Voice-Based vs. Text-Based WhatsApp Messaging for Maternal Health Knowledge and Attitudes Among Pregnant and Breastfeeding Women in Abuja, Nigeria

Maternal mortality remains unacceptably high in sub-Saharan Africa, and gaps in maternal health knowledge contribute to delayed care-seeking behavior. While mobile health (mHealth) interventions offer scalable solutions, evidence directly comparing voice-based and text-based delivery formats in African settings is lacking.

This three-arm pilot randomized controlled trial was designed to address this gap by evaluating the preliminary effectiveness of voice-based versus text-based WhatsApp messaging on maternal health knowledge and attitudes. The theoretical rationale for the intervention is grounded in media richness and social presence frameworks, which suggest that voice communication conveys paralinguistic cues and creates greater social presence than text alone, potentially reducing cognitive load for women with limited literacy.

Participants were randomized into three arms: voice-based WhatsApp messaging, text-based WhatsApp messaging, or a standard care control group. The intervention consisted of weekly maternal health messages delivered over an eight-week period. The educational content, adapted from WHO and UNICEF maternal health materials, covered topics including nutrition, danger signs, breastfeeding, and postnatal care. Messages were delivered by a trained research assistant via a WhatsApp broadcast list, with the voice arm receiving audio recordings and the text arm receiving written messages of equivalent content.

The primary objective is to determine if voice-based messaging results in greater improvements in maternal health knowledge and attitudes compared to text-based messaging and standard care. Secondary exploratory objectives include assessing changes in a binary knowledge battery. The results of this pilot trial are intended to establish preliminary effect sizes and inform the design of a definitive, multi-site randomized controlled trial to evaluate voice messaging at scale.