Evidence map›Paper›PMID 42870955›Full record

ArticleFrontiers in public health2026

The relationship between proactive health management, perceived social support, and symptoms in perimenopausal women: a latent profile and mediation analysis.

Ying Yuan, Xuyan Liu, Tingting Ruan, Lijia Hou, Yiwei Li, Fan Zhang

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Article in Frontiers in public health, 2026. 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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5 · Who and what money

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6 authors.

Ying YuanDepartment of Gynecology, De Yang People's Hospital, Deyang, Sichuan, China.
Xuyan LiuDepartment of Gynecology, De Yang People's Hospital, Deyang, Sichuan, China.
Tingting RuanDepartment of Gynecology, De Yang People's Hospital, Deyang, Sichuan, China.
Lijia HouDepartment of Gynecology, De Yang People's Hospital, Deyang, Sichuan, China.
Yiwei LiDepartment of Gynecology, De Yang People's Hospital, Deyang, Sichuan, China.
Fan ZhangDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Perimenopausal women experience various physical and psychological symptoms. Proactive health management and perceived social support are considered important factors in alleviating these symptoms. However, previous studies have largely overlooked the heterogeneity of proactive health management among individuals. This study aims to identify latent profiles of proactive health management in perimenopausal women and to explore the mediating role of perceived social support between different levels of proactive health management and perimenopausal symptoms. Methods: Between February 2025 and August 2025, a total of 491 perimenopausal women were recruited from four tertiary hospitals in Sichuan Province. Data were collected using the perimenopausal women's proactive health management scale, the Perceived Social Support Scale, and the Menopause Rating Scale. Latent profile analysis and mediation analysis were performed. Results: Three latent profiles of proactive health management in perimenopausal women were identified through latent profile analysis (LPA): the low-level passive health group (39.5%), the low-ability high-driven group (24.6%), and the high-level proactive health group (35.9%). Significant differences in perceived social support and perimenopausal symptoms were found between the low-level passive health group and the other groups. Mediation analysis revealed that the indirect association of perceived social support between the high-level proactive health group and perimenopausal symptoms was significant compared with the low-level passive management group ( Conclusion: There is heterogeneity in the level of proactive health management among perimenopausal women. Perceived social support shows a significant mediating association between the high-level proactive health management and perimenopausal symptoms. This mediating association was not observed in the low-ability high-driven group. It is recommended that community-based healthcare service systems and families pay attention to individual differences in health management among perimenopausal women. Beyond improving health literacy and self-management skills, health promotion programs should also leverage family and community support networks. Stratified and precise health promotion and intervention strategies should be developed for different subgroups to alleviate perimenopausal symptoms, thereby helping women transition through the perimenopausal period more smoothly and successfully.

Indexed as

PerimenopauseSocial SupportChinaFemaleHumansMediation AnalysisMiddle AgedSurveys and Questionnaireslatent profile analysisperceived social supportperimenopausal symptomsperimenopausal womenproactive health management

Identifiers

PMID42870955
PMCPMC13658004

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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.