Monitoring of Pregnant Women Using the “Risk Identification, Evaluation Counseling, Systematic Monitoring, Troubleshooting” (REST) Mobile App: Protocol for a Cluster Randomized Controlled Trial
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".