Mobile Apps for Oral Health Promotion: Content Review and Heuristic Usability Analysis
Notice bibliographique
Résumé
BACKGROUND: There has been an increase in consumer-facing mobile health (mHealth) apps in recent years. Prior reviews have characterized the availability, usability, or quality of popular mHealth apps targeting a range of health behaviors, but none has examined apps that promote better oral health care. Oral disease affects billions of people worldwide and mobile phone use is on the rise, so the market for well-designed and effective oral health apps is substantial. OBJECTIVE: We examined the content and usability of popular oral health promotion apps to better understand the current state of these self-help interventions and inform the need and opportunity for future app development. METHODS: Between February and March 2018, we identified oral health-focused apps that were designed for Android or iOS, available in English, and targeted adult consumers (as opposed to children or dental health professionals). The sample was limited to the most popular and highly rated apps on each platform. For each app reviewed, we assessed its basic descriptive characteristics (eg, platform, cost), evidence of a theoretical basis or empirical validation, key program functionality, and the extent to which the app addressed diet and tobacco and alcohol use as risk factors for oral disease. We characterized the framing (ie, gain vs loss) of all persuasive messaging and conducted a heuristic analysis to assess each app's usability as a persuasive health technology. RESULTS: Thirty-three apps were eligible for review based on the selection criteria. Two-thirds (22/33, 67%) were geared toward the general public as opposed to dental clinic patients, insurance plan members, or owners of specific electric toothbrushes. Most (31/33, 94%) were free to download, and a majority (19/33, 58%) were sponsored by software developers as opposed to oral health experts. None offered any theoretical basis for the content or had been empirically validated. Common program features included tools for tracking or reminding one to brush their teeth and assistance scheduling dental appointments. Nineteen apps (58%) included educational or persuasive content intended to influence oral health behavior. Only 32% (6/19) of these included a larger proportion of gain-framed than loss-framed messaging. Most of the apps did not mention diet, alcohol or tobacco-important risk factors for oral disease. Overall, the apps performed poorly on standard usability heuristics recommended for persuasive health technologies. CONCLUSIONS: The quality of the reviewed apps was generally poor. Important opportunities exist to develop oral health promotion apps that have theoretically grounded content, are empirically validated, and adhere to good design principles for persuasive health technologies.
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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 | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».