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February 26, 2026SHILAP Revista de lepidopterología0 citationsOpen Access

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

ZXZheyuan XiaYMYukuan MiaoLTLeran Tang

Key Result

Four distinct proactive health behavior profiles were identified among 321 hypertensive patients in China: Positive-Proactive (15.8%), Self-Regulating-Proactive (35.4%), Medically Compliant-Proactive (30.2%), and Passive-Proactive (18.6%).

Key Points

  • This research aims to identify distinct profiles of proactive health behaviors in individuals with hypertension and explore factors influencing these behaviors.
  • Conducted a cross-sectional study with 352 hypertensive patients from multiple communities.
  • Utilized various self-designed surveys and established scales to collect data on health behaviors, self-efficacy, and health literacy.
  • Applied latent profile analysis (LPA) to classify patients into behavioral profiles based on their proactive health behaviors.
  • Employed multinomial logistic regression to analyze factors related to the identified behavioral profiles.
  • Four profiles of proactive health behavior were identified: positive, self-regulating, medically compliant, and passive.
  • The total proactive health behavior score averaged 89.57 with a standard deviation of 22.99.
  • High entropy value (0.856) confirmed accurate classification of behavior profiles.
  • Significant influencing factors included age, education, marital status, employment, disease duration, and self-efficacy levels.

Study Design

Type

Cross-Sectional (n=321)

Multicenter

Yes

Structured PICO

P
Population
321 patients with hypertension from 8 communities in Anhui Province, China
O
Outcome
Latent profiles of proactive health behavior and their influencing factorspatient reported

Identifying distinct latent profiles of proactive health behaviors in hypertensive patients enables the development of tailored, profile-specific interventions to improve disease management.

Limitations

  • Cross-sectional design cannot establish causality.
  • Single-province sample may limit generalizability.
  • Self-reported behaviors subject to bias.
  • No intervention tested, descriptive study only.

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, n = 50, 15.8%), the self-regulating proactive health behavior profile (Class 2, n = 114, 35.4%), the medically compliant-proactive health behavior profile (Class 3, n = 96, 30.2%), and the passive proactive health behavior profile (Class 4, n = 61, 18.6%). The entropy value was high (0.856), indicating a correct classification. Multivariate regression analyses showed that age, educational level, marital status, employment status, disease duration, hospitalization due to hypertension, self-management level, self-efficacy level and health literacy as factors influencing proactive health behavior profiles. 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.

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Cite This Study

Xia et al. (2026) conducted a cross-sectional in Hypertension (n=321). None (Observational study of proactive health behavior profiles) was evaluated on Latent profiles of proactive health behavior among hypertensive patients identified by latent profile analysis. Four distinct proactive health behavior profiles were identified among 321 hypertensive patients in China: Positive-Proactive (15.8%), Self-Regulating-Proactive (35.4%), Medically Compliant-Proactive (30.2%), and Passive-Proactive (18.6%).

synapsesocial.com/papers/699fe2eb95ddcd3a253e6637https://doi.org/10.3389/fpubh.2026.1789975
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