Co‐designing a just‐in‐time adaptive mHealth intervention to improve parental support for child physical activity using a no‐code app design platform: Development study
Bibliographic record
Abstract
Parental support significantly influences children's physical activity (PA) levels. Just-in-time adaptive interventions (JITAIs) delivered through mobile health (mHealth) may provide personalized, dynamic support to parents, though research is limited. 1) Describe the co-design process of a family-based JITAI app designed to help parents support their children's PA, and 2) evaluate the resources required to co-design this app using a "no-code" platform, Pathverse. Following the Integrate, Design, Assess, Share (IDEAS) and Multi-Process Action Control (M-PAC) frameworks, parents of children 8-12 years not meeting PA guidelines participated in semi-structured interviews (Phase 1). Feedback-informed app features, JITAI tailoring strategies, and prototype refinement (Phase 2). Six parents participated in Phase 1 guided by the IDEAS framework, with parental feedback directly shaping the app design. Parents emphasized family-based content, gamification, and diverse PA activities, while barriers (e.g., time, weather) informed JITAI tailoring. The M-PAC framework guided the selection and delivery of behavior change techniques (e.g., self-monitoring, social support). Development required 320 hours over four months, including decision-tree creation (50), uploading dynamic content (70), and testing (80). A family-based JITAI app was co-designed leveraging the M-PAC framework and Pathverse to integrate parental support for PA, laying the groundwork for future testing.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".