Development of a Healthy Lifestyle Mobile App for Overweight Pregnant Women: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps are becoming an increasingly ubiquitous platform for delivery of health behavior interventions among overweight and obese perinatal women. However, only a few methodological guidelines on integrating theory, evidence, and qualitative research for their designs are available. OBJECTIVE: The aim of this study was to develop a theory-based, evidence-driven, and user-centered healthy lifestyle app targeting overweight and obese multiethnic pregnant women. METHODS: This paper illustrates how intervention development may be enriched with theoretical basis, systematic review, and qualitative study. An individual face-to-face interview was performed to incorporate the user's involvement in the design. These interviews were audiotaped and transcribed. Thematic analysis technique was used for emerging themes. RESULTS: Integrated concepts of social cognitive theory of self-regulation, self-regulation model, and strength model of self-control were selected as bases of the intervention. Evidence from our systematic review and meta-analysis provided the strongest evidence for the development of intervention. We invited 16 obese or overweight pregnant women to participate in a semistructured interview . The following key themes emerged: content, platform, interactivity, format, and functionality. Apps are a favorable technology platform for healthy diet advice, appropriate physical exercise, and weight management because they are user-friendly and convenient. The app used in this study contains culture-specific, pregnancy-related, and credible contents, including educational, professional and peer support, and self-monitoring domains. The design should include aesthetic appeal, visualized features, and interactive multimedia. CONCLUSIONS: A 3-step process integrating theoretical basis, evidence from systematic review, and research findings from target users can be considered a guide for future app development.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".