ROOM to Grow, a Mobile Well-Being Intervention for University Students: Overview of the Design Process and Outcomes
Notice bibliographique
Résumé
BACKGROUND: University students are facing a multitude of challenges and an increase in mental health issues that affect their academic performance and overall well-being. In response, Erasmus University Rotterdam launched the Student Wellbeing Programme in 2019, offering comprehensive, tailored support through a stepped-care framework to enhance student success and well-being. One of the tools developed for students is ROOM to Grow, an anonymous and accessible preventative mental health app. OBJECTIVE: This paper describes the process and outcomes of ROOM's design and development, guided by the Centre for eHealth Research (CeHRes) road map and privacy-by-design principles, and highlights the lessons learned throughout this process. METHODS: This paper describes the first 4 phases of the CeHRes Road map: contextual inquiry, value specification, design, and operationalization. It outlines the population (ie, stakeholders), methods (ie, literature reviews, expert groups, cognitive walkthroughs, interviews, experimental designs), and outcomes of each phase. RESULTS: The most common mental health struggles among our target population were stress, anxiety symptoms, perfectionistic tendencies, and loneliness. Students often recognized these issues only once they became overwhelming. Regarding digital tools, students seek credible content specific to their experiences, as well as adaptable and intuitive systems; they are mindful of data privacy and aim to reduce their screen time. ROOM was developed to address the diverse needs and preferences of university students through a transdiagnostic approach to mental health. It targets emotion regulation (ER) skills and self-awareness, which underlie the mental health challenges experienced by our target users. ROOM comprises 26 brief exercises (ie, micro-interventions) that support the development and use of adaptive ER. The exercises incorporate techniques from various therapeutic approaches (ie, self-compassion, positive psychology, mindfulness, cognitive behavioral therapy, and acceptance and commitment therapy) to accommodate students' diverse content preferences. To help students recognize their struggles earlier, ROOM includes a mood tracker and a self-assessment module with questionnaires that evaluate both traits (eg, perfectionistic tendencies) and states (eg, stress levels), providing personalized feedback. ROOM further implements an intelligent recommender system that connects users to relevant content, enhancing the tool's personalization and responsiveness to users' needs. As students aim to minimize screen time, ROOM's goal is not prolonged app use but the application of ER skills in real life, supported by features that facilitate skill transfer into everyday settings. Finally, ROOM was developed within a "privacy-by-design" framework to address students' privacy concerns, implementing strict privacy and security regulatory standards. CONCLUSIONS: Compromises were necessary to balance user needs, resource constraints, and privacy-by-design and security standards, often limiting ROOM's interactivity. Other challenges included simplifying complex psychological concepts into brief formats, fostering interdisciplinary collaboration, balancing academic rigor with industry production pace, and operating with fixed resources while maintaining an iterative process. This paper may serve as a primer for designing transdiagnostic, adaptive mental health interventions for youth, blending therapeutic approaches and promoting skill transfer.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,020 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».