Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA.
Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
Autism Res. 2020 Aug;13(8):1373-1382. doi: 10.1002/aur.2293. Epub 2020 Mar 25.
To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. To that end, a previous study demonstrated that a tablet-based application (app) that assessed several autism risk behaviors distinguished between toddlers with ASD and non-ASD toddlers. Using vocal data collected during this study, we investigated whether vocalizations uttered during administration of this app can distinguish among toddlers aged 16-31 months with typical development (TD), language or developmental delay (DLD), and ASD. Participant's visual and vocal responses were recorded using the camera and microphone in a tablet while toddlers watched movies designed to elicit behaviors associated with risk for ASD. Vocalizations were then coded offline. Results showed that (a) children with ASD and DLD were less likely to produce words during app administration than TD participants; (b) the ratio of syllabic vocalizations to all vocalizations was higher among TD than ASD or DLD participants; and (c) the rates of nonsyllabic vocalizations were higher in the ASD group than in either the TD or DLD groups. Those producing more nonsyllabic vocalizations were 24 times more likely to be diagnosed with ASD. These results lend support to previous findings that early vocalizations might be useful in identifying risk for ASD in toddlers and demonstrate the feasibility of using a scalable tablet-based app for assessing vocalizations in the context of a routine pediatric visit. LAY SUMMARY: Although parents often report symptoms of autism spectrum disorder (ASD) in infancy, we are not yet reliably diagnosing ASD until much later in development. A previous study tested a tablet-based application (app) that recorded behaviors we know are associated with ASD to help identify children at risk for the disorder. Here we measured how children vocalize while they watched the movies presented on the tablet. Children with ASD were less likely to produce words, less likely to produce speechlike sounds, and more likely to produce atypical sounds while watching these movies. These measures, combined with other behaviors measured by the app, might help identify which children should be evaluated for ASD. Autism Res 2020, 13: 1373-1382. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.
为了提高自闭症谱系障碍(ASD)的早期识别能力,我们需要客观、可靠和易于使用的评估方法。为此,先前的一项研究表明,一种基于平板电脑的应用程序(app)可以评估多种自闭症风险行为,从而区分自闭症儿童和非自闭症儿童。利用该研究中收集的声音数据,我们研究了在使用该 app 进行评估期间,发声是否可以区分 16-31 个月大、具有典型发育(TD)、语言或发育迟缓(DLD)和 ASD 的幼儿。参与者的视觉和声音反应是通过平板电脑上的摄像头和麦克风记录的,而幼儿则观看旨在引发与 ASD 相关风险行为的电影。然后对发声进行离线编码。结果表明:(a)与 TD 参与者相比,患有 ASD 和 DLD 的儿童在 app 管理期间更不可能说出单词;(b)与 ASD 或 DLD 参与者相比,音节发声与所有发声的比例在 TD 参与者中更高;(c)无音节发声的比率在 ASD 组中高于 TD 组或 DLD 组。那些产生更多无音节发声的人被诊断为 ASD 的可能性要高 24 倍。这些结果支持了先前的研究结果,即早期发声可能有助于识别幼儿患 ASD 的风险,并证明了使用可扩展的基于平板电脑的 app 在儿科常规就诊中评估发声的可行性。
尽管父母通常会在婴儿期报告自闭症谱系障碍(ASD)的症状,但我们直到发育后期才能可靠地诊断出 ASD。先前的一项研究测试了一种基于平板电脑的应用程序(app),该程序记录了我们已知与 ASD 相关的行为,以帮助识别有患病风险的儿童。在这里,我们测量了儿童在观看平板电脑上播放的电影时的发声方式。与观看这些电影时,ASD 儿童更不可能说出单词,更不可能发出类似言语的声音,更有可能发出异常声音。这些措施与 app 测量的其他行为相结合,可能有助于识别哪些儿童应接受 ASD 评估。自闭症研究 2020, 13: 1373-1382. © 2020 自闭症国际研究协会,威利父子出版公司