Mobile Apps for Dental Caries Prevention: Systematic Search and Quality Evaluation
Notice bibliographique
Résumé
BACKGROUND: Dental caries is the most common multifactorial oral disease; it affects 60% to 90% of the global population. Dental caries is highly preventable through prevention behaviors aimed at improving oral hygiene, adequate fluoride usage, and dietary intake. Mobile apps have the potential to support patients with dental caries; however, little is known about the availability, target audience, quality, and features of these apps. OBJECTIVE: This review aims to systematically examine dental caries prevention apps; to describe their content, availability, target audience, and features; and to assess their quality. METHODS: We systematically identified and evaluated apps in a process paralleling a systematic review. This included a search strategy using search terms; an eligibility assessment using inclusion and exclusion criteria focused on accessibility and dental caries self-management behaviors, including oral hygiene, dietary intake, and fluoride usage; data extraction on app characteristics, including app store metrics; prevention behavior categorization; feature identification and description; a quality appraisal of all apps using the validated Mobile App Rating Scale (MARS) assessment tool; and data comparison and analysis. RESULTS: Using our search strategy, we retrieved 562 apps from the Google Play Store and iTunes available in Australia. Of these, 7.1% (40/562) of the apps fit our eligibility criteria, of which 55% (22/40) targeted adults, 93% (37/40) were free to download, and 65% (26/40) were recently updated. Oral hygiene was the most common dental caries prevention behavior domain, addressed in 93% (37/40) of the apps, while dietary intake was addressed in 45% (18/40) of the apps and fluoride usage was addressed in 42% (17/40) of the apps. Overall, 50% (20/40) of the apps addressed only 1 behavior, and 38% (15/40) of the apps addressed all 3 behaviors. The mean MARS score was 2.9 (SD 0.7; range 1.8-4.4), with 45% (18/40) of the apps categorized as high quality, with a rating above 3.0 out of 5.0. We identified 21 distinctive features across all dental caries prevention behaviors; however, the top 5 most common features focused on oral hygiene. The highest-ranking app was the Brush DJ app, with an overall MARS score of 4.4 and with the highest number of features (n=13). We did not find any apps that adequately addressed dental caries prevention behaviors in very young children. CONCLUSIONS: Apps addressing dental caries prevention commonly focus on oral hygiene and target young adults; however, many are not of high quality. These apps use a range of features to support consumer engagement, and some of these features may be helpful for specific patient populations. However, it remains unclear how effective these apps are in improving dental caries outcomes, and further evaluation is required before they are widely recommended.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».