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Randomized clinical trial of an adapted marijuana e-CHECKUP TO GO compared to a healthy stress management condition
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Program effects on reductions in heavy use were transmitted by decreased marijuana use while studying.
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Marijuana e-CHECKUP TO GO may be most effective at reducing student marijuana use while studying.
Abstract
The objective of this study was to test indirect effects of the Marijuana e-CHECKUP TO GO program on college students' frequent marijuana use through decreased use in specific social and academic activities. This study randomly assigned college students who reported frequent marijuana use (i.e., approximately five times per week) in fall 2016 to receive Marijuana e-CHECKUP TO GO or healthy stress management (HSM) strategies. The final baseline sample included 298 participants. Path analyses tested direct program effects on marijuana use at six-week posttest, as well as the indirect effect via use within four activities frequently participated in by college students: socializing, being physically active, studying, and being in class. Direct Marijuana e-CHECKUP TO GO effects on reductions in frequent use were transmitted by decreased marijuana use while studying and no use while socializing, being physically active, or in class. Marijuana e-CHECKUP TO GO may be most effective at reducing use of marijuana among college students while studying.
Sluggish cognitive tempo is associated with academic functioning and internalizing symptoms in college students with and without attention deficit/hyperactivity disorder.
). Regular marijuana use is also related to decreased cognitive function and increased emotional problems (e.g., depression, anxiety), both of which may affect student achievement and retention (
Sluggish cognitive tempo is associated with academic functioning and internalizing symptoms in college students with and without attention deficit/hyperactivity disorder.
). For example, heavy use is associated with decreased higher-order cognitive processing (e.g., executive function), and decrements in learning and memory, self-conscious awareness, and IQ (
The academic, cognitive, and psychosocial consequences associated with misuse of marijuana by college students serve as rationale for developing effective marijuana misuse interventions for college students. However, to date, few evidence-based interventions for marijuana use reduction among college students exist. Research has shown personalized normative feedback (PNF) interventions, such as Alcohol e-CHECKUP TO GO,
). One study showed that Alcohol e-CHECKUP TO GO reduced normative perceptions of peer drinking, positive alcohol expectancies, and alcohol use. Based on the efficacy of Alcohol e-CHECKUP TO GO, researchers have developed similar interventions for marijuana misuse (
Changing social norms: The impact of normative feedback included in motivational enhancement therapy on cannabis outcomes among heavy-using adolescents.
; Brief Alcohol Screening & Intervention for College Students) and the recently developed CASICS (Cannabis Screening & Intervention for College Students) include 2 in-person sessions that last 60–90 min with a trained facilitator. Session content includes assessment of student alcohol or marijuana use patterns, history, and use-related consequences, personalized feedback, and strategies to reduce substance related risks. While BASICS is well established in the literature (
is a commercially available, online intervention providing personalized PNF designed to motivate college students to reduce marijuana use by correcting misperceived descriptive norms (i.e., misperceptions of the prevalence of use) and providing marijuana use education (
). Although widely implemented, few studies have tested the efficacy of Marijuana e-CHECKUP TO GO. One exception—a study of 245 college student abstainers—found that participants reported more precise perceptions of descriptive (i.e., use prevalence) and injunctive (i.e., attitudes about use) norms at one-month posttest than did an assessment only control group (
). A second study demonstrated Marijuana e-CHECKUP TO GO intervention effects on decreasing “extreme” descriptive norms of college students who report marijuana use, but not on student use or consequences of use for students who reported relatively heavy (i.e., 2+ times per week) use (
). Our research group worked with the developers of Marijuana e-CHECKUP TO GO to adapt the program to include content focusing on increasing knowledge and use of protective behavioral strategies (PBS), strategies individuals can use to prevent or reduce substance use that have been found to mediate college student alcohol misuse outcomes in three randomized controlled trials (
). Some of the PBS added to the intervention include avoiding marijuana use before school or work, limiting use to weekends, avoid mixing with other drugs, and avoiding use to cope with emotions (
) of this adapted version of the Marijuana e-CHECKUP TO GO with students who reported frequent (i.e., 5 times per week average reported use frequency) cannabis use with the expectation that students who report frequent use would have a) the greatest misperceptions of social norms, b) the greatest need for PBS education, and c) the greatest need for intervention given their elevated risk for negative consequences of use. Thus, the study anticipated that those who report frequent use would be most responsive to the PNF intervention. This study demonstrated that among Marijuana e-CHECKUP TO GO participants, females reported greater use of PBS; more precise descriptive norms; and being high fewer hours per week, days per week, and weeks per month than participants in a comparison condition receiving strategies for healthy stress management (HSM), but that there were no direct program effects on marijuana use consequences (
). Effect sizes ranged from small to medium and were entirely due to reductions in use among e-CHECKUP TO GO participants rather than between-group differences in use escalation.
The study did not test indirect intervention effects on marijuana use through hypothesized mechanisms of change w in the first round of analyses. These analyses go beyond testing whether an intervention was efficacious to testing how the intervention contributed to decreased marijuana use. One important potential mechanism of change for this study was marijuana use by college students in specific social contexts (i.e., socializing, physical activity, studying, and in the classroom). A better understanding of the specific social contexts within which the Marijuana e-CHECKUP TO GO program reduces marijuana use will inform continuous program improvements, including adaptations that support reduced use in social contexts that may not be affected by the intervention in its present form. As such, this study is the first to test indirect program effects on marijuana use during specific activities to determine where college students reduced their use as a result of the Marijuana e-CHECKUP TO GO program and whether reductions in use during these activities are contexts within which direct program effects result in reduction in frequent marijuana use.
The purpose of this study was to test the indirect effects of the Marijuana e-CHECKUP TO GO program on frequent marijuana use through reductions in use during four activities commonly in which college students commonly participate: being social/partying, being physically active, studying, and in class. Significant indirect effects would demonstrate specific pathways, or activities, through which Marijuana e-CHECKUP TO GO had its effects. This study hypothesized that there would be significant indirect effects of the intervention on cannabis use through reductions in marijuana use during time spent in each of these four specific activities.
2. Method
2.1 Participants & procedures
Undergraduate college students were recruited in the Fall of 2016 via emails to on-campus residents and fraternity/sorority life, on-campus fliers, Facebook advertisements, and word-of-mouth. Students expressing interest in the study were e-mailed a screener to determine eligibility (see Fig. 1, Consort Flow Diagram). Eligibility criteria were that participants were 18 years of age or older, an undergraduate university student, self-reporting recreational marijuana use (i.e., non-medicinal) of at least twice per week. Of the 918 completed screeners, 527 (57%) met eligibility requirements. Participants were invited to participate on a rolling basis until the target number of 300 participants was achieved. One additional student was added to the study sample in between the time the target number of participants was achieved and when study staff were able to discontinue enrollment. Thus, the sample at baseline was 301 participants. Participants received $20 for completing the baseline survey and $10 for completing the 6-week posttest survey. The research described was conducted in accordance with the Institutional Review Board at the Colorado State University.
Participants were randomly assigned to either the Marijuana e-CHECKUP TO GO (n = 146) or HSM (n = 155) comparison condition. Prior to intervention, all participants completed a 203-item survey asking about participants' personal substance use, perceived marijuana use norms, and PBS use. Baseline survey responses from 3 participants indicated that they did not meet study eligibility (n = 1 no reported use, n = 2 non-students), despite indicating as such on the screener. These participants were removed from further analyses. Therefore, the final baseline sample included 298 participants (PNF = 144, 48%; HSM = 154, 52%). The sample was 51% male and had a mean age of 19.97 years (SD = 2.0). No significant differences existed between the two study conditions on sex, racial/ethnic background, or age (
Following survey completion Marijuana e-CHECKUP TO GO participants received PNF regarding personal marijuana use, perceptions of marijuana use norms versus actual use prevalence at their university and nationally, and suggested PBS (
). Comparison condition participants were provided with strategies for HSM (e.g., deep breathing, mindfulness, exercise). Participants were then sent up to three e-mails at four-day intervals inviting them to complete the same survey at 6-week posttest. Two hundred and twenty-seven (75%) participants (PNF = 109, 48%; HSM = 118, 52%) completed this survey. Retained participants reported significantly fewer hours high per week (t = −3.71, p < .001), hours high per use day (t = −3.60, p < .001), and days high per week (t = −2.46, p < .05) than those who did not complete six-week posttest surveys. Retained participants were also significantly less likely to be male than female (OR 0.52, 95% C.I. 0.30–0.89, p < .05). However, there were no statistically significant differences in the number of retained vs. non-retained participants across condition (OR 1.05, 95% CI 0.62–1.79, p > .05). There were also no statistically significant differences by condition in the attrition of students who identify as male or who report frequent use.
2.2 Measures
The independent variable of primary interest was intervention condition (PNF = 1, HSM = 0). The dependent variable was Periods High per Week. To calculate Periods High per Week, we asked participants to indicate if they are typically high during 6-h time blocks of each day of the week. The total number of endorsed time blocks during a typical week were summed to evaluate Periods High per Week. This method of assessing marijuana use has been validated in previous research (
The mediator variables were the proportion of time high while partying/socializing, exercising/playing sports, studying, and in class were measured by asking participants “During a typical school week, how many hours do you estimate you spend in total: partying/socializing, exercising/playing sports, studying, or in class?” with open text fields for each activity. They were then asked to report the number of hours they engaged in each activity during a typical week while under the influence of marijuana. Proportions were calculated by dividing the number of hours participating in each of these activities while under the influence of marijuana by the total amount of time participating in the four activities. The proportion of time high while participating in each activity was computed, rather than the total amount of time high while engaged in each activity, due to potential fluctuations in the number of total hours engaged in the activities over the study duration. At baseline, the proportion of time participants reported being high while social, physically active, studying, and in class was 0.65, 0.21, 0.17, and 0.11, respectively. Hours participating in activities high raw proportions are best modeled using beta regression models (cf.
). Table 1 provides univariate descriptive statistics for each variable at baseline, by intervention condition. There were no significant differences between the two conditions.
Table 1Variable univariate statistics at baseline by treatment condition.
Condition
Marijuana e-CHECKUP TO GO
HSM
(n = 144) (n = 109)
(n = 154) (n = 118)
Baseline Mean (SD)
Follow-up Mean (SD)
Baseline Mean (SD)
Follow-up Mean (SD)
Number of time periods high per week
9.24 (6.89)
7.43 (6.67)
8.90 (6.60)
8.41 (6.11)
High while social/partying
0.62 (0.31)
0.64 (0.32)
0.66 (0.32)
0.63 (0.31)
High while exercising/playing sports
0.18 (0.32)
0.19 (0.35)
0.23 (0.33)
0.19 (0.32)
High while studying
0.19 (0.28)
0.13 (0.24)
0.16 (0.23)
0.14 (0.22)
High while in class
0.10 (0.22)
0.09 (0.21)
0.11 (0.24)
0.10 (0.21)
Note: HSM = healthy stress management. For all High While… variables values represent the proportion of time high while engaging in each activity.
) to handle attrition to assess mechanisms of change. Per-protocol analysis includes data from those who complete all aspects of the study. First, we tested the direct Marijuana e-CHECKUP TO GO program effects on use during the four activities potentially mediating direct intervention effects on marijuana use at the six-week follow-up using separate beta regressions (
). Beta regressions are appropriate when the dependent variable ranges from 0 to 1 and is the best practice for analyzing proportions as outcomes. In beta regression models the extremes (i.e., 0 and 1) should be transformed using the following (y ∗ (n − 1) + 0.5)/n where y is each score on the dependent variable and n is the sample size (
). Results were used to build hypothesis testing models by trimming activities not affected by the intervention to create the final indirect effects model.
Next, a residualized gains path analysis was conducted to test for indirect effects of treatment on marijuana use via activities that were identified in the direct effects tests using the MPlus 8 statistical package (
). The marijuana use variable, a highly skewed count variable, was analyzed using negative binomial regression for paths leading to marijuana use. The residualized gains approach controls for baseline values of each outcome in predictions of follow-up values (cf.
). In this case baseline levels of proportion of time high while studying predicted proportion of time high while studying at follow-up, and baseline marijuana use predicted follow-up marijuana use. Sex was also controlled for on both proportion of time high while studying and marijuana use follow-up variables. Biological sex (male = 0, female = 1) was included as a covariate due to research demonstrating that males are approximately twice as likely to report heavy marijuana use (
Monitoring the future national survey results on drug use. 1975–2016: Volume II, college students and adults ages 19–55. Ann Arbor: Institute for Social Research.
). Due to the negative binomial specification of the outcome variable (i.e., marijuana use at follow-up), typical model fit statistics are not available because count regression models are estimated using maximum likelihood with robust standard errors (MLR). MLR relies on raw data rather than means, variances, and covariances which eliminates the ability to calculate typical model fit indices. Further, the best practices approach for testing indirect effects is to use the product of coefficients method (
). The product of coefficients method violates the normality assumption making p-values not trustworthy. Instead, the best approach for determining significance of indirect effects with count distributed outcomes is to evaluate Monte Carlo Confidence Intervals (MCCIs,
). MCCIs that do not include zero are considered to be statistically significant. Further, regression coefficients from negative binomial regressions can be exponentiated to calculate Rate Ratios to ease interpretation (
The direct effects beta regression model results are presented in Table 2. Direct Marijuana e-CHECKUP TO GO program effects on proportion of time high while participating in each of the four activities demonstrated direct program effects on proportion of time high while studying (b = −0.31, SE = 0.14, p = .02, OR = 0.73), but not with proportion of time high while socializing/partying, being physically active, or in class. This can be interpreted as those in the Marijuana e-CHECKUP TO GO program reported 27% lower proportion of time high while studying compared to those in the healthy stress management condition at follow-up. Based on the results of the direct effects tests, only proportion of time high while studying was included in the path model examining indirect effects.
Table 2Beta regression results examining the direct effects of treatment condition predicting proportion of time engaging in activities while high.
Predictor variable
Estimate
Standard error
P-Value
Proportion time high while studying at follow-up
Treatment condition
−0.31
0.14
0.02
Proportion of time high while studying at baseline
4.30
0.33
<0.01
Proportion time high while in class at follow-up
Treatment condition
−0.08
0.14
0.55
Proportion of time high while in class at baseline
4.34
0.40
<0.01
Proportion time high while exercising/playing sports at follow-up
Treatment condition
0.07
0.17
0.65
Proportion of time high while exercising/playing sports at baseline
2.92
0.33
<0.01
Proportion time high while partying/socializing at follow-up
Treatment condition
0.09
0.17
0.57
Proportion of time high while partying/socializing at baseline
1.63
0.28
<0.01
Note: Treatment condition coded 0 = Healthy Stress Management Condition; 1 = marijuana e-CHECKUP TO GO condition.
for individual outcomes, Marijuana e-CHECKUP TO GO participants significantly decreased weekly use as indicated by a significantly lower self-reported 6-week marijuana use when compared to participants in the HSM comparison condition. Among the covariates, sex did not predict either follow-up marijuana use or follow-up proportion time high while studying; however, baseline marijuana use and baseline proportion of hours high while studying were significantly associated with the respective 6-week follow-up variables. Moreover, a significant indirect effect (b = −0.03, SE = 0.02, MCCI = −0.064, −0.004) demonstrated that the Marijuana e-CHECKUP TO GO program effect on marijuana use was partially explained by decreased time high while studying.
Table 3Indirect effect analysis results: treatment condition → proportion of time high while studying → marijuana use.
Direct effects
RR
Estimate
SE
P-Value
Marijuana use at follow-up (negative binomial)
Treatment condition
0.82
−0.19
0.07
0.01
Periods of time high while studying at follow-up
1.82
0.60
0.24
0.01
Sex
0.92
−0.08
0.07
0.30
Marijuana use at baseline
1.08
0.08
0.01
0.00
Periods of time high while studying at follow-up
Treatment condition
−0.05
0.02
0.01
Periods of time high while studying at baseline
0.80
0.06
0.00
Sex
0.00
0.02
0.94
Indirect Effect
Estimate
S.E.
MCCI
Treatment condition → periods of time high while studying at follow-up → marijuana use at follow-up
−0.03
0.02
−0.064, −0.004
Note: RR = rate ratio; SE = standard error; MCCI = Monte Carlo Confidence Intervals.
Study results add to the limited research on web-based PNF approaches to marijuana use reduction among college students. Our previous research established a small direct effect of the Marijuana e-CHECKUP TO GO program on reducing cannabis use. What we did not know at the time was the mechanism of change for that direct effect. Results add to prior research by demonstrating that decreases in marijuana use are partly due to decreases in the proportion of time that college students are high while studying. Specifically, college students participating in Marijuana e-CHECKUP TO GO who reported frequent marijuana use reported 27% lower proportion of time high while studying compared to those in the healthy stress management condition at follow-up. Further, the indirect effects model showed that time spent high while studying mediated the relation between the treatment condition and marijuana use. In other words, the treatment condition decreased time spent studying while high, which decreased marijuana use. Future research should explore whether reductions in the amount of time studying high translates into increased achievement and retention.
The Marijuana e-CHECKUP TO GO program did not decrease the proportion of time high while socializing, in class, or being physically active. One potential explanation for null findings in the socializing/partying context is that college students may choose to first decrease use within solitary contexts and/or those considered to be most normatively irresponsible (e.g., while studying) versus social contexts considered to be relatively normatively responsible (e.g., socializing/partying). College students who report frequent marijuana use may also initially reduce use within contexts where they feel most in control of their use (i.e., studying, which in many cases is a solitary activity) and are less likely to be socially sanctioned for not using (i.e., being social or partying with others who use heavily).
Null findings for being high while in class are notable given academic similarities between studying and being in class. One potential explanation here is a floor effect in the proportion of time spent high while in class. Specifically, participants reported being high only 11% of the time while in class. This relatively low amount of time using in this context may have rendered it difficult to detect significant reductions in use. Future studies with larger samples and greater power to detect group differences will help to determine whether the Marijuana e-CHECKUP TO GO program can significantly reduce use while in class.
Another notable finding was the lack of reduction in the time spent high while engaging in physical exercise. This may have resulted because the PNF intervention did not directly address physical exercise. Prior research has shown that the explicit recommendation of physical activity in brief cannabis intervention results in greater reductions in cannabis use (
A preliminary test of a brief intervention to lessen young-adults’ cannabis use: Episode-level smartphone data highlights the role of protective behavioral strategies and exercise.
Experimental and Clinical Psychopharmacology.2020; 28: 150-156
). Future adjustments to the intervention should address exercise or physical activity explicitly.
This study shows the importance of measuring use during specific activities in which college students frequently participate for a better understanding of the collegiate contexts where use is decreasing, as well as for systematic intervention adaptation and improvement. For example, future efforts to decrease marijuana use in social situations, where use is most prevalent, could adapt the intervention to increasingly emphasize PBS, or alter messaging around PBS that can be used in social contexts including partying. Such program adaptations have the potential to enhance already promising intervention effects.
4.1 Limitations
Study results should be considered in light of study limitations. There was 24% attrition of participants at six-week post-test. Further, participants not completing post-test surveys reported using marijuana at significantly higher rates and were more likely to report being male. Therefore, whether PNF was an effective intervention approach for those using at the highest frequency is unclear. Data were self-reported, a strategy limited by several threats to the internal validity including recall, which may be of particular concern due the effects of frequent marijuana on certain aspects of memory (
). Related, the program developers developed the mediating variables, and they lack established psychometric properties. The relatively brief six-week interval between baseline and follow-up surveys was also a limitation, a longer follow-up window may have shown that the intervention was more effective. Finally, daily diary or ecological momentary assessment strategies would be beneficial to provide a more nuanced view of marijuana use and time spent high while engaging in a variety of activities.
4.2 Conclusions
This study builds on research that initially supported marijuana e-CHECKUP TO GO as a low-cost, easily diffused, web-based PNF approach to reduce marijuana use among college students. Importantly, the research reported herein demonstrates one pathway through which intervention effects were transmitted. Findings support that college students who report frequent use reduced their use while studying from 19% to 13% of the time, which mediated the relation between the treatment condition and self-reported marijuana use. Given that studying is likely to be more effective when sober compared to when high following marijuana use, the intervention may also be able to address concomitant academic difficulties associated with frequent marijuana use while in college. Study results describe a mechanism of change operating as a result of this PNF intervention. Frequency of marijuana use was the recruitment criteria for the current study, not a desire to reduce marijuana use. In other words, direct and indirect study effects occurred for individuals who were not specifically looking to alter their marijuana use. This may explain the effect size while also identifying a mechanism of change, among those who are not seeking to change their behavior.
CRediT authorship contribution statement
Each author contributed to the manuscript across the following domains. Mark A. Prince: conceptualization; methodology; data analysis; editing; draft preparation. Alexander J. Tyskiewicz: reviewing and editing. Bradley T. Conner: conceptualization; methodology; editing; draft preparation. Jamie E. Parnes: data curation; analyses; draft preparation. Audrey M. Shillington: reviewing and editing. Melissa W. George: reviewing and editing Nathaniel R. Riggs: conceptualization; methodology; data analysis; editing; draft preparation.
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Sluggish cognitive tempo is associated with academic functioning and internalizing symptoms in college students with and without attention deficit/hyperactivity disorder.
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A preliminary test of a brief intervention to lessen young-adults’ cannabis use: Episode-level smartphone data highlights the role of protective behavioral strategies and exercise.
Experimental and Clinical Psychopharmacology.2020; 28: 150-156
Monitoring the future national survey results on drug use. 1975–2016: Volume II, college students and adults ages 19–55. Ann Arbor: Institute for Social Research.
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