Codevelopment of an mHealth App With Health Care Providers, Digital Health Experts, Community Partners, and Families for Childhood Obesity Management: Protocol for a Co-Design Process
Bibliographic record
Abstract
BACKGROUND: Childhood obesity is increasing in Singapore, with most cases persisting into adulthood and leading to poor health outcomes. The current evidence for childhood obesity interventions shows a clear dose-response effect, where effectiveness improves with an increasing number of treatment hours. A minimum threshold of ≥26 hours over a 2- to 12-month period is required to achieve significant outcomes. The Kick Start Move Smart program is the first online community-based multidisciplinary program to treat pediatric obesity in Singapore. It has demonstrated feasibility and acceptability, with 70% of participants completing the recommended ≥26 hours of intervention. Preliminary data show significantly lower BMI and improved quality of life in participants compared to controls. Successful families are positive outliers who developed strategies for health in the context of an obesogenic environment. This positive outlier approach indicates that solutions to challenges that a community faces exist within certain individual members, and these strategies can be generalized and promoted to improve the health of others in the same community. A mobile health (mHealth) app targeting parents is a critical missing link in the currently available interventions to support parental self-management of childhood obesity. Using a combination of behavioral theory and user-centered design approaches is important for designing mHealth apps. One recommended framework is Integrate, Design, Assess, and Share (IDEAS), which aims to facilitate the development of more effective interventions by engaging perspectives from different stakeholders. OBJECTIVE: This study aims to (1) describe the co-design protocol of an mHealth app using the IDEAS framework as a low-intensity intervention or as an adjunct to more intensive existing pediatric obesity interventions and (2) assess the usability, acceptability, and engagement of the app by parents. METHODS: A clinician-led co-design approach will be undertaken with a multidisciplinary team using the IDEAS framework. Phase 1 involves stakeholder engagement and the formation of a core committee and a parent advisory board. Phase 2 involves developing the app content through focus group and expert panel discussions. Phase 3 involves developing a prototype app and gathering feedback. Phase 4 involves piloting the minimum viable product by parent users and evaluating its effectiveness through interviews and questionnaires. RESULTS: In April 2023, a parent advisory board was formed, and stakeholders were engaged as part of phase 1. Phases 2 and 3 were completed in June 2024. Focus group discussions were held with the parent advisory board and stakeholders to identify family strategies and patient-centric outcomes and provide feedback on the app. As of January 2025, the app is complete, and we are now in the middle of data collection from participants. Participants will provide feedback to the research team, and the app will be updated accordingly. CONCLUSIONS: An evidence-based, theory-driven mHealth app developed using a structured design framework can bridge the gap in delivering multidisciplinary care in community settings for families with overweight children. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59238.
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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 | 0.063 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.094 | 0.019 |
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".