Mobile Health Intervention in the Maternal Care Pathway: Protocol for the Impact Evaluation of hAPPyMamma
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth) has great potential to both improve the quality and efficiency of care and increase health literacy and empowerment of patient users. There are several studies related to the introduction of mHealth tools for supporting pregnancy and the postnatal period, with promising but not yet rigorously evaluated impacts. This article presents the protocol for evaluating an mHealth intervention (hAPPyMamma) applied in the maternal and child care pathway of a high-income country (in a pilot area of Tuscany Region, Italy). OBJECTIVE: The protocol describes hAPPyMamma and the methods for evaluating its impact, including the points of view of women and practitioners. The research hypothesis is that the use of hAPPyMamma will facilitate a more appropriate use of available services, a better care experience for women, and an improvement in the maternal competencies of the women using the app compared to the control group. The protocol also includes analysis of the organizational impact of the introduction of hAPPyMamma in the maternal pathway. METHODS: A pre-post quasiexperimental design with a control group is used to undertake difference-in-differences analysis for assessing the impact of the mHealth intervention from the mothers' points of view. The outcome measures are improvement of maternal health literacy and empowerment as well as experience in the maternal care pathway of the control and intervention groups of sampled mothers. The organizational impact is evaluated through a quantitative and qualitative survey addressing professionals and managers of the maternal care pathway involved in the intervention. RESULTS: Following study recruitment, 177 women were enrolled in the control group and 150 in the intervention group, with a participation rate of 97%-98%. The response rate was higher in the control group than in the intervention group (96% vs 67%), though the intervention group had less respondent loss at the postintervention survey (10% compared to 33% of the control group). Data collection from the women was completed in April 2018, while that from professionals and managers is underway. CONCLUSIONS: The study helps consolidate evidence of the utility of mHealth interventions for maternal and child care in developed countries. This paper presents a protocol for analyzing the potential role of hAPPyMamma as an effective mHealth tool for improving the maternal care pathway at individual and organizational levels and consequently helps to understand whether and how to scale up this intervention, with local, national, and international scopes of application. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19073.
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 | 0,018 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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 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 ».