Engagement With and Impact of an mHealth App for Childhood Obesity Prevention and Management: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
Background: Childhood obesity is a serious global health concern that affects approximately 20% of children worldwide. Digital health behavior change interventions have the potential to improve behaviors that can contribute to childhood obesity, such as diet and physical activity, but often lack sufficient user engagement to achieve significant impact. Objective: The aim of this project is to develop evidence to better understand how users engage with digital interventions and how behavior change techniques can be leveraged to support engagement. Specifically, the study will examine the impact of a family-focused app for childhood obesity prevention on health behaviors, health outcomes, and communication between families and health care professionals (HCPs). Methods: A pretest-posttest, mixed methods evaluation will examine the impact of the NoObesity app on families' physical activity and dietary behaviors and on HCPs' self-efficacy at communicating with families about childhood obesity. Secondary outcomes will include well-being, usability, and users' engagement with and perceptions of the intervention. An initial sample of 1000 families (children and young people of any weight and age under 18 years and their parents) and 180 HCPs will be recruited to participate in the study; a subset of these participants will be invited to take part in qualitative semistructured interviews. The study implementation and follow-up period will last for 6 months, with the outcomes measured at baseline and 3 and 6 months after baseline. Quantitative outcomes will be compared over time using repeated measures ANOVA, and qualitative data will be analyzed thematically and triangulated with app use data. Results: Ethics approval was granted by the Newcastle University Faculty of Medical Science Ethics Committee (2688/41816) on March 22, 2024. Recruitment has not yet started but will involve capturing informed consent (and assent from participants younger than 16 years). Conclusions: The project's key contributions will be to generate evidence of the potential for a family-based digital intervention to support families' health behavior change and HCPs' confidence in their ability to support them and to improve our understanding of how particular behavior change techniques can be used to support engagement with the intervention and its target behavior. Findings will be disseminated through peer-reviewed journals and shared with the general public, with support from patient and public involvement representatives.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.014 |
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".