Park Jisook, Miller Carol A, Sanjeevan Teenu, Van Hell Janet G, Weiss Daniel J, Mainela-Arnold Elina
Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada.
Department of Communication Sciences and Disorders, Pennsylvania State University, University Park, PA, USA.
Int J Lang Commun Disord. 2021 Jul;56(4):858-872. doi: 10.1111/1460-6984.12632. Epub 2021 Jun 16.
BACKGROUND & AIMS: Given that standardized language measures alone are inadequate for identifying functionally defined developmental language disorder (fDLD), this study investigated whether non-linguistic cognitive abilities (procedural learning, motor functions, executive attention, processing speed) can increase the prediction accuracy of fDLD in children in linguistically diverse settings.
METHODS & PROCEDURES: We examined non-linguistic cognitive abilities in mono- and bilingual school-aged children (ages 8-12) with and without fDLD. Typically developing (TD) children (14 monolinguals, 12 bilinguals) and children with fDLD (28 monolinguals, 12 bilinguals) completed tasks measuring motor functions, procedural learning, executive attention and processing speed. Children were assigned as fDLD based on parental or professional concerns regarding children's daily language functioning. If no concerns were present, children were assigned as TD. Standardized English scores, non-verbal IQ scores and years of maternal education were also obtained. Likelihood ratios were used to examine how well each measure separated the fDLD versus TD groups. A binary logistic regression was used to test whether combined measures enhanced the prediction of identifying fDLD status.
OUTCOMES & RESULTS: A combination of linguistic and non-linguistic measures provided the best distinction between fDLD and TD for both mono- and bilingual groups. For monolingual children, the combined measures include English language scores, functional motor abilities and processing speed, whereas for bilinguals, the combined measures include English language scores and procedural learning.
CONCLUSIONS & IMPLICATIONS: A combination of non-linguistic and linguistic measures significantly improved the distinction between fDLD and TD for both mono- and bilingual groups. This study supports the possibility of using non-linguistic cognitive measures to identify fDLD in linguistically diverse settings.
What is already known on the subject Given that standardized English language measures may fail to identify functional language disorder, we examined whether supplementing English language measures with non-linguistic cognitive tasks could resolve the problem. Our study is based on the hypothesis that non-linguistic cognitive abilities contribute to language processing and learning. This is further supported by previous findings that children with language disorder exhibit non-linguistic cognitive deficits. What this paper adds to existing knowledge The results indicated that a combination of linguistic and non-linguistic cognitive abilities increased the prediction of functional language disorder in both mono- and bilingual children. What are the potential or actual clinical implications of this work? This study supports the possibility of using non-linguistic cognitive measures to identify the risk of language disorder in linguistically diverse settings.
鉴于仅靠标准化语言测试不足以识别功能性定义的发育性语言障碍（fDLD），本研究调查了在语言环境多样的儿童中，非语言认知能力（程序学习、运动功能、执行性注意力、处理速度）是否能提高fDLD的预测准确性。
我们检查了有和没有fDLD的单语和双语学龄儿童（8至12岁）的非语言认知能力。发育正常（TD）儿童（14名单语儿童、12名双语儿童）和患有fDLD的儿童（28名单语儿童、12名双语儿童）完成了测量运动功能、程序学习、执行性注意力和处理速度的任务。根据家长或专业人士对儿童日常语言功能的担忧，将儿童判定为患有fDLD。如果没有担忧，则将儿童判定为TD。还获取了标准化英语成绩、非言语智商分数和母亲受教育年限。似然比用于检验每项测量在区分fDLD组与TD组方面的效果。使用二元逻辑回归来测试综合测量是否能增强对fDLD状态识别的预测。
语言和非语言测量相结合，在单语和双语组中都能最好地区分fDLD和TD。对于单语儿童，综合测量包括英语语言成绩、功能性运动能力和处理速度，而对于双语儿童，综合测量包括英语语言成绩和程序学习。
非语言和语言测量相结合，在单语和双语组中都显著改善了fDLD和TD之间的区分。本研究支持在语言环境多样的情况下使用非语言认知测量来识别fDLD的可能性。
关于该主题已有的知识 鉴于标准化英语语言测量可能无法识别功能性语言障碍，我们研究了用非语言认知任务补充英语语言测量是否能解决这一问题。我们的研究基于这样的假设，即非语言认知能力有助于语言处理和学习。先前的研究结果进一步支持了这一点，即患有语言障碍的儿童存在非语言认知缺陷。本文对现有知识的补充 结果表明，语言和非语言认知能力相结合，提高了对单语和双语儿童功能性语言障碍的预测。这项工作潜在或实际的临床意义是什么？本研究支持在语言环境多样的情况下使用非语言认知测量来识别语言障碍风险的可能性。
