An Evidence-Based Serious Game App for Public Education on Antibiotic Use and Resistance: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The misuse and overuse of antibiotics accelerate the development of antimicrobial resistance (AMR). Serious games, any form of games that serve a greater purpose other than entertainment, could augment public education above ongoing health promotion efforts. Hence, we developed an evidence-based educational serious game app-SteWARdS Antibiotic Defence-to educate players on good antibiotic use practices and AMR through a game quest comprising 3 minigames and interaction with the nonplayer characters. OBJECTIVE: We aimed to evaluate the effectiveness of the SteWARdS Antibiotic Defence app in improving the knowledge of, attitude toward, and perceptions (KAP) of appropriate antibiotic use and AMR among the public in Singapore. METHODS: We conducted a 2-arm parallel randomized controlled trial, recruiting visitors aged 18-65 years from 2 polyclinics in Singapore. Intervention group participants had to download the SteWARdS Antibiotic Defence app (available only in English and on the Android platform) on their smartphones and complete the quest in the app. Participants took half a day to 2 weeks to complete the quest. The control group received no intervention. Knowledge questions on antibiotic use and AMR (11 binary questions) were self-administered at baseline, immediately after the intervention, and 6-10 weeks post intervention, while attitudes and perception questions (14 three-point Likert-scale questions) were self-administered at baseline and 6-10 weeks post intervention. We also collected participants' feedback on app usage. RESULTS: Participants (n=348; intervention: n=142, control: n=206) had a mean age of 36.9 years. Intervention group participants showed a statistically significant improvement in mean knowledge score (effect size: 0.58 [95% CI 0.28-0.87]) compared with controls after accounting for age, educational level, and exposure to advertisements on antibiotics and AMR. Intervention participants also showed a statistically significant improvement in mean attitude-perception scores (effect size: 0.98 (95% CI 0.44-1.52)) after adjusting for marital status and race. A majority of participants agreed that the "SteWARdS Antibiotic Defence" app improved their awareness on antibiotic use (135/142, 95.1%) and AMR (136/142, 95.8%). About 73.9% (105/142) of the participants agreed that the app is easy to use, 70.4% (100/142) agreed that the app was enjoyable, and 85.2% (121/142) would recommend the app to others. CONCLUSIONS: Our educational serious game app improves participants' KAP on appropriate antibiotic use and AMR. Public education apps should be engaging, educational, easy to use, and have an attractive user interface. Future research should assess the effectiveness of interventions in facilitating long-term knowledge retention and long-lasting behavioral change. TRIAL REGISTRATION: ClinicalTrials.gov NCT05445414; https://clinicaltrials.gov/ct2/show/NCT05445414. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/45833.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».