Impact of Reduced Cannabis Use on Functional Outcomes (R33 Phase)
Phase: NA
Conditions: Cannabis, Cannabis Use
Sponsor: Duke University

Impact of Reduced Cannabis Use on Functional Outcomes 
NCT04567238 
Protocol
 Document Date: 8/16/2022 

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SPECIFIC AIMS 
 
More than 8 million Americans reported heavy cannabis use in 2013, representing a 59% increase since 
2005 (Substance Abuse and Mental Health Services Administration, 2014). Heavy cannabis use (i.e., use 
on ≥ 40 of last 90 days) is associated with addiction, unemployment, neuropsychological deficits, lower 
educational attainment, and reduced physical activity (Substance Abuse and Mental Health Services 
Administration, 2014; Volkow et at., 2014; Hasin et al., 2015; Fergusson, Horwood & Beautrais, 2003; 
Compton et al., 2014; Jin et al., 2017; Pate et al., 1996; Hall & Degenhardt, 2009; McCaffrey et al., 
2010; Zwerling, Ryan & Orav, 1990). Studies by our laboratory and others indicate that heavy cannabis 
use is associated with increased mental health problems as well as suicidal and nonsuicidal self-injury 
(Volkow et al., 2014; Hall & Degenhardt, 2009; Borges, Bagge, & Orozco, 2016); Kimbrel, Newins et al., 
2017; Gentes et al., 2015; Kimbrel, Meyer et al., in press; Adkisson et al., manuscript submitted). 
Cannabis is also the illicit drug most strongly associated with drugged driving and traffic accidents, 
including fatal accidents (Volkow et al., 2014; Hall & Degenhardt, 2009; Kelly, Darke & Ross, 2004; 
National Institute on Drug Abuse, 2016). While there is evidence that sustained abstinence can lead to 
improvements in the functioning of former users (Bonnet et al., 2015; Schreider & Dunn, 2012; Bruins 
et al., 2016; Davis et al., 2014; Kimbrel, Calhoun et al., manuscript in preparation), the degree to which 
reductions in frequency and quantity of cannabis use alone (i.e., in the absence of sustained 
abstinence) are associated with positive changes in functional outcomes is largely unknown at the 
present time (Tiffany et al., 2011: Donovan et al., 2011). This is a critical gap in the literature, as many 
interventions for cannabis use disorder (CUD) are associated with decreases in frequency and quantity 
of use, but fail to reduce overall rates of sustained abstinence (Tiffany et al., 2011: Donovan et al., 
2011; Copeland & Pokorski, 2016; Sherman & McRae-Clark, 2016). 
 
Across multiple epidemiologic samples, Black persons (versus White persons) are more likely to: 1) use 
cannabis as their first drug, which is associated with a higher odds of CUD (Fairman, Furr-Holden, & 
Johnson, 2019); 2) report higher weekly and monthly cannabis use (Wu, Zhu, & Swartz, 2016); 3) meet 
criteria for CUD (Hasin, Shmulewitz, & Sarvet, 2019); and 4) are more likely to test positive for cannabis 
use after traffic deaths (Azagba, Shan, & Latham, 2020). Although racial inequities in cannabis use and 
CUD have been well characterized, the determinants of these disparities are not well understood.  
 
There is a robust association between the experience of racial discrimination and psychological 
distress. Black Americans who report chronic exposure to racial discrimination are more likely to report 
experiencing stressful life events, are more likely to view stressful life events as threats rather than 
challenges and are more likely to engage in maladaptive coping. In contrast to chronic stressors, time-
limited daily stressors may also impact psychological health. Current evidence indicate that repeated 
everyday racial hassles are also associated with psychological distress and physical health (Ong, Ruller-
Rowell, & Burrow, 2009). Thus, both chronic exposure to racial discrimination and repeated daily 
discrimination has been shown to impact psychological health.  
 
Currently, limited data exist examining the relationship between perceived discrimination and cannabis 
use outcomes although several recent studies suggest perceived stress and discrimination may be 
important determinants of cannabis use trajectories (Walsh, Kolobov, & Harel-Fisch, 2018). These 

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studies are limited, however, by a reliance on retrospective reports of perceived exposure to 
discrimination (Potter, Brondolo, & Smyth, 2019). Ecological momentary assessment (EMA) studies are 
needed to examine the association between perceived discrimination and cannabis use. We propose to 
supplement our project and state-of-the-art EMA protocol with multiple measures of perceived 
discrimination including EMA assessment of daily discrimination.  
 
The overall objective of the present research is to use ecological momentary assessment (EMA), a real-
time, naturalistic data collection method, to prospectively study the impact of reduced cannabis use on 
functional outcomes in heavy cannabis users. EMA addresses several limitations of traditional 
assessment techniques by enhancing ecological validity, minimizing memory bias, and enabling 
examination of the impact of context on participants’ behavior. Our central hypothesis is that 
reductions in frequency and quantity of cannabis use will lead to positive changes in cannabis users’ 
functional outcomes. The rationale for this research is that it will provide real-time data concerning the 
impact of reductions in cannabis use on functioning and will support efforts to include reductions in 
illicit drug use as a valid outcome measure in pharmacotherapy studies (Tiffany et al., 2011: Donovan 
et al., 2011).  
 
Contingency management (CM) will be used to promote reductions in frequency and quantity of 
cannabis use. CM is an intensive behavioral therapy that is highly effective at producing short-term 
reductions in illicit drug use. Moreover, we have developed a novel approach that leverages mobile 
technology and recent developments in cannabis testing to make daily CM for cannabis more portable 
and feasible. We have pilot-tested this approach with heavy cannabis users and found that it is an 
acceptable and feasible method to reduce cannabis use. The present research will use this technology 
in conjunction with state-of-the-art EMA methods to study the impact of reduced cannabis use on key 
functional outcomes.  
 
We will pursue the following specific aims during the R21 phase:   
 
R21 Aim 1: Refine the study procedures and the CM reinforcement schedule to ensure that a 
majority of participants will be adherent, find the study procedures acceptable, and 
substantially reduce their frequency and quantity of cannabis use. 
 
Milestone 1: The majority (i.e., ≥ 65%) of participants will be adherent and rate procedures as 
acceptable. 
 
Milestone 2: We will identify a CM reinforcement schedule that reliably produces ≥ 50% 
reductions in frequency and quantity of cannabis use among at least half of the heavy cannabis 
users enrolled. 
 
R21 Aim 2: Examine the association between days of cannabis use and total amount of cannabis 
used. 
 
Milestone 3: We will calculate the association between frequency of cannabis use (days) and total 

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quantity of cannabis consumed per week based on participants’ EMA data concerning quantity of 
cannabis use. 
 
If the proposed R21 milestones are met, we will complete the following specific aims during the R33 
phase:  
 
R33 Aim 1: Assess the impact of reduced frequency and quantity of cannabis use on functioning.  
 
Hypothesis 1a: Decreased cannabis use will be associated with significant improvements in 
mental health symptoms, self-efficacy, physical activity, and health-related quality of life. 
 
Hypothesis 1b: Decreased cannabis use will be associated with significant improvements in 
working memory, impulsivity, and drugged driving. 
 
R33 Supplementary Aim 1: Explore momentary (e.g., craving, sleep) and between-person factors 
(e.g., sex, baseline use) that might moderate the association between cannabis use and 
functioning. 
 
R33 Supplementary Aim 2: Evaluate whether exposure to chronic discrimination is related to a) 
reported cannabis-related problems, b) ad lib cannabis use in grams, and c) magnitude of cannabis 
reduction during a four-week intervention period.  
 
Hypothesis: Higher racial discrimination at baseline will be associated with increased cannabis 
related problems, increased use in grams, and smaller reductions in cannabis use in grams 
during the intervention period.  
 
R33 Supplementary Aim 2: Use ecological momentary assessment (EMA) technology to measure 
the daily association between perceived discrimination and cannabis use.  
 
Hypothesis: Higher daily discrimination will be associated with increased quantity of daily 
cannabis use in grams and lower cannabis reduction in grams across a four-week intervention.  
 
Expected Outcomes: The proposed research will be the first study to systematically assess the impact 
of reduced frequency and quantity of cannabis use on functioning. As such, these findings will directly 
inform ongoing efforts to include reductions in illicit drug use as a valid, clinically-meaningful outcome 
measure in clinical trials of pharmacotherapies for the treatment of CUD and other substance use 
disorders (Tiffany et al., 2011: Donovan et al., 2011). 
 
 
Cannabis is the most widely used illicit drug in the U.S. (Substance Abuse and Mental Health Services 
Administration, 2014). While there are ongoing efforts to legalize cannabis for medicinal and 
recreational use throughout the U.S., many questions and concerns regarding the safety of this 
A. SIGNIFICANCE 

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controversial drug exist, as prior research demonstrates that heavy cannabis use is associated with a 
host of negative outcomes (Volkow et al., 2014; Hasin et al., 2015; Fergusson, Horwood & Beautrais, 
2003; Compton et al., 2014; Jin et al., 2017; Pate et al., 1996; Hall & Degenhardt, 2009; McCaffrey et 
al., 2010; Zwerling, Ryan & Orav, 1990; Borges, Bagge & Orozco, 2016; Kimbrel, Newins et al., 2017; 
Gentes et al., 2015; Kimbrel, Meyer et al., in press; Kelly, Darke & Ross, 2004; National Institute on 
Drug Abuse, 2016). While there is evidence that sustained abstinence may reduce some of the negative 
consequences associated with heavy cannabis use [e.g., neuropsychological deficits (Bonnet et al., 
2015), psychiatric symptoms (Schreiner & Dunn, 2012; Bruins et al., 2016)], the degree to which 
reductions in cannabis use alone (i.e., reductions in frequency or quantity of use without sustained 
abstinence) might be associated with positive changes in functional outcomes, such as mental health 
and health-related quality of life, is unclear at the present time (Tiffany et al., 2011; Donovan et al., 
2011; Copeland & Pokorski, 2016; Sherman & McRae-Clark, 2016). Our central hypothesis is that 
reductions in frequency and quantity of cannabis use will lead to positive changes in heavy cannabis 
users’ functional outcomes. The rationale for the proposed research is that it will provide the first and 
only real-time, naturalistic data concerning the potential impact of reductions in cannabis use on a 
wide range of key functional outcomes. The objective of the current proposal is to prospectively study 
the impact of reduced cannabis use on functioning. Our central hypothesis is that reductions in 
cannabis use will lead to positive changes in heavy cannabis users’ functioning. The rationale for this 
research is that it will provide the first and only real-time data on this important, but understudied 
topic. Thus, our findings will directly inform ongoing efforts to include reductions in illicit drug use as a 
valid, clinically-meaningful outcome measure for pharmacotherapy trials for the treatment of CUD and 
other SUDs (Tiffany et al., 2011: Donovan et al., 2011). 
 
B. PRELIMINARY STUDIES 
 
B.1. Development of Mobile CM for Smoking 
Contingency management (CM) will be used to promote reductions in cannabis use in the present 
study so that the impact of these reductions on functioning can be systematically studied. CM is a 
behavioral therapy that provides positive reinforcers (e.g., vouchers, money) to individuals misusing 
substances contingent upon bioverification of abstinence from drug use. CM is a highly effective 
method for producing short-term abstinence among individuals who misuse substances, including 
cannabis (Schuster et al., 2016; Budney, Stanger et al., 2015; Kadden et al., 2007; Carroll et al., 2006). 
Further, CM has demonstrated effect sizes in excess of other behavioral treatments in several 
substance use trials (Dutra et al., 2008). We have previously developed and completed testing of a 
smartphone app that makes CM for smoking cessation portable (Carpenter et al., 2015; Hertzberg et 
al., 2013). Using the app, individuals generate video recordings of themselves blowing into a small CO 
monitoring device. The app uploads the video to a secure website using encrypted network 
connections so that study staff can view the video via a web-interface and confirm abstinence based 
on the participant’s CO reading. Upon confirmation, the program automatically calculates the 
compensation earned based on the current CO reading (as well as their previous readings). Abstinence 
rates across the studies were high (82-87%), and were substantially higher in the active mobile CM 
condition [82% vs. 45% in the yoked condition; (Carpenter et al., 2015; Hertzberg et al., 2013)]. These 
studies demonstrate our experience in the development of complex mobile CM protocols as well as 

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the fact that participants are highly adherent to mobile CM protocols, uploading 96% of videos across 
studies.  
 
B.2. Adaptation of Mobile CM App to Reduce Cannabis Use  
Our programmer, Jeff Hertzberg, has already adapted our existing mobile CM application for cannabis. 
Though most of the procedures used in our previous mobile CM studies were easily applied to 
cannabis use, we have made several important changes, including adding a videotaped saliva 
assessment component that records participants self-administering the saliva test kit to themselves. In 
addition, because reinforcers are most effective when delivered immediately after a target behavior is 
performed (Lattal, 2010), the app now also provides daily feedback on amount of money earned to 
date as well as how the amount was calculated.  
 
B.3. Bioverification of Cannabis Use via Saliva Test Kits  
For many years, the standard for detection of cannabis use has been urinalysis examining excretion of 
the cannabis metabolite 11-nor-∆9-tetrahydrocannabinolic acid (THC-COOH) via immunoassay 
completed in a clinic setting (Budney, Stanger, et al., 2015). There are several drawbacks to this 
approach for CM. While multiple factors affect detection times for cannabis use via urine screening 
(e.g., frequency of use, dosage, individual metabolism), THC-COOH levels are typically elevated in 
regular cannabis users (e.g., background levels ≥1,000 ng/ml). As a result, a washout period (1-2 weeks 
or longer) is needed between cessation of use and submission of negative urine samples to verify daily 
abstinence. As a result, implementation of CM for CUD has been discouraged in health care settings 
because the lag-time between cessation of use and submission of negative samples makes CM for CUD 
more complicated to administer (Petry, DePhilippis, Rash, Drapkin, & McKay et al., 2014). As a result, 
most previous CM approaches for CUD have required clinic-based monitoring at least twice a week to 
verify abstinence. Consequently, detection of cannabis use via traditional urinalysis methods makes it 
impossible to contingently reinforce reductions in daily cannabis use. In contrast, saliva [i.e., oral fluid 
(OF)] testing is non-invasive and has the benefits of directly observable sample collection methods 
(reducing potential for sample adulteration), lower biohazard risk during collection, ease of multiple 
sample collections, and stronger correlation with blood-based drug-testing results than urine 
concentrations (Lee & Huestis, 2014). Moreover, the reliability/validity of OF drug testing has improved 
significantly over the past decade (Lee & Huestis, 2014) and there is currently one FDA-approved saliva 
testing method (Oratect® Oral Fluid Drug Screen Device) that can be used to detect THC use (40 ng/mL) 
in the past 12-14 hours. In order to evaluate the accuracy of this saliva test kit compared to newer test 
kits that are more sensitive to THC use, at the baseline, post-ad lib, and follow-up visits, we will ask 
participants to use two additional saliva test kits. We will compare results from the three tests to urine 
cannabinoid analyses to determine the most accurate test.  
 
B.4. Preliminary Data on Saliva Testing with Heavy Cannabis Users 
We have now collected feasibility and acceptability data from 4 heavy cannabis users on the use of the 
saliva test as part of the mobile CM for cannabis protocol (PRO00072366; Beckham, Adkisson et al., 
manuscript submitted). Participants were trained to self-administer the test. They were then asked to 
videotape themselves twice daily (at least 8-hours apart) while taking the test during a 1-week ad lib 
period followed by 4-weeks of mobile CM. During each video recording, participants: 1) started a video 

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recording session using the smartphone; 2) showed the unused test strip to the camera; 3) swabbed 
his/her cheek while on camera; 4) placed the strip on a flat surface for 5 minutes; and 5) recorded the 
final result with the camera. Saliva sticks were numbered to ensure they were not reused or 
substituted. Videos were uploaded and transmitted to our secure server using the app described 
above. Two raters independently reviewed each video and indicated if the saliva test was positive or 
negative. In 1% of videos, coordinators identified a problem (i.e., control strip was not legible) and the 
sample was rated as invalid (participants were given the benefit of the doubt and were paid in these 
rare instances). Agreement between raters was excellent (100%) as was agreement between 
participant self-report and saliva results (96%).  
 
B.5. Preliminary Pilot Data on Mobile CM for Cannabis 
As noted above, 4 participants have completed the mobile CM for cannabis protocol to date. All of the 
heavy cannabis users who completed the 4-week mobile CM for cannabis protocol reduced their 
cannabis use. On average, participants reduced the number of days they used cannabis from 17.5 days 
to 10.3 days per month, representing a 41% reduction in frequency of cannabis use on average. These 
preliminary findings demonstrate that: (1) we can recruit and retain heavy cannabis users in the 
proposed mobile CM protocol; (2) mobile CM for cannabis is feasible and accepted by heavy cannabis 
users; and (3) mobile CM produces a range of reductions in frequency of daily cannabis use (18-93%) 
that make it an ideal platform from which to study the impact of reduced frequency of cannabis use on 
functional outcomes.  
  
C. R21 APPROACH 
 
C.1. R21 Aims and Milestones 
In Aim 1 of the R21 phase of the project, we will evaluate the acceptability of the study procedures and 
further refine the CM reinforcement schedule to ensure that a majority of participants will be 
adherent, find the study procedures acceptable, and substantially reduce their cannabis use. In Aim 2, 
we will examine the association between frequency of cannabis use and total amount of cannabis 
consumed per week.  
 
C.2. Overview of the R21 Study Design and Participant Flow 
Figure 1 depicts the R21 study design and participant flow. Interested participants that meet initial 
eligibility criteria during a phone screen will complete an in-person baseline assessment to determine 
final eligibility. We anticipate that we will need to complete 24 baseline assessments to enroll a final 
sample of 18 eligible participants. Eligible participants will complete 2 weeks of ad lib monitoring 
during which time they will use cannabis typically to establish baseline patterns of use. Following the 2-
week ad lib period, participants will complete the 6-week mobile CM for cannabis protocol aimed at 
reducing their cannabis use. Afterwards, participants will return to the laboratory to return equipment 
and to complete an in-person 8-week follow-up.  

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C.3. R21 Inclusion/Exclusion Criteria 
Inclusion criteria include: (a) cannabis use other than ingested cannabis 
(i.e., edibles) on ≥40 of past 90 days; (b) ability to speak and write fluent 
English; (c) 18-80 years of age; and (d) willingness to attempt to 
temporarily reduce cannabis use. Participants will be excluded if they: (a) 
expect to have an unstable medication regimen during the study; (b) are 
currently receiving non-study CUD treatment; (c) meet criteria for serious 
mental illness (e.g., bipolar disorder, schizophrenia); (d) become 
imprisoned; (e) become hospitalized for psychiatric reasons; (f) become 
pregnant; (g) report imminent risk for suicide or homicide; or (h) meet 
criteria for a substance use disorder other than CUD or tobacco.  
 
C.4. Recruitment & Enrollment  
We estimate that we will need to recruit 24 participants to meet our 
overall goal of enrolling 18 eligible participants. Participants will be 
recruited from the community and from outpatient clinics at Duke 
University Medical Center (DUMC). Several methods will be used to recruit potential participants at 
these sites. Recruitment materials such as IRB-approved study flyers will be placed in mental health 
clinic areas at DUMC. In addition, study flyers will be placed throughout these medical centers in 
centrally located posting areas.  
 
We will identify potentially eligible participants using searches using Duke’s Deduce and Maestro Care 
systems. We will identify potentially eligible participants, using diagnostic and contact information in 
those systems. Potentially eligible participants will be sent an introductory letter or email asking them 
to consider participation, and they will be provided contact information for the study coordinator. 
Potential participants may also be called about a week after they are sent the letter to determine 
interest. Potential participants will be contacted no more than three times, as indicated by DOCR 
guidelines. They will also be provided information about how to opt out of additional contact from the 
study team re: this study. 
 
The study will be registered at clinicaltrials.gov, which will allow for online recruitment during the 
course of the study. As needed, we will advertise the study in local area newspapers, on Duke 
University’s clinical research website, and on online classified advertising websites such as 
Craigslist.com, DukeList, or JobFinder.com. In addition, we will post study recruitment materials at 
local community areas such as laundromats, Bull Connector bus lines, substance use treatment 
centers, restaurants, and grocery stores. We will place advertisements in local area newspapers (print 
and/or online) such as The Daily Tarheel, Duke’s Chronicle, and Indy Week. 
 
Our study team will use social media to reach potentially eligible participants. We have developed a 
Facebook page for posting IRB-approved study flyers and information for this and other studies in the 
Traumatic Stress and Health Research Laboratory, https://www.facebook.com/Duke-Traumatic-Stress-
and-Health-Research-Lab-379366159145563/. We plan to place pictures of our study flyers on the 
 
Figure 1. R21 Study Design & Participant Flow 

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Facebook page, and use Facebook’s post boost to draw attention to the post. The post itself will say 
“Enroll now!” or “Now enrolling!” We will also plan to use Facebook ads to target potential participants 
within a 50-mile radius of Duke. If any participant contacts the email associated with the Facebook 
page (TSHRLab@dm.duke.edu, he/she will be sent an automatic email response. 
 
We will plan to use a recruitment method referred to as respondent-driven sampling, or “seed 
recruitment” (Christina Meade, Ph.D., personal communication). Seed recruitment is suitable for 
sampling “hidden populations” of participants who are best known by their own peers (Heckathorn, 
1997).  It includes providing incentives to participants for referral of other eligible participants. In our 
model, each participant, or seed, will receive a coupon to recruit another person in his/her social 
networks. The recruitment coupon will provide a brief description of the survey and a phone number 
for contacting the study coordinator. The coupon will be marked with a unique identification number 
(not the study identification number) so that when the coupons are returned to us, the ID number can 
be used to provide payment ($20) to the participant who made the referral. The key connecting the 
participant’s study ID number with the seed ID number will be kept in a database separate from other 
PHI, creating two layers of separation between the seed ID and the already-participating person’s 
identifying information. Any participant who does not wish to recruit in this manner will not be 
required to do so. 
 
Any participant who contacts by telephone the study coordinator or other study staff regarding the 
study will be provided more information, and will be interviewed using an IRB-approved telephone 
screening. If a participant is deemed potentially eligible in the telephone screen, he/she will be 
scheduled to attend a formal screening visit. We will send, via Duke secured email, appointment 
reminders to any participant who wants to receive them. Once a participant reports to the laboratory 
to begin the study, the study staff member obtaining consent will explain the study in detail, provide 
the participant with an IRB-approved written consent form explaining the procedures and risks, and 
answer any questions. The initial consent process and documentation takes place in a quiet, private 
office at Duke University Medical Center, and participants are given the chance to thoroughly read the 
consent prior to participation. Participants are given a copy of the signed informed consent form, and 
are given phone numbers to call if they have additional questions about the consent form or the 
research, if they have any problems during the study, or if they have questions about participating in 
research studies in general. With regards to determination of decision making capacity of potential 
participants, our laboratory has a standard procedure for determining understanding of the study 
procedures, risks, and benefits. We utilize this procedure if we have any reason to suspect that the 
participant may have difficulty in the consent process (e.g., traumatic brain injury impacting cognitive 
function, active psychotic symptoms). In this procedure, the study coordinator providing the informed 
consent information evaluates understanding of the procedures at several different time points during 
the process by asking questions like “Do you understand what we’re asking you to do?” and “Do you 
have any questions about the risks of the study? Can you tell me what you understand the risks to be?” 
Prior to having a participant sign consent, the study coordinator may ask the potential participant to 
outline the study procedures, risks, and benefits so that he can make sure that the participant is aware 
of them. If the participant is unable to summarize these, he/she will not be allowed to sign the 
informed consent form, and may be referred for other treatment. No study procedures will begin until 

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informed consent has been obtained. 
 
C.5. Screening Procedures 
Prior to study entry, potential participants will complete screening procedures as part of the baseline 
assessment, including informed consent, diagnostic interview, self-report measures, demographic 
data, and cannabis history. Urine and saliva samples will be collected to assess for cannabis use and 
other illicit drugs. Women of childbearing potential will be given a pregnancy test. Urine samples will 
be assayed by a trained staff member using a Quidel QuickVue or Cardinal pregnancy test, whichever is 
available and approved for use per DOCR. We have developed a short interview for female 
participants; this interview will help us determine which female participants must have a urine 
pregnancy test, and when the test should be done. Female participants of childbearing potential who 
are not pregnant must agree to use appropriate contraception during the course of the study, and to 
notify study staff if they become pregnant during the study. During the screening visit, if there is any 
indication that a participant is at greater than minimal risk for suicide (using the Columbia Suicide 
Scale), he/she/they will work with a trained study staff member to complete a safety planning 
worksheet (Stanley & Brown, 2012). This will help mitigate suicide risk. If necessary, this safety plan will 
be reviewed during the post-treatment visit as well.  
 
C.6. Refining the Study Procedures and the CM Reinforcement Schedule (R21 Aim 1) 
In Aim 1 of the R21, we will use an iterative process to refine the study procedures and the CM 
reinforcement schedule in 3 cohorts of 6 participants each (total N=18) to ensure that a majority of 
participants will be adherent, find the study procedures acceptable, and substantially reduce their 
cannabis use. Note that while we will pilot test all of the EMA procedures during this phase, inclusion 
of these procedures is solely to determine feasibility, acceptability, and impact of the CM schedule on 
cannabis use, as we will not be powered to analyze the functional outcomes of interest during this 
phase. Note that we anticipate that the majority of adjustments to the protocol that will be made 
during the R21 phase of this project will be related to “fine tuning” the CM reinforcement schedule to 
ensure optimal reductions in use. 
 
C.6.1. Determining the Initial CM Reinforcement Schedule. Building upon our pilot findings, we will 
use an escalating reinforcement schedule (see Table 1), such that each subsequent day of abstinence 
will be reinforced with a greater amount of money; however, to ensure that participants are not 
motivated to achieve sustained abstinence, we will program our CM schedule such that participants 
will be able to use cannabis one day each week without penalty. That is, there will be 1 “free” day per 
week when participants will not be penalized for using cannabis. This procedure will help to ensure 
there is an upper limit on reduced use, as our goal is to motivate participants to substantially reduce 
their use, but not to the point that they achieve sustained abstinence. Participants will earn $10.00 for 
their first day of abstinence. Each subsequent day of bioverified abstinence/free day will result in 
participants’ compensation increasing by $1.00. We also considered use of a reset contingency. Reset 
contingencies are designed to promote sustained abstinence because any post-abstinence substance 
use results in the level of reinforcement being reset to the initial (i.e., day 1) amount. While we have 
used reset contingencies successfully in the past, we have elected to not use a reset contingency in this 
study because our goal is to promote reduced use, not sustained abstinence. We have also elected to 

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increase (relative to our pilot) the amount of money that participants can receive to increase the 
likelihood that a majority (i.e., ≥ 50%) of participants will decrease their use by 50% or more.  
Table 1. R21 phase otential payment schedule with “free day” allowance 
Week 
Day 
Saliva 
videos  
(Regardless 
of use) 
Daily Saliva 
Video 
Uploads 
Smoking? 
Daily Saliva 
Video 
Uploads 
Explanation 
Total 
Running 
Total 
Week 1 & 2 
(Ad Lib) 
 
1-
14 
1 
$2.50 
As Usual 
No Bonus yet 
$7.50 ($2.50 per video) payment 
for uploading saliva testing 
videos (regardless of cannabis 
use). $5 for providing a self-
initiated reading.  
$12.50 
each 
day 
175.00 
2 
$2.50 
As Usual 
3 
$2.50 
As Usual 
SI* 
$5.00 
As Usual 
Weeks 3 to 7 (CM Phase 1), one example week shown 
15 
1 
$2.50 
No 
$10.00 
Participants will earn $10 for 
their first day of verified 
abstinence. $1 bonus marijuana 
payment for verified marijuana-
free day or if the once-a-week 
cheat day is used. 
$17.50 
$192.50 
2 
$2.50 
No 
3 
$2.50 
No 
16 
1 
$2.50 
No 
$11.00 
$18.50 
$211.00 
2 
$2.50 
No 
3 
$2.50 
No 
17 
1 
$2.50 
No 
$12.00 
$19.50 
$230.50 
2 
$2.50 
No 
3 
$2.50 
No 
18 
1 
$2.50 
No 
$13.00 
$20.50 
$251.00 
2 
$2.50 
No 
3 
$2.50 
No 
19 
1 
$2.50 
Yes 
$14.00 
$21.50 
$272.50 
2 
$2.50 
Yes 
3 
$2.50 
Yes 
20 
1 
$2.50 
No 
$15.00 
$22.50 
$295.00 
2 
$2.50 
No 
3 
$2.50 
No 
21 
1 
$2.50 
No 
$16.00 
$23.50 
$318.50 
2 
$2.50 
No 
3 
$2.50 
No 
Week 8 (CM Phase 6) 
50 
1 
$2.50 
No 
$45.00 
1 dollar increase to bonus 
marijuana payment for verified 
marijuana-free day or if the 
once-a-week cheat day is used. 
$52.50 
$1435.00 
2 
$2.50 
No 
3 
$2.50 
No 
51 
1 
$2.50 
No 
$46.00 
$53.50 
$1488.50 
2 
$2.50 
No 
3 
$2.50 
No 
52 
1 
$2.50 
No 
$47.00 
$54.50 
$1543.00 
2 
$2.50 
No 
3 
$2.50 
No 
53 
1 
$2.50 
No 
$48.00 
$55.50 
$1598.50 
2 
$2.50 
No 
3 
$2.50 
No 

Full Protocol, PI version date 8/16/2022  
 
Page 11 of 30  
 
C.6.2. Saliva Testing Schedule. Saliva testing will occur contemporaneously with EMA alarms. 
Participants will be required to video record themselves taking a saliva test each time they are alarmed 
to complete EMA, i.e., three times per day. During the ad lib phase of the monitoring, we will ask 
participants to do a self-initiated EMA and saliva test if they use marijuana. Participants will be asked 
to begin a reading when beginning to use marijuana. An alarm will be set for 60 minutes later. At the 
alarm, participants will complete a saliva test. Participants will be paid $5 each day if they do a self-
initiated reading. During the active CM phases of the study, if a participant tests negative at each time 
point, he/she will by reinforced for abstinence that day. In addition, participants will be granted one 
“free day” per week in which they can use cannabis and still be reinforced if they upload their saliva 
tests. Please note that participants are reinforced separately for uploading both positive and negative 
saliva tests (they receive $2.50 per upload, regardless of cannabis use), enabling us to compare 
participants’ saliva test results with their self-report. Participants will be given half of the required 
saliva screening strips at their screening visits, and then will be mailed remaining strips about halfway 
into the treatment period.  
  
C.6.3. Cohort 1: Protocol Refinement. After the first 6 participants have completed the mobile CM for 
cannabis protocol, we will ask each of them to rate the acceptability of the procedures. We will also 
conduct interviews with participants to identify and resolve any issues that may have contributed to 
poor adherence and/or poor response to the CM procedures. A summary of this feedback will then be 
evaluated by the study team (i.e., Co-PIs, Co-I’s, Consultant, and computer programmer) and 
incorporated into the revised protocol. We will also evaluate compliance data and calculate the 
reduction in cannabis use that participants achieved during CM to determine if further adjustments to 
the reinforcement schedule are needed. 
 
C.6.4. Cohort 2: Further Protocol Refinement. Once the protocol and/or reinforcement schedule have 
been modified based on information learned from the first cohort, we will administer the revised 
mobile CM for cannabis protocol to a second cohort of heavy cannabis users (n=6) and repeat the 
evaluative measures described above. Any additional modifications needed to improve adherence, 
acceptability, or efficacy of the mobile CM for cannabis protocol will be implemented at this time. We 
Table 1. R21 phase otential payment schedule with “free day” allowance 
Week 
Day 
Saliva 
videos  
(Regardless 
of use) 
Daily Saliva 
Video 
Uploads 
Smoking? 
Daily Saliva 
Video 
Uploads 
Explanation 
Total 
Running 
Total 
54 
1 
$2.50 
Yes 
$49.00 
$56.50 
$1655.00 
2 
$2.50 
Yes 
3 
$2.50 
Yes 
55 
1 
$2.50 
No 
$50.00 
$57.50 
$1712.50 
2 
$2.50 
No 
3 
$2.50 
No 
56 
1 
$2.50 
No 
$51.00 
$58.50 
$1771.00 
2 
$2.50 
No 
3 
$2.50 
No 

Full Protocol, PI version date 8/16/2022  
 
Page 12 of 30  
have found this second small cohort particularly helpful in previous treatment development studies, as 
it allows for careful evaluation of changes in procedures and reinforcement schedules. 
 
C.6.5. Cohort 3: Milestone Assessment. Once the EMA protocol and CM reinforcement schedule have 
been finalized based on information learned from cohort 2, we will administer the final version of the 
protocol to a third cohort (n=6) to determine if our first two milestones have been met. Our first 
milestone will be to demonstrate that a majority (i.e., 4/6) of cohort 3 participants will be adherent to 
the study protocol and rate the study procedures as acceptable. Protocol adherence will be defined as 
completing the baseline assessment, 8-week follow-up assessment, and 1 or more EMA assessments 
per day (total ≥56) for the duration of the 8-week EMA protocol. Acceptability will be assessed with a 
questionnaire specifically designed for this study. Our second milestone will be to demonstrate that we 
have identified a reinforcement schedule that reliably produces ≥ 50% reductions in frequency and 
quantity of cannabis use in at least half of the participants in cohort 3 (i.e., 3/6). By aiming to reliably 
produce clinically meaningful reductions in frequency and quantity of cannabis use in at least half of 
the participants, we will maximize our ability to study the impact of reduced cannabis use on key 
functional outcomes during the R33 phase of the project. To evaluate if this milestone has been met, 
we will calculate the percentage reduction in bioverified abstinent days and overall cannabis quantity 
by comparing the ad lib monitoring period to the mobile CM period.    
 
C.7. Association between Frequency of Use and Total Amount of Cannabis Consumed (R21 Aim 2) 
In Aim 2 of the R21, we will examine the association between number of bioverified abstinent days and 
self-report of total quantity of cannabis use per week. Quantity of cannabis consumed during EMA 
assessments will be calculated as the inverse of the self-reported average number of joints made from 
1 gram of cannabis (joints/gram) consumed per EMA period, as this approach has been demonstrated 
to reliably assess cannabis quantity (van der Pol, Liebregts, Graff, Korf, van den Brink & van Laar, 2013).  
We will use van der Pol et al.’s (van der Pol, Liebregts, Graff, Korf, van den Brink & van Laar, 2013) 
methods to assess cannabis potency via self-report, and total THC load will be calculated as 
joints/gram x potency. Participants will also be prompted at the end of each day (~11 pm) to estimate 
the total amount of joints/gram they used that day. Our third milestone involves examining the 
association between frequency of cannabis use (i.e., days) and total amount of cannabis consumed per 
week based on participants’ EMA data on quantity of cannabis use. By tracking the total amount of 
cannabis that participants use, we will be well-positioned to account for such an effect in the R33 
analyses. To assess this milestone, we will calculate days since last use for each report of cannabis use. 
We will use count-adjusted (i.e., negative binomial or Poisson) MLM to model the equivalent number 
of joints/gram smoked on a given day as a function of days since last use. We anticipate the association 
of latency between days since last use and amount consumed will be small (i.e., Cohen’s d ≤ 0.10). 
 
C.8. Additional Bioverification of Cannabis Use.  
At three time points (screening, end of the ad lib period, end of treatment), we will ask participants to 
provide a urine sample. These urine samples will be sent to Lab Corp for creatinine normalization 
analyses to determine cannabinoid concentrations. Measurement of cannabinoid concentration will 
allow analyses of reduced cannabis use.  
 

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Page 13 of 30  
D. R33 APPROACH 
 
D.1. Overview of the R33 Study Procedures 
If the proposed R21 milestones are met, the R33 phase of the project will be used to conduct a fully 
powered test of our central hypothesis that reductions in frequency and quantity of cannabis use will 
lead to positive changes in cannabis users’ functional outcomes. The R33 study will be directly modeled 
after the R21 study and will use the same recruitment, enrollment, and screening procedures 
described herein. Based on evaluation of the R21 data, we have modified the contingency 
management procedures such that participants are only asked to respond to two daily alarms (instead 
of three), and they are no longer asked to do self-initiated alarms during the ad lib period.  Each 
subsequent day of bioverified abstinence/free day will result in participants’ compensation increasing 
by 50 cents instead of $1.00.  Note, this decreases the potential amount of money earned.   

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Page 14 of 30  
 
Seventy-two participants will be randomized using a 2:1 allocation ratio to either the Reduced Use 
Condition (n = 48) or a Control Condition (n = 24). Inclusion of a control group will allow clear 
inferences to be drawn regarding the effects of reduced cannabis 
use on functional outcomes. Study participation will entail completion of the baseline assessment, 
completion of the 8-week EMA study, and completion of the 8-week follow-up assessment. 
  
D.2. R33 Inclusion/Exclusion Criteria and Recruitment Timeline 
The final inclusion/exclusion criteria for the R33 phase of the project will be similar to the R21 
Table 2. R33 Phase potential payment schedule with “free day” allowance 
Week 
Day 
Saliva 
videos  
(Regardless 
of use) 
Daily Saliva 
Video 
Uploads 
Smoking? 
Daily Saliva 
Video 
Uploads 
Explanation 
Total 
Running 
Total 
Week 1 & 2 
(Ad Lib) 
 
1-
14 
1 
$2.50 
As Usual 
No Bonus yet 
$5.00 ($2.50 per video) payment 
for uploading saliva testing 
videos (regardless of cannabis 
use).  
$5 each 
day 
$70.00 
2 
$2.50 
As Usual 
Weeks 3 to 7 (CM Phase 1), one example 
week shown 
15 
1 
$2.50 
No 
$10.00 
Participants will earn $10 for 
their first day of verified 
abstinence. Bonus payment 
increases after verified 
marijuana-free day or if the 
once-a-week cheat day is used. 
$15.00 
$85.00 
2 
$2.50 
No 
16 
1 
$2.50 
No 
$10.50 
$15.50 
$100.50 
2 
$2.50 
No 
17 
1 
$2.50 
No 
$11.00 
$16.00 
$116.50 
2 
$2.50 
No 
18 
1 
$2.50 
No 
$12.50 
$16.50 
$133.00 
2 
$2.50 
No 
19 
1 
$2.50 
Yes 
$13.00 
$17.00 
$150.00 
2 
$2.50 
Yes 
20 
1 
$2.50 
No 
$13.50 
$17.50 
$167.50 
2 
$2.50 
No 
21 
1 
$2.50 
No 
$14.00 
$18.00 
$185.500 
2 
$2.50 
No 
Week 8 (CM Phase 6) 
50 
1 
$2.50 
No 
$27.50 
1 dollar increase to bonus 
marijuana payment for verified 
marijuana-free day or if the 
once-a-week cheat day is used. 
$32.50 
$925.00 
2 
$2.50 
No 
51 
1 
$2.50 
No 
$28.00 
$33.00 
$958.00 
2 
$2.50 
No 
52 
1 
$2.50 
No 
$28.50 
$33.50 
$991.50 
2 
$2.50 
No 
53 
1 
$2.50 
No 
$29.00 
$34.00 
$1025.50 
2 
$2.50 
No 
54 
1 
$2.50 
Yes 
$29.50 
$34.50 
$1060.00 
2 
$2.50 
Yes 
55 
1 
$2.50 
No 
$30.00 
$35.00 
$1095.00 
2 
$2.50 
No 
56 
1 
$2.50 
No 
$30.50 
$35.50 
$1130.50 
2 
$2.50 
No 
 
 
Figure 2. R33 Study Design & Participant Flow 

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Page 15 of 30  
inclusion/exclusion criteria with two exceptions. First, with regards to the substance use disorder 
exclusion, for alcohol use disorder (AUD) only, if a person has had fewer than two symptoms in the 
past three months, they are not excluded from the study, regardless of their prior AUD history. Second, 
participants who are found to meet criteria for bio-verified sustained abstinence (i.e., all of their saliva 
tests are negative during the 6-week experimental phase of the study) will be excluded from the 
statistical analyses to ensure that there is an upper limit on reduced use. Thus, we will exclude any 
participants who achieve sustained abstinence to ensure that any improvements in functioning 
observed among participants in the experimental condition are not being driven by inclusion of a 
subset of participants who achieve sustained abstinence. Please also note that we can exclude up to 12 
of the 72 enrolled participants from the statistical analyses (remaining N = 60) and still be adequately 
powered to conduct all of the proposed analyses. We anticipate that we will need to consent up to 125 
participants in order to reach our goal of enrolling 72 participants (i.e., a total of 149 participants 
enrolled over the entire R21 and R33 phases of the study). If any participant’s self-reported cannabis 
use is not bioverified during the screen and ad lib portions of the study, they will be withdrawn from 
the study. 
 
D.3. Diagnostic Reliability and Training of Clinical Interviewers  
Interviewers will be trained to conduct the interviews using our standardized laboratory procedures, 
including rating videotaped interviews and participation in ongoing weekly supervision. These 
procedures have produced consistently high interrater reliability, with Fleiss’ kappa values ranging 
from .92-.94 in previous studies (Kimbrel, Newins et al., 2017; Kimbrel, Calhoun et al., 2014). 
 
D.4. Participant Reimbursement 
In addition to the compensation that participants will receive from the CM procedures, participants will 
also receive $100 for completion of the baseline assessment, $25 for the post ad lib period urine 
testing, $150 for the 8-week follow-up, and $400 for the EMA study ($50 per week). They will also be 
eligible to receive a $25 bonus per week for not missing more than 1 EMA alarm per day, and a $50 
bonus for returning their equipment on time. These procedures have worked well (high compliance 
and equipment return rates) in previous studies (Carpenter et al., 2015; Hertzberg et al., 2013; 
Beckham, Calhoun et al., 2013; Dennis et al., 2016; Volz et al., 2014). Participants may also receive $20 
for successfully recruiting another participant in his/her/their social network.  
 
 
D.5. Description and Timing of In-Person Assessments 
Table 3 describes the laboratory and self-report assessments that will be collected at the baseline and 
8-week follow-up assessments as well as their administration schedule. Whenever possible, we have 
selected Common Data Elements from the PhenX Toolkit (Hamilton et al., 2011) to maximize the 
impact of the proposed research. We have also elected to use brief measures when possible to reduce 
burden. Whenever possible, participants will complete questionnaires remotely using Duke’s instance 
of RedCap. Of the measures listed below, only the impulsivity and working memory tasks need to be 
completed in person.  
 
D.6. EMA and CM Procedures 

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Page 16 of 30  
EMA addresses the limitations of traditional assessment techniques by (a) repeatedly assessing 
participants in their normal daily environment, which enhances ecological validity; (b) assessing 
experiences and behaviors at the time they occur, which minimizes memory biases associated with 
retrospective recall; and (c) allowing for examination of the context of participants’ experiences. 
Eligible participants will be trained in the EMA procedures following established procedures at the end 
of the baseline assessment (Beckham, Calhoun 2013). Participants will then practice with the electronic 
diary at home for 24 hours. Once the training period is complete, participants will complete an 8-week 
EMA study in which they will carry the diaries with them on a daily basis. EMA will include four 
readings per day, including a morning diary entry, a before-bed assessment, and two random daily 
alarms. EMA entries will be programmed to present full assessments about once daily, and other 
assessments will be abbreviated. Specifically, we will abbreviate EMA assessments of mental health, 
health-related quality of life, and cannabis withdrawal symptoms at these times to reduce participant 
burden, as these are the most time-intensive sections. Full entries are estimated to take ~3-5 minutes, 
whereas abbreviated entries are expected to take ≤ 2 minutes. We will add one question at every 
assessment point to assess daily discrimination: “Since the last assessment, have you experienced any 
discrimination? (yes/no).” If “yes” is endorsed a drop down menu will appear with the following 
question and choices: “What do you think is the main reason for these experiences?” and choices 
(derived from the Everyday Discrimination Scale will be “Your Ancestry or National Origins”, “Your 
Gender”, “Your Race”, “Your Age”, “Your Religion”, “Your Height”, “Your Weight”, “Some other Aspect 
of Your Physical Appearance”, “Your Sexual Orientation”, and “Your Education or Income Level”. 
Ecological momentary assessments of daily discrimination will be collected randomly during the daily 
assessments. The first 2 weeks of the EMA study will involve daily assessments of cannabis use and 
participants’ functional outcomes during the ad lib period to establish baseline patterns of use.  
 
The functional outcomes assessed with EMA include items about mood and suicidal ideation. 
Participants are made aware during the informed consent process that these EMA responses are not 
reviewed in real time. The EMA has been programmed to include the telephone number for the 
national suicide hotline in the event that a participant endorses suicidal ideation. We will urge 
participants who receive the suicide hotline information during their diary reading to contact the 
hotline.    
 
On day 15, participants in the Reduced Use Condition (n = 48) will begin reducing their cannabis use 
through participation in the mobile CM for cannabis protocol, whereas participants in the Control 
Condition (CC, n = 24) will not be asked to reduce cannabis use. Participants in CC will receive payment 
for uploading video recordings (up to $5 per day). Those who upload videos as asked will be eligible to 
receive a bonus payment. Any bonus payments they receive will be non-contingent payments based on 
the earnings of a participant in the Reduced Use Condition to whom they are yoked. All participants 
will continue to provide EMA assessments of their functional outcomes during the 6-week 
experimental period so that we can study the impact of reduced cannabis use on functioning. 
Electronic diary data stored on the smart phone are encrypted at rest. Data will be downloaded from 
the smart phones to duhsnas-pri\dusom_psych\private\irb\kimbrel\Marijuana.  
 
 
Table 3. Description and Timing of In-Person Assessments 

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Page 17 of 30  
MEASURE 
DESCRIPTION 
BL 
End 
of ad 
lib 
8-wk 
Demographic Assessment 
PhenX (Hamilton et al., 2011) protocols will assess age (#010101), 
race (#010601), ethnicity (#010501), gender (#010700), and sexual 
orientation (#101401). 
X 
 
 
Structured Clinical Interview for 
DSM-5 (First et al., 2015) 
Will be used to diagnose CUD and other psychiatric disorders based 
on DSM-5 criteria in order to determine study eligibility and 
characterize sample. 
X 
 
 
Columbia Suicide Severity Rating 
Scale (Posner et al., 2011) 
State-of-the-art interview that assesses the full continuum of 
suicidal behavior. Will be used to determine study eligibility and 
assess suicidal behavior. 
X 
 
X 
Medication List 
Patients will be asked to bring their medications to the baseline visit 
and record their current prescription medication use. 
X 
 
 
Timeline Followback for 
Cannabis (Robinson, Sobell, 
Sobell & Leo, 2014) 
TFLB will be used to establish participants’ cannabis use in the past 
90 days to determine eligibility; will be re-administered at follow-up. 
X 
 
X 
Marijuana Problems Scale 
(Stephens, Roffman & Curtin, 
2000) 
Self-report of cannabis-related symptoms and problems. 
X 
 
X 
Quality of Life Scale (Burckhardt 
& Anderson, 2003) 
16-item self-report measure of satisfaction in 16 areas. Good 
internal consistency and test-retest reliability. 
X 
 
X 
Marijuana Motives 
Questionnaire (Lee et al., 2009) 
Self-report assessment of motives for using cannabis. 
X 
 
X 
Marijuana Withdrawal Checklist 
(Budney, Novy & Hughes, 1999) 
Self-report assessment of cannabis withdrawal symptoms 
X 
 
X 
Recreational and Medicinal 
Marijuana Use Questionnaire 
Self-report assessment of cannabis use symptoms (based on Metrik 
et al., 2018) 
X 
 
X 
Alc. Use Dis. Identification Test 
(Babor et al., 1989) 
Self-report of alcohol use and related consequences. 
X 
 
X 
Symptom Checklist-90 
(Derogatis, 1983) 
90-item measure of mental health symptoms that has previously 
been shown to be sensitive to sustained abstinence from cannabis. 
X 
 
X 
Beck Scale for Suicidal Ideation 
(Beck & Steer, 1991) 
21-item self-report of suicidal thoughts and behaviors. 
X 
 
X 
Deliberate Self-Harm Inventory 
(Gratz, 2001) 
17-item self-report of frequency of nonsuicidal self-injurious 
behaviors. 
X 
 
X 
PTSD Checklist – (PCL-5; 
Weathers, Litz et al., 2013) 
20-item self report of posttraumatic stress disorder (PTSD) 
symptoms. 
X 
 
X 
Traumatic Life Events 
Questionnaire (TLEQ; Kubany et 
al., 2000) 
23-item self report of history of traumatic events 
X 
 
 
Dimensions of Anger Reactions 
(DAR; Novaco, Swanson et al., 
2012)  
7-item scale measuring the frequency, duration, and behavioral 
response to anger, and anger-related functional impairment on 
social relationships, health, and work 
X 
 
X 
Conflict Tactics Scale, version 2 
(CTS2; Straus, Hamby, Boney-
McCoy, & Sugarman, 1996) 
Scale measuring interpersonal aggression. 6-week and 6-month 
version given at screen; 6-week version given at post-treatment. 
X 
 
X 
PEG (Krebs et al., 2009) 
3-item self report measure of pain (brief version of the Brief Pain 
Inventory). 
X 
 
X 
Insomnia Severity Index (ISI; 
Morin et al., 2011) 
7-item self report of insomnia symptoms. 
X 
 
X 

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Page 18 of 30  
STOP-Bang (Chung et al. 2012) 
Brief measure to screen participants for obstructive sleep apnea. 
X 
 
 
WHOQOL-BREF (The WHOQOL 
Group, 1994) 
26-item measure of health-related quality of life developed by WHO. 
X 
 
X 
Sheehan Disability Scale 
(Sheehan, 1983) 
3-item assessment of work, social, and family impairment. 
X 
 
X 
 
 
Marijuana Reduction Strategies 
Self-Efficacy Scale (Davis et al., 
2014) 
21-item assessment of self-efficacy related to strategies intended to 
reduce the amount and/or frequency of cannabis consumption. 
X 
 
X 
 
 
Godin Leisure Time Exercise 
Questionnaire (Godin, 2011) 
4-item measure of physical activity 
X 
 
X 
Int’l Physical Activity 
Questionnaire (Booth, 2000) 
27-item physical activity scale that focuses on the past 7 days. 
X 
 
X 
Working Memory Tasks from 
Wechsler Adult Intelligence 
Scale (WAIS) and Wechsler 
Memory Scale (WMS) (Wechsler, 
2008; Wechsler, 2008) 
Symbol Span and Spatial Addition will assess visual working memory 
(WM). Digit Span and Letter-Number Sequencing will assess auditory 
WM. Arithmetic will measure concentration, quantitative reasoning, 
and mental manipulation.  
X 
 
X 
Behavior Rating Inventory of 
Executive Function (Gioia, 
Isquith, Guy & Kenworthy, 2000) 
75-item self-report of perception of working memory and executive 
functioning; contains nine subscales (e.g., Working Memory, Inhibit, 
Shift, Plan/Organize). 
X 
 
X 
Impulsivity Tasks (Richards, 
Zhang, Mitchell, De Wit, 1999; 
Bechara et al., 1994; Lejuez et 
al., 2002) 
The Delay Discounting Task, Iowa Gambling Task, and Balloon 
Analogue Risk Task will assess impulsivity, particularly impulsive 
choice. 
X 
 
X 
UPPS-P (Lynam et al., 2006) 
59-item self-report that assesses multiple facets of impulsivity, 
including: urgency, premeditation, perseverance, sensation seeking, 
positive urgency. 
X 
 
X 
Brief Interpersonal Psychosocial 
Functioning Measure (Bovin et 
al., 2018) 
7-item measure of PTSD-related psychosocial functional impairment. 
X 
 
X 
Cannabis Use Measure 
(Goodman, Leos-Toro, & 
Hammond, 2019 
Measure of cannabis use adapted from 2020 scale used in the 
International Cannabis Policy Study 
(http://cannabisproject.ca/methods/.  
X 
 
 
Cannabis Problems 
Questionnaire (Copeland, 
Gilmour, Gates, & Swift, 2005) 
22-item measure to evaluate cannabis-related problems. 
X 
 
X 
Medical Problems List 
Measure designed to evaluate presence of major medical problems 
ever and in the past year.  
X 
 
 
Fagerström Test of Nicotine 
Dependence (Heatherton, et al., 
1991) 
Measure designed to evaluate nicotine dependence 
X 
 
X 
World Health Organization 
Disability Assessment Schedule 
(Üstün, 2010) 
36-item interview version of functional disability. 
X 
 
X 
Marijuana Ladder (Slavet et al., 
2006) 
Measure of motivation to change marijuana use 
X 
 
X 
Brief Perceived Ethnic 
Discrimination Questionnaire – 
Community Version (Brondolo et 
al., 2005; Keum et al., 2018) 
Multidimensional measure of exposure to ethnic/racial 
discrimination.  
X 
 
 

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D.6.1. Electronic Diary System Hardware and Data Security. We have established methods to limit risk 
of breach of confidentiality in EMA studies (Carpenter et al., 2015; Hertzberg et al., 2013; Beckham, 
Calhoun et al., 2013; Dennis et al., 2016; Volz, et al., 2014). The smart phone used will be any one of 
four models of Droid phones: 1) Droid Turbo 2 (Motorola Mobility Inc, Libertyville, IL) with a Qualcomm 
Snapdragon 810 processor, 5.4” Quad HD display; 2)Droid Turbo, with 5.2" Quad HD Super AMOLED™ 
Corning® Gorilla® Glass 3 Display; 3) Droid MAXX, with a dual-core 1.2 Ghz processor, I GB DDR2 RAM, 
and 4.3” gHD display; or 4) Droid MAXX 2 DROID MAXX 2 with an octa-core 1.7 Ghz processor, 2 GB 
RAM, and 5.5” full HD display. Each will be equipped with an Android operating system that is 
compliant with Federal Information Processing Standard (FIPS) 140-2 standards. Features other than 
the electronic diary, calling, and texting features will be locked out. The phone will be programmed so 
that staff can set up the phone by simply entering a participant’s code. Encrypted TLS connections will 
be used to upload data to the server. The study’s website will use a Virtual Private Server provided by 
InMotion Hosting, Inc. The data at InMotion Hosting are Advanced Encryption Standard (AES)-256 
encrypted at rest, and the data being transferred are encrypted at transfer AES-256 & Transport Layer 
Security (TLS). Website properties include TLS 1.0, AES w/ 128 bit encryption (High); Rivest-Shamir-
Adleman (RSA) with 2048 bit exchange. The web application written for this study has been checked 
for SQL injection, Code Injection, XSS, and RFI vulnerabilities and has passed. The site will only be 
accessible to staff via 512-bit SHA-2 hashed passwords.  
 
The software used in this study will be developed by our IT specialist, Mr. Jeffrey Hertzberg, who has 
developed and implemented similar programs for other EMA and mobile health studies conducted by 
our laboratory (see PRO00072366 and PRO00067912) (Carpenter et al., 2015; Hertzberg et al., 2013; 
Beckham, Calhoun et al., 2013; Dennis et al., 2016; Volz, et al., 2014). Participants will be prompted to 
respond to EMA alarms and diary alarms. For the EMA alarms, after the alarm sounds, participants will 
have 5 minutes to respond. If participants do not respond in 5 minutes, a missed alarm will be 
recorded, and a subsequent alarm will be prompted 30-40 minutes later. If participants respond in 5 
minutes, they can choose to “snooze” the alarm for 5 minutes, “skip” the alarm until 30-40 minutes 
later, or “answer” the alarm. Participants will be instructed on how to turn off the alarm while they 
sleep or during short periods of time when it might be dangerous (e.g., driving) or disturbing (e.g., 
during a work meeting) to respond. To reduce burden, EMA entries will be programmed to present full 
assessments ~1x daily, whereas the remaining assessments will be abbreviated. Specifically, we will 
abbreviate EMA assessments of mental health, health-related quality of life, and cannabis withdrawal 
symptoms at these times to reduce participant burden, as these are the most time-intensive sections. 
Experiences of Discrimination 
(Krieger et al., 2005)  
Measures “self-reported experiences of discrimination.”  
X 
 
 
The Everyday Discrimination 
Scale (Williams, Yu, Jackson, & 
Anderson, 1997) 
Measure used to assess the relative frequency of chronic, routine, 
and subtle experiences of unfair treatment that occur in day-to-day 
life.  
X 
 
 
Timeline Followback of 
Drugged Driving (Robinson, 
Sobell, Sobell, & Leo, 2014) 
Participants will be asked to report days they have driven within 2 
hours of using cannabis use in the past 28 days using Timeline 
Followback method. 
X 
 
X 
Sound Sensitivity Measures 
(Hiller & Goebel, 1992; Khalfa et 
al., 2002; Wu et al., 2014) 
Several measures, including the Mini Tinnitus Questionnaire, 
Misophonia Questionnaire, and Hyperacusis Questionnaire, will 
measure sound sensitivity. 
X 
 
X 
 
  

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Full entries are estimated to take ~3-5 minutes, whereas abbreviated entries are expected to take ≤ 2 
minutes. If any participant would like to have text messages sent to his/her/their personal phones 
when the study phone alarms, we will provide this service to them. Participants who opt in for text 
messaging may receive other study-related communications, such as “You missed an alarm. Remember 
to respond to the next one!” and “Contact [name of study coordinator] if you’re having problems with 
your equipment.” Participants can opt out of text messages during the initial informed consent 
process, or at any time during the study. Text messages sent will not contain PHI or PII.  
 
 
EMA of mental health symptoms will include administration of the K-10 (Kessler et al., 2002) and 
PANAS (Watson, Clark & Tellegen, 1988), well-validated measures of psychological distress and positive 
and negative affect, respectively. Given our prior work demonstrating associations between cannabis 
use and self-injury (Kimbrel, Newins et al., 2017; Kimbrel, Meyer et al., in press), we will also assess 
self-injurious thoughts and behaviors using a state-of-the-art protocol developed and implemented by 
Dr. Kimbrel (Co-PI) as part of his ongoing EMA study of NSSI (I01CX001486). The WHOQOL-BREF and 
the Sheehan Disability Scale (SDS; Sheehan, 1983) will be used to assess health-related quality of life. 
We will evaluate impulsivity with the Momentary Impulsivity Scale (MIS; Tomko et al., 2014) is a brief 
4-item EMA of momentary impulsivity that correlates with gold standard measures of impulsivity and 
has good psychometric properties. 
 
Cannabis reduction self-efficacy will be assessed at each time point. EMA of self-efficacy will be 
modeled after prior EMA self-efficacy work (Gwaltney et al., 2005) and will ask participants how 
confident they are in their ability to reduce their cannabis use on an 11-point scale where 0 = “not 
confident at all” and 10 = “extremely confident.”  
 
Participants will be asked at each EMA assessment if they have used cannabis, alcohol, or any other 
illicit drugs, and, if so, how much of each substance they used. Participants will also be prompted each 
night (~11pm) to estimate the total amount of cannabis, alcohol, and illicit drugs they used in the past 
24 hours. Quantity and potency of cannabis consumed during each EMA assessment will be calculated 
using van der Pol’s methods (van der Pol et al., 2014). Participants will also be given a small scale to use 
to measure their marijuana stash at the end of each day (to assist them in estimating daily use).  If 
participants report use, they will also be asked if they drove a vehicle within two hours of use. We will 
also ask participants to report the total number of times they engaged in any form of drugged driving 
during the past 24 hours at the end of each night. EMA-based assessment of cannabis craving will use 
an 11-point Likert scale that ranges from 0 = “No urge” to 10 = “Extreme urge (Buckner et al., 2012). 
EMA of cannabis withdrawal will be completed with the Marijuana Withdrawal Checklist (Budney, 
Novy & Hughes, 1999; Budney et al., 2003). In our review of data from the first cohorts, we noticed 
that participants who do not do at least 10 of the 14 nighttime diary readings during the ad lib period 
are much less likely to participate in the study fully, and are much more likely to be lost to contact 
during the treatment phase. Therefore, we would like to include in the treatment phase only those 
participants who complete 10 of the 14 nightly diaries. Any participant who does not meet this 
criterion will be withdrawn from the study. 
 
EMA of physical activity will be conducted with the Fitbit Charge 2, a light-weight wristband capable of 

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tracking steps, calorie consumption, and sleep. The Vivofit has been validated previously (Simunek et 
al., in press) and features a 1-year battery life, making it an attractive alternative to more expensive 
devices. Our programmer, Mr. Jeffrey Hertzberg, has already written a program that enables us to 
capture participants’ daily step data via Fitbit’s Bluetooth syncing technology.  
 
EMA of working memory will be assessed with the visual working memory (VWM) EMA task developed 
by Schuster et al. (Schuster, Mermelstein & Hedeker, 2015). This task is quite brief (average 
assessment time of 7 sec following stimulus presentation), and participant compliance was high (87%) 
over a 1 week period across 38 prompts (Schuster, Mermelstein & Hedeker, 2015). Most important, 
performance on this task was associated with gold standard neuropsychological measures of visual 
working memory (Symbol Span, r = .60, p<.001; Spatial Addition, r = .53, p<.001) and auditory working 
memory (Digit Span, r = .42, p<.01; Letter-Number Sequencing, r = .44, p<.001). 
 
Sleep will be carefully monitored as disturbed sleep is a common symptom of cannabis withdrawal 
(Budney, NOvy & Hughes, 1999; Budney et al., 2003) that could moderate the association between 
cannabis use and functioning. EMA of sleep will consist of daily sleep diaries and objective actigraphy 
data captured by the Fitbit Charge 2. Sleep estimates will be obtained each morning using the 
Consensus Sleep Diary (Carney et al., 2012), whereas the Fitbit will track total hours of sleep (including 
naps) and sleep movement.  We have used these methods previously (5R01MH062482) and have 
experience analyzing this type of data (Ulmer et al., 2009; Ulmer et al., 2013; Calhoun et al., 2007). 
 
D.6.2. Temporary Measures During COVID-19 Pandemic. Given that exposure to COVID-19 stressors 
could have an impact on cannabis use, we would like to ask participants to complete measures related 
to stress, trauma, and coping strategies. Participants will be informed about these new study 
procedures verbally, and the study team member who informs them will provide a note-to-fil 
indicating so. Participants can refuse to complete these measures. We will ask ongoing participants to 
complete these measures at their next study visit or follow-up session. When study enrollment begins 
again, participants will complete these measures at the screening session. We are adding the CAIR 
Pandemic Impact Questionnaire (Lang, 2020; https://www.nlm.nih.gov/dr2/CAIR-PIQ_scoring.pdf), and 
another measure, COVID Core Questions, with variables of interest. If a participant endorses any item 
marked with an asterisk on the COVID Core Questions measure, we will ask them to complete a PTSD 
Checklist 5 related to that specific event. Finally, we have added a measure designed to evaluate 
participants’ satisfaction with remote study procedures. 
 
 
E. DATA ANALYSIS PLAN 
 
Multilevel modeling (MLM) will be used to analyze the EMA data. MLM is a technique for analyzing 
repeated observations of data across multiple individuals. Unlike repeated-measures ANOVA, MLM can 
incorporate time-varying (Level-1) and time-invariant (Level-2) predictors. MLM can also accommodate 
imbalanced data and unequal variances. The main conclusions drawn from this study will be based on 
the pre-specified hypotheses, which will be tested with two-sided statistical tests at an alpha of .05. 
Analyses will be performed with SAS for Windows (Version 9: SAS Institute, Cary, NC).  

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E.1. Missing Data.  
Although we do not anticipate much missing baseline data, we do anticipate missing values in the EMA 
data. MLM procedures, which will be used to analyze H1A, H1B, and the supplementary aim, are based 
on maximum likelihood estimation and use all available data. As such, MLM can accommodate data 
missing at random. Missing data will be examined to determine whether missingness is random or 
systematically associated with baseline variables. Those baseline parameters associated with 
missingness will be covaried. If data are suspected to be missing not at random (i.e., only certain values 
are likely to be missing), multiple imputation will be used to analyze non-random patterns of missing 
EMA data (Schafer & Graham, 2002).  
 
E.2. Electronic Diary Compliance and EMA Data Reduction.  
During the 8-week EMA data collection, we will calculate the percentage of responses recorded within 
5 minutes of the random prompts. We will exclude responses delayed by longer than 5 minutes from 
the analyses. Data will be aggravated at the day-to-day level to indicate the following: whether 
cannabis was used; the total number of joints/grams consumed; mean daily potency; mean daily levels 
of mental health symptoms, self-efficacy, health-related quality of life, working memory, and 
impulsivity; whether participants drove while intoxicated; and total amount of physical activity. By 
using multi-daily records to examine day-level phenomena, we will minimize measurement error 
related to poor recall and random variability. For the 2-week baseline period and each week of the 6-
week experimental period, we will calculate frequency of cannabis use as the number of days used per 
week. To quantify change in frequency of cannabis use from baseline, change scores will be calculated 
by subtracting number of days per week of cannabis use during the 2-week baseline from those levels 
recorded during each week of the mCM treatment period. A dichotomous variable will also be 
calculated reflecting whether a given participant achieved at least a 50% reduction in cannabis use 
during a given week. Thus, the data will be structured such that each participant will have up to 56 
rows of data corresponding to each day of the 2-week baseline period and 6-week mCM/non-
contingent control. Each row will contain a dichotomous indicator of whether or not cannabis was 
consumed that day, total quantity of cannabis consumed that day, total THC load of the cannabis used 
that day, daily estimates of functioning, a baseline estimate of cannabis use, and three variables 
capturing weekly varying frequency, quantity, and total THC load of cannabis use: the aforementioned 
dichotomous variable measuring whether a 50% reduction from baseline use was achieved and a 
change score, with a 0 change-score value for days falling during the 2-week baseline.  
 
E.3. Hypotheses 1a and 1b. 
To examine the effect of reduced cannabis use on functioning assessed with EMA, day-by-day outcome 
variables will be modeled via MLM as a function of treatment group (Level 2), week (Level 1; with the 
two baseline weeks treated as a single week), and treatment-by-week interactions. Daily consumption 
(total equivalent number of joints/gram consumed and total potency; Level 1) will be examined as 
potential covariates. Linear and quadratic time effects will be explored. Week-by-week effects of 
cannabis reduction will be examined in separate models. We will also examine the effects of reduced 
use by modeling day-by-day and week-by-week outcomes as a function of week-by-week reductions in 
cannabis, independent of treatment group. Linear MLM will be used to model normally distributed 

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outcome variables via PROC MIXED. Generalized linear MLM will be used to model dichotomous and 
otherwise non-normally distributed outcome variables via PROC GLIMMIX. 
 
E.4. Supplementary Aim 1.  
To examine whether reduced cannabis use moderates or is moderated by other factors (e.g., sex 
differences), the models examined in Hypotheses 1a and 1b will be modified to include potential Level-
1 (e.g., day-to-day craving, sleep) and Level-2 (e.g., sex, baseline cannabis use, self-efficacy) predictors 
as well as their interactions with week-by-week change in cannabis use. For instance, to determine 
whether a week-by-week reduction in cannabis use moderates the association of day-by-day cannabis 
use (or abstinence) with daily working memory, whether or not participants used cannabis that day 
(Level 1) will be added as a predictor of working memory as will its interaction with week-by-week 
change in frequency or quantity of cannabis use. This particular model could thus determine whether 
cannabis users demonstrate greater cognitive functioning on abstinent days when they reduce their 
cannabis use. 
 
E.5. Supplementary Aim 2.  
We will first examine the association between race (dichotomous) and perceived discrimination with 
logistic regression. As described in the parent application, multilevel modeling (MLM) will be used to 
analyze EMA data. Generalized linear MLM will be used to evaluate the association of discrimination 
(baseline scores from the PEDQ-CV-B, EOD, EDS) with reported cannabis-related problems b) 
magnitude of ad lib cannabis use (grams) and b) magnitude of cannabis reduction during a 6-week 
intervention period (grams reduction). Specifically, daily levels of cannabis-related problems and 
cannabis use (grams) from the 2-week ad lib period will be analyzed in separate models as a function of 
perceived discrimination. A third model will analyze daily cannabis use (grams) from the 6-week 
intervention period as a function of perceived discrimination, controlling for mean daily cannabis use 
during the ad lib period. All models will include group assignment as a covariate. 
 
 
E.6. Supplementary Aim 3.  
To examine the effect of daily discrimination on day-to-day outcome variables daily cannabis 
consumption (total equivalent number of joints/gram consumed and total potency) will be modeled 
via generalized linear MLM as a function of daily levels of discrimination, treatment phase (ad lib vs. 
treatment), and the interaction between daily discrimination and treatment phase. Additionally, 
treatment group and race will be covaried. 
 
Drs. Kimbrel and Beckham have a VA Merit Review in which they are evaluating the impact of reduced 
cannabis use on veterans with posttraumatic stress disorder. We anticipate that we may want to 
combine the data collected in the Duke study with those VA data. We have added a check box to the 
informed consent form so that participants can indicate their willingness to have their deidentified 
data combined with VA data. These data will be combined for secondary data analyses, to be 
determined.  
 
E.5. Power Calculations. 

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Power calculations are based on Hypotheses 1a and 1b and were performed with Power Analysis and 
Sample Size software (PASS, Version 12: NCSS LLC, Kaysville, Utah). We conservatively estimate that as 
many as 12 participants could potentially need to be excluded due to sustained abstinence, leaving a 
minimum of 60 participants available for analysis. The corresponding analyses make use of multilevel 
data with a total sample of up to 3,360 records (60 participants X 56 daily observations). Because there 
will only be 7 unique estimates per participant of week-to week change in frequency of cannabis use, a 
conservative minimum estimate of the effective sample size is 420 (60 participants X 7 weekly 
observations). Although these observations are non-independent given clustering within participants, 
hence the use of MLM, accounting for variation between participants will reduce the error variance in 
the dependent variables. Thus, the effective sample size will very likely exceed 420. An effective 
sample size of 420 has 90% power to detect small-to-medium treatment effects equivalent to Cohen’s 
d = 0.30 or associations equivalent to r = .16. 
 

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References 
 
Adkisson, K., Cunningham, K.C., Dedert, E.A., Dennis, M.F., Calhoun, P.S., Elbogen, E.B., Beckham, J.C., 
& Kimbrel, N.A. (manuscript under review). Cannabis use disorder and post-deployment suicide 
attempts in Iraq/Afghanistan-era veterans. 
Azagba, S., Shan, L., & Latham, K. (2020). Rural-urban differences in cannabis detected in fatally injured 
drivers in the United States. Preventive medicine, 132, 105975. 
Babor TF, de la Fuente JR, et al., 1989. AUDIT The Alcohol Use Disorders Identification Test: Guidelines 
for use in primary health care (WHO/MNH/DAT 89.4). Geneva, Switzerland. 
Bechara, A., Damasio, A.R… Anderson, S.W., 1994. Insensitivity to future consequences following 
damage to human prefrontal cortex. Cognition, 50, 7-15.  
Beck AT, Steer RA. (1991). Manual for Beck Scale for Suicide Ideation. Psychological Corp: San Antonio. 
Beckham, J.C., Adkisson, K.A., Hertzberg, J., Kimbrel, N.A., Budney, A.J., Stephens, R.S., Moore, S.D., & 
Calhoun, P.S. (manuscript under review). Mobile contingency management as an adjunctive 
treatment for co-morbid cannabis use disorder and cigarette smoking. 
Beckham, J.C., Calhoun, P.S. et al., 2013. Predictors of lapse in first week of smoking abstinence in PTSD 
and non-PTSD smokers. Nicotine & Tobacco Research, 15, 1122-9. PMCID: PMC3646653. 
Bonnet, U., Borda, T…Specka, M., 2015. Abstinence phenomena of chronic cannabis-addicts 
prospectively monitored during controlled inpatient detoxification (Part II): Psychiatric complaints 
and their relation to delta-9-tetrahydrocannibinol and its metabolites in serum. Drug and Alcohol 
Dependence, 155, 302-306.  
Booth, M.L., 2000. Assessment of Physical Activity: An International Perspective. Research Quarterly for 
Exercise and Sport, 71, s114-20. 
Borges, G., Bagge, C.L., Orozco, R., 2016. A literature review and meta-analyses of cannabis use and 
suicidality. J Affect Disord., 195, 63-74.  
Bovin, M.J., Black, S.K., Rodriguez, P., Lunney, C.A., Kleiman, S.E., Weathers, F.W., Schnurr, P.P., Spira, 
J., Keane, T.M., & Marx, B. (2018). Development and validation of a measure of PTSD-related 
psychosocial functional impairment: The Inventory of Psychosocial Functioning. Psychological 
Services, 15, 216-229. 
Brondolo, E., Kelly, K.P., Coakley, V., Gordon, T., Thompson, S., Levy, E. & Contrada, R.J. (2005). The 
Perceived Ethnic Discrimination Questionnaire: Development and preliminary validation of a 
community version 1. Journal of Applied Social Psychology, 35, 335-65.  
Bruins, J., Pijnenborg, M.G…Jorg, F., 2016. Cannabis use in people with severe mental illness: The 
association with physical and mental health—a cohort study. A pharmacotherapy monitoring and 
outcome survey study. Journal of Psychopharmacology, 30, 354-362.  
Buckner, J.D., Crosby, R.D…Schmidt, N.B., 2012. Immediate antecedents of marijuana use: An analysis 
from ecological momentary assessment. Journal of Behavior Therapy and Experimental Psychiatry, 
43, 647-655. 
Budney, A.J., Moore, B A…Hughes, J.R., 2003. The time course and significance of cannabis withdrawal. 
Journal of Abnormal Psychology, 112(3), 393-402. 
Budney, A.J., Novy, P.L., Hughes, J.R., 1999. Marijuana withdrawal among adults seeking treatment for 
marijuana dependence.  Addiction, 94(9), 1311-1322. 
Budney, A.J., Stanger, C…Walker, D.D., 2015. Computer-assisted behavioral therapy and contingency 

Full Protocol, PI version date 8/16/2022  
 
Page 26 of 30  
management for cannabis use disorder. Psychology of Addictive Behaviors, 29, 501-511. 
Burckhardt, C.S.,  & Anderson, K.L. (2003). The Quality of Life Scale: Reliability, validity, and utilization. 
Health Qual Life Outcomes, 1, 60. 
Calhoun, P.S., Wiley, M…Beckham, J.C., 2007. Objective evidence of sleep disturbance in women with 
posttraumatic stress disorder. Journal of Traumatic Stress, 20, 1009-1018. 
Carpenter VL, Hertzberg JS… Beckham, J.C., 2015. Multicomponent smoking cessation treatment 
including mobile contingency management for smoking cessation in homeless veteran smokers. 
Journal of Clinical Psychiatry, 76, 959-964. PMCID: PMC4522209. 
Carney, C.E., Buysse, D.J…Morin, C.M., 2012. The consensus sleep diary: Standardizing prospective 
sleep self-monitoring. Sleep, 35, 287-302. 
Carroll, K.M., Easton, C.J…Rounsaville, B.J., 2006. The use of contingency management and 
motivational/skills-building therapy to treat young adults with marijuana dependence. Journal of 
Consulting & Clinical Psychology, 74, 955-966. 
Chung, F., Subramanyam, R., Liao, P., Sasaki, E., Shapiro, C., & Sun, Y. (2012). High STOP-Bang score 
indicates a high probability of obstructive sleep apnoea. British Journal of Anaesthesia, 108, 768-
775. 
Compton, W.M., Gfroerer, J…Finger, M.S., 2014. Unemployment and substance abuse outcomes in the 
United States 2002-2010. Drug and Alcohol Dependence, 142, 350-353.  
Copeland, J., Gilmour, S., Gates, P., & Swift, W. (2005). The Cannabis Problems Questionnaire: Factor 
structure, reliability, and validity. Drug and Alcohol Dependence, 80, 313-319.  
Copeland, J., Pokorski, I., 2016. Progress toward pharmacotherapies for cannabis-use disorder: An 
evidence-based review. Substance Abuse and Rehabilitation, 7, 41-53.  
Davis, A.K., Osborn, L.A…Baik, K.D., 2014. Development and evaluation of the Marijuana Reduction 
Strategies Self-Efficacy Scale. Psychology of Addictive Behaviors, 28, 575-579. 
Dennis, P.A., Kimbrel, N.A…Beckham, J.C., 2016. Trauma and autonomic dysregulation: Episodic – 
versus systemic – negative affect underlying cardiovascular risk in posttraumatic stress disorder. 
Psychosomatic Medicine, E-pub ahead of print. 
Derogatis, L.R., 1983. SCL-90: Administration, Scoring and Procedures Manual-I for the Revised Version 
and other Instruments of the Psychopathology Rating Scale Series. Baltimore, MD: Johns Hopkins 
University School of Medicine, Clinical Psychometrics Research Unit.Donovan, D.M., Bigelow, 
G.E…Wells, E.A., 2011. Primary outcome indices in illicit drug dependence treatment research: 
Systematic approach to selection and measurement of drug use end-points in clinical trials. 
Addiction, 107, 694-708. 
Dutra, L., Stathopoulou, G… Otto, M.W., 2008. A meta-analytic review of psychosocial interventions for 
substance use disorders. The American Journal of Psychiatry, 165, 179-87. 
Fairman, B. J., Furr-Holden, C. D., & Johnson, R. M. (2019). When marijuana Is used before cigarettes or 
alcohol: Demographic predictors and associations with heavy use, cannabis use disorder, and other 
drug-related outcomes. Prevention science: The Official Journal of the Society for Prevention 
Research, 20(2), 225–233. 
Godin, G. (2011). The Godin-Shephard leisure-time physical activity questionnaire. Health & Fitness 
Journal of Canada, 4(1), 18-22. 
Goodman, S., Leos-Toro, C., & Hammond, D. (2019). Methods to assess cannabis consumption in 
population surveys: Results of cognitive interviewing. Qualitative Health Research, 29(10), 1474-

Full Protocol, PI version date 8/16/2022  
 
Page 27 of 30  
1482.  
Gratz, K L., 2001. Measurement of deliberate self-harm: Preliminary data on the Deliberate Self-Harm 
Inventory. Journal of Psychopathology & Behavioral Assessment, 23(4), 253-263. 
Gwaltney, C.J., Shiffman, S…Paty, J.A., 2005. Dynamic self-efficacy and outcome expectancies: 
Prediction of smoking lapse and relapse. J of Abnormal Psychology, 114, 661-675. 
Hall, W., Degenhardt, L., 2009. Adverse health effects of non-medical cannabis use. Lancet, 374, 1383-
1391. 
Hamilton et al., 2011. The PhenX toolkit: Get the most from your measures. Am J Epidem, 174, 253-
260. 
Hasin, D.S., Saha, T.D…Grant, B.F., 2015. Prevalence of marijuana use disorders in the United States 
between 2001-2002 and 2012-2013. JAMA Psychiatry, 72, 1235-1242.  
Hasin, D. S., Shmulewitz, D., & Sarvet, A. L. (2019). Time trends in US cannabis use and cannabis use 
disorders overall and by sociodemographic subgroups: A narrative review and new findings. The 
American Journal of Drug and Alcohol Abuse, 45(6), 623–643. 
Heatherton, T.F., Kozlowski, L.T., Frecker, R.C. & Fagerström, K.O. (1991). The Fagerström Test for 
Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of 
Addiction, 86, 1119-27. 
Hiller, W., & Goebel, G. (1992). Rapid assessment of tinnitus-related psychological distress using the 
Mini-TQ. International Journal of Audiology, 43, 600-604. 
Fergusson, D.M., Horwood, L.J., Beautrais, A.L., 2003. Cannabis and educational achievement. 
Addiction, 98, 1681-1692. 
First MB, Williams JBW, Karg RS, et al., 2015. Structured Clinical Interview for DSM-5. American 
Psychiatric Association: Arlington, VA.  
Gentes, E.L., Schry, A.R…Calhoun, P.S., 2015. Prevalence and correlates of cannabis use in an 
outpatient VA posttraumatic stress disorder clinic. Psychology of Addictive Behaviors, 30, 415-421.  
Gioia G.A., Isquith P. K., Guy S. C., Kenworthy L., 2000. Behavior Rating Inventory of Executive Function. 
Odessa, FL: Psychological Assessment Resources. 
Hertzberg, J.S., Carpenter, V.L…Beckham, J.C., 2013. Mobile contingency management as an adjunctive 
smoking cessation treatment for smokers with posttraumatic stress disorder. Nicotine & Tobacco 
Research, 15, 1934-1938. PMCID: PMC3790624.  
Jin, L.Z., Rangan, A…Heitmann, B.L., 2017. Association between use of cannabis in adolescence and 
weight change into midlife. PLoS One, 12, e0168897. 
Kadden, R.M., Litt, M.D… Petry, N.M., 2007. Abstinence rates following behavioral treatments for 
marijuana dependence. Addictive Behaviors, 32, 1220-36. 
Kelly, E., Darke, S., Ross, J., 2004. A review of drug use and driving: epidemiology, impairment, risk 
factors and risk perceptions. Drug and Alcohol Review, 23, 319-344.  
Kessler, R.C., Andrews, G., et al., 2002. Short screening scales to monitor population prevalence and 
trends in nonspecific psychological distress. Psychological Medicine. 32(6), 959-976. 
Keum, B., Thai, C., Truong, N., Ahn, L. & Lu, Y. (2018). Factor structure and measurement invariance of 
the Perceived Ethnic Discrimination Questionnaire-Community Version Brief. International Journal 
of Culture and Mental Health, 11, 498-512. 
Khalfa, S., Dubal, S., Veuillet, E., Perez-Diaz, F., Jouvent, R., & Collet, L. (2002). Psychometric 
normalization of a hyperacusis questionnaire. ORL, 64, 436-442.  

Full Protocol, PI version date 8/16/2022  
 
Page 28 of 30  
Kimbrel, N.A., Calhoun, P. S… Beckham, J. C., 2014. The factor structure of psychiatric comorbidity 
among Iraq/Afghanistan veterans and its relationship to violence, incarceration, suicide attempts, 
and suicidality. Psychiatry Research, 220, 397-403. PMCID: PMC4697758. 
Kimbrel, N.A., Calhoun, P.S…Beckham, J.C., manuscript in preparation. Mental health outcomes as a 
function of cannabis use disorder remission status.  
Kimbrel, N.A., Meyer, E.C…Morissette, S.B., in press. The impact of cannabis use disorder on suicidal 
and nonsuicidal self-injury in Iraq/Afghanistan-era veterans with and without mental health 
disorders. Suicide and Life-Threatening Behavior.   
Kimbrel, N.A., Newins, A.R... Calhoun, P.S., 2017. Cannabis use disorder and suicide attempts in 
Iraq/Afganistan-era veterans. Journal of Psychiatric Research, 89, 1-5. 
Krebs, E., Lorenz, K., Bair, M., Damush, T,M., Wu, J., Sutherland, J.M., Asch, S.M., & Kroenke, K. (2009). 
Development and initial validation of the PEG, a three-item scale assessing pain intensity and 
interference. Journal of General Internal Medicine, 24, 733–8. 
Krieger, N., Smith, K., Naishadham, D., Hartman, C. & Barbeau, E.M. (2005). Experiences of 
discrimination: Validity and reliability of a self-report measure for population health research on 
racism and health. Social Science & Medicine, 61, 1576-96. 
Kubany, E.S., Haynes, S.N., Leisen, M.B. et al. (2000). Development and preliminary validation of a brief 
broad-spectrum measure of trauma exposure: The traumatic life events questionnaire. 
Psychological Assessment, 12, 210-24. 
Lattal, K.A., 2010. Delayed reinforcement of operant behavior. Journal of the Experimental Analysis of 
Behavior, 93, 129-39.  
Lee, D., Huestis, M.A., 2014. Current knowledge on cannabinoids in oral fluid. Drug Testing and 
Analysis, 6, 88-111. 
Lee, C.M., Neighbors, C… Grossbard, J.R., 2009. Development and preliminary validation of a 
comprehensive marijuana motives questionnaire. Journal of Studies on Alcohol and Drugs, 70, 279-
287.  
Lejuez, C.W., Read, J.P…Brown, R.A., 2002. Evaluation of a behavioral measure of risk taking: The 
Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied, 8, 75-84.  
Lynam, D.R, Smith, G.T., et al., 2006. The UPPS-P: Assessing five personality pathways to impulsive 
behavior. West Lafayette, IN: Purdue University. 
McCaffrey, D.F., Pacula, R.L…Ellickson, P., 2010. Marijuana use and high school dropout: The influence 
of the unobservables. Health Economics, 19, 1281-1299. 
Metrik, J., Bassett, s., Aston, E.R., Jackson, K.M., & Borsari, B. (2018). Medicinal versus recreational 
cannabis use among returning veterans. Translational Issues in Psychological Science, 4, 6-20. 
Morin, C.M., Belleville, G., Belanger, L., & Ivers, H. (2011). The Insomnia Severity Index: Psychometric 
indicators to detect insomnia cases and evaluate treatment response. Sleep, 34, 601-608.  
National Institute on Drug Abuse, 2016. Drug facts: Drugged driving. [Internet]. Retrieved 2/11/17. 
https://www.drugabuse.gov/publications/drugfacts/drugged-driving  
Novaco, R. W., Swanson, R. D., Gonzalez, O. I., Gahm, G. A., & Reger, M. D. (2012). Anger and 
postcombat mental health: validation of a brief anger measure with U.S. soldiers postdeployed 
from Iraq and Afghanistan. Psychological Assessment, 24(3), 661-675. doi:10.1037/a0026636 
Ong, A. D., Fuller-Rowell, T., & Burrow, A. L. (2009). Racial discrimination and the stress 
process. Journal of personality and social psychology, 96(6), 1259. 

Full Protocol, PI version date 8/16/2022  
 
Page 29 of 30  
Pate, R.R., Heath, G.W…Trost, S.G., 1996. Associations between physical activity and other health 
behaviors in a representative sample of U.S. adolescents. American Journal of Public Health, 86, 
1577-1581.  
Petry, N.M., DePhilippis, D., Rash, C.J., Drapkin, M., McKay, J.R., 2014. Nationwide dissemination of 
contingency management: The Veterans Administration initiative. The American Journal on 
Addictions, 23, 205-210. 
Posner, K., Brown, G.K., Stanley, B., et al. (2011). The Columbia-Suicide Severity Rating Scale: Initial 
validity and internal consistency findings from three multisite studies with adolescents and adults. 
Am J Psychiatry, 168, 1266-1277.  
Potter, L. N., Brondolo, E., & Smyth, J. M. (2019). Biopsychosocial correlates of discrimination in daily 
life: A review. Stigma and health, 4(1), 38–61. https://doi.org/10.1037/sah0000120 
Richards, J.B., Zhang, L., Mitchell, S.H., De Wit, H., 1999. Delay or probability discounting in a model of 
impulsive behavior: Effect of alcohol. Journal of the Experimental Analysis of Behavior, 71, 121-143. 
Robinson, S.M., Sobell, L.C., Sobell, M.B., Leo, G.I., 2014. Reliability of the Timeline Followback for 
Cocaine, Cannabis, and Cigarette Use. Psychol Addict Behav, 28, 154-162.  
Schafer, J.L., Graham, J.W., 2002. Missing data: Our view of the state of the art. Psychological 
methods, 7, 147-177. 
Schreiner, A.M., Dunn, M.E., 2012. Residual effects of cannabis use on neurocognitive performance 
after prolonged abstinence: A meta-analysis. Experimental and Clinical Psychopharmacology, 20, 
420-429. 
Schuster, R.M., Hanly, A., Gilman, J., Budney, A.J., Vandrey, R., Evins, A.E., 2016. A contingency 
management method for 30-days abstinence in non-treatment seeking young adult cannabis users. 
Drug Alcohol Depend, 167, 199-206.  
Schuster, R.M., Mermelstein, R.J., Hedeker, D., 2015. Acceptability and feasibility of a visual working 
memory task in an ecological momentary assessment paradigm. Psychological Assessment, 27, 
1463-1470. 
Sheehan, D.V., 1983. The anxiety disease. Charles Scribner and Sons. 
Sherman, B.J., McRae-Clark, A.L., 2016. Treatment of cannabis use disorder: Current science and future 
outlook. Pharmacotherapy, 36, 511-535.  
Slavet, J.D., Stein, L.A.R., Colby, S.M., Barnett, N.P., Monti, P.M., Golembeske, C., & Lebeau-Craven, R. 
(2006). The Marijuana Ladder: Measuring motivation to change marijuana use in incarcerated 
adolescents. Drug and Alcohol Dependence, 83, 42-48. 
Stanley, B., & Brown, G.K. (2012). Safety planning intervention: A brief intervention to mitigate suicide 
risk. Cognitive and Behavioral Practice, 19, 256-264. 
Stephens, R.S., Roffman, R.A., Curtin, L., 2000. Comparison of extended versus brief treatments for 
marijuana use. Journal of Consulting & Clinical Psychology, 68, 898-908.  
Straus, M., Hamby, S.L., Boney-McCoy, S., & Sugarman, D. (1996). The Revised Conflict Tactics Scales 
(CTS2): Development and preliminary psychometric data. Journal of Family Issues, 17, 286-316 
Substance Abuse and Mental Health Services Administration, 2014. Results from the 2013 National 
Survey on Drug Use and Health: Summary of National Findings (HHS Publication No. (SMA) 14-4887. 
NSDUH Series H-49). Rockville, MD: Substance Abuse and Mental Health Services Administration.  
The WHOQOL Group, 1994.The development of the World Health Organization quality of Life 
Assessment instrument (the WHOQOL),”in Quality of Life Assessment: International Perspectives, 

Full Protocol, PI version date 8/16/2022  
 
Page 30 of 30  
eds J. Orley and W. Kuyken (Heidelberg: Springer). 
Tiffany, S.T., Friedman, L… Jackson, R., 2011. Beyond drug use: A systematic consideration of other 
outcomes in evaluation of treatments for substance use disorders. Addiction, 107, 709-718.  
Tomko, R.L., Carpenter, R.W…Trull, T.J., 2014. Measuring impulsivity in daily life: The Momentary 
Impulsivity Scale. Psychological Assessment, 26, 339-349. 
Ulmer, C.S., Calhoun, P.S…Beckham, J.C., 2009. Sleep disturbance and baroreceptor sensitivity in 
women with posttraumatic stress disorder. Journal of Traumatic Stress, 22. PMCID: PMC2798909. 
Ulmer, C.S., Calhoun, P.S…Beckham, J.C., 2013. Nocturnal blood pressure non-dipping, posttraumatic 
stress disorder, and sleep quality in woman. Behavioral Medicine, 39, 111-121. PMCID: 
PMC3964784. 
Üstün TB. (2010). Measuring health and disability: Manual for WHO disability assessment schedule 
WHODAS 2.0. WHO. 
van der Pol, P., Liebregts, N., Graaf, R.D., Korf, D.J., van den Brink, W., & van Laar, M. (2013). Validation 
of self-reported cannabis dose and potency: An ecological study. Addiction, 108, 1801–1808. 
Volkow, N.D., Baler, R.D… Weiss, S.R.B., 2014. Adverse health effects of marijuana use. New England 
Journal of Medicine, 370, 2219-2227.  
Volz, A.R., Dennis, P.A…Beckham, J.C., 2014. The role of daily hassles and distress tolerance in 
predicting abstinence among smokers. Nicotine and Tobacco Research, 16, 872-5.  
Walsh, S. D., Kolobov, T., & Harel-Fisch, Y. (2018). Social capital as a moderator of the relationship 
between perceived discrimination and alcohol and cannabis use among immigrant and non-
immigrant adolescents in Israel. Frontiers in psychology, 9, 1556 
Watson, D., Clark, L.A., Tellegen, A., 1988. Development and validation of brief measures of positive 
and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–
1070. 
Weathers, F.W., Litz, B.T., Keane, T.M., Palmieri, P. A., Marx, B. P., & Schnurr, P.P. (2013). The PTSD 
Checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov. 
Wechsler, D., 2008. Wechsler Memory Scale (4th ed.). San Antonio, TX: Pearson. 
Wechsler, D., 2008. Wechsler Adult Intelligence Scale (4th ed.). San Antonio, TX: Pearson. 
Williams, D.R., Yu. Y., Jackson, J.S., & Anderson, N.B. (1997). Racial differences in physical and mental 
health: Socioeconomic status, stress, and discrimination. Journal of Health Psychology, 2, 335-351. 
Wu, M.S., Lewin, A.B., Murphy, T.K., & Storch, E.A. (2014). Misophonia: Incidence, phenomenology, 
and clinical correlates in an undergraduate student sample. Journal of Clinical Psychology, 70, 994-
1007.  
Wu, L. T., Zhu, H., & Swartz, M. S. (2016). Trends in cannabis use disorders among racial/ethnic 
population groups in the United States. Drug and alcohol dependence, 165, 181–190. 
Zwerling, C., Ryan, J., Orav, E.J., 1990. The efficacy of preemployment drug screening for marijuana and 
cocaine in predicting unemployment outcome. JAMA, 264, 2639-2643.