Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study
Notice bibliographique
Résumé
BACKGROUND: While early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country. OBJECTIVE: This study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals. Individual interviews and focus group discussions were also conducted among participants to develop themes from their comments and suggestions to help structure further improvement and future development of the app. METHODS: User experience sessions were conducted among participants, and the Mobile Application Rating Scale (MARS) professional and user versions (uMARS) were administered followed by individual interviews and focus group discussions. Descriptive statistical analysis of the MARS and uMARS score ratings was performed. The total app mean score ratings by sociodemographic and dengue fever history using nonparametric mean difference analyses were also conducted. Thematic synthesis was used to develop themes from the comments and suggestions raised in individual interviews and focus group discussions. RESULTS: Mozzify obtained an overall >4 (out of 5) mean score ratings in the MARS and uMARS app objective quality (4.45), subjective (4.17), and specific (4.55) scales among 948 participants (79 health care professionals and 869 school-based samples). Mean difference analyses revealed that total app mean score ratings were not significantly different across ages and gender among health care professionals and across age, income categories, and self and family dengue fever history but not gender (P<.001) among the school-based samples. Thematic syntheses revealed 7 major themes: multilanguage options and including other diseases; Android version availability; improvements on the app's content, design, and engagement; inclusion of users from low-income and rural areas; Wi-Fi connection and app size concerns; data credibility and issues regarding user security and privacy. CONCLUSIONS: With its acceptable performance as perceived by health care professionals and school-based young adults, Mozzify has the potential to be used as a strategic health intervention system for early detection of disease outbreaks in the Philippines. It can be used by health care professionals of any age and gender and by school-based samples of any age, socioeconomic status, and dengue fever history. The study also highlights the feasibility of school-based young adults to use health-related apps for disease prevention.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».