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首页> 外文期刊>Physiotherapy theory and practice >Use of the International Classification of Functioning, Disability and Health as a framework for analyzing the Stroke Impact Scale-16 relative to falls
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Use of the International Classification of Functioning, Disability and Health as a framework for analyzing the Stroke Impact Scale-16 relative to falls

机译：使用国际功能，残疾与健康分类作为分析中风相对于跌倒的中风影响等级16的框架

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Objective: To determine if subscores based on grouping Stroke Impact Scale 16 (SIS-16) items according to International Classification of Functioning, Health and Disability (ICF) components are more accurate in identifying individuals with a history of falls than the total SIS-16 score. Design: Case series. Subjects: 43 community-dwelling people with chronic stroke. Methods: Participants were grouped based on six month fall history (no fall versus one or more falls). The SIS-16 items were categorized as belonging to the Body Structure and Function (BSF), Activity (ACT) or Participation (PART) component of the ICF. SIS-16 total score and ICF component subscores were analyzed for their association with falls. Receiver Operating Characteristic Curves were (ROC) analyzed. Results: There were significant differences between groups on SIS-16 total (p=0.006), BSF (p=0.041) and ACT (p=0.003) scores. The BSF and ACT component subscores had the highest specificity (0.91) and sensitivity (0.80), respectively, for categorizing participants according to fall history. The BSF+ACT component subscore demonstrated greater accuracy than the total SIS-16 for identifying people with falls (area under the curve=0.78). Conclusion: The ICF may be a useful model for analysis of fall screening tools for people with chronic stroke. ICF component subscores are more accurate than the SIS-16 total score for this purpose.
机译：目的：根据国际功能，健康与残障分类（ICF）组件，根据对卒中影响量表16（SIS-16）项进行分组来确定子评分是否比总SIS-16更准确地识别有跌倒史的人得分了。设计：案例系列。受试者：43名慢性卒中社区居民。方法：根据六个月的跌倒历史（不跌倒或一次或多次跌倒）对参与者进行分组。 SIS-16项被归类为属于ICF的身体结构和功能（BSF），活动（ACT）或参与（PART）组件。分析SIS-16总得分和ICF组件子得分与跌倒的相关性。分析了接收器工作特性曲线（ROC）。结果：SIS-16总分（p = 0.006），BSF（p = 0.041）和ACT（p = 0.003）得分之间存在显着差异。 BSF和ACT组件子得分分别根据跌倒历史对参与者进行分类，分别具有最高的特异性（0.91）和敏感性（0.80）。 BSF + ACT组件子得分比总的SIS-16更高，能够更好地识别跌倒人员（曲线下面积= 0.78）。结论：ICF可能是分析慢性卒中患者的秋天筛查工具的有用模型。为此，ICF组件子得分比SIS-16总得分更准确。

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