Question · asked by 1 review

Gamified oral health interventions (mobile apps, serious games, augmented reality, game-based education) vs Non-digital gamified oral health interventions (card/board games, storytelling, printed aids): Long-term behavior change and overall oral health outcomes

Population
Children or adolescents
Intervention
Gamified oral health interventions (mobile apps, serious games, augmented reality, game-based education)
Comparison
Non-digital gamified oral health interventions (card/board games, storytelling, printed aids)
Outcome
Long-term behavior change and overall oral health outcomes

Digital gamified interventions appear more effective than non-digital alternatives for long-term behavior change and oral health outcomes, but the evidence is narrative and ungraded.

What each review found

Current versions only
FindingEffectStudiesGRADE certainty
Digital gamified oral health interventions (mobile apps, serious games, augmented reality) vs Non-digital gamified oral health interventions (card/board games, storytelling, printed aids)
Long-term behavior change and overall oral health outcomes · Children and adolescents aged <=18 years · Long-term follow-up (sustained behavior change), Schools, dental clinics, and mobile-app user-testing environments
Digital gamified interventions appear more effective than non-digital alternatives for long-term behavior change and oral health outcomes, but the evidence is narrative and ungraded.
The Application of Gamification in Children's Oral Health Management: Systematic Review. (2025) All outcomes
Digital-based interventions were more effective in fostering long-term behavior change compared with nondigital alternatives and consistently outperformed nondigital alternatives in improving oral health outcomes;…Not reportedGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
The Application of Gamification in Children's Oral Health Management: Systematic Review.
2025 All outcomes
January 1, 2014 to February 9, 202541Low / Critically Low
How reviews are matched to this question

Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.