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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".