Acceptability, Feasibility, and Outcome Responsiveness of the Joint Effort Mobile App for Promoting Lower-Risk Cannabis Use Among Young Adults: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background Cannabis use (CU) among young adults continues to be an important public health issue. Interventions to support lower-risk CU during young adulthood can improve health outcomes. Mobile applications constitute a promising mode of service delivery. However, there is a lack of evidence-based apps specifically developed for young adult cannabis users. Objective This study aimed to evaluate the acceptability of a novel mobile app intervention (Joint Effort) and to assess the feasibility and outcome responsiveness of the study procedures used. Methods A pilot study with a parallel-group randomized trial design was conducted with Canadian-based university students aged 18‐30 years reporting using cannabis ≥1 day in the past month. Participants were randomly assigned on a 1:1 ratio to either an experimental group (EG) involving the use of the Joint Effort mobile app or to a control group (CG) involving a web-based brief normative feedback message. The Joint Effort mobile app was designed to support CU self-management. This theory-informed behavior change intervention aims to reinforce the use of protective behavioral strategies by targeting intention, attitude, social norms, and self-efficacy. The app’s acceptability was assessed via uptake, engagement, and appreciation. The feasibility of study procedures was assessed via recruitment time, recruitment rate, and attrition rate. Outcome responsiveness was informed by participant-reported outcomes: CU frequency, intention to take action on CU, protective behavioral strategies use, severity of dependence, and psychological distress. All data were collected using a web-based survey at baseline, one-month (T1), and 2-month (T2) postbaseline. Descriptive analyses were carried out on all outcomes. Results The recruitment period lasted 124 days, and the recruitment rate was 56% (99/178). The final dataset analyzed included 80 participants (39 in EG and 41 in CG). Mean age was 23.4 (SD 2.6) years, and 66% (53/80) self-identified as women. Study attrition was 18% (14/80). User uptake of the Joint Effort app (ie, proportion of participants in the EG who downloaded the app) was estimated at 59% (23/39), and the average time spent on it per participant was 8.2 minutes (SD 7.3; median 7.5, IQR 5.7). The app obtained a mean total score on the User Engagement Scale-Short Form of 3.8/5 (SD 0.5) and a mean app quality total score of 4.2/5 (SD 0.5) on the end user version of the Mobile App Rating Scale. The proportion of participants who reported daily CU in the past month decreased from 13% (5/39) at baseline to 4% (1/24) at T2 in the EG and from 7% (3/41) to 6% (2/36) in the CG. Conclusions Joint Effort appears to be a promising, acceptable, and scalable mobile app to help young adult cannabis users who wish to better manage their CU. Findings should inform future randomized controlled trials to assess the efficacy of this mobile-based intervention for cannabis users.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".