Autism Spectrum Disorders, hereafter referred to as autism, emerge early and persist throughout life, contributing significantly to global years lived with disability. Typically, an autism diagnosis depends on clinical assessments by highly trained professionals. This high resource demand poses a challenge in resource-limited areas where skilled personnel are scarce and awareness of neurodevelopmental disorder symptoms is low. We have developed and tested a novel app, START, that can be administered by non-specialists to assess several domains of the autistic phenotype (social, sensory, motor functioning) through direct observation and parent report. N=131 children (2-7 years old; 48 autistic, 43 intellectually disabled, and 40 typically developing) from low-resource settings in the Delhi-NCR region, India were assessed using START in home settings by non-specialist health workers. We observed a consistent pattern of differences between typically and atypically developing children in all three domains assessed. The two groups of children with neurodevelopmental disorders manifested lower social preference, higher sensory sensitivity, and lower fine-motor accuracy compared to their typically developing counterparts. Parent-report further distinguished autistic from non-autistic children. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups (ASD, ID, TD). Qualitative analysis of the interviews with health workers and families (N= 15) of the participants suggest high acceptability and feasibility of the app. These results provide proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental disorders in low-resource settings.

中文翻译：

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在印度资源匮乏的社区环境中使用移动健康技术评估儿童自闭症：解决检测差距的创新

孤独症谱系障碍，以下简称孤独症，早期出现并持续一生，对全球的残疾年数做出了重大贡献。通常，自闭症的诊断取决于训练有素的专业人员的临床评估。这种高资源需求给资源有限的地区带来了挑战，在这些地区，技术人员稀缺且对神经发育障碍症状的认识较低。我们开发并测试了一个新的应用程序 START，它可以由非专家管理，通过直接观察和父母报告来评估自闭症表型的几个领域（社交、感觉、运动功能）。N=131 名来自德里-NCR 地区资源匮乏地区的儿童（2-7 岁；48 名自闭症，43 名智障，40 名正常发育），印度由非专业卫生工作者在家庭环境中使用 START 进行评估。我们观察到在所有三个评估领域中典型和非典型发育儿童之间存在一致的差异模式。与典型的发育障碍儿童相比，这两组患有神经发育障碍的儿童表现出较低的社会偏好、较高的感觉敏感性和较低的精细运动准确性。家长报告进一步区分了自闭症儿童和非自闭症儿童。结合所有 START 衍生测量的机器学习分析表明，三组（ASD、ID、TD）的分类准确度为 78%。对参与者的卫生工作者和家属（N = 15）的采访进行的定性分析表明该应用程序的可接受性和可行性很高。这些结果为 START 提供了原理证明，