Abstract
Research on the mechanisms of behavior change (MOBC) for the treatment of cannabis use disorder (CUD) has limitations due to an overemphasis on abstinence, rigid definitions, and a lack of consideration for demographic variation. Our objective was to address these limitations by investigating the following emerging MOBC properties: (1) the viability of craving and use reductions as MOBCs, (2) if immediate treatment outcomes facilitate longer-term behavioral changes, and (3) if the course of CUD treatment differs between men and women. Treatment-seeking individuals (n = 186; 70.1% male; 57.2% White) with CUD, aged 18-50 (M = 30.90, SD = 8.95), participated in a 12-week multi-site clinical trial with a 4-week follow-up. We collected weekly self-reports and biweekly creatinine-corrected cannabinoid urine concentrations. We employed moderated multigroup four-timepoint longitudinal path analyses to analyze treatment progression. We examined (H1) if mid-treatment reductions in craving and cannabis use mediated the direct effect of CUD severity at the screening visit on immediate treatment outcomes (anxiety, depression, and cannabis-related problems), (H2) if immediate treatment outcomes mediated the direct effect of mid-treatment MOBCs on a four-week follow-up outcome (quality-of-life challenges), and (H3) if gender moderated these effects. We found that craving reduction may be a MOBC for the full and men samples. However, for women, depression functioned concurrently as an immediate outcome and a MOBC for follow-up quality-of-life challenges. Additionally, we observed gender differences in treatment progressions; for men, the MOBC was craving reduction, while for women, it was reducing cannabis use. These findings indicate that our understanding of CUD treatments may be more nuanced than the existing literature suggests.
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Notes
The participants completed the PhenX Toolkit demographics form at the screening, which included questions about gender, date of birth, ethnicity, race, education, employment, and marital status.
We used list-wise deletion to ensure a consistent sample size across the parameter estimates within our models to ensure we could not attribute the difference within and between the models to power differences from varying sample sizes.
For full output on these demographic differences, please visit the OSF at https://doi.org/10.17605/OSF.IO/NSWBK.
Researchers set these cut-offs for these fit indices to provide evidence that the model has significant fit issues. If a model does not pass this threshold, it is unlikely that power is relevant to the model fit because increasing the sample size is unlikely to improve the fit.
We recognize that power analyses are designed to predict future events and have different interpretations when performed after study completion (Quach et al., 2022; Zhang et al., 2019). Given differing views in the literature, we opted to include this information for interested readers. Further, although the RMSEA is the most common fit index for power analyses, it is notably biased in sample sizes below 200 (Taasoobshirazi & Wang, 2016). Therefore, we used the GFI, which is less likely to be biased by sample size (Bagozzi & Yi, 1988; Jöreskog & Sörbom, 1982).
We do not use fit indices to determine model fit in this study, so overpowered models do not compromise our interpretations. See the Fit Assessment subsection for more details.
We used the terms "men" and "women" to describe gender instead of “male” and female,” which typically describe sex. We acknowledge that the demographic form may not align with the current understanding of sex and gender. No participant selected unknown or refused.
All measures in the current study were symptom or negative outcome measures, so all the scales followed the same direction (i.e., higher scores indicated worse characteristics).
Type III errors involved failing to reject the null hypothesis because of a significant effect in the opposing direction.
Although the estimation methods and sample sizes varied between the groups, these differences were not confounding because the parameter estimates across the groups used the same method, we determined gender differences with multigroup path analyses, and the power was sufficient for all samples.
The significant indirect effects and mediations involved ≅ 47% of paths, thereby explaining all notable differences between men and women. Thus, there are no significant differences between a model where gender moderates only mediations compared to a model where gender moderates all paths, Δχ2(8) = 5.15, p = .741. We interpreted the simpler model (Kline, 2015), where gender moderates all paths because the equality restraints provide no meaningful information or better fit.
We replicated these effects with a treatment condition variable controlling the five MOBCs variables and found that treatment groups did not significantly explain any variability or MOBC effects across all samples. We provide these findings on the Open Science Framework: https://tinyurl.com/54w5y9wj.
We emphasize the importance of prioritizing idiosyncratic strategies for patients to reduce the potential of stereotyping and prejudice. These findings may provide important considerations but applying them to all individuals may be problematic.
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Acknowledgements
I want to express my heartfelt gratitude to my passionate research mentors - Janet, Acacia, Kathleen, David, and Brian - who have guided me with their wisdom and expertise. Lifelong friends - Anthony, Leah, Taylor, Rudy, Amanda, and Emily - have enriched my journey with their companionship and encouragement. I am profoundly thankful for my incredibly supportive family - Marcie, Jeremy, Michael, Marilyn, Catya, and Eric - whose love and belief in me have been the bedrock of my resilience and perseverance. I am grateful to the thousands of participants who devoted their time to improving our understanding of substance use disorders and the hundreds of patients who have shared their stories with me. I cannot overstate your collective impact on my work and growth.
Data and Code Availability
All data (raw and cleaned), syntax and raw output (SPSS and R), and trial protocol (materials) are available on an anonymous OSF (https://doi.org/10.17605/OSF.IO/NSWBK).
Funding
The National Institute of Drug Abuse (NIDA) supported this work through grants awarded to Kevin M. Gray (NIDA U10 DA013727-CTN0053) and Brian J. Sherman (NIDA K23 DA045099). The National Institute of Drug Abuse supported Bryant M. Stone while writing this manuscript through their postdoctoral fellowship training grant (T32DA007292).
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BMS: Conceptualization, Methodology, Software, Validation, Formal Analysis, Writing – Original Draft, Writing – Review & Editing, Visualization, Project Administration. KMG: Methodology, Validation, Investigation, Resources, Data Curation, Writing – Review & Editing, Project Administration, Funding Acquisition. RLT: Formal Analysis, Writing – Original Draft; Writing – Review & Editing. ARM: Writing – Review & Editing. BJS: Conceptualization, Validation, Writing – Original Draft, Writing – Review & Editing, Supervision, Project Administration.
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The Institutional Review Board at each site approved the trial.
Informed Consent
Participants read and agreed to an informed consent document and were debriefed at the end of the clinical trial. Participants provided informed consent for publishing their anonymized data.
Conflict of Interest
KMG has provided consultation to Jazz Pharmaceuticals and received research support from Aelis Farma. RLT has provided consultation to the American Society of Addiction Medicine. All remaining authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.
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Preregistration
Kevin Gray originally collected the data during the ACCENT pharmacotherapy trial (NCT01675661; Gray, 2018), which was pre-registered. We registered anonymously on OSF (https://doi.org/10.17605/OSF.IO/UEFN8).
Open Science Practices
We reported how we determined our sample size, all data exclusions, all manipulations, and all measures (Simmons et al., 2012). An anonymous registration for this study is available at https://doi.org/10.17605/OSF.IO/UEFN8. In addition, all data (raw and cleaned), syntax, raw output, protocols, materials, and manuscript files are available on the anonymous Open Science Framework: https://doi.org/10.17605/OSF.IO/NSWBK, (Anonymous view-only link; we will add author details and project updates upon publication.) See “Appendix A” for more information on our open science practices.
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Highlights
• Mechanisms of behavior change (MOBCs) are constructs that enable therapeutic changes
• We aimed to improve MOBC research for cannabis use disorder
• Reducing cannabis use and craving improves outcomes irrespective of abstinence
• Immediate treatment outcomes facilitate longer-term behavioral changes
• The course of CUD treatment differs between men and women
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Stone, B.M., Gray, K.M., Tomko, R.L. et al. Differential Mechanisms of Behavior Change in Cannabis Use Disorder Treatments: Functional Improvements and Clinical Implications. Int J Ment Health Addiction (2024). https://doi.org/10.1007/s11469-023-01231-7
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DOI: https://doi.org/10.1007/s11469-023-01231-7