A Mobile App (Joint Effort) to Support Cannabis Use Self-Management and Reinforce the Use of Protective Behavioral Strategies: Development Process and Usability Testing (Preprint)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».