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<p>Stroke is a leading cause of global disability, with survivors often experiencing complex, co-occurring symptoms that significantly impair health-related quality of life (HRQoL). Conventional analytical methods often fail to capture the interdependent nature of these symptoms. Network analysis offers a novel approach to understanding symptom interactions and their collective impact on HRQoL. A cross-sectional study using symptom network analysis. A total of 311 convalescent stroke survivors were recruited using a convenience sampling method from two Grade A tertiary hospitals in Zhejiang Province, China, between October 20, 2024, and July 31, 2025. Data were collected using a general information questionnaire, the Stroke Symptom Experience Scale, and the Stroke-Specific Quality of Life Scale (SS-QOL). Symptom network analysis was performed using R 4.4.2 software. The EBICglasso model was applied to construct the partial correlation network using Spearman rank correlation as input. The stability of the network structure was also evaluated using the case-dropping bootstrap method. The most frequently reported symptom was a decline in self-care ability (83.28%). Symptom network analysis revealed that limited limb mobility was the central symptom, with the highest strength centrality (strength centrality = 1.144). Symptoms most strongly associated with poorer HRQoL included decline in self-care ability (edge weight = -0.229) and slurred speech (edge weight = -0.181). Stability tests indicated that the network model performed well, with a correlation stability coefficient of 0.437 for strength centrality. Symptoms in convalescent stroke patients are interrelated, and different symptoms are associated with HRQoL through distinct mechanisms. Our findings suggest that intervention strategies based on central symptoms and their interrelationships may offer potential benefits for improving rehabilitation outcomes and enhancing patients’ HRQoL. These propositions require confirmation through future longitudinal or interventional studies.</p>

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Symptom network structure and its association with health-related quality of life in convalescent stroke survivors: a cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月18日

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  • Yiqing Zhang,
  • Qihang Xu,
  • Lina Chen,
  • Jingjing Ma

摘要

Stroke is a leading cause of global disability, with survivors often experiencing complex, co-occurring symptoms that significantly impair health-related quality of life (HRQoL). Conventional analytical methods often fail to capture the interdependent nature of these symptoms. Network analysis offers a novel approach to understanding symptom interactions and their collective impact on HRQoL. A cross-sectional study using symptom network analysis. A total of 311 convalescent stroke survivors were recruited using a convenience sampling method from two Grade A tertiary hospitals in Zhejiang Province, China, between October 20, 2024, and July 31, 2025. Data were collected using a general information questionnaire, the Stroke Symptom Experience Scale, and the Stroke-Specific Quality of Life Scale (SS-QOL). Symptom network analysis was performed using R 4.4.2 software. The EBICglasso model was applied to construct the partial correlation network using Spearman rank correlation as input. The stability of the network structure was also evaluated using the case-dropping bootstrap method. The most frequently reported symptom was a decline in self-care ability (83.28%). Symptom network analysis revealed that limited limb mobility was the central symptom, with the highest strength centrality (strength centrality = 1.144). Symptoms most strongly associated with poorer HRQoL included decline in self-care ability (edge weight = -0.229) and slurred speech (edge weight = -0.181). Stability tests indicated that the network model performed well, with a correlation stability coefficient of 0.437 for strength centrality. Symptoms in convalescent stroke patients are interrelated, and different symptoms are associated with HRQoL through distinct mechanisms. Our findings suggest that intervention strategies based on central symptoms and their interrelationships may offer potential benefits for improving rehabilitation outcomes and enhancing patients’ HRQoL. These propositions require confirmation through future longitudinal or interventional studies.