EARLY STRESS DETECTION DURING PREGNANCY USING E-HEALTH IN THE PANDEMIC

Abstract

Background: Women are more prone to stress during pregnancy than during the postpartum period. Stress during pregnancy is correlated with pregnancy and birth outcomes. Early detection using the e-health system is an alternative to health services during the pandemic. Aims: The research objective was to produce innovation in early stress detection using an information system based on the e-Health system. Methods: This study was conducted in the Ngaliyan Primary Healthcare Centre with 34 pregnant women. This study utilized both qualitative and quantitative research. Qualitative research used the System Development Life Cycle (SDLC), while quantitative research used an experimental design with a one-shot case study approach. Results: The e-Health system could automatically identify stress during pregnancy, with the TAM questionnaire yielding a very effective result of 85.4%. The average time needed to detect pregnant women’s stress was 230.94 seconds. This system can analyze 374 pregnant women within one day (24 hours), provide services, and report pregnant women’s stress detection results. Conclusions: The e-Health system effectively conserves time and can be used to record and report early stress in pregnant women. Keywords: early detection, information system, pregnancy, smartphone, stress