A Mobile App (Joint Effort) to Support Cannabis Use Self-Management and Reinforce the Use of Protective Behavioral Strategies: Development Process and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Canada's legalization of recreational cannabis use (CU) has further highlighted the need for innovative interventions that promote lower-risk CU. Young adults aged 18-25 years represent the age group with the highest prevalence of CU. Protective behavioral strategies (PBSs) have been shown to help manage CU and reduce its negative consequences. To date, only a few interventions have focused on PBSs. To address this gap, a mobile app prototype using PBSs to influence CU was developed with and for young adults. OBJECTIVE: This study aims to describe the development process and usability testing of Joint Effort, a CU self-management mobile app prototype centered on promoting the use of PBSs among young adults with any past 30-day CU. METHODS: Intervention mapping (IM) and a co-design approach were used. Six steps were followed: (1) focus groups were conducted to identify needs and preferences regarding CU interventions; (2) a matrix of change objectives was used to select target behaviors and determinants; (3) theory-based intervention methods and practical applications were selected; (4) focus groups were held to validate the intervention structure and examples of tailored messages; (5) preliminary intervention content was created; and (6) the intervention content was transposed into a mobile app prototype. Usability was assessed through qualitative semistructured interviews and the User Version of the Mobile Application Rating Scale (uMARS), completed by a sample of 20 university students with a mean age of 21.8 (median 22) years, 14 (70%) of whom were women and 15 (75%) were undergraduates. Qualitative data were analyzed using thematic analysis. RESULTS: Four themes were identified from the interviews: Joint Effort was visually pleasing and easy to use; the content was well-adapted to the target audience and nonjudgmental; customization functions were appreciated; and the app was perceived as helpful and relevant for initiating behavior change. The prototype received a mean quality score of 4.43/5.0 (SD 0.53) per item on the uMARS. The mean scores on the 5 subscales were as follows: engagement (4.14, SD 0.53), functionality (4.60, SD 0.47), aesthetics (4.53, SD 0.52), information quality (4.44, SD 0.61), and subjective quality (3.36, SD 0.53). CONCLUSIONS: Our findings highlight the added value of IM and a co-design approach, underscoring the importance of incorporating user feedback in the development of mobile apps. Building on the strong usability results, the Joint Effort prototype has since been developed into an iOS mobile app, and larger-scale evaluations are currently underway to assess its acceptability, feasibility, and efficacy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".