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Whitney Guthrie 等人

Robust evidence supports the efficacy of early autism intervention. Despite broad consensus that earlier intervention leads to better outcomes, evidence for this has been limited to correlational studies. This study examined timing effects of the Early Social Interaction (ESI) model, a parent-implemented intervention, using a complete crossover randomized controlled trial (RCT). Effects of Individual-ESI were compared when initiated at 18 or 27 months of age, and also contrasted with effects of the less intensive Group-ESI as an active control condition. Participants included 82 autistic toddlers who received 9 months of Individual-ESI and 9 months of Group-ESI, with the timing/order randomized. Blinded clinicians completed assessments at baseline (18 months of age), end of Condition 1 (27 months), and end of Condition 2 (36 months). Toddlers randomized to Individual-ESI at 18 months showed greater gains during treatment than those starting Individual-ESI at 27 months in receptive/expressive language, social communication, and daily living skills. This pattern was not observed for Group-ESI, demonstrating that timing effects were specific to Individual-ESI and ruling out maturation effects. This RCT demonstrated that earlier intensive, individualized intervention led to greater improvements, and suggests that even a narrow window of 18 versus 27 months may impact child outcomes.

强有力的证据支持早期自闭症干预的有效性。尽管人们普遍认为早期干预会带来更好的结果，但这方面的证据仅限于相关研究。本研究通过一项完全交叉随机对照试验（RCT），考察了由家长实施干预的早期社会互动（ESI）模式的时间效果。研究比较了个体-ESI 在幼儿 18 个月或 27 个月时启动的效果，并将其与强度较低的群体-ESI 作为积极对照条件的效果进行了对比。研究对象包括 82 名自闭症幼儿，他们分别接受了 9 个月的个体-ESI 和 9 个月的群体-ESI，时间/顺序随机安排。蒙眼临床医生分别在基线（18 个月大）、条件 1 结束（27 个月大）和条件 2 结束（36 个月大）时完成评估。与 27 个月时开始接受个体-ESI 的幼儿相比，18 个月时随机接受个体-ESI 的幼儿在接受/表达语言、社交沟通和日常生活技能方面的进步更大。在群体-ESI 中没有观察到这种模式，这表明时间效应是个体-ESI 所特有的，并排除了成熟效应。这项研究表明，更早的强化、个性化干预能带来更大的改善，并表明即使是 18 个月与 27 个月的狭窄窗口期也可能影响儿童的结果。

Ying Hao ；Yao Du； Saijun Zhang；Lin Sun； Gina Keene； Toshikazu Ikuta

This systematic review and meta-analysis evaluated the effectiveness of telehealth social communication intervention on language skills of children with autism spectrum disorder (ASD). Twenty-one studies were included, among which 17 were single subject experimental design (SSED). Language outcomes were categorized into five linguistic domains (phonology, morphology, syntax, semantics, and pragmatics), and meta-analysis was planned for each domain. Meta-analysis was only performed for pragmatic outcomes, due to the small number of studies that included outcomes in other domains. The results showed significant pre- to post-intervention improvement, thus supporting the use of telehealth social communication intervention to improve pragmatic skills among children with ASD. More diverse measures should be used to target linguistic domains beyond pragmatics. The predominant use of SSED warrants large-scale studies for robust evidence in the future.

本系统综述和荟萃分析评估了远程医疗社会沟通干预对自闭症谱系障碍（ASD）儿童语言能力的影响。共纳入 21 项研究，其中 17 项为单人实验设计（SSED）。语言成果分为五个语言领域（语音学、形态学、句法学、语义学和语用学），并计划对每个领域进行元分析。由于包含其他领域结果的研究数量较少，因此只对语用结果进行了元分析。研究结果表明，干预前和干预后的效果都有明显改善，因此支持使用远程医疗社会沟通干预来提高 ASD 儿童的实用技能。除了语用能力之外，还应该使用更多样化的测量方法来测量语言领域。由于主要使用的是社会交往能力评估（SSED），因此今后需要进行大规模研究，以获得可靠的证据。

Sam Perochon

Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17–36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.

自闭症是一种与社交沟通障碍有关的神经发育疾病，早期发现自闭症可确保及时干预。与研究相比，自闭症筛查问卷在基层医疗机构等实际环境中使用时的准确性较低，尤其是对有色人种儿童和女童而言。在此，我们报告了一项多临床、前瞻性研究的结果，该研究评估了自闭症筛查数字应用程序（App）的准确性，该应用程序是在儿科健康检查期间对 475 名（17-36 个月大）儿童（269 名男孩和 206 名女孩）进行的，其中 49 名儿童被诊断为自闭症，98 名儿童被诊断为发育迟缓，但没有自闭症。该应用程序显示的刺激物可诱发自闭症的行为表现，并通过计算机视觉和机器学习进行量化。结合多种数字表型的算法显示出很高的诊断准确性，接收者工作特征曲线下面积 = 0.90，灵敏度 = 87.8%，特异性 = 80.8%，阴性预测值 = 97.8%，阳性预测值 = 40.6%。该算法在按性别、种族和民族划分的亚组中具有相似的灵敏度表现。这些结果表明，数字表型技术有可能为现实环境中的自闭症筛查提供一种客观、可扩展的方法。此外，将数字表型和照顾者问卷调查的结果结合起来可能会提高自闭症筛查的准确性，并有助于减少诊断和干预方面的差异。