Wang Yuanyuan, Williams Rondeline, Dilley Laura, Houston Derek M
Department of Otolaryngology-Head & Neck Surgery, The Ohio State University, 915 Olentangy River Road # 4000, Columbus, OH.
Department of Communicative Sciences & Disorders, Michigan State University, East Lansing, MI 48824.
Dev Rev. 2020 Sep;57. doi: 10.1016/j.dr.2020.100921. Epub 2020 Jun 11.
Early language environment plays a critical role in child language development. The Language ENvironment Analysis (LENA™) system allows researchers and clinicians to collect daylong recordings and obtain automated measures to characterize a child's language environment. This meta-analysis evaluates the predictability of LENA's automated measures for language skills in young children. We systematically searched reports for associations between LENA's automated measures, specifically, adult word count (AWC), conversational turn count (CTC), and child vocalization count (CVC), and language skills in children younger than 48 months. Using robust variance estimation, we calculated weighted mean effect sizes and conducted moderator analyses exploring the factors that might affect this relationship. The results revealed an overall medium effect size for the correlation between LENA's automated measures and language skills. This relationship was largely consistent regardless of child developmental status, publication status, language assessment modality and method, or the age at which the LENA recording was taken; however, the effect was weakly moderated by the gap between LENA recordings and language measures taken. Among the three measures, there were medium associations between CTC and CVC and language, whereas there was a small-to-medium association between AWC and language. These findings extend beyond validation work conducted by the LENA Research Foundation and suggest certain predictive strength of LENA's automated measures for child language. We discussed possible mechanisms underlying the observed associations, as well as the theoretical, methodological, and clinical implications of these findings.
早期语言环境在儿童语言发展中起着关键作用。语言环境分析（LENA™）系统使研究人员和临床医生能够收集一整天的录音，并获得自动化测量结果以描述儿童的语言环境。这项荟萃分析评估了LENA自动化测量结果对幼儿语言技能的预测能力。我们系统地检索了有关LENA自动化测量结果（具体而言，成人单词计数（AWC）、对话轮次计数（CTC）和儿童发声计数（CVC））与48个月以下儿童语言技能之间关联的报告。使用稳健方差估计，我们计算了加权平均效应大小，并进行了调节因素分析，以探究可能影响这种关系的因素。结果显示，LENA自动化测量结果与语言技能之间的相关性总体为中等效应大小。无论儿童的发育状况、发表状态、语言评估方式和方法，还是进行LENA录音时的年龄如何，这种关系在很大程度上都是一致的；然而，LENA录音与语言测量之间的时间间隔对效应有微弱的调节作用。在这三项测量中，CTC和CVC与语言之间存在中等关联，而AWC与语言之间存在小到中等的关联。这些发现超出了LENA研究基金会进行的验证工作范围，表明LENA自动化测量结果对儿童语言具有一定的预测能力。我们讨论了观察到的关联背后可能的机制，以及这些发现的理论、方法和临床意义。
