Abstract:

Objective To explore the potential categories and influencing factors of fatigue trajectories in stroke patients, to provide information of the nursing management of patients with post-stroke fatigue after subsequent stroke. Methods 265 stroke patients hospitalized in the rehabilitation department of a tertiary A general hospital in Lishui from January 2022 to June 2023 were conveniently selected as the research subjects. The general information questionnaire, the Fatigue Severity Scale, the Health Behaviour Scale for Stroke Patient, and the Pittsburgh Sleep Quality Index were employed. The degree of fatigue was evaluated at 1~ 2 weeks，1 month，3 months and 6 months after the onset of the disease. The latent category growth model was used to identify the potential categories of fatigue trajectory，and logistic regression was used to analyze the influencing factors of fatigue trajectory. Results A total of 232 stroke patients were included，among which 128（55.17%） had fatigue，and there were 4 trajectories of fatigue，including 44 cases（18.97%） in the “continuous fatigue group”，13 cases（5.60%） in the “increased fatigue group”，71 cases（30.60%） in the “fatigue relief group”，104 cases（44.83%） in the “no fatigue group”. Logistic regression analysis showed that age，education level，health behavior and sleep quality were the influencing factors of fatigue trajectory in stroke patients（P<0.05）. Conclusion The fatigue of stroke patients can be divided into 4 kinds of change trajectories，and there is group heterogeneity. Nursing staff should carry out targeted nursing interventions for patients according to different fatigue change trajectories.

Key words: Stroke, Post-Stroke Fatigue, Latent Class Growth Model, Root Cause Analysis, Nursing Care

摘要：

目的 探讨脑卒中患者疲劳轨迹的潜在类别及影响因素，为后续脑卒中后疲劳患者的护理管理提供依据。 方法 便利选取2022年1月—2023年6月在浙江省丽水市某三级甲等综合医院康复科住院的265例脑卒中患者作为调查对象。采用一般资料调查表、疲劳严重度量表、脑卒中患者健康行为量表、匹兹堡睡眠质量指数进行调查，分别在发病后1~2周、发病后1个月、发病后3个月及发病后6个月对其疲劳程度进行评估，运用潜类别增长模型识别疲劳轨迹的潜在类别，采用Logistic回归分析疲劳轨迹的影响因素。 结果 最终纳入脑卒中患者232例，共128例（55.17%）发生了疲劳，其疲劳程度存在4种轨迹，分别为“持续疲劳组”44例（18.97%）；“疲劳升高组”13例（5.60%）；“疲劳缓解组”71例（30.60%）；“无疲劳组”104例（44.83%）。Logistic回归分析结果显示，年龄、文化程度、健康行为及睡眠质量是脑卒中患者疲劳轨迹的影响因素（P<0.05）。 结论 脑卒中患者疲劳分为4种变化轨迹，并且存在着群体异质性，护理人员应根据不同的疲劳变化轨迹对患者进行针对性护理干预。

关键词: 脑卒中, 卒中后疲劳, 潜类别增长模型, 影响因素分析, 护理