Monitoring of Pregnant Women Using the “Risk Identification, Evaluation Counseling, Systematic Monitoring, Troubleshooting” (REST) Mobile App: Protocol for a Cluster Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The maternal mortality rate (MMR) in Indonesia is still quite high and has not reached the national target. The success of maternal health programs can be assessed through the main indicator of the MMR. Pregnancy monitoring is one of the efforts to reduce the increase in the MMR. OBJECTIVE: This study aims to improve maternal and child safety and health during pregnancy and childbirth through pregnancy monitoring using a mobile app called REST (Risk Identification, Evaluation Counseling, Systematic Monitoring, Troubleshooting). METHODS: The research used the cluster randomized controlled trial (RCT) design, involving pregnant women from 11 subdistricts in Purworejo Regency, who were randomized to 11 clusters in the intervention group and 11 clusters in the control group. The 22 ANC class clusters comprised 22 midwives and 220 pregnant women. The intervention group received monitoring using the REST mobile app, while the control group received standard pregnancy monitoring in the antenatal care (ANC) class. The mentoring program included the use of the REST mobile app, which consists of pregnancy checks according to the 10T pregnancy examination standard. The app was used by midwives and pregnant women, starting from the second trimester of pregnancy to childbirth. RESULTS: In July 2023, the REST mobile app was prepared and tested in small community groups, including midwives and pregnant women, through simulation, and in September 2023, the app was further refined based on feedback from these groups and introduced to study participants. The majority of participants were aged 20-35 years (n=168, 76.4%), consistent with national demographic trends. Approximately 185 (84.1%) had secondary education (junior and senior high school), and 57 (25.9%) had tertiary education (college graduates). The participants were evenly distributed across economic quintiles, reflecting diverse socioeconomic backgrounds, and most lived approximately 1 km from a health facility. Ethical approval was obtained in April 2024. Staff training was conducted from July to October 2023. Participants were recruited from November 2023 to January 2024, the intervention was implemented from February to July 2024, and data were collected from August 2024 to February 2025. Data were analyzed in April 2025, and dissemination of results is expected by the end of 2025. CONCLUSIONS: Pregnancy monitoring using the REST mobile app is expected to have a significant influence on the number of ANC visits, reduce pregnancy complications, improve normal delivery methods, and ensure the birth weight of the baby stays within normal limits (≥2500 g). TRIAL REGISTRATION: ClinicalTrials.gov NCT05741931; https://clinicaltrials.gov/study/NCT05741931. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66774.
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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 | 0,029 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,061 | 0,008 |
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 ».