Development, Implementation, and Usability Evaluation of the CANMI App to Monitor the Quality of Maternal and Child Nutrition Care in Primary Health Units: Mixed Methods Pilot Study
Notice bibliographique
Résumé
Background: In Mexico, the maternal and child population continues to face a high burden of malnutrition, posing a persistent public health challenge. The health care system plays a crucial role, not only in addressing existing cases but also in preventing and detecting malnutrition early. Mobile health technologies have the potential to strengthen maternal and child health services by improving the quality, accessibility, and timeliness of nutritional care. Objective: The aim was to design, develop, and assess the usability and acceptance of a mobile app-CANMI (Calidad de la Atención Nutricional Materno Infantil; its Spanish acronym)-to monitor the quality of maternal and child nutritional care in primary health care units in Mexico. Methods: The framework of the CANMI app was based on 16 validated indicators designed to assess the quality of nutritional care during the preconception, pregnancy, postpartum, early childhood, and preschool stages. The app was developed for both iOS and Android systems using a user-centered design approach. Following development, we conducted a pilot usability study in a randomized sample of 18 primary health care units in Guanajuato, Mexico. Trained nutritionists implemented the app and collected usability data at the end of the initial use period and again 6 weeks later. To further explore user experience, semistructured online interviews were conducted to identify barriers, facilitators, and overall satisfaction with the app. Results: The CANMI app allows the systematic registration of key indicators to assess the quality of nutritional care in primary health care settings. Users described the app as simple, intuitive, and visually appealing. Overall usability was rated positively, with a mean score of 71.13 (SD 11.68) on the System Usability Scale, indicating good acceptability. The app's offline functionality, streamlined interface, and efficiency in data collection were identified as key facilitators of use. Reported benefits included reduced time for data entry and perceived improvements in the quality of nutritional care. Identified barriers to integration included the need to use personal devices, user fatigue due to prolonged screen time, inconsistent clinical records, and limited time to incorporate the app into routine workflows. Importantly, the app encouraged and promoted improvements in documentation practices and heightened awareness among health personnel regarding the precision and clarity of their nutritional recommendations. Conclusions: The CANMI app provides a feasible and effective solution for monitoring the quality of maternal and child nutritional care in primary health settings. Its high usability and offline capabilities make it particularly suitable for low-connectivity environments. Beyond facilitating data collection, the app contributed to improved clinical documentation practices and enhanced health care provider awareness of care quality. Consequently, the app represents a promising digital tool to support the implementation of evidence-based, user-centered strategies aimed at strengthening maternal and child health services in resource-limited contexts.
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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 | 0,024 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| 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 ».