Reducing Dental Anxiety in Children Using a Mobile Health App: Usability and User Experience Study
Bibliographic record
Abstract
BACKGROUND: Dentistry interventions cause common anxiety and fear problems in children (6-11 years), and according to scientific evidence, this causes a decrease in their quality of life. Therapies mediated by IT-based tools have been shown to positively influence children's mood based on distraction as well as relaxing activities, but there is no evidence of their use to reduce dental anxiety in children. OBJECTIVE: The aim of this study was to answer the following research question: Does our new children-centered codesign methodology contribute to achieving a usable mobile-based product with a highly scored user experience? METHODS: A mobile health app was developed to reduce dental anxiety in children using rapid application development following the usage-centered design methodology. Structured interviews were conducted to test the usability and user experience of the app prototype among 40 children (n=20, 50%, boys and n=20, 50%, girls; age 6-11 years) using a children-adapted questionnaire and the 7-point Single Ease Question rating scale. The Smiley Faces Program-Revised questionnaire was used to assess the level of dental anxiety in participants. RESULTS: There were no significant differences between girls and boys. The task completion rate was 95% (n=19) for children aged 6-8 years (group 1) and 100% (n=20) for children aged 9-11 years (group 2). Group 1 found watching the relaxing video (task C) to be the easiest, followed by playing a video minigame (task B) and watching the narrative (task A). Group 2 found task C to be the easiest, followed by task A and then task B. The average time spent on the different types of tasks was similar in both age groups. Most of the children in both age groups were happy with the app and found it funny. All children thought that having the app in the waiting room during a dental visit would be useful. CONCLUSIONS: The findings confirmed that the app is usable and provides an excellent user experience. Our children-adapted methodology contributes to achieving usable mobile-based products for children with a highly scored user experience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".