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高血压患者自我管理能力及其潜在类别预测因素分析

Predictors for self-management competence and latent class in hypertensive patients
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摘要 目的探究高血压患者的自我管理能力及其潜在类别预测因素。方法选取2020年5月至2022年12月于河南中医药大学第五临床医学院(郑州人民医院)就诊的218例原发性高血压患者为研究对象,通过填写一般信息调查表、高血压患者自我管理行为评估表、高血压疾病知识水平量表、高血压患者治疗态度与信念量表、高血压患者自我效能量表收集患者一般资料以及高血压管理相关数据,依据高血压自我管理的6个维度作为分类指标并进行潜在剖面分析以得出不同自我管理潜在类别,采用Logistic回归分析潜在类别的预测因素。结果共212例原发性高血压患者纳入本研究,其自我管理行为评估得分为(149.27±4.92)分。综合模型实际意义考虑,最终选择2类别为拟合模型。其中1类别(静态管理剖面)的132例(62.26%)高血压患者在用药管理、工作与休息管理、病情监测这3类静态管理维度上得分较高,2类别(动态管理剖面)的80例(37.74%)高血压患者在静态管理维度上得分较低,而在饮食管理、运动管理、情绪管理这3类动态维度的得分较高。两种类别管理剖面的患者性别、居住地、教育程度比较差异均无统计学意义(P>0.05),而在月收入、高血压病程、年龄、疾病知识水平、治疗态度与信念、自我效能方面比较差异均具有统计学意义(P<0.05)。Logistic回归分析结果显示,月收入、高血压病程、年龄、疾病知识水平、治疗态度与信念、自我效能均是高血压患者自我管理能力的潜在类别预测因素(P<0.05)。结论高血压患者自我管理能力可分为静态管理剖面与动态管理剖面,月收入、高血压病程、年龄、疾病知识水平、治疗态度与信念、自我效能均是高血压患者自我管理能力潜在类别预测因素。针对不同管理水平与潜在类别的原发性高血压患者采取不同的健康教育模式,可以提高患者的健康素养,改善健康状况。 Objective To explore the predictors for self-management competence and latent class in hypertensive patients.Methods Patients with essential hypertension who visited the Fifth Clinical Medical College of Henan University of Traditional Chinese Medicine(Zhengzhou People's Hospital)from May 2020 to December 2022 were selected as the study subjects,and the general data and hypertension management-related data of the patients were collected by filling in the general information questionnaire,the self-management behavior assessment form of hypertensive padoitients,the knowledge level scale of hypertensive diseases,the treatment attitude and belief scale of hypertensive patients,and the self-efficacy scale of hypertensive patients.The six dimensions of self-management of hypertension were used as classification indicators,and potential cross-sectional analysis was performed to obtain different potential categories of self-management.Logistic regression analysis was used to analyze predictors of potential categories.Results A total of 212 patients with essential hypertension were included in this study,and their self-management behavior assessment score was(149.27±4.92)points.Considering the practical significance of the model,2 categories were finally selected as the fitted model.Among them,132(62.26%)hypertensive patients in category 1(static management profile)scored higher on the three static management dimensions of medication management,work and rest management,and disease monitoring,and 80(37.74%)hypertensive patients in category 2(dynamic management profile)scored lower on the static management dimension but higher on the three dynamic dimensions of diet management,exercise management,and mood management.There were no significant differences in gender,residence,and education between patients with the two categories of management profiles(P>0.05),but there were significant differences in monthly income,duration of hypertension,age,level of disease knowledge,treatment attitude and beliefs,and self-efficacy(P<0.05).Logistic regression analysis showed that monthly income,duration of hypertension,age,disease knowledge level,treatment attitude and belief,and self-efficacy were all potential category predictors of self-management ability in hypertensive patients(P<0.05).Conclusion The self-management competence of hypertensive patients can be divided into static and dynamic management profiles.Monthly income,duration of hypertension,age,level of knowledge,treatment attitude and belief,and self-efficacy are all potential predictors of self-management competence of hypertensive patients.Furthermore,the application of different health education models for essential hypertension patients with different self-management competence and latent class can effectively improve patients'health literacy and health status.
作者 李雅琼 陈涛 陈冰冰 薛春丽 LI Ya-qiong;CHEN Tao;CHEN Bing-bing;XUE Chun-li(Health Medical Center,the Fifth Clinical Medical College of Henan University of Traditional Chinese Medicine(People's Hospital of Zhengzhou),Zhengzhou 450000,Henan,CHINA)
出处 《海南医学》 CAS 2024年第12期1689-1693,共5页 Hainan Medical Journal
基金 河南省医学科技攻关计划项目(编号:LHGJ20220015)。
关键词 原发性高血压 自我管理 潜在类别 预测因素 Essential hypertension Self-management Latent class Predictor
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