A School-Based Mobile App Intervention for Enhancing Emotion Regulation in Children: Exploratory Trial
Bibliographic record
Abstract
BACKGROUND: Most mental health disorders are first experienced in childhood. The rising rates of mental health difficulties in children highlight the need for innovative approaches to supporting children and preventing these difficulties. School-based digital interventions that address shared risk factors and symptoms, such as emotion dysregulation, present exciting opportunities to enhance mental health support for children on a larger scale. OBJECTIVE: This study investigates the use of a new app-based intervention designed to support children's emotion regulation in schools. The aim is to optimize the usability, acceptability, and utility of the app and explore its scope for implementation with the target user in the school context. METHODS: As part of an interdisciplinary development framework, the app is being evaluated in a 3-month trial across 4 primary schools. In total, 144 children (aged 10-12 years) took part and accessed the intervention app in the classroom or at home. Outcomes regarding usability, acceptability, and implementation opportunities were assessed through digital user data, self-report questionnaires (132/144, 91.6%), and semistructured interviews with children (19/144, 13.2%) and teachers (6/8, 75%). RESULTS: The app usage data showed that 30% (128/426) of the users were returning users. Self-report data indicated that 40.1% (53/132) of the children had not used the app, whereas 57.5% (76/132) had used it once or more. Of the children who had used the app, 67% (51/76) reported that the app was helpful. Interviews with children and teachers suggested positive experiences with the app and that it helped them to calm down and relax. Children reported that they perceived the app as acceptable, usable, and helpful. In terms of the intervention's usability, most features functioned well; however, certain technical issues were reported, which may have led to reduced engagement levels. Teachers not only reported overall positive experiences but also discussed access difficulties and reported a lack of content as one of the main barriers to implementing the app. Having a web-based app significantly enhanced accessibility across devices and settings and provided teachers with more opportunities to use it. We identified the need for new, activating app features in addition to the existing, primarily relaxing ones. The findings indicated that it is possible to use and evaluate an app intervention in the school context and that the app could help enhance children's emotion regulation. We discuss areas for improvement regarding the app, study design, and future implementation strategies. CONCLUSIONS: We share important insights with regard to the development, implementation, and evaluation of a new app for supporting children's emotion regulation in schools. Our results demonstrate that mental health apps represent a promising means to facilitate effective mental health service provision in and outside of the school context. Important lessons learned are shared to support other researchers and clinicians on similar journeys.
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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".