scholar]
  92. Shannon S, Lewis N, Lee H, & Hughes S (2019). Cannabidiol in anxiety and sleep: A large case series. The Permanente Journal, 23, 18–041. 10.7812/TPP/18-041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Sharpe L, Sinclair J, Kramer A, De Manincor M, & Sarris J (2020). Cannabis, a cause for anxiety? A critical appraisal of the anxiogenic and anxiolytic properties. Journal of Translational Medicine, 18(1), 1–21. 10.1186/s12967-020-02518-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Sobell L, & Sobell M (1992). Timeline follow-back: A technique for assessing self-reported alcohol consumption. In Measuring Alcohol Consumption (pp. 41–72). 10.1007/978-1-4612-0357-5_3 [DOI] [Google Scholar]
  95. Spanagel R, & Bilbao A (2021). Approved cannabinoids for medical purposes – Comparative systematic review and meta-analysis for sleep and appetite. Neuropharmacology, 196, 108680. 10.1016/J.NEUROPHARM.2021.108680 [DOI] [PubMed] [Google Scholar]
  96. Stanciu CN, Brunette MF, Teja N, & Budney AJ (2021). Evidence for use of cannabinoids in mood disorders, anxiety disorders, and ptsd: A systematic review. In Psychiatric Services (Vol. 72, Issue 4, pp. 429–436). American Psychiatric Association. 10.1176/appi.ps.202000189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. StataCorp. (2011). Stata Statistical Software (Release 12). StataCorp LP. [Google Scholar]
  98. Stephens RS, Roffman RA, & Curtin L (2000). Comparison of extended versus brief treatment for marijuana use. Journal of Consulting and Clinical Psychology, 68(5), 898–908. 10.1037/0022-006X.68.5.898 [DOI] [PubMed] [Google Scholar]
  99. Suraev AS, Marshall NS, Vandrey R, McCartney D, Benson MJ, McGregor IS, Grunstein RR, & Hoyos CM (2020). Cannabinoid therapies in the management of sleep disorders: a systematic review of preclinical and clinical studies. Sleep Medicine Reviews, 101339. 10.1016/j.smrv.2020.101339 [DOI] [PubMed] [Google Scholar]
  100. Sznitman SR, Shochat T, & Greene T (2020). Is time elapsed between cannabis use and sleep start time associated with sleep continuity? An experience sampling method. Drug and Alcohol Dependence, 208(December 2019), 107846. 10.1016/j.drugalcdep.2020.107846 [DOI] [PubMed] [Google Scholar]
  101. Troxel WM, Ewing B, & D’Amico EJ (2015). Examining racial/ethnic disparities in the association between adolescent sleep and alcohol or marijuana use. Sleep Health. 10.1016/j.sleh.2015.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Tsuno N, Besset A, & Ritchie K (2005). Sleep and depression. In Journal of Clinical Psychiatry (Vol. 66, Issue 10, pp. 1254–1269). Physicians Postgraduate Press Inc. 10.4088/JCP.v66n1008 [DOI] [PubMed] [Google Scholar]
  103. Vandrey R, Stitzer ML, Mintzer MZ, Huestis MA, Murray JA, & Lee D (2013). The dose effects of short-term dronabinol (oral THC) maintenance in daily cannabis users. Drug and Alcohol Dependence, 128(1–2), 64–70. 10.1016/j.drugalcdep.2012.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Winiger EA, Hitchcock LN, Bryan AD, & Cinnamon Bidwell L (2021). Cannabis use and sleep: Expectations, outcomes, and the role of age. Addictive Behaviors, 112. 10.1016/j.addbeh.2020.106642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Zhang L, & Zhao ZX (2007). Objective and subjective measures for sleep disorders. Neuroscience Bulletin, 23(4), 236–240. 10.1007/s12264-007-0035-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, CB. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

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