Using Augmented Reality to Motivate Oral Hygiene Practice in Children: Protocol for the Development of a Serious Game
Bibliographic record
Abstract
BACKGROUND: New technologies create possible new ways of action, interaction, and learning which is extremely relevant in the field of oral health education. There is a lack of protocol in using an immersive interactive ludic-educational interface to motivate oral hygiene practice in children by means of augmented reality. OBJECTIVE: This study aims to present a protocol on the development of a serious game to motivate oral hygiene practice in children. METHODS: A serious game will be designed by augmented reality techniques to improve toothbrushing effectiveness of children aged 6 to 10 years. The functional structure of this interface is activated by means of movements recognized by Kinect (Microsoft Corp). The toothbrushing technique will be available in the game, enabling the children to execute the movement in the virtual environment. By identifying errors, this game will be tailored to improve the oral health of children by correcting the technique and teaching the user the adequate toothbrushing method. A template analysis will be performed to identify barriers and facilitators in each scenario. RESULTS: After the implementation of the virtual interactive and immersive panels, enrollment will begin and evaluations will be made by means of questionnaires distributed to participants who interact with the game. Thus, an analysis of the product efficacy will be conducted. The expected outcome will be to obtain a digital instrument to motivate oral hygiene practice and enhance health awareness in children. CONCLUSIONS: The serious game will support the prevention of oral diseases by sharing scientific research in the school environment and community. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/10987.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".