Effect of Mobile Health Integrated Antenatal Care Intervention in Improving Maternity Continuum of Care, Obstetric Health Literacy, Maternal and Perinatal Outcomes in Public Hospitals, Ethiopia: A Randomized Control Trial
Effect of Mobile Health Integrated Antenatal Care Intervention in Improving Maternity Continuum of Care, Obstetric Health Literacy, Maternal and Perinatal Outcomes in Public Hospitals, Ethiopia: A Randomized Control Trial
The World Health Organization (WHO) has released wide-ranging guidelines on antenatal care (ANC) with the aiming to provide ongoing maternity care and "positive pregnancy experience". In this regard, the Safe Delivery App (SDA) and Health for All App provide animated clinical instruction videos in basic emergency obstetric and neonatal care, has been developed and recommended to be implemented in maternity health care. Though mobile health (mHealth) technology shows immense potential in improving healthcare services, evidence of its effectiveness had not been previously seen in Ethiopia.
Objectives: This study aims to assess effect of mHealth integrated ANC intervention in improving maternity continuum of care utilization, obstetric health literacy, and maternal and perinatal outcomes in Public Hospitals in East Gojjam Zone, North-West, Ethiopia, 2024/2025.
Methods: an interventional two-arm pragmatic cluster randomized controlled trial (RCT) will be conducted at six (three for each arm) primary Public Hospitals in East Gojjam Zone, North-West, Ethiopia, from April 1st to December 30th 2024. About 520 (260 for each arm) low-risk pregnant women having an access to smartphones and coming for 1st ANC contact within the first 12 weeks of gestation will be identified, requested and recruited for the study in both arms. Thereafter, those eligible women in the intervention arm will receive the application and practical training of mHealth solution in addition to the routine ANC services. Data will be collected 4 times, just at the baseline, end of ANC contact, at delivery and within 7 days of post natal period through record review, client interview and observation. The quantitative data will be analyzed using a chi-squared test, independent t-test, paired t-test, intention-to-treat (ITT) and per protocol (PP) analysis. Generalized linear mixed effect modeling will be used and the intervention's effect on our pre-specified objectives will be assessed using a relative risk (RR) measure of association along with non-inferior and superior analysis. Qualitative data will be collected from both the healthcare providers and clients to experience about the acceptability, feasibility and sustainability of integrating mHealth interventions.
Expected outcomes: includes maternity continuum of care, obstetric health literacy, birth preparedness and complication readiness plan and satisfaction, maternal and perinatal outcomes
Background: Maintaining the best possible health for the mother and newborns is still a challenge in Sub-Saharan Africa (SSA), including Ethiopia (1). The high toll of maternal and child mortalities remains the single most important challenge to the health sector (2). In low-income countries, hypertensive disorders of pregnancy (HDP), obstetric hemorrhage (OH), and sepsis are the most common drivers of maternal morbidity and mortality (3). Preventive measures that are straightforward and evidence-based are likely to prevent catastrophic morbidity and mortality during pregnancy (4). ANC is crucial for ensuring positive maternal and perinatal outcomes (5).
The WHO recommends establishing a broader package of interventions to increase individual, family, and community capacity to contribute to maternal and newborn health improvements and to increase use of skilled care during pregnancy, for childbirth, and after birth (5,6). The Health for All application is developed by scholars from the University of Gondar and granted by the International Institute for Primary Health Care - Ethiopia and the Ethiopian Ministry of Health. The Health for All app contains detailed information on family planning packages, ANC packages (updated 8 contacts), birth preparedness and complication readiness, skilled delivery, maternal nutrition, danger signs during ANC, perinatal periods, post-partum family planning, and postnatal care, but has not yet been implemented in Ethiopia. Therefore, this study will implement integrated services during ANC that may contribute to changes in healthcare-seeking during the perinatal period, ensure ongoing maternity care, and provide a positive pregnancy experience.
Even though maternal and neonatal health has received significant attention during the present Sustainable Development Goal (SDG) period, maintaining the best possible health for both the mother and newborns is still a challenge in Sub-Saharan Africa (SSA) (7). Ethiopia is one of the sub-Saharan countries with the highest toll of maternal and child mortalities; it remains the single most important challenge to the health sector (i.e., 401 MMR per 100,000 live births,and the perinatal mortality rate is 33 deaths per 1000 (8). The continuum of maternal care completion rate was extremely low (2). According to the 2019 Ethiopian mini demographic health survey report, about 57% of pregnant women didn't get optimum antenatal care (14), 69.1% of women dropped out of the maternity continuum of care (9). To increase pregnant women's adherence to the continuum of care, thereby reducing morbidity and mortality, mobile Health (mHealth) Apps are essential tools accessible through smartphones, empowering women with the health information they need to take control of their well-being like never before (10). These innovations are essential to improve women's limited understanding of the need for maternal health services, and harmful cultural norms pose obstacles to women's healthcare service utilization (11). Moreover, mHealth innovative applications enable users to learn about their conditions, promote lifestyle modifications during pregnancy, treatment options, and preventive measures, promoting informed decision making, access health information, and seek care (7,12-15). However, there is limited integrated uptake of this digital health solution in the maternal and child healthcare service delivery processes due to a lack of adequate information about the presence of these updated and evidence-based mHealth Apps (i.e., Safe Delivery and Health for All). Moreover, the lack of practical implementation experience is limiting Ethiopia's ability to move toward maturing its digital healthcare. Hence, healthcare providers and women's engagement are key components of effective healthcare, and mHealth apps have emerged as a game-changer in this domain.
Evidence revealed that mHealth interventions have emerged as a promising solution to improve maternal and child health outcomes. Despite the acceptance and effectiveness of mHealth being widely documented in literature, little to no research has been done in Ethiopia.
Research question: Does implementation of the mHealth integrated antenatal care intervention have a significant effect on improving maternity continuum of care, obstetric health literacy, and maternal and perinatal outcomes? Research hypothesis: Implementation of the mHealth integrated antenatal care intervention will improve maternity continuum of care, obstetric health literacy, and maternal and perinatal outcomes.
Objectives of the study:
General objective: To assess the effect of mHealth on integrated antenatal care intervention in improving maternity continuum of care, obstetric health literacy, and maternal and perinatal outcomes in Primary Public Hospitals of East Gojjam Zone, North-West Ethiopia, 2024 Specific objectives
Population:
Source population: All pregnant women attending antenatal care in East Gojjam Zone Public Health Institutions Study population: Low-risk pregnant women having access to a smartphone and coming for 1st ANC contact within the first 12 weeks of gestation in randomly selected Primary Public Hospitals Sample size determination and Sampling procedure: The following assumptions were considered to calculate the sample size using STATA 17: α = 5%, β = 80%, lost to follow up = 20%, ICC (ῤ) = 0.03, We assumed P1 at baseline of complete utilization of maternity continuum of care amongst control group (13.9%) based on previous literature (40). Implementation was expected to increase P1 by 14% and thus P2 was estimated as 27.9 %, d = 15% and cluster size (m) = 46. Sample size is calculated for each objective, and the largest sample size is obtained to be 260 per arm and 520 in total.
Unit and randomization of the clusters (Sampling procedure): The level of cluster will be Primary Public Hospitals, and a total of four (i.e., two non-adjacent pairs) of health institutions will be included and randomly assigned into intervention and control arms (i.e., two clusters for each arm). The 260 eligible study population in each arm will be selected consecutively until we get the desired sample, and an identification code will be given.
Implementation: The intervention involves the provision of training for both maternity service providers and users. Initially, advocating and training will be given for maternity healthcare providers. These maternity service providers in the intervention clusters are anticipated to be midwives and health extension workers (HEWs). In the first phase, hands-on practice practical training about the application of the mHealth solution (i.e., Health for All Apps) will be provided for midwives and HEWs. In the second phase, those trained maternity service providers will share the mHealth solution application (i.e., Health for All) with those eligible pregnant women and provide education about the benefits, application, and utilization of the mHealth solution. In addition to health center-based education, HEWs are also expected to provide education through maternity forums at the community level within the intervention catchment areas. After initiating the intervention and recruiting the required number of eligible women in the intervention arm, ongoing supportive supervision and monitoring will be undertaken throughout the follow-up period.
Data Collection Methods: Data will be collected 4 times, that is, at the baseline (during the 1st contact), end of ANC contact, delivery, and within the seventh day of the postpartum period of the intervention through chart review, client interview, and observation.
Statistical Analysis: The collected data will be entered using EpiData version 4.6 and will be exported to Stata version 17 for analysis. Descriptive statistics will be used to describe the participants' baseline data. We will figure out the statistical proportion difference between the two arms. The intervention's effect on between-group differences will be assessed using a chi-squared test for categorical variables and an independent t-test for continuous variables. A paired t-test will be used to assess differences within groups. Two pre-specified analyses will be used in this study: The primary analysis is an intention-to-treat (ITT) analysis that includes all recruited participants in the arm to which they were initially assigned, regardless of whether they have follow-up maternal continuum of care utilization status findings or completed the trial. The secondary analysis is as per protocol (PP) analysis, which examines treatment effects among just those participants who had available outcome status results at the end of the study. Generalized linear mixed effect modeling with standard errors adjusted for clustering at the health facility level will be employed. The intervention's effect on our pre-specified objectives will be assessed using a relative risk (RR) measure of association. The upper bound of a 95% confidence interval will be used to determine whether the RR falls below the non-inferiority margin (d=14%). If the upper bound of this confidence interval for the RR comparing the intervention to the control is less than 1.14, the intervention will not be inferior to the control. At the same time superiority analysis will be implemented and if the lower boundary of the CI is greater than 1.14, the alternative will be accepted. Post-intervention qualitative data will be collected from the stakeholders to assess pregnancy experiences, and the acceptability and feasibility as well as challenges and opportunities of the mHealth intervention, and will be analyzed using ATLAS.ti software.
Data quality assurance: To ensure the quality of data in the questionnaires will be pre-tested. Randomization of the samples will be done by taking different primary hospitals for the intervention and control groups, geographically far apart, to minimize Data contamination. A close follow-up will be done by supervisors. Training will be given to data collectors and supervisors Ethical approval: An Ethical approval letter will be obtained from the Institutional Review Board of Debre Markos University, and a trial registration number will be obtained from the concerned body. A formal letter of administrative approval will be obtained from each health facility manager. Written informed consent will be received from each of the study participants after a clear explanation of the aim of the study.
Inclusion criteria:
Exclusion Criteria:
Debre Markos, Amhara Regional State 269, Ethiopia