Feasibility and Acceptability of an Adapted Mobile Phone Message Program and Changes in Maternal and Newborn Health Knowledge in Four Provinces of Afghanistan: Single-Group Pre-Post Assessment Study
Bibliographic record
Abstract
BACKGROUND: Mobile phone apps for health promotion have expanded in many low- and middle-income countries. Afghanistan, with high maternal and newborn morbidity and mortality rates, a fragile health infrastructure, and high levels of mobile phone ownership, is an ideal setting to examine the utility of such programs. We adapted messages of the Mobile Alliance for Maternal Action (MAMA) program, which was designed to promote healthy behaviors during pregnancy and a newborn's first year of life, to the Afghan context. We then piloted and assessed the program in the provinces of Kabul, Herat, Kandahar, and Balkh. OBJECTIVE: The aim of this study was to assess the feasibility and acceptability of the MAMA pilot program, and to examine changes in reported maternal, newborn, and child health (MNCH) knowledge and attitudes among participants from baseline to follow up. METHODS: We conducted a single-group study with data collected within 10 weeks of enrollment, and data collection was repeated approximately 6 months later. Data were collected through face-to-face interviews using structured questionnaires. Eligible participants included pregnant women who had registered to receive fully automated mobile health messages and their husbands. Assessment questionnaires queried sociodemographic details; knowledge, attitudes, and health care-seeking practices; and intervention experience and acceptability at follow up. The number of messages received by a given phone number was extracted from the program database. We descriptively analyzed the feasibility and acceptability data and compared the change in MNCH knowledge between baseline and follow-up measures using the McNemar Chi square test. RESULTS: Overall, 895 women were enrolled in the MAMA program. Data from 453/625 women (72.5% of the pretest sample) who received voice (n=302) or text (n=151) messages, and 276/427 men (64.6% of the pretest sample) who received voice (n=185) or text (n=91) messages contributing data at both time points were analyzed. At follow up, 699/729 (95.9%) participants were still enrolled in the MAMA program; voice message and SMS text messaging subscribers received 43 and 69 messages on average over the 6-month period, respectively. Participants who were voice message subscribers and female participants more commonly reported missing messages compared with the text message subscribers and men; predominant reasons for missed messages were the subscribers being busy with chores or not having their shared phone with them. Over 90% of men and women reported experiencing benefits from the program, mainly increased knowledge, and 226/453 (49.9%) of the female participants reported referring someone else to the program. Most of the participants (377/453, 83.2% women and 258/276, 93.5% men) believed it was beneficial to include husbands in the program. Joint decision making regarding maternal and child health care increased overall. The proportions of participants with correct knowledge significantly increased for all but one MNCH measure at follow up. CONCLUSIONS: This assessment indicates that the pilot MAMA program is feasible and acceptable in the Afghan context. Further research should be conducted to determine whether program participation leads to improved MNCH knowledge, health practices, and health service utilization in this fragile setting prior to larger scale up.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".