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Introduction: Prelacteal feeding, the practice of giving newborns substances other than breast milk within the first few days of life, remains a common yet harmful practice in many low- and middle-income countries, including Ethiopia. No evidence in Ethiopia indicates that mHealth can help improve prelacteal feeding. This study aimed to evaluate the effect of mobile health (mHealth) intervention on reducing prelacteal feeding practices and improving antenatal care (ANC) and postnatal care (PNC) utilization among mothers in South Ethiopia. Methods: A cluster-randomized controlled trial (CRT) was conducted in rural areas of South Ethiopia. A total of 20 clusters were selected using simple random sampling for intervention (mHealth) and control groups, each containing 340 women. Mothers in the intervention group received automated weekly SMS messages and reminders on exclusive breastfeeding, prelacteal feeding risks, ANC, and PNC. Mothers were only selected if they could read, write, and use mobile phones. Results: The mHealth intervention significantly reduced prelacteal feeding practice (AOR = 0.19, 95% CI: 0.06–0.58); p < 0.05). Higher ANC visits related to decreased prelacteal feeding (AOR = 0.28, 95% CI: 0.21–0.39; p < 0.001). The log count of ANC visit increased by 0.14 among intervention groups (IRR = 1.15, 95% CI: 1.06–1.25; p < 0.001). The PNC time was delayed 2.05 days among controls (β = −2.05, 95% CI: −2.66–−1.42; p < 0.001). Maternal and partner education, postnatal time, and ANC visits influenced prelacteal feeding. Conclusions: This finding might suggest that mHealth can reduce prelacteal feeding practices and improve maternal healthcare behaviors such as ANC attendance and timely PNC. These findings highlight the potential of mobile health interventions in promoting healthy maternal and infant practices in rural settings, where healthcare access is limited. Further research is needed to explore the long-term impacts of such interventions on maternal and child health outcomes. Multi-level analysis reduced variability. However, an unexplained variance could be reduced by including more cluster-level variables.
Gilano et al. (Tue,) studied this question.