Integration of a Patient-Centered mHealth Intervention (Support-Moms) Into Routine Antenatal Care to Improve Maternal Health Among Pregnant Women in Southwestern Uganda: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) interventions that leverage social support (SS) can improve partner involvement and pregnancy experiences and promote antenatal care (ANC) attendance and skilled births. In our previous studies, we used behavioral frameworks to develop a user-centered mHealth-based, audio SMS text messaging app to support pregnant individuals to use maternity care services in rural Uganda (Support-Moms app). In our pilot study, we observed high intervention uptake, acceptability, and feasibility, as well as increased ANC attendance and skilled births. OBJECTIVE: With the promising pilot data, we propose a type 1 hybrid implementation-effectiveness trial to test if this novel patient-centered automated and customized mHealth-based SS intervention is effective and cost-effective enough to warrant future large-scale implementation into Uganda's routine maternity care. METHODS: We will physically recruit 824 pregnant women at <20 weeks of gestation living in Mbarara and Mitooma districts, southwestern Uganda, and randomize them (1:1) to receive standard of care or the Support-Moms app, with at least 2 of their identified social supporters. Our primary outcome will be the proportion of skilled births. Secondary outcomes will include number of ANC visits, institution-based delivery, mode of infant delivery, preterm birth, birth weight, SS, obstetric complications, and deaths (maternal, fetal, and newborn). We will assess other implementation, service, and client outcomes through study records, the mHealth platform, and questionnaires with all women in the intervention, their social supporters, health care providers (HCPs), and managers from participating facilities. We will conduct face-to-face in-depth exit interviews with 30 purposively selected intervention participants and 15 facility HCPs and managers to explore implementation strategies for scale-up. Annual maternity resource allocations, costs, number of ANC visits, and deliveries will be assessed from facility records up to 36 months after implementation. We will estimate incremental cost-effectiveness ratios concerning cost per additional HCP-led delivery, per death averted, and per quality-adjusted life year gained as cost-effectiveness measures. RESULTS: This study was funded in September 2023. Ethics approval was obtained in February 2024, and actual data collection started in March 2024. As of January 2025, 75% (618/824) of all projected study participants provided consent and were recruited into the study. Participants are expected to be followed up until delivery, and 15% (124/824) have so far exited. Data analysis for the trial is expected to start as soon as the last participant exits from the study. The qualitative interviews will start in April 2025, and data will be analyzed and published as soon as data collection is done, which is expected in March 2027. CONCLUSIONS: We are testing the feasibility, acceptability, and cost-effectiveness of implementing Support-Moms into routine maternity care from individual and facility perspectives. We hypothesize that Support-Moms will be an effective and cost-effective strategy to improve maternity service use for women in rural Uganda and similar settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05940831; https://clinicaltrials.gov/study/NCT05940831. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67049.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".