Mobile Apps Leveraged in the COVID-19 Pandemic in East and South-East Asia: Review and Content Analysis
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Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic increased attention to digital tools to support governmental public health policies in East and South-East Asia. Mobile apps related to the COVID-19 pandemic continue to emerge and evolve with a wide variety of characteristics and functions. However, there is a paucity of studies evaluating such apps in this region, with most of the available studies conducted in the early days of the pandemic. OBJECTIVE: This study aimed to examine free apps developed or supported by governments in the East and South-East Asian region and highlight their key characteristics and functions. We also sought to interpret how the release dates of these apps were related to the commencement dates of other COVID-19 public health policies. METHODS: We systematically searched for apps in Apple App Store and Google Play Store and analyzed the contents of eligible apps. Mobile apps released or updated with COVID-19-related functions between March 1 and May 7, 2021, in Singapore, Taiwan, South Korea, China (mainland), Japan, Thailand, Hong Kong, Vietnam, Malaysia, Indonesia, and the Philippines were included. The CoronaNet Research Project database was also examined to determine the timeline of public health policy commencement dates in relation to the release dates of the included apps. We assessed each app's official website, media reports, and literature through content analysis. Descriptive statistics were used to summarize relevant information gathered from the mobile apps using RStudio. RESULTS: Of the 1943 mobile apps initially identified, 46 were eligible, with almost 70% of the apps being intended for the general public. Most apps were from Vietnam (n=9, 20%), followed by Malaysia, Singapore, and Thailand (n=6 each, 13%). Of note, most apps for quarantine monitoring (n=6, 13%) were mandatory for the target users or a population subset. The most common function was health monitoring (32/46, 70%), followed by raising public health awareness (19/46, 41%) through education and information dissemination. Other functions included monitoring quarantine (12/46, 26%), providing health resources (12/46, 26%). COVID-19 vaccination management functions began to appear in parallel with vaccine rollout (7/46, 15%). Regarding the timing of the introduction of mobile solutions, the majority of mobile apps emerged close to the commencement dates of other public health policies in the early stages of the pandemic between March and April 2020. CONCLUSIONS: In East and South-East Asia, most governments used mobile health apps as adjuncts to public health measures for tracking COVID-19 cases and delivering credible information. In addition, these apps have evolved by expanding their functions for COVID-19 vaccination.
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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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 découle