Chinese General Practice ›› 2026, Vol. 29 ›› Issue (27): 3947-3955.DOI: 10.12114/j.issn.1007-9572.2025.0538

• Article • Previous Articles     Next Articles

Study on the Influencing Factors of Self-management Ability of Atrial Fibrillation Patients in Rural Areas

  

  1. 1. School of Health Policy & Management, Nanjing Medical University, Nanjing 211166, China
    2. Laboratory for Digital Intelligence & Health Governance, Nanjing Medical University, Nanjing 211166, China
  • Received:2025-12-10 Revised:2026-04-20 Published:2026-09-20 Online:2026-08-11
  • Contact: LI Zhong

农村心房颤动患者自我管理能力影响因素研究

  

  1. 1.211166 江苏省南京市,南京医科大学医政学院
    2.211166 江苏省南京市,南京医科大学数智技术与健康治理实验室
  • 通讯作者: 李忠
  • 作者简介:

    作者贡献:

    付梦茹负责统计学分析、撰写初稿、跟进修正;薛梅启旸、蒋雨桐负责现场调查、数据收集;陈鸣声、钱东福负责提供资源、调查现场协调联系;李忠负责文章的监督指导、调查现场协调联系、论文修订和审校。

  • 基金资助:
    国家科技重大专项"癌症、心脑血管、呼吸和代谢性疾病防治研究"慢病防治技术推广行动实施项目(江苏方案)课题一"慢病防治技术推广机制及模式构建"(2024ZD0524001)

Abstract:

Background

Rural areas are the weak link in the prevention and treatment of heart and brain vascular diseases such as atrial fibrillation. However, there are relatively few studies on the self-management ability and influencing factors of atrial fibrillation patients in rural areas.

Objective

Based on the health ecology model, this study explores the factors influencing self-management ability and ability of each dimension among patients with atrial fibrillation, aiming to provide a basis for formulating personalized intervention strategies.

Methods

By combining convenience sampling and cluster sampling methods, a questionnaire survey was conducted among patients with atrial fibrillation in Jiangdu District of Yangzhou City and Suining County of Xuzhou City, Jiangsu Province. Based on the health ecology model, independent variables were included for single-factor analysis and multiple linear regression analysis.

Results

Among the 431 respondents, 250 were male (58.0%) and 181 were female (42.0%); the majority were aged between 70 and 79 years old, with 236 individuals (54.8%); 172 had a health score of 71 or above (39.9%); 247 had paroxysmal atrial fibrillation (57.3%); 173 had the disease duration of no more than 5 years (40.1%). The average self-management ability score for atrial fibrillation patients was (3.17±0.56) points. Among the five dimensions, the score for managing bad habits was the highest at 5.00 (1.33); followed by emotional and social management at 3.50 (1.40), medical compliance behavior management at 3.29 (1.00), anticoagulant drug management at 2.33 (2.00), and disease prevention and monitoring management at 2.00 (1.25) points. The results of the multiple linear regression analysis showed that women is a protective factor for the management of bad habits [B (95%CI)=-1.500 (-2.939 to -0.061), P<0.001], and a risk factor for anticoagulant drug management [B (95%CI)=-1.500 (-2.939 to -0.061), P=0.041]; age is a protective factor for the management of bad habits [70-79 years old: B (95%CI)=1.605 (0.670 to 2.540), P<0.001;≥80 years old: B (95%CI)=1.400 (0.339 to 2.460), P=0.010]; health status scores is a protective factor for emotional and social management [60-70 scores: B (95%CI)=1.734 (0.129 to 3.340), P=0.034;≥71 scores: B (95%CI)=3.152 (1.414 to 4.890), P<0.001] and disease prevention and monitoring management [B (95%CI)=1.803 (0.435 to 3.171), P=0.010]; longer disease duration is a protective factor for medical compliance behavior management [B (95%CI)=2.311 (0.871 to 3.751), P=0.002], and a risk factor for bad habit management [B (95%CI)=-0.859 (-1.621 to -0.096), P=0.027); multiple chronic diseases is a protective factor for medical compliance behavior management [1 chronic condition: B (95%CI)=2.349 (0.659 to 4.038), P=0.007; three over chronic conditions: B (95%CI)=2.992 (1.133 to 4.850), P=0.002]; moderate to severe anxiety and depression is a risk factor for emotional and social management [B (95%CI)=-1.504 (-2.849 to -0.159), P=0.029], and a protective factor for disease prevention and monitoring management [B (95%CI)=1.156 (0.092 to 2.221), P=0.033]; not sure if anticoagulant drugs are being used is a risk factor for self-management ability [B (95%CI)=-10.349 (-19.206 to -1.491), P=0.022], while being currently using anticoagulant drugs is a protective factor for medical compliance management [B (95%CI)=2.718 (1.292 to 4.143), P<0.001] and anticoagulant drug management [B (95%CI)=1.571 (0.107 to 3.035), P=0.036]; receiving chronic disease management services through online tools is a protective factor for medical compliance behavior management [B (95%CI)=3.235 (1.803 to 4.667), P<0.001]; a higher level of education is a protective factor for disease prevention and monitoring management [B (95%CI)=2.140 (0.323 to 3.958), P=0.021]; not seeking medical treatment due to economic reasons is a risk factor for self-management ability [B (95%CI)=-5.816 (-11.169 to -0.463), P=0.033]; doctors adjusting health management plans is a protective factor for medical compliance behavior management [B (95%CI)=1.482 (0.110 to 2.853), P=0.034], anticoagulant drug management [B (95%CI)=1.453 (0.034 to 2.872), P=0.045], and disease prevention and monitoring management [B (95%CI)=1.081 (0.103 to 2.059), P=0.030].

Conclusion

The self-management ability of rural patients with atrial fibrillation is at a medium to low level. The overall and individual dimensions of their ability are affected by multiple factors such as gender, age, health status score, disease duration, number of chronic diseases, degree of anxiety and depression, whether they use anticoagulant drugs, whether they receive chronic disease management services through online tools, educational level, whether they do not seek medical treatment due to economic reasons, and whether their health management plans are adjusted by doctors. Policy designers and service providers should comprehensively consider multiple intervention targets and construct an integrated intervention strategy in order to enhance the self-management ability and health level of rural patients with atrial fibrillation.

Key words: Atrial fibrillation, Self-management, Health ecology model, Rural areas, Root cause analysis, Multivariate linear regression analysis

摘要:

背景

农村地区是心房颤动(AF)等心脑血管疾病防治的薄弱环节。然而,农村AF患者自我管理能力及影响因素相关研究较少。

目的

基于健康生态学模型(HEM),探讨AF患者自我管理及各维度能力的影响因素,为制订整合干预策略提供依据。

方法

2025年7—8月,采用方便抽样和整群抽样相结合的方法,对江苏省扬州市江都区和徐州市睢宁县AF患者开展问卷调查。以AF患者自我管理能力总分及各维度得分为因变量,基于HEM纳入自变量,进行单因素分析和多元线性回归分析。

结果

431例调查对象中男250例(58.0%),女181例(42.0%);年龄以70~79岁居多,为236例(54.8%);健康状况71分及以上172例(39.9%);阵发性AF 247例(57.3%);病程≤5年173例(40.1%)。AF患者自我管理能力平均得分为(3.17±0.56)分。5个维度中,不良嗜好管理得分最高,为5.00(1.33)分;其次是情绪与社交管理3.50(1.40)分、遵医行为管理3.29(1.00)分、抗凝药物管理2.33(2.00)分、疾病预防与监测管理2.00(1.25)分。多元线性回归分析结果显示,女性是不良嗜好管理的保护因素[B(95%CI)=2.826(2.049~3.604),P<0.001]和抗凝药物管理的危险因素[B(95%CI)=-1.500(-2.939~-0.061),P=0.041];年龄是不良嗜好管理保护因素[70~79岁:B(95%CI)=1.605(0.670~2.540),P<0.001;≥80岁:B(95%CI)=1.400(0.339~2.460),P=0.010];健康状况得分是情绪与社交管理[60~70分:B(95%CI)=1.734(0.129~3.340),P=0.034;≥71分:B(95%CI)=3.152(1.414~4.890),P<0.001]和疾病预防与监测管理[B(95%CI)=1.803(0.435~3.171),P=0.010]的保护因素;病程较长是遵医行为管理保护因素[B(95%CI)=2.311(0.871~3.751),P=0.002]和不良嗜好管理的危险因素[B(95%CI)=-0.859(-1.621~-0.096),P=0.027];多重慢病是遵医行为管理的保护因素[1种:B(95%CI)=2.349(0.659~4.038),P=0.007;≥3种:B(95%CI)=2.992(1.133~4.850),P=0.002];中/重度焦虑抑郁是情绪与社交管理的危险因素[B(95%CI)=-1.504(-2.849~-0.159),P=0.029]和疾病预防与监测管理的保护因素[B(95%CI)=1.156(0.092~2.221),P=0.033];不知道是否在使用抗凝药物是自我管理能力的危险因素[B(95%CI)=-10.349(-19.206~-1.491),P=0.022],正使用抗凝药物是遵医行为管理[B(95%CI)=2.718(1.292~4.143),P<0.001]和抗凝药物管理[B(95%CI)=1.571(0.107~3.035),P=0.036]的保护因素;通过在线工具接受慢病管理服务是遵医行为管理的保护因素[B(95%CI)=3.235(1.803~4.667),P<0.001];受教育程度较高是疾病预防与监测管理的保护因素[B(95%CI)=2.140(0.323~3.958),P=0.021];因经济原因不看病是自我管理能力的危险因素[B(95%CI)=-5.816(-11.169~-0.463),P=0.033];医生调整健康管理方案是遵医行为管理[B(95%CI)=1.482(0.110~2.853),P=0.034]、抗凝药物管理[B(95%CI)=1.453(0.034~2.872),P=0.045]、疾病预防与监测管理[B(95%CI)=1.081(0.103~2.059),P=0.030]的保护因素。

结论

农村AF患者自我管理能力处于中低水平,总体及各维度能力受性别、年龄、健康状况得分、病程、慢病数量、焦虑抑郁程度、是否使用抗凝药物、是否通过在线工具接受慢病管理服务、受教育程度、是否因经济原因不看病、医生是否调整健康管理方案多层次因素的影响。政策制定者和服务提供方应综合考虑多层面干预靶点,构建整合干预策略,提高农村AF患者自我管理能力和健康水平。

关键词: 心房颤动, 自我管理, 健康生态学模型, 农村地区, 影响因素分析, 多元线性回归分析