A Transdiagnostic, Emotion Regulation App (Eda) for Children: Design, Development, and Lessons Learned
Bibliographic record
Abstract
BACKGROUND: Digital interventions, including mobile apps, represent a promising means of providing effective mental health support to children and young people. Despite the increased availability of mental health apps, there is a significant gap for this age group, especially for children (aged 10-12 years). Research investigating the effectiveness and development process of child mental health apps is limited, and the field faces persistent issues in relation to low user uptake and engagement, which is assumed to be a result of limited user involvement in the design process. OBJECTIVE: This study aims to present the development and design process of a new mental health app for children that targets their emotion regulation abilities. We describe the creation of a new interdisciplinary development framework to guide the design process and explain how each activity informed different app features. METHODS: The first 2 stages of the framework used a variety of methods, including weekly classroom observations over a 6-month period (20 in total); public engagement events with the target group (N=21); synthesis of the existing evidence as part of a meta-analysis; a series of co-design and participatory workshops with young users (N=33), clinicians (N=7), researchers (N=12), app developers (N=1), and designers (N=2); and finally, testing of the first high-tech prototype (N=15). RESULTS: For the interdisciplinary framework, we drew on methods derived from the Medical Research Council framework for complex interventions, the patient-clinician framework, and the Druin cooperative inquiry. The classroom observations, public engagement events, and synthesis of the existing evidence informed the first key pillars of the app and wireframes. Subsequently, a series of workshops shaped and reshaped the content and app features, including games, psychoeducational films, and practice modules. On the basis of the prototype testing sessions, we made further adjustments to improve the app. CONCLUSIONS: Although mobile apps could be highly suitable to support children's mental health on a wider scale, there is little guidance on how these interventions could be designed and developed. The involvement of young users across different design activities is very valuable. We hope that our interdisciplinary framework and description of the used methods will be helpful to others who are hoping to develop mental health apps for children and young people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".