Internet Of Things Based Model For Preeclampsia Monitoring In Antenatal Care

In the health sector, the health of women is a significant public health issue, which impacts the personal well-being, family reproduction, and societal development. Therefore, knowledge about the health of women has led to an emerging requirement for healthcare sectors to obtain the real-time status and data of various applications that can improve the performance and accuracy of the health production. Globally, it has been found that women die due to pregnancy and childbirth consequences. The major effects of maternal morbidity and mortality include haemorrhage, infection, high blood pressure, unsafe abortion, and obstructed labour. Some of the maternal challenges that cause long term effects when not controlled include preeclampsia, which is caused by hypertension, one of the leading identifiable risk factors in pregnancy. Hypertension also results in stillbirth, oedema, and even death. Hospitals in developing countries have been using several devices in the detection of blood pressure fluctuations, though not reading real-time data. This study, therefore, sought to implement an Internet of Things (IoT) based model for preeclampsia monitoring in antenatal care. To achieve this overall objective, the study identified suitable smart armband for measuring blood pressure based on functionality, hardware, software, and affordability. In addition, the study sought to develop IoT model and implement it to read pregnant mother’s real-time data that can be accessed by a Health care provider and family caregiver in case of an emergency. The developed IoT prototype was tested with fifty pregnant mothers who were selected using purposive and simple random sampling. The sample size selection was done using Cochran formula. The study was undertaken using mixed research design that involved exploratory, rapid prototyping approach and a quasi-experimental research designs. The respondents were selected from Thika level 5 hospital and Embu Level 5 hospital in Kiambu and Embu counties respectively. The study used consistency, response rate, accuracy, reliability, and output as metrics to evaluate IoT system performance. The T-test was used to determine the significance of performance metrics. The study found out that the IoT based model for preeclampsia monitoring was feasible and practical during the testing and also performed as expected during its evaluation. Based on the findings, the study recommends the approach to be scaled up and adopted in maternal health care to address preeclampsia conditions while addressing issues of cost in its adoption. In addition, the study recommends fabrication of suitable smart armband for measuring blood pressure in pregnant mothers.