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. Author manuscript; available in PMC: 2026 Apr 1.
Published in final edited form as: Addict Behav. 2024 Dec 31;163:108239. doi: 10.1016/j.addbeh.2024.108239

Associations Between Cannabis Use and Same-day and Substance Use Health Behaviors

Irene De La Torre 1, Emily T Hébert 2, Krista M Kezbers 1, Danielle Walters 1, Zachary C Pope 1,3, Bingjing Mao 1, Lizbeth Benson 4, Dingjing Shi 5, Nadia Stanley 6, Michael S Businelle 1,7
PMCID: PMC11805613  NIHMSID: NIHMS2046241  PMID: 39756126

Abstract

Objective:

Cannabis use has increased in the United States as states have legalized/decriminalized recreational and/or medicinal use. The primary aim of this study was to examine daily associations between cannabis use and health (physical activity [PA]) and substance use (alcohol consumption, cigarette use) behaviors.

Method:

Data from a 28-day nationwide study that prompted daily ecological momentary assessments (EMAs) were used to assess daily cannabis use, moderate-to-vigorous PA (MVPA), alcohol containing drinks consumed, and cigarettes smoked. Only participants who reported cannabis use on at least one day during the study period were included in the analysis (N = 98). Generalized linear mixed models were used to examine associations between cannabis use and same-day health and substance use behaviors while adjusting for race, biological sex, and age.

Results:

Daily cannabis use was positively associated with daily PA (p = 0.04, 3.31-minute higher PA duration on use vs. non-use days), number of alcohol containing drinks consumed (p = 0.01, 0.45 more drinks on use vs. non-use days), and number of cigarettes smoked (p = 0.01, 0.63 more cigarettes on use vs. non-use days).

Conclusions:

This study was among the first to use EMAs to examine associations between daily cannabis use and same-day PA, alcohol consumption, and cigarette use. Overall, findings indicated that daily cannabis use is associated with higher engagement in these daily health and substance use behaviors. Further research is needed to understand mechanisms linking cannabis use with these and other behaviors.

Keywords: Physical Activity, Alcohol, Tobacco, Marijuana, Cannabis, Ecological Momentary Assessment

1. Introduction

Cannabis, also known as marijuana, is the most frequently used federally illicit drug in the United States (U.S.) (Centers for Disease Control and Prevention [CDC], 2022b; Substance Abuse and Mental Health Services Administration, 2022). Attitudes towards cannabis use have become more positive as states have legalized cannabis for medicinal and recreational purposes (Hasin et al., 2019; Urits et al., 2021). The “Monitoring the Future Panel” study showed an overall 9% increase in cannabis users between 2017 and 2022, with a 3% increase in daily cannabis use among those aged 19–30 years and an 11% increase in daily cannabis use among those aged 35–50 years over this same time period (Patrick et al., 2022). Despite these increases, research on how daily cannabis use relates to other behaviors is limited, partially due to continued federal regulations, research-related cannabis sourcing difficulties, and limited cannabis-related research funding (Cooper et al., 2021; Piomelli et al., 2019). Research examining associations between daily cannabis use and same-day health and substance use behaviors may yield important insights that could inform policy as well as multiple behavior change interventions. Current research on the relationship of cannabis use with common health and substance use behaviors is briefly reviewed below.

1.1. Cannabis Use and Physical Activity (PA)

Daily PA improves cardiovascular, musculoskeletal, and psychological function (Bouchard et al., 2012; Miles, 2007; U.S. Department of Health and Human Services, 2018). Yet, insufficient PA remains a major public health issue leading to premature mortality, loss of function, and increased risk for multiple chronic diseases (Saunders et al., 2020). According to Behavioral Risk Factor Surveillance System data, only 25.3% of U.S. adults report being physically active (CDC, 2023). Increasing PA is therefore important and can increase life expectancy (World Health Organization, 2018), partially by preventing the development of noncommunicable diseases (Lee et al., 2012). Examining how cannabis use may relate to PA is important in the context of health behavior promotion to ensure cannabis use is not precluding PA participation. Cross-sectional research that has examined relationships between cannabis use and PA has been mixed, with some studies finding that cannabis use may be associated with higher PA (Ong et al., 2021; YorkWilliams et al., 2019) and others finding that cannabis use may be associated with lower PA (Vidot et al., 2017). Scant research is available using more intensive survey methods to examine this relationship. Gibson et al. (2023) used an email-delivered daily diary to examine the relationship between cannabis use and exercise (i.e., planned, repetitive PA done for health-promoting purposes) over 30 days. Observations suggested cannabis users were no more likely to engage in exercise than non-users and that cannabis use days were no more likely to be associated with exercise engagement than non-use days. Using more intensive survey methods to examine how cannabis use is related to overall daily PA, and not the more specific concept of ‘exercise’, may therefore be critical for public health policy.

1.2. Cannabis Use and Alcohol Consumption

Alcohol is among the most frequently used substances in the U.S. and is a leading cause of preventable death nationwide behind tobacco, illicit drugs, and physical inactivity (CDC, 2022a; National Institute on Alcohol Abuse and Alcoholism, 2023; Pilar et al., 2020; Substance Abuse and Mental Health Services Administration, 2022). Co-substance use is defined as the use of two substances on the same day or using two substances at the same time on any given day. Co-use of alcohol and cannabis is common and increasing (McCabe et al., 2021). Narrative reviews (Yurasek et al., 2017) and longitudinal analyses of cross-sectional survey data (Novak et al., 2016) have shown that cannabis use is associated with increased alcohol consumption and that individuals meeting cannabis use disorder criteria have heightened risk for alcohol use disorder (Stinson et al., 2006). Gunn et al. (2022) showed that co-use of cannabis and alcohol may be substitutionary (e.g., an individual using cannabis to help reduce alcohol consumption) or complementary (e.g., an individual using cannabis to increase the feelings of intoxication associated with alcohol consumption). Short-term studies evaluating the daily use of cannabis and alcohol have been completed. Lee et al. (2020) used SMS text-based online surveys to examine this relationship, observing that alcohol consumption was higher on days when cannabis was used relative to days with no cannabis use. This research group has also observed that days with simultaneous cannabis and alcohol use are also associated with a higher number of 1) hours feeling high and 2) consequences related to alcohol consumption (positive and negative) (Fairlie et al., 2023). However, these studies were conducted in community-based young adult samples. As cannabis use has increased among older populations (Patrick et al., 2022), investigating this relationship among older cohorts is important.

1.3. Cannabis Use and Cigarette Smoking

Cigarette smoking contributes to approximately 480,000 annual deaths in the U.S. (Carter et al., 2015). Smoking increases the likelihood of developing lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease (Chen & Boreham, 2002; Fabbri, 2016; Walser et al., 2008). Individuals who smoke cigarettes are more likely to use cannabis than individuals who do not smoke cigarettes (Goodwin et al., 2018; Schauer et al., 2017). Cannabis use is also associated with decreased smoking cessation, increased smoking relapse, and increased cigarette smoking initiation (Weinberger et al., 2020). In a 28-day long study of daily cannabis and tobacco use assessed via an automated phone system, Wilhelm et al. (2020) observed combustible tobacco use (cigarette, cigar, hookah/waterpipe tobacco, e-cigarettes) was significantly more likely to occur following cannabis use among young adult co-users. In a 21-day study of this relationship using SMS text-based surveys, Berg et al. (2019) observed young adults were more likely to smoke cigarettes if they also used cannabis. However, similar to literature on daily cannabis use and alcohol consumption, investigating the relationship between daily cannabis use and cigarette smoking among older samples would extend the literature base.

1.4. Limitations of Current Literature

There is a clear need for prospective investigations into how daily cannabis use is associated with same-day health and substance use behaviors. Ecological momentary assessments (EMA) can be used to assess an individual’s daily engagement in these behaviors in a more ecologically valid manner and in a way subject to less recall bias than traditional survey-based methods (Businelle et al., 2024b; Shiffman et al., 2008). Given the high percentage of smartphone ownership across sociodemographic categories (Pew Research Group, 2024), smartphone-based EMA represents a survey delivery methodology with the necessary reach to complete larger, geographically-diverse investigations that gather more generalizable results. Smartphone-based EMA investigations on the associations of daily cannabis use with same-day health and substance use behaviors among cannabis users across the U.S. could therefore allow for more precise estimates of these associations. This could increase the potential to inform future multiple behavior change interventions—particularly within study designs that seek to use smartphone-based methods to deliver the right intervention to the right person at the correct time (e.g., just-in-time adaptive interventions).

1.5. The Present Study

The current study examined associations between daily cannabis use and same day: 1) PA, 2) alcohol consumption, and 3) cigarette smoking. Specifically, smartphone-based EMAs were used to examine within- and between-person associations of cannabis use with same-day: 1) moderate-to-vigorous PA (MVPA), 2) number of alcohol containing drinks consumed, and 3) number of cigarettes smoked. Owing to mechanistic research suggesting that the acute impact of cannabis use on the endocannabinoid and dopaminergic systems may heighten the psychological reward associated with PA (Laksmidewi & Soejitno, 2021), it was hypothesized that MVPA would be higher 1) on cannabis use vs. non-use days and 2) among more vs. less frequent cannabis users. Similarly, it was hypothesized that the number of alcohol containing drinks consumed and cigarettes smoked would be higher 1) on cannabis use vs. non-use days and 2) among more vs. less frequent cannabis users.

2. Material and Methods

The current manuscript was constructed per the Journal Article Reporting Standards for Quantitative Research in Psychology (Applebaum et al., 2018). We report how we determined our sample size, all data exclusions (if any), all manipulations, and all study measures.

2.1. Study Design and Participants

The larger study from which these data were drawn is described in Businelle et al. (2024a). The parent study used a 32-group factorial design to investigate smartphone-based EMA best practices. All participants were recruited through nationwide Facebook advertisements from November 2021 to September 2022. Interested participants completed a secure REDCap pre-screening survey to determine initial study eligibility. If deemed initially eligible, participants completed a screening and enrollment call during which informed consent and HIPAA forms were completed, with eligibility verified against the following eligibility criteria: 1) ≥18 years old; 2) literacy level >6th grade; 3) own a data-enabled Android smartphone with operating system 6.0 or higher to ensure compatibility with the Insight™ mHealth Platform smartphone application (hereafter, ‘Insight™’) (Mobile Health Technology, 2015); 4) willing to install Insight™ on their smartphone; 5) able to send study staff a picture of their state-issued identification—necessary to reduce fraudulent attempts at study enrollment; and 6) willing to complete EMAs throughout study participation. Insight™ was then downloaded, with study staff providing a unique activation code and training participants in its use.

2.2. Procedures

The University of Oklahoma Health Sciences Center Institutional Review Board approved all study procedures (IRB# 13684). Within seven days of the screening and enrollment call, participants completed the 30-minute baseline survey via Insight™. Insight™ started prompting daily EMAs 2–4 times per day the day after the baseline survey was completed and continued for 28 days. After 28 days, participants completed a 15-minute Insight™-based follow-up survey. Participants were paid $10 and $30 for completing the baseline and follow-up surveys, respectively. Payment for EMA completion depended on group allocation as outlined in Businelle et al. (2024a). Only participants that reported using cannabis at least once during the 28-day EMA period were included in the current secondary data analyses (N=98).

2.3. Measures

2.3.1. Baseline Survey

The baseline survey was used to collect sociodemographic information, health history, and self-reported health behaviors. Of relevance to the present analyses, information on race (0 = Minoritized, 1 = White), biological sex at birth (male = 0, female = 1), and age (years) were collected.

2.3.2. EMAs

Morning EMAs asked about the prior day’s cannabis use as well as any PA participation, alcohol consumption, and cigarettes smoked. Participants answered the following question about prior day cannabis use, “In the past 24 hours, which of the following have you used? (select all that apply)” (response options: 1=Smoked marijuana, 2=Vaped marijuana, 3=Edible marijuana, 4=Used marijuana some other way, 7=None of the above). A dichotomous variable (yes/no) was created to indicate whether a participant reported using cannabis of any kind on a given day. The following two questions assessed prior day’s PA, “How many minutes of MODERATE leisure time physical activity did you get yesterday? Examples of moderate physical activity include brisk walking, bicycling or anything else that causes a small increase in breathing or heart rate.” and “How many minutes of VIGOROUS leisure time physical activity did you get yesterday? Examples of vigorous physical activity include running, aerobics, heavy yard work, or anything else that causes large increases in breathing or heart rate.” The moderate and vigorous PA questions had identical response options (0=0–9 minutes, 1=10–19 minutes, 2=20–29 minutes, 3=30–39 minutes, 4=40–49 minutes, 5=50–59 minutes, 6=60–69 minutes, 7=70 or more minutes). To convert these categorical responses into a proxy of MVPA duration in minutes per day, the lower bound for each participant’s selected range of moderate and vigorous PA was used for each day and MVPA was calculated as: (moderate PA minutes + vigorous PA minutes). Taking the lower bound of each PA category was a conservative approach that reflects the tendency for how social desirability can lead individuals to overreport PA, particularly vigorous PA (Sallis & Saelens, 2000) on self-report questionnaires; thus contributing to the low-to-moderate correlations observed between self-reported PA and objectively-assessed PA (e.g., accelerometry) (Prince et al., 2008). Participants answered the following question about prior day alcohol use, “How many standard drinks of alcohol did you have yesterday? A standard drink is a 12-ounce beer (a 24-ounce beer = 2 standard drinks), a 5-ounce glass of wine, or a shot of liquor.” (response options: 0 to 15+ in one drink increments). Finally, participants answered the following question about the number of cigarettes smoked the prior day, “How many cigarettes did you smoke yesterday?” (response options: 0 to 25+ in one cigarette increments).

2.4. Transparency and Openness

All data, analysis code, and research materials are available by emailing the corresponding author. Data were analyzed using SPSS v29. The parent study’s design and approach were pre-registered on ClinicalTrials.gov (NCT05194228).

2.5. Statistical Analyses

The present analyses included all individuals who reported cannabis use ≥1 day during the 28-day study period. Descriptives for sociodemographic and variables of interest were reported as mean ± standard deviation or frequency (%), as applicable. Prior to inferential analyses, Level 1 residuals were examined for normality and homoscedasticity using histograms, Q-Q plots, and scatterplots of residuals against fitted values as were the distributions of Level 2 residuals (random effects for intercepts and slopes)—all performed following guidelines by Santos Nobre and da Motta Singer (2007). Statistical tests indicated only mild normality deviations at both levels; however, visual diagnostics did not reveal any extreme departures or trends suggesting systematic issues with the model. Importantly, mixed models are robust to minor normality deviations, especially in large samples such as ours (n = 2,032 observations at Level 1 and 98 participants at Level 2). Generalized linear mixed models with restricted maximum likelihood were used to examine how daily cannabis use (0 = no use within a day; 1 = use within a day) was related to same-day MVPA, number of alcohol containing drinks consumed, and number of cigarettes smoked. Each model included a fixed effect for the between-person component of cannabis use (reflecting the proportion of observed study days on which each participant reported cannabis use [possible range: 1–28 days]). All models also included a random intercept for participant and random slope for cannabis use. Finally, unadjusted and adjusted models were reported. Adjusted models included race (Minoritized vs. White), biological sex at birth (male vs. female), and age in years as covariates given literature noting differences in cannabis use by race (Montgomery et al., 2022), biological sex (Greaves & Hemsing, 2020), and age (Livne et al., 2024).

3. Results

3.1. Descriptive Statistics

A total of 98 participants met inclusion criteria for the current secondary data analyses. Table 1 provides descriptive information on participant sociodemographic characteristics and variables of interest.

Table 1.

Demographic Characteristics (N = 98)

Mean (SD) Frequency (%)
Sex
 Female 69 (70.4)
 Male 29 (29.6)
Age (years) 46.6 (12.1)
Race
 White 66 (67.3)
 Black 20 (20.4)
 Asian 3 (3.1)
 American Indian/Alaska Native 3 (3.1)
 More than one race 6 (6.1)
Cannabis Use (days during study perioda) 12.2 (8.9)
Daily MVPAb (minutes/day) 27.89 (35.13)
Alcohol
 Alcohol Use (days during study perioda) 4.7 (6.2)
 Number of drinks consumed on drinking days 3.3 (2.4)
 Proportion having ≥1 drink during study perioda 62 (63.3)
Smoking
 Cigarette Use (days during study perioda) 8.1 (10.3)
 Number of cigarettes smoked on smoking days 10.6 (6.6)
 Proportion having ≥1 cigarette during study perioda 47 (48.0)
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Note.

a

Study period was 28 days;

b

MVPA: moderate-to-vigorous physical activity.

3.2. Same-day Associations Between Cannabis Use and MVPA

Table 2 provides results from unadjusted and adjusted models examining same-day associations between cannabis use and MVPA. In the adjusted model, daily cannabis use was positively associated with MVPA (p = 0.04), with participants reporting an additional 3.31 more minutes of MVPA/day on cannabis use vs. non-use days. Additionally, there was a positive between-person association between proportion of days of cannabis use and average MVPA/day (16.90 more minutes; p = 0.02). Of our covariates, being male was associated with higher MVPA/day (p < 0.01), but race and age were not (p = 0.64 and p = 0.90, respectively). Random effects results highlighted considerable individual variability in how daily cannabis use was associated with same-day MVPA (SD: 5.92).

Table 2.

Associations Between Cannabis Use and Same-day MVPAa

Unadjusted Model
 Adjusted Model
Coef p-value 95% CI Coef. p-value 95% CI
Fixed Effects
 Within-person component of daily cannabis use 3.18 0.045 (0.07, 6.28) 3.31 0.04 (0.18, 6.45)
 Between-person component of daily cannabis use 14.75 0.04 (0.69, 28.81) 16.90 0.02 (3.15, 30.64)
 White 2.40 0.64 (−7.72, 12.52)
 Male 15.85 0.00 (5.57, 26.12)
 Age −0.02 0.90 (−0.42, 0.37)
Intercept 17.24 <.001 (8.55, 25.92) 10.79 0.29 (−9.12, 30.7)
Random Effects
 SD Daily cannabis use 5.61 (3.26, 9.67) 5.92 (3.48, 10.05)
 SD Intercept 22.91 (19.3, 27.19) 21.86 (18.26, 26.16)
 Correlation intercept, daily cannabis use 1.00 (−1.00, 1.00) 1.00 (−1.00, 1.00)
 Residual SD 20.93 (20.28, 21.6) 20.93 (20.28, 21.6)
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Note.

a

MVPA: moderate-to-vigorous physical activity

3.3. Same-day Associations Between Cannabis Use and Alcohol Consumption

Table 3 provides results from unadjusted and adjusted models examining the same-day associations between cannabis use and alcohol consumption. The adjusted model shows that participants consumed an average of 0.45 more drinks containing alcohol on cannabis use vs. non-use days, a significant positive association (p = 0.01). For the between-person component, the proportion of days individuals used cannabis was not associated with the average number of alcohol containing drinks consumed (p = 0.09). Like PA, being male was associated with more alcohol consumption (p = 0.049), but race and age were not (p = 0.32 and p = 0.94, respectively). Random effects indicated significant individual variability for the association between daily cannabis use and same-day alcohol consumption (SD: 1.19).

Table 3.

Associations Between Cannabis Use and Same-day Alcohol Consumption

Unadjusted Model
 Adjusted Model
Coef. p-value 95% CI Coef. p-value 95% CI
Fixed Effects
 Within-person component of daily cannabis use 0.46 0.01 (0.11, 0.8) 0.45 0.01 (0.11, 0.80)
 Between-person component of daily cannabis use −0.81 0.07 (−1.67, 0.06) −0.74 0.09 (−1.60, 0.12)
 White −0.32 0.32 (−0.94, 0.31)
 Male 0.64 0.05 (0.00, 1.28)
 Age 0.00 0.94 (−0.02, 0.03)
 Intercept 1.18 <0.01 (0.68, 1.69) 1.13 0.07 (−0.09, 2.35)
Random Effects
 SD Daily cannabis use 1.18 (0.91, 1.54) 1.19 (0.92, 1.55)
 SD Intercept 1.31 (1.09, 1.58) 1.27 (1.05, 1.54)
 Correlation intercept, daily cannabis use 0.01 (−0.31, 0.32) 0.03 (−0.29, 0.34)
 Residual SD 1.41 (1.36, 1.45) 1.41 (1.36, 1.45)
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3.4. Same-day Associations Between Cannabis Use and Cigarettes Smoked

Table 4 provides results from unadjusted and adjusted models examining the same-day associations between cannabis use and cigarettes smoked. In the adjusted model, a significant positive association was observed for cannabis use and number of cigarettes smoked such that participants smoked 0.63 more cigarettes on cannabis use vs. non-use days (p = 0.01). However, the proportion of days of cannabis use was not significantly associated with the number of cigarettes smoked (p = 0.11), nor did race, sex, or age demonstrate significant associations (p = 0.91, 0.60, and 0.92, respectively). Random effects analyses revealed significant individual differences in the impact of daily cannabis use on cigarette consumption (SD: 1.45).

Table 4.

Associations Between Cannabis Use and Same-day Cigarette Use

Unadjusted Model
 Adjusted Model
Coef. p-value 95% CI Coef. p-value 95% CI
Fixed Effects
 Within-person component of daily cannabis use 0.63 0.01 (0.17, 1.09) 0.63 0.01 (0.17, 1.09)
 Between-person component of daily cannabis use −2.95 0.12 (−6.62, 0.73) −3.06 0.11 (−6.84, 0.71)
 White −0.17 0.91 (−3.11, 2.78)
 Male −0.80 0.60 (−3.82, 2.22)
 Age 0.01 0.92 (−0.11, 0.12)
 Intercept 6.10 <.001 (3.67, 8.54) 6.23 0.04 (0.37,12.09)
Random Effects
 SD Daily cannabis use 1.44 (0.93, 2.23) 1.45 (0.94, 2.24)
 SD Intercept 6.64 (5.74, 7.69) 6.74 (5.81, 7.81)
 Correlation intercept, daily cannabis use 0.26 (−0.13, 0.59) 0.25 (−0.14, 0.58)
 Residual SD 2.05 (1.98, 2.11) 2.05 (1.98, 2.11)
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4. Discussion

This study is among the first to use EMA data to examine associations between cannabis use and same-day MVPA, alcohol consumption, and cigarettes smoked. The observed positive between- and within-person associations between cannabis use and MVPA aligned with our hypothesis and prior cross-sectional observations that people who use cannabis (vs. non-users) tend to report more minutes of weekly PA (YorkWilliams et al., 2019) and have higher accelerometer-measured light PA and MVPA (Ong et al., 2021). However, observations conflict with Vidot et al.’s (2017) findings that showed that current and past cannabis users have lower overall and recreational PA as compared with never users. Further, while Gibson et al. (2023) did not observe any greater likelihood of daily exercise engagement among cannabis users vs. non-users or on cannabis use days vs. non-use days using an emailed daily diary-based assessment, it is notable that this study was among a notably younger sample than the current study (29.3 vs. 46.6 years, respectively), with results possibly also influenced by the assessment of exercise instead of overall PA. Mechanisms have been proposed regarding how cannabis use may influence PA participation, including how cannabis use may increase enjoyment and motivation to be physically active (Gillman et al., 2015; YorkWilliams et al., 2019), enhance recovery from PA (Gillman et al., 2015; Pinzone et al., 2023), and activate the endocannabinoid system and, subsequently, the dopaminergic system—increasing feelings of psychological reward associated with PA (Laksmidewi & Soejitno, 2021). As the current study’s sample was composed of healthy middle-aged adults, it may be that cannabis use increased PA enjoyment and/or subsequent feelings of psychological reward rather than supported recovery from PA—the latter more likely among those completing intense PA bouts (e.g., athletes). Future EMA studies on this topic should consider using objective PA data collection methodologies (e.g., research-grade accelerometry, wearable technology) over longer time periods to better discern the influence of cannabis use on PA. Future studies may also consider including an assessment of whether cannabis was used pre- or post-PA and for what reasons.

While no between-person associations were observed between cannabis use and same-day alcohol consumption and cigarettes smoked—ill-aligned with our hypothesis—the observed within-person positive associations between cannabis use and same-day alcohol consumption and cigarettes smoked are consistent with previous research that has shown a tendency for substance use behaviors to co-occur (Gunn et al., 2022; McCabe et al., 2021; Weinberger et al., 2020; Yurasek et al., 2017). Like the association between cannabis use and PA participation, the impact of cannabis use on the endocannabinoid system may reinforce the use of alcohol (Gunn et al., 2022) and tobacco (Lemyre et al., 2019; Rabin & George, 2015) through mechanisms related to psychological reward. Yet, there is nuance to these observations. For instance, doses of cannabis and alcohol, when used concurrently, can contribute to greater or lesser feelings of intoxication and, thus, differentially reinforce co-use behaviors (Gunn et al., 2022; Yurasek et al., 2017). For example, using SMS text-based daily surveys, Fairlie et al. (2023) observed that simultaneous cannabis and alcohol use among young adults was associated with a greater number of hours feeling high. Further, bidirectionality must be considered. Cannabis use could increase the likelihood of alcohol and/or cigarette smoking, but the inverse could also be true given changes in inhibition and/or the use of one substance to help address effects of another substance (e.g., withdrawal) (Weinberger et al., 2020; Yurasek et al., 2017). Using an automated phone survey system, Wilhelm et al. (2020) showed that use of combustible tobacco products (cigarettes, cigars, hookah/waterpipe tobacco, e-cigarettes) was higher following cannabis use among young adults. The authors hypothesized that participants may be using tobacco products to enhance the feelings of being high from cannabis and/or given reduced tobacco use inhibition from cannabis use. Interestingly, the authors also suggested that cannabis may increase tobacco cravings due to less explored mechanisms. Future mechanistic research should use EMA methods to gain insights into this bidirectional relationship and how cannabis use may lead to increases in alcohol consumption and cigarette use or vice versa.

This study had strengths in its 1) prospective design; 2) use of EMA methodology to collect data over a 28-day study period; and 3) a relatively large number of cannabis users older than is typically observed in cannabis use research (mean age: 46.6 years old). However, some limitations should be mentioned. First, cannabis use, MVPA, alcohol use, and cigarettes smoked were all collected via morning daily self-reports of the previous day’s behaviors. Thus, analyses could not determine causality. Future research should include time stamps for substance use and PA to determine causal relationships between cannabis use and health and substance use behaviors. Further, although EMA-based data collection has been shown to reduce recall bias and enhance ecological validity (Knell et al., 2017; Mun et al., 2021; Shiffman et al., 1997), objective measurement of substance use and PA would further reduce bias while also assisting in determining causal relationships. Second, the PA measure used in this study did not have a true ‘zero’ minutes of PA option and was categorical in nature. Future studies that do not collect objective PA measurements (e.g., research-grade accelerometry, wearable technology) should allow for a response option of zero minutes of daily moderate and vigorous PA—perhaps via a slider-type response format.

5. Conclusions

Observations from the current study indicated that cannabis use was related to increased same-day MVPA, alcohol consumption, and cigarettes smoked. The use of EMA to gather data on these behaviors has two implications. First, the enhanced ecological validity and reduced recall bias that smartphone-based EMA affords allowed for more precise estimates of the same-day associations between cannabis use and these health and substance use behaviors across a nationwide sample. Health professionals could use these data within multiple behavior change interventions that use just-in-time adaptive intervention strategies to deliver health behavior promotion- and substance use cessation-oriented content to individuals reporting higher cannabis use on a given day. Second, as governments legalize cannabis for medicinal and recreational purposes, this study offers a good starting point for further mechanistic investigations of how cannabis use may influence other health and substance use behaviors. Better understanding these mechanisms is important when refining multiple behavior change intervention strategies for greatest scalability and maximum public health impact.

Highlights.

  • Cannabis use is increasing due to legalization for medicinal/recreational purposes.

  • We need to better understand how cannabis use relates to other behaviors.

  • Relations between daily cannabis use and health and substance use behaviors were examined.

  • Cannabis use is related to increased physical activity, alcohol, and cigarette use.

Acknowledgements:

We would like to acknowledge all those that participated in this study.

Funding:

Programming and technological support was provided through the mobile health shared resource of the Stephenson Cancer Center via an NCI Cancer Center Support Grant (P30CA225520). Data analysis and manuscript preparation were additionally supported through R00DA046564, and through the Oklahoma Tobacco Settlement Endowment Trust grant R22–02.

Footnotes

Declarations of Interest: Dr. Michael S. Businelle is the primary inventor of the Insight™ mHealth Platform, which was used to develop the Exemplar app. Businelle receives royalties related to the Insight™ Platform, but he did not receive royalties in this case because he was the study PI.

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