Mobile App for Improving the Mental Health of Youth in Out-of-Home Care: Development Study Using an Intervention Mapping Approach
Bibliographic record
Abstract
BACKGROUND: Youth in out-of-home care encounter substantial mental health challenges because of the absence of stable family and social support systems. Their vulnerability is heightened by trauma, neglect, and abuse. They struggle, especially when transitioning to independent living, coping with loneliness, anxiety, and pressure. OBJECTIVE: This study aimed to develop a mobile app with high accessibility and long-term continuous effects to support independent living and improve mental health among youth in out-of-home care. The approach used was the systematic and step-by-step intervention mapping (IM) framework. METHODS: The program was created using the IM framework and had 6 steps. Drawing from data from individual and focus group interviews and literature reviews, we developed a logical model of the problem. We established program outcomes and objectives, defining performance objectives and variable determinants. We identified theoretical and evidence-based methods that influence determinants. The app design integrated these methods into practical applications, allowing for the creation of self-management and emotional support tools. The development process included ongoing discussions between app designers and the research team to ensure that user needs and preferences were addressed. RESULTS: Individual interviews and focus group discussions revealed challenges in managing daily routines and regulating emotions. The program design was based on the transtheoretical model, social cognitive theory, and elaboration likelihood model. Key features included goal setting, structured routines, emotion recognition flashcards, character models demonstrating emotion regulation strategies, verbal persuasion, and self-monitoring tools to support habit formation and emotion regulation. An implementation plan was developed to facilitate the app's adoption, execution, and maintenance, while an evaluation plan was established, including app usage analytics, user logs, and feedback surveys. A randomized controlled trial will be conducted to assess the app's impact on mental health outcomes, focusing on reducing anxiety and depressive symptoms, improving emotion regulation, and enhancing daily living skills. CONCLUSIONS: The IM framework was beneficial in developing a mobile app to enhance the mental health of youth in out-of-home care. The study produced a program grounded in theory and evidence that caters to the needs of these individuals. Further research should aim to verify the app's effectiveness in real-world settings and refine it continuously based on user input.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".