ROOM to Grow, a Mobile Well-Being Intervention for University Students: Overview of the Design Process and Outcomes
Bibliographic record
Abstract
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.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".