PE-CARE: An Artificial Intelligence (AI)-Based Mobile Health Application to Improve Maternal Knowledge of Early Preeclampsia Detection – A Quasi-Experimental Study
Abstract
Background: Preeclampsia remains a leading cause of maternal mortality worldwide, yet awareness and early detection remain limited in low- and middle-income countries. While artificial intelligence (AI)-based applications have been increasingly utilized in hospital settings, their adoption in Indonesian primary care remains minimal. This study aimed to evaluate the effectiveness of an AI-based mobile health application (PE-CARE) in improving maternal knowledge on early detection of preeclampsia. Methods: A quasi-experimental pretest–posttest control group design was conducted at Puskesmas Parongpong, West Bandung Regency, from February to March 2025. A total of 100 pregnant women (≤20 weeks gestation) were recruited using purposive sampling and assigned equally to the intervention (n=50) and control (n=50) groups. The intervention group used the PE-CARE application for 14 days, while the control group received conventional health education. Knowledge was assessed using a validated 15-item questionnaire. Data were analyzed using paired and independent t-tests, complemented by effect size (Cohen’s d) and 95% confidence intervals. Results: Knowledge scores improved significantly in both groups, with a larger gain in the intervention group (mean difference 28.1; Cohen’s d=3.79, 95% CI 25.7–30.5, p<0.001) compared to the control group (mean difference 11.5; Cohen’s d=1.56, 95% CI 9.3–13.7, p<0.001). Between-group comparison of posttest scores confirmed a significant effect favoring the intervention (mean difference 21.3; Cohen’s d=4.05, 95% CI 18.8–24.8, p<0.001). Conclusion: The PE-CARE application was effective in improving maternal knowledge of preeclampsia in a primary care setting. While these findings demonstrate the potential of AI-based mobile health tools to complement antenatal education, further research is needed to evaluate long-term behavioral and clinical outcomes as well as implementation feasibility in diverse primary care contexts.
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Copyright (c) 2025 Erni Hernawati, Firsha Ilvany Mutiara, Sofa Nurul Hidayati (Author)
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References
Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A. (2023). A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus, 15(10), 1–14. https://doi.org/10.7759/cureus.46454
Alkhodari, M., Xiong, Z., Khandoker, A. H., Hadjileontiadis, L. J., Leeson, P., & Lapidaire, W. (2023). The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Review of Cardiovascular Therapy, 21(7), 531–543. https://doi.org/10.1080/14779072.2023.2223978
Atluri, N., Beyuo, T. K., Oppong, S. A., Moyer, C. A., & Lawrence, E. R. (2023). Challenges to diagnosing and managing preeclampsia in a low-resource setting: A qualitative study of obstetric provider perspectives from Ghana. PLOS Global Public Health, 3(5), e0001790. https://doi.org/10.1371/journal.pgph.0001790
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/https://doi.org/10.1146/annurev.psych.52.1.1
BPS. (2023, July 18). Angka Kematian Ibu/AKI (Maternal Mortality Rate/MMR) Hasil Long Form SP2020 Menurut Provinsi, 2020 - Tabel Statistik - Badan Pusat Statistik Indonesia. Badan Pusat Statistik. https://www.bps.go.id/id/statistics-table/1/MjIxOSMx/angka-kematian-ibu-aki-maternal-mortality-rate-mmr-hasil-long-form-sp2020-menurut-provinsi-2020.html
Bülez, A., Hansu, K., Çağan, E., Şahin, A., & Dokumacı, H. (2024). Artificial Intelligence in Early Diagnosis of Preeclampsia. Nigerian Journal of Clinical Practice, 27(3), 383–388. https://doi.org/10.4103/njcp.njcp_222_23
Butler, L., Gunturkun, F., Chinthala, L., Karabayir, I., Tootooni, M. S., Bakir-Batu, B., Celik, T., Akbilgic, O., & Davis, R. L. (2024). AI-based preeclampsia detection and prediction with electrocardiogram data. Frontiers in Cardiovascular Medicine, 11(1), 1–8. https://doi.org/10.3389/fcvm.2024.1360238
Champion, V. L., & Skinner, C. S. (2008). The health belief model. Health Behavior and Health Education: Theory, Research, and Practice, 4, 45–65.
Dinkes Jabar. (2023). Profil Kesehatan Provinsi Jawa Barat Tahun 2023. https://diskes.jabarprov.go.id
Firoz, T., Sanghvi, H., Merialdi, M., & von Dadelszen, P. (2011). Pre-eclampsia in low and middle income countries. Best Practice & Research Clinical Obstetrics & Gynaecology, 25(4), 537–548. https://doi.org/10.1016/j.bpobgyn.2011.04.002
Geller, S. E., Koch, A. R., Garland, C. E., MacDonald, E. J., Storey, F., & Lawton, B. (2018). A global view of severe maternal morbidity: moving beyond maternal mortality. Reproductive Health, 15(S1), 98–112. https://doi.org/10.1186/s12978-018-0527-2
Hassan, M., Kushniruk, A., & Borycki, E. (2024). Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review. JMIR Human Factors, 11(1), 1–19. https://doi.org/10.2196/48633
Hernawati, E., Che Hassan, H., Nambiar, N., & Heriawanti, R. (2024). The Relationship Between Knowledge and Attitude of Pregnant Women about Anc with The Suitability of Antenatal Care Visits. The Malaysian Journal of Nursing, 15(03), 54–61. https://doi.org/10.31674/mjn.2024.v15i03.007
Joseph, K. S., Boutin, A., Lisonkova, S., Muraca, G. M., Razaz, N., John, S., Mehrabadi, A., Sabr, Y., Ananth, C. V., & Schisterman, E. (2021). Maternal Mortality in the United States: Recent Trends, Current Status, and Future Considerations. In Obstetrics and Gynecology (Vol. 137, Issue 5, pp. 763–771). Lippincott Williams and Wilkins. https://doi.org/10.1097/AOG.0000000000004361
Liu, L., Pu, Y., Fan, J., Yan, Y., Liu, W., Luo, K., Wang, Y., Zhao, G., Chen, T., Puiu, P. D., & Huang, H. (2024). Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review. In Sensors (Vol. 24, Issue 19). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/s24196426
Ministry of Health of the Republic of Indonesia. (2023). Indonesian Health Survey 2023 In Number: Accurate Data Precise Policy. https://www.badankebijakan.kemkes.go.id/
Owoche, P. O., Shisanya, M. S., Mayeku, B., & Namusonge, L. N. (2025). The role of AI in reducing maternal mortality: Current impacts and future potentials: Protocol for an analytical cross-sectional study. PLOS One, 20(5), e0323533. https://doi.org/10.1371/journal.pone.0323533
Paas, F., & Ayres, P. (2014). Cognitive Load Theory: A Broader View on the Role of Memory in Learning and Education. Educational Psychology Review, 26(2), 191–195. https://doi.org/10.1007/s10648-014-9263-5
Parsa, S., Khajouei, R., Baneshi, M. R., & Aali, B. S. (2019). Improving the knowledge of pregnant women using a pre-eclampsia app: A controlled before and after study. International Journal of Medical Informatics, 125(1), 86–90. https://doi.org/10.1016/j.ijmedinf.2019.03.001
Profil Kesehatan Indonesia. (2023). Profil Kesehatan Indonesia. Direktorat Jenderal Pelayanan Kesehatan Republik Indonesia. https://kemkes.go.id/id/profil-kesehatan-indonesia-2023
Salam, R., Das, J., Ali, A., Bhaumik, S., & Lassi, Z. (2015). Diagnosis and management of preeclampsia in community settings in low and middle-income countries. Journal of Family Medicine and Primary Care, 4(4), 501–506. https://doi.org/10.4103/2249-4863.174265
Sharma, P., & Kaur, P. D. (2017). Effectiveness of web-based social sensing in health information dissemination—A review. Telematics and Informatics, 34(1), 194–219. https://doi.org/https://doi.org/10.1016/j.tele.2016.04.012
Souza, J. P., Day, L. T., Rezende-Gomes, A. C., Zhang, J., Mori, R., Baguiya, A., Jayaratne, K., Osoti, A., Vogel, J. P., Campbell, O., Mugerwa, K. Y., Lumbiganon, P., Tunçalp, Ö., Cresswell, J., Say, L., Moran, A. C., & Oladapo, O. T. (2024). A global analysis of the determinants of maternal health and transitions in maternal mortality. The Lancet Global Health, 12(2), e306–e316. https://doi.org/10.1016/S2214-109X(23)00468-0
Vijayan, V., Connolly, J., Condell, J., McKelvey, N., & Gardiner, P. (2021). Review of wearable devices and data collection considerations for connected health. Sensors, 21(16). https://doi.org/10.3390/s21165589
WHO. (2025, April 7). Maternal mortality. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/maternal-mortality
