Evidence map›Paper›PMID 41473756›Full record

ArticleFrontiers in psychiatry2025

The effect of artificial intelligence-empowered mobile health on psychological distress in women following abortion: protocol for a mixed-methods study.

Wei Zhang, Yuqi Yang, Meimei Liu, Lirong Wang, Qing Lei, Qiumei Zhang, Jing Wang, Hui Li, Gumula Wuri

Abstract read
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Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wei ZhangDepartment of Gynecology, Inner Mongolia Maternity and Child Health Care Hospital, Hohhot, Inner Mongolia, China.
Yuqi YangSchool of Nursing, Henan University of Science and Technology, Luoyang, Henan, China.
Meimei LiuDepartment of Gynecology, Inner Mongolia Maternity and Child Health Care Hospital, Hohhot, Inner Mongolia, China.
Lirong WangDepartment of Gynecology, Inner Mongolia Maternity and Child Health Care Hospital, Hohhot, Inner Mongolia, China.
Qing LeiDepartment of Gynecology, Inner Mongolia Maternity and Child Health Care Hospital, Hohhot, Inner Mongolia, China.
Qiumei ZhangDepartment of Gynecology, Inner Mongolia Maternity and Child Health Care Hospital, Hohhot, Inner Mongolia, China.
Jing WangSchool of Public Health, Peking University, Beijing, China.
Hui LiNursing Department, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Gumula WuriDepartment of Gynecology, Inner Mongolia Maternity and Child Health Care Hospital, Hohhot, Inner Mongolia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The number of abortions worldwide continues to rise, and abortion can have adverse physical and psychological effects. At present, there is little attention paid to the psychological distress experienced by women after abortion, such as anxiety and depression. Current interventions rely heavily on specific personnel, time, and location, which can be costly. Artificial Intelligence-Empowered Mobile Health can compensate for the shortcomings of current interventions, and these interventions are guided by Swanson's Theory of Caring. Methods: This study is a mixed-methods research protocol to be implemented at the Inner Mongolia Maternity and Child Health Care Hospital. A total of 100 women after abortion will be included and randomly assigned to either the intervention group or the standard care group. The intervention group will receive two weeks of Artificial Intelligence-Empowered Mobile Health intervention guided by Swanson's Theory of Caring, including medical knowledge about abortion, post-operative diet, and exercise guidance. The standard care group will receive standard prenatal care. The primary outcome is the change in depression levels, while secondary outcomes include anxiety, perceived stress, perceived social support, and other factors. This study uses IBM SPSS Statistics 27.0 and NVivo 12.0 for data analysis, employing descriptive statistics, normality tests, Mann-Whitney U, Wilcoxon, and chi-square tests. Discussion: This study pioneers an Artificial Intelligence-Empowered Mobile Health guided by Swanson's Theory of Caring, providing continuous post-abortion support to reduce psychological distress. It applies Large Language Models to Artificial Intelligence-Empowered Mobile Health for women experienced abortion, delivering timely, specialized care. This approach overcomes traditional barriers: offering real-time interaction, breaking spatiotemporal limits, lowering costs, and integrating expert knowledge to mitigate regional resource disparities, and also promoting health equity.

Indexed as

abortionartificial intelligencelarge language modelmixed-methodsmobile healthpsychological distressSwanson’s theory of caring

Identifiers

PMID41473756
PMCPMC12746660

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.