ABSTRACT Background While persons with aphasia (PwA) are often dichotomised as fluent or nonfluent, agreement that fluency is not an all-or-nothing construct has led to the use of continuous variables as a way to quantify fluency, such as multi-dimensional rating scales, speech rate, and utterance length. Though these measures are often used in research, they provide little information about the underlying fluency deficit. Aim The aim of the study was to identify how well commonly used continuous measures of fluency capture variability in spontaneous speech variables at lexical, grammatical, and speech production levels. Methods & Procedures Speech samples of 254 English-speaking PwA from the AphasiaBank database were analyzed to examine the distributions of four continuous measures of fluency: the WAB-R fluency scale, utterance length, retracing, and speech rate. Linear regression was used to identify spontaneous speech predictors contributing to each fluency outcome measure. Outcomes & Results All the outcome measures reflected the influence of multiple underlying dimensions, although the predictors varied. The WAB-R fluency scale, speech rate, and retracing were influenced by measures of grammatical competence, lexical retrieval, and speech production, whereas utterance length was influenced only by measures of grammatical competence and lexical retrieval. The strongest predictor of WAB-R fluency was aphasia severity, whereas the strongest predictor for all other fluency proxy measures was grammatical complexity. Conclusions Continuous measures allow a variety of ways to objectively quantify speech fluency; however, they reflect superficial manifestations of fluency that may be affected by multiple underlying deficits. Furthermore, the deficits underlying different measures vary, which may reduce the reliability of fluency diagnoses. Capturing these differences at the individual level is critical to accurate diagnosis and appropriately targeted therapy.

中文翻译：

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有多流畅？B 部分：持续测量失语症流畅度的潜在因素

摘要 背景 虽然失语症 (PwA) 的人通常被分为流利或不流利，但一致认为流利不是一个全有或全无的结构，这导致使用连续变量作为量化流利的一种方式，例如多维评分音阶、语速和话语长度。尽管这些措施经常用于研究，但它们提供的关于潜在流畅性缺陷的信息很少。目的 本研究的目的是确定常用的流畅度连续测量在词汇、语法和语音生成水平上如何捕捉自发语音变量的可变性。方法和程序 分析了来自 AphasiaBank 数据库的 254 个说英语的 PwA 的语音样本，以检查四种连续性流利度量的分布：WAB-R 流利程度、话语长度、回溯和语速。线性回归用于识别对每个流利度结果测量有贡献的自发语音预测因子。结果与结果 尽管预测因素各不相同，但所有结果指标都反映了多个潜在维度的影响。WAB-R 流利量表、语速和回溯受语法能力、词汇检索和语音生成的影响，而话语长度仅受语法能力和词汇检索的影响。WAB-R 流畅性的最强预测因子是失语症严重程度，而所有其他流畅性替代指标的最强预测因子是语法复杂性。结论 连续测量允许通过多种方式客观地量化语言流畅度；然而，它们反映了可能受多种潜在缺陷影响的流利程度的表面表现。此外，不同措施所依据的缺陷各不相同，这可能会降低流畅性诊断的可靠性。在个体层面捕捉这些差异对于准确诊断和适当的靶向治疗至关重要。