“I Just Wanted a Dentist in My Phone”—Designing Evidence-Based mHealth Prototype to Improve Preschool Children’s Oral and Dental Health: Multimethod Study of the Codevelopment of an App for Children’s Teeth
Bibliographic record
Abstract
BACKGROUND: Dental caries in preschool children is a global health concern. With increased access to technology and the disruption of health care during the pandemic, mobile health apps have been of interest as potential vehicles for individuals' health maintenance. However, little is known about caring for their child's teeth and what their preferences would be regarding the content or design of an oral health app. OBJECTIVE: This study aims to co-design the prototype of an app named App for Children's Teeth with parents, providing a source of information for them about caring for their children's teeth and promoting positive dental habits. METHODS: This multimethod study conducted user involvement research with a purposive sample of parents or carers of children aged ≤6 years to (1) understand their use of the internet through the eHealth Literacy Scale and interviews, (2) determine their opinions about content related to children's oral health, and (3) collect feedback about the app's acceptability using the Theoretical Framework of Acceptability. There were three stages: (1) interviews with parents to understand their needs, preferences, and abilities; (2) prototype design with app developers; and (3) parent feedback interviews using the think aloud method for data collection. Data were deductively analyzed using a codebook strategy, whereas data from the think aloud sessions were analyzed inductively using reflexive thematic analysis. RESULTS: The prototype design stage involved 10 parents who reported using the internet for health information but found it to be scattered and contradictory. Parents generally welcomed the App for Children's Teeth but expressed concerns about screen time and practicality. They suggested guidance regarding oral hygiene practices, teething symptoms, and pain relief. Parents appreciated features such as clear fonts, categorization according to their child's age, and "In a Nutshell" bullet points. Topics that resonated with parents included information about teething, finding a dentist, and breastfeeding. They believed that the app aligned with their goals and offered suggestions for future developments, such as outlining the process of finding a dentist and incorporating a forum for parents to communicate and exchange ideas. CONCLUSIONS: The coproduction design approach highlighted parents' need for solutions such as mobile health apps to access reliable information about oral health. Parents identified key design concepts for the app, including a simple and uncluttered interface, content categorization according to their child's age, and practical guidance supported by visual aids. Despite potential challenges related to screen time restrictions, parents provided insights into how such an app could fit seamlessly into their lives. TRIAL REGISTRATION: Open Science Framework; https://osf.io/uj9az.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".