Examining the Impact of Simultaneous Alcohol and Cannabis Use on Alcohol Consumption and Consequences: Protocol for an Observational Ambulatory Assessment Study in Young Adults
Bibliographic record
Abstract
BACKGROUND: There is significant conflicting evidence as to how using cannabis while drinking alcohol (ie, simultaneous alcohol and cannabis use) impacts alcohol volume consumed, patterns of drinking, and alcohol-related consequences. The impact of simultaneous use on drinking outcomes may be influenced by several within-person (eg, contextual) and between-person (individual) factors. OBJECTIVE: This study was designed to examine naturalistic patterns of alcohol and cannabis use to understand how simultaneous use may impact drinking outcomes. The primary aims were to understand the following: (1) if simultaneous use is associated with increased alcohol consumption and riskier patterns of drinking, (2) if simultaneous use leads to increased alcohol consequences, and (3) how contextual circumstances moderate the impact of simultaneous use on consumption and consequences. METHODS: Data collection involves a 28-day ambulatory assessment protocol in which a sample of non-treatment-seeking young adults who report simultaneous use of alcohol and cannabis complete ecological momentary assessments (random, event-contingent, and time-contingent surveys) of alcohol and cannabis use, contexts, motives, and consequences on their personal smartphones while continuously wearing an alcohol biosensor bracelet. Participants also complete a baseline assessment, brief internet-based check-in on day 14, and a final session on day 28. Community-based recruitment strategies (eg, social media and flyers) were used to enroll 95 participants to obtain a target sample of 80, accounting for attrition. RESULTS: Recruitment and data collection began in May 2021 and continued through June 2024. Initial results for primary aims are expected in October 2024. As of March 2024, the project had recruited 118 eligible participants, of whom 94 (79.7%) completed the study, exceeding initial projections for the study time frame. Remaining recruitment will provide the capacity to probe cross-level interactions that were not initially statistically powered. Strengths of the project include rigorous data collection, good retention and compliance rates, faster-than-expected enrollment procedures, use of a novel alcohol biosensor, and successful adaptation of recruitment and data collection procedures during the COVID-19 pandemic. CONCLUSIONS: This is the first investigation to assess the key momentary predictors and outcomes of simultaneous use as well as self-reported and objective (via alcohol biosensor) measures of alcohol consumption and patterns. The results of this study will inform prevention efforts and studies of individuals who use cannabis who are engaged in alcohol treatment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58685.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".