A diagnosis of autism typically depends on clinical assessments by highly-trained professionals. This high resource demand poses a challenge in low-resource settings. Digital assessment of neurodevelopmental symptoms by non-specialists provides a potential avenue to address this challenge. In this study, we provide the proof of principle for such a digital assessment, with a cross-sectional case control field study using mixed methods. We developed and tested an app, START, that can assess autism phenotypic domains (social, sensory, motor) through child performance and parent reports. N=131 children (2-7 years old; 48 autistic, 43 intellectually disabled, and 40 non-autistic typically developing) from low-resource settings in India were assessed using START in home settings by non-specialist health workers. 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. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups. Qualitative analysis of the interviews with health workers and families of the participants demonstrated high acceptability and feasibility of the app. These results provide feasibility, acceptability, and proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental conditions in low-resource settings.
- digital health