mHealth-Based Health Promotion Intervention to Improve Use of Maternity Care Services Among Women in Rural Southwestern Uganda: Iterative Development Study
Bibliographic record
Abstract
BACKGROUND: Antenatal care (ANC) prevents perinatal morbidity and mortality, but use of these services in Uganda remains low and maternal mortality rates are among the highest in the world. There is growing evidence that mobile health (mHealth) approaches improve timely communication of health-related information and produce positive health behavior change as well as health outcomes. However, there are limited data to guide development of such interventions in settings where ANC attendance and uptake of skilled maternity care are low. OBJECTIVE: The aim of this study is to develop a novel patient-centered mHealth intervention to encourage and support women to use maternity care services in Mbarara district, southwestern Uganda. METHODS: Using an iterative development approach, we conducted formative stakeholder interviews with 30 women and 5 health care providers (HCPs) to identify preferred key ANC topics and characterize the preferred messaging intervention; developed content for SMS text messaging and audio messaging with the help of 4 medical experts based on the identified topics; designed an app prototype through partnership with an mHealth development company; and pilot-tested the prototype and sought user experiences and feedback to refine the intervention through 3 sets of iterative interviews, a focus group discussion, and 5 cognitive interviews. Qualitative data were coded and analyzed using NVivo (version 12.0; QSR International). RESULTS: Of the 75 women who completed interviews during the development of the prototype, 39 (52%) had at least a primary education and 75 (100%) had access to a mobile phone. The formative interviews identified 20 preferred perinatal health topics, ranging from native medicine use to comorbid disorders and danger signs during pregnancy. In all, 6 additional topics were identified by the interviewed HCPs, including birth preparedness, skilled delivery, male partner's involvement, HCP interaction, immunization, and caring for the baby. Positive audio messaging and SMS text messaging content without authoritative tones was developed as characterized by the interviewed women. The postpilot iterative interviews and focus group discussion revealed a preference for customized messaging, reflecting an individual need to be included and connected. The women preferred short, concise, clear actionable messages that guided, supported, and motivated them to keep alert and seek professional help. Complementary weekly reminders to the women's significant others were also preferred to encourage continuity or prompt the needed social support for care seeking. CONCLUSIONS: We used an iterative approach with diffuse stakeholders to develop a patient-centered audio messaging and SMS text messaging app designed to communicate important targeted health-related information and support rural pregnant women in southwestern Uganda. Involving both HCPs and end users in developing and formulating the mHealth intervention allowed us to tailor the intervention characteristics to the women's preferences. Future work will address the feasibility, acceptability, and effectiveness of this design approach.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".