Cluster analysis of self-management behaviors in stroke patients and study of influencing factors
10.3760/cma.j.cn211501-20250430-01334
- VernacularTitle:脑卒中患者自我管理行为聚类分析及影响因素研究
- Author:
Hui WEI
1
;
Jing WANG
;
Xuyun JIANG
;
Yun XU
;
Yuting SHI
;
Juan LI
Author Information
1. 复旦大学附属华山医院神经内科，上海 200040
- Publication Type:Journal Article
- Keywords:
Stroke;
Self-management;
Influencing factors;
Cluster analysis;
Disease control
- From:
Chinese Journal of Practical Nursing
2025;41(31):2440-2449
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the types and characteristics of self-management behaviors among stroke patients, as well as to analyze the influencing factors associated with these different types, providing a reference for developing intervention programs aimed at enhancing self-management behaviors in stroke patients.Methods:This study adopted a cross-sectional survey design. The stroke inpatients were selected through convenience sampling from the Department of Neurology at Huashan Hospital, Fudan University between October 2023 and August 2024. Data collection was conducted using the following instruments: the General Information Questionnaire, Stroke Self-Management Behavior Rating Scale, Self-Efficacy for Managing Chronic Disease 6-Item Scale, Stroke Health Knowledge Questionnaire, Social Support Rating Scale, Generalized Anxiety Disorder Scale, Patient Health Questionnaire-9, and the modified Rankin Scale. Stroke patients' self-management behaviors were categorized using systematic cluster analysis, and disordered multi-class Logistic regression was employed to identify the influencing factors associated with each category.Results:Finally, 210 stroke patients were enrolled, there were 148 males and 62 females, aged (60.82 ± 13.05) years. The total score of self-management behavior in stroke patients was (144.18 ± 23.24) points, with a score rate of 56.54%. Systematic cluster analysis identified four distinct self-management behaviors patterns: consistent implementers (25.71%, 54/210); unrealistically optimistic(54.76%, 115/210); optimistically proactive (13.81%, 29/210); and passive and resigned (5.71%, 12/210). Disordered multi-class Logistic regression analysis indicated that higher scores in stroke-related health knowledge were associated with a greater likelihood of being categorized as stable practice type and optimistic proactive type ( OR=1.130, 1.254, both P<0.05). Conversely, increased levels of depression correlate with a higher probability of being classified as passive waiting type ( OR=0.684, 0.722, 0.540, all P<0.05). Additionally, lower modified Rankin Scale scores were linked to an increased tendency to fall into the categories of stable practice type and blind optimism type ( OR=19.759, 23.148, both P<0.05). Conclusions:The self-management behaviors of stroke patients are generally suboptimal and exhibited distinct classification features. Significant differences are observed in stroke health knowledge, depression, and the modified Rankin Scale scores among the four patient types. Healthcare professionals should tailor intervention measures to the characteristics of each type to enhance patients' self-management capacity.
