Reducing Cannabis Use in Young Adults With Psychosis Using iCanChange, a Mobile Health App: Protocol for a Pilot Randomized Controlled Trial (ReCAP-iCC)
Bibliographic record
Abstract
BACKGROUND: Cannabis use is the most prevalent among adolescents and young adults; frequent consumption is associated with cannabis use disorder (CUD) and psychosis, with a high prevalence (up to 50%) of CUD in individuals with first-episode psychosis (FEP). Early Intervention Services (EIS) for psychosis include face-to-face psychosocial interventions for CUD, because reducing or discontinuing cannabis use improves clinical and health care service use outcomes. However, multiple barriers (eg, staff availability and limited access to treatment) can hinder the implementation of these interventions. Mobile health (mHealth) interventions may help circumvent some of these barriers; however, to date, no study has evaluated the effects of mHealth psychological interventions for CUD in individuals with FEP. OBJECTIVE: This study describes the protocol for a pilot randomized controlled trial using a novel mHealth psychological intervention (iCanChange [iCC]) to address CUD in young adults with FEP. iCC was developed based on clinical evidence showing that in individuals without psychosis, integrating the principles of cognitive behavioral therapy, motivational interviewing, and behavioral self-management approaches are effective in improving cannabis use-related outcomes. METHODS: Consenting individuals (n=100) meeting the inclusion criteria (eg, aged 18-35 years with FEP and CUD) will be randomly allocated in a 1:1 ratio to the intervention (iCC+modified EIS) or control (EIS) group. The iCC is fully automatized and contains 21 modules that are completed over a 12-week period and 3 booster modules available during the 3-month follow-up period. Validated self-report measures will be taken via in-person assessments at baseline and at 6, 12 (end point), and 24 weeks (end of trial); iCC use data will be collected directly from the mobile app. Primary outcomes are intervention completion and trial retention rates, and secondary outcomes are cannabis use quantity, participant satisfaction, app use, and trial recruiting parameters. Exploratory outcomes include severity of psychotic symptoms and CUD severity. For primary outcomes, we will use the chi-square test using data collected at week 12. We will consider participation in iCC acceptable if ≥50% of the participants complete at least 11 out of 21 intervention modules and the trial feasible if attrition does not reach 50%. We will use analysis of covariance and mixed-effects models for secondary outcomes and generalized estimating equation multivariable analyses for exploratory outcomes. RESULTS: Recruitment began in July 2022, and data collection is anticipated to be completed in July 2024. The main results are expected to be submitted for publication in 2024. We will engage patient partners and other stakeholders in creating a multifaceted knowledge translation plan to reach a diverse audience. CONCLUSIONS: If feasible, this study will provide essential data for a larger-scale efficacy trial of iCC on cannabis use outcomes in individuals with FEP and CUD. TRIAL REGISTRATION: ClinicalTrials.gov NCT05310981; https://www.clinicaltrials.gov/ct2/show/NCT05310981. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40817.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.080 | 0.013 |
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".