Data-Driven Analysis of Husband Support and Psychological Anxiety in Pregnant Women Using Educational Health Analytics
DOI:
https://doi.org/10.33050/itee.v4i2.966Keywords:
Husband’s Support, Anxiety, PrimigravidaAbstract
This study examines the relationship between husband support and psycholog- ical anxiety among pregnant women using a data-driven educational health ana- lytics approach. A cross-sectional quantitative design was employed at Pratama Suradita Clinic, Tangerang Regency, Indonesia. The sample consisted of 85 pregnant women selected from a target sample of 88 respondents using Slovin’s formula; three questionnaires were excluded due to incomplete responses. Psy- chological anxiety was measured using the Hamilton Anxiety Rating Scale (HARS), while husband support was assessed through a structured questionnaire covering emotional, informational, instrumental, and appraisal support. Instru- ment validity and reliability were confirmed prior to data collection (Cronbach’s alpha> 0.70). Chi-square analysis showed a statistically significant relation- ship between husband support and anxiety (p = 0.001). Pregnant women receiv- ing adequate husband support had an 88.1% lower probability of experiencing anxiety (OR = 0.119). These findings highlight the importance of involving husbands in prenatal educational programs to improve maternal mental health.
References
[1] N. Irfan, S. Zafar, K. A. Shakil, M. A. Wani, S. Kumar, A. Jaiganesh, and K. Abubeker, “Integrating ai predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes,” Scientific Reports, vol. 15, no. 1, p. 23878, 2025.
[2] C. Espinosa, M. Becker, I. Mari´c, R. J. Wong, G. M. Shaw, B. Gaudilliere, N. Aghaeepour, D. K. Steven- son, I. A. Stelzer, L. S. Peterson et al., “Data-driven modeling of pregnancy-related complications,” Trends in molecular medicine, vol. 27, no. 8, pp. 762–776, 2021.
[3] B. Rawat, A. S. Bist, D. Supriyanti, V. Elmanda, and S. N. Sari, “Ai and nanotechnology for healthcare: A survey,” Aptisi Transactions on Management (ATM), vol. 7, no. 1, pp. 86–91, 2023.
[4] V. Sai Shradha and B. R. Prathap, “Artificial intelligence-driven perspectives on maternal health: Reveal- ing important aspects and improving pregnancy results via machine learning,” in International Conference on Data Science, Computation and Security. Springer, 2024, pp. 227–243.
[5] N. Irfan, S. Zafar, and I. Hussain, “Holistic analysis and development of a pregnancy risk detection frame- work: Unveiling predictive insights beyond random forest,” SN Computer Science, vol. 5, no. 8, p. 1074, 2024.
[6] S. C. Esteves, C. J. De Jonge, A. Pacey, and M. H. Vazquez-Levin, “The 2025 who infertility guideline: value in focusing on clinical varicocele and recognizing gaps in male diagnosis and treatment,” Human Reproduction, p. deaf251, 2026.
[7] J. L. Willson, A. Nuche, and R. Widayanti, “Ethical considerations in the development of ai-powered healthcare assistants,” International Transactions on Education Technology (ITEE), vol. 2, no. 2, pp. 109–119, 2024.
[8] M. A. Mahant and V. Pellakuri, “Data-driven insights using decision trees and k-nearest neighbors (knn) for improving child and women’s health,” Multimedia Tools and Applications, vol. 84, no. 26, pp. 30 781– 30 804, 2025.
[9] S. Harnan, M. Franklin, C. Angus, J. B. Soriano, A. Johannessen, A. Sutton, J. Garcia-Aymerich, A. Auer, C. Carroll, E. Simpson et al., “The epidemiological burden and societal cost of 14 respiratory conditions in the who european region: systematic evidence map and economic analysis,” ERJ Open Research, 2026.
[10] M. Dehghan-Bonari, M. Alipour-Vaezi, M. M. Nasiri, and A. Aghsami, “A diagnostic analytics model for managing post-disaster symptoms of depression and anxiety among students using a novel data-driven optimization approach,” Healthcare Analytics, vol. 4, p. 100238, 2023.
[11] U. Rahardja, P. A. Sunarya, Q. Aini, S. Millah, and S. Maulana, “Technopreneurship in healthcare: Evalu- ating user satisfaction and trust in ai-driven safe entry stations,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 404–417, 2024.
[12] M. S. Miah and M. O. Ullah, “Identifying the determinants of depression among ever married women in bangladesh: A machine learning approach,” International Journal of Statistical Sciences, vol. 25, no. 2, pp. 33–46, 2025.
[13] A. Baird and Y. Xia, “Applying analytics to sociodemographic disparities in mental health,” Nature Mental Health, vol. 3, no. 1, pp. 124–138, 2025.
[14] R. AlSaad, A. Youssef, S. Kashani, M. AlAbdulla, A. Abd-Alrazaq, S. M. Khaled, A. Ahmed, and J. Sheikh, “Multimodal large language models for women’s reproductive mental health,” Archives of Women’s Mental Health, pp. 1–16, 2025.
[15] S. Dlamini, E. Kurniyaningrum, K. Kustiyono, U. Rahardja, Q. Aini, and S. Mahfudz, “Machine learning- based big data analytics for personalized health insurance services,” Sundara Advanced Research on Artificial Intelligence, vol. 2, no. 1, pp. 26–36, 2026.
[16] K. Lakhdadi, A. Ezzine, K. Boujraine, and H. Satori, “Data collection, datasets and sensors for pregnancy risk factors prediction: A literature review,” in 2025 4th International Conference on Embedded Systems and Artificial Intelligence (ESAI). IEEE, 2025, pp. 1–10.
[17] Y. Zhang, J. Fang, and A. Liu, “Leveraging machine learning to uncover ethnic-specific predictors of maternal postpartum depression,” Prevention Science, pp. 1–12, 2026.
[18] N. Lutfiani, D. A. Astrieta, V. Wildan, H. Sulistyaningrum, M. R. Anwar, and E. D. Astuti, “Emotional well-being and psychological support in infertility a multi-modal ai approach,” International Journal of Cyber and IT Service Management (IJCITSM), vol. 5, no. 1, pp. 81–92, 2025.
[19] H. M. A. Al Sliti, “Addressing maternal health disparities: An integrative approach using machine learn- ing and process mining,” Ph.D. dissertation, State University of New York at Binghamton, 2025.
[20] F. T. Zohora, “Machine learning for predicting postpartum depression and suicidal tendencies in bangladesh,” 2025.
[21] F. Fitriani, F. Anita, and L. Lilis, “Impact of husband support on maternal psychological well-being during pregnancy: a systematic review,” Jurnal Edukasi Ilmiah Kesehatan, vol. 3, no. 2, pp. 55–62, 2025.
[22] S. N. Abidah, F. D. Anggraini, M. Nuriyana, R. Halwa et al., “Husband support correlates with maternal anxiety levels during pregnancy in the third trimester,” Journal of Health Sciences, vol. 14, no. 3, pp. 174–180, 2021.
[23] D. Y. Kristiyanto, H. D. Purnomo, G. P. Cesna, and N. Ani, “The strategic role of orange technology in cultivating innovation and well-being,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 27–37, 2025.
[24] Y. T. Wijayanti, U. Khair, and G. Gustini, “Increasing the role of husbands in supporting the mental health of pregnant women in the third trimester through childbirth preparation education,” Jurnal Pengabdian Masyarakat Edukasi Indonesia, vol. 2, no. 3, pp. 112–120, 2025.
[25] N. Votruba, D. Praveen, L. Mellers, E. Rajan, S. R. Thout, V. Arora, Y. Malik, A. Kashyap, S. Majumdar, J. Hirst et al., “Smarthealth pregnancy and mental health study: protocol for a situational analysis of perinatal mental health in women living in rural india,” Frontiers in global women’s health, vol. 4, p. 1143880, 2023.
[26] R. Xie and F. Xie, “Effect of health education on mood and pregnancy rate among infertile patients undergoing assisted reproduction: A systematic review and meta-analysis,” Medicine, vol. 104, no. 47, p. e46165, 2025.
[27] C. Lukita, Y. M. Kareem, and B. Rawat, “The dynamics of adaptation and innovation of msmes in facing changes in consumer behavior post pandemic,” Startupreneur Business Digital (SABDA Journal), vol. 5, no. 1, pp. 23–32, 2026.
[28] P. Mulgund, Y. Li, R. Singh, S. Purao, and L. Agrawal, “The design and evaluation of a mental health edu- cational app for paternal postpartum depression,” International Journal of Human–Computer Interaction, vol. 41, no. 20, pp. 13 192–13 211, 2025.
[29] E. Lobo, R. Deepa, S. Mandal, J. S. Menon, A. Roy, S. Dixit, R. Gupta, S. Swaminathan, P. Thankachan, S. Bhavnani et al., “Protocol of the nutritional, psychosocial, and environmental determinants of neurode- velopment and child mental health (coincide) study,” Wellcome Open Research, vol. 9, p. 486, 2024.
[30] E. Murdaningsih, “Relationship between the husband support and anxiety in pregnant mothers third trimester in butuh district, purworejo regency central java in 2022,” 2022, accessed: 2026-05-21. [Online]. Available: https://rama.kemdiktisaintek.go.id/document/detail/oai:eprints.poltekkesjogja.ac.id:9860-78
[31] H. D. Purnomo, S. Y. Prasetyo, I. R. Widiasari, U. Rahardja et al., “Explainable ai with shap for data-driven growth prediction in smart poultry farming,” in 2025 2nd International Conference on Information System and Information Technology (ICISIT). IEEE, 2025, pp. 1–6.
[32] K. M. Miley, “Understanding social functioning deficits in health and first episode schizophrenia: A data-driven approach towards improved identification and treatment,” Ph.D. dissertation, University of Minnesota, 2022.
[33] W. H. Organization et al., “Population health management in primary health care: a proactive approach to improve health and well-being: primary health care policy paper series,” World Health Organization. Regional Office for Europe, Tech. Rep., 2023.
[34] Q. Hao, W. J. Choi, and J. Meng, “Retracted article: A data mining-based analysis of cognitive interven- tion for college students’ sports health using apriori algorithm: Q. hao et al.” Soft Computing, vol. 27, no. 21, pp. 16 353–16 371, 2023.
[35] E. Pranckeviciene and J. Kasperiuniene, “Global suicide mortality rates (2000–2019): Clustering, themes, and causes analyzed through machine learning and bibliographic data,” International journal of environ- mental research and public health, vol. 21, no. 9, p. 1202, 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Imella Annisah, Sania Ihroma, Anggarani Prihatiningsih, Irvan Arivianto, Arsita Pratiwi
This work is licensed under a Creative Commons Attribution 4.0 International License.
