Evidence map›Paper›PMID 41810284›Full record

ArticleFrontiers in public health2026

Latent profile analysis and influencing factors of proactive health behaviors in hypertensive patients from the perspective of the health belief model.

Zheyuan Xia, Yukuan Miao, Leran Tang, Ting Yao, Qiao Hu, Xiao Wang, Xiang Wang

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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. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Zheyuan Xia *The School of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Yukuan Miao *Emergency Intensive Care Unit (EICU), The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Leran Tang *The School of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Ting YaoThe School of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Qiao HuThe School of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Xiao WangThe School of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Xiang WangThe School of Nursing, Anhui University of Chinese Medicine, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: To identify latent profiles of proactive health behaviors in patients with hypertension, examine the category-specific influencing factors. Background: Proactive health behavior, as an emerging concept, refers to a self-motivated approach to systematically managing health-related factors in order to actively maintain and promote one's health status. However, existing studies have largely focused on describing the overall level of such behaviors among patients with hypertension, with insufficient exploration of behavioral heterogeneity within this population. Moreover, there has been a lack of systematic integration of established behavioral theories to explain the multifactorial mechanisms underlying different behavioral patterns, which limits the development of precise nursing interventions. Methods: A cross-sectional study was performed, involving 352 patients with hypertension from 8 communities in Anhui Province from September to December 2025. The survey tools included self-designed demographic and clinical instrument, the Proactive Health Behavior Scale for Hypertensive Patients, the Self-Efficacy Scale for Hypertensive Patients, the Health Literacy Management Scale (HeLMS). Latent profile analysis (LPA) was used to identify subtypes of proactive health behavior among hypertension patients. Multinomial logistic regression analysis was applied to determine the factors associated with the identified subtypes. Results: A total of 352 questionnaires were distributed, yielding 321 valid responses (a response rate of 91.2%). The total score of proactive health behavior was 89.57 ± 22.99 points. The LPA revealed four profiles of proactive health behavior: the positive proactive health behavior profile (Class 1, Conclusion: The proactive health behavior among hypertension patients was at a moderate level, revealing four distinct behavioral categories with significant differences. Guided by the Health Belief Model, profile-specific influencing factors were analyzed, which informed the development of tailored intervention strategies.

Indexed as

Health BehaviorHealth Belief ModelHypertensionAdultAgedChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnaireshealth belief modelhypertensionlatent profile analysisnursing careproactive health

Identifiers

PMID41810284
PMCPMC12969933

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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.