Abstract
Background
The 4th Industrial Revolution with the advent of the smart era, in which artificial intelligence, such as big data analysis and machine learning, is expected, and the provision of healthcare services using smartphones has become a reality. In particular, high-risk mothers who experience gestational diabetes, gestational hypertension, and prenatal and postpartum depression are highly likely to have adverse effects on the mother and newborn due to the disease. Therefore, continuous observation and intervention in health management are needed to prevent diseases and promote healthy behavior for a healthy life.
Methods
This randomized controlled trial will provide mothers 18 years of age or older with health care information collected based on evidence-based literature data using a smartphone app for 6 weeks. About 500 mothers will be selected in consideration of the dropout rate due to the characteristics of mothers. The study group and control group will be computer-generated in a 1:1 ratio through random assignment. The research group will receive health management items through the app, and health management information suitable for the pregnancy cycle is pushed to an alarm. The control group will receive the health management information of the paper. We also followed the procedure for developing mobile apps using the IDEAS framework.
Discussion
These results show the effectiveness of smart medical healthcare services and promote changes in health behaviors throughout pregnancy in high-risk mothers.
Trial registration
Clinical trial registration information for this study has been registered with WHO ICTRP and CRIS (Korea Clinical Research Information Service, CRIS). Clinical trial registration information is as follows:
Study of development of integrated smart health management service for the whole life cycle of high-risk mothers and newborns based on community, KCT0007193. Registered on April 14, 2022, prospectively registered. This protocol version is Version 1.0. April 14, 2022.
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Details
1 Gachon University College of Medicine, Department of Preventive Medicine, Incheon, Republic of Korea (GRID:grid.256155.0) (ISNI:0000 0004 0647 2973); Gachon University Gil Medical Center, Center for Public Health, Incheon, Republic of Korea (GRID:grid.411653.4) (ISNI:0000 0004 0647 2885); Konkuk University, Department of Literature and Art Therapy, Seoul, Republic of Korea (GRID:grid.258676.8) (ISNI:0000 0004 0532 8339)
2 Gachon University College of Medicine, Department of Preventive Medicine, Incheon, Republic of Korea (GRID:grid.256155.0) (ISNI:0000 0004 0647 2973); Gachon University Gil Medical Center, Center for Public Health, Incheon, Republic of Korea (GRID:grid.411653.4) (ISNI:0000 0004 0647 2885); Gachon University Gil Medical Center, Artificial Intelligence and Big-Data Convergence Center, Incheon, Republic of Korea (GRID:grid.411653.4) (ISNI:0000 0004 0647 2885)
3 Yonsei University College of Medicine, Department of Preventive Medicine & Institute of Health Services Research, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)
4 Gachon University of Gil Medical Center, Department of Obstetrics and Gynecology, Incheon, Republic of Korea (GRID:grid.411653.4) (ISNI:0000 0004 0647 2885)
5 Harvard TH Chan School of Public Health, Department of Social & Behavioral Sciences, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)