关键词: 自我管理；潜在剖面分析；高血压；预测指标

Abstract: Objective: To analyze the latent category characters of self-management of patients with hypertension and the risk indicators for defect categories of self-management. Methods: From November 2019 to June 2020, 243 hypertension patients admitted to department of cardiology of a tertiary grade A hospital in Tianjin were sampled conveniently as the research subjects. Hypertension Knowledge-Level Scale, Anti-hypertensive Therapy Related Attitudes and Beliefs Scale, Social Support Rating Scale, Duke Activity Status Index, Hypertension Patients Self-efficacy Rating Scale and Hypertension Patients Self-management Behavior Rating Scale were adopted to conduct a questionnaire survey. The latent class characters of self-management were explored by latent profile analysis and the predictor indicators of different categories were analyzed. Results: Four latent classes of self-management in patients with hypertension were identified: high self-management class, low self-management class, static self-management class and dynamic self-management class. Compared with high self-management class, the independent predictor of static self-management class were monthly income of 2000-5000, no hospitalization for hypertension, low therapy related attitudes and beliefs, low duke activity status index and low self-efficacy (P<0.05). The risk factors of dynamic self-management class were lake of knowledge of hypertension and low self-efficacy (P<0.05). The independent predictor of low self-management class were monthly income of 2000-5000, low therapy related attitudes and beliefs, duration of hypertension less than 5 years and low social support (P<0.05). Conclusion: Significant subgroups of patients with different self-management characters were identified. Nurses could identify the self-management characters and risk factors for each type of patients in early stage, and take corresponding support measures to improve the level of self-management of hypertension patients.

Key words: self-management; latent profile analysis; hypertension; predictors