Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap

Article


Dubey, I., Dasgupta, J., Bhavnani, S., Belmonte, M. K., Gliga, T., Mukherjee, D., Lockwood Estrin, G., Johnson, M. H., Chandran, S., Patel, V., Gulati, S., Divan, G. and Chakrabarti, B. 2024. Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap. Autism. 28 (3), pp. 755-769. https://doi.org/10.1177/13623613231182801
AuthorsDubey, I., Dasgupta, J., Bhavnani, S., Belmonte, M. K., Gliga, T., Mukherjee, D., Lockwood Estrin, G., Johnson, M. H., Chandran, S., Patel, V., Gulati, S., Divan, G. and Chakrabarti, B.
Abstract

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. This cross-sectional case-control field study establishes proof of principle for such a digital assessment. We developed and tested an app, START, that can be administered by non-specialists to 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 Delhi-NCR, 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, greater sensory interest 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. 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.

JournalAutism
Journal citation28 (3), pp. 755-769
ISSN1362-3613
Year2024
PublisherSAGE Publications
Publisher's version
License
File Access Level
Anyone
Supplemental file
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1177/13623613231182801
Publication dates
Online17 Jul 2023
PrintMar 2024
Publication process dates
Accepted31 May 2023
Deposited07 Aug 2023
FunderMedical Research Council, Global Challenges Research Fund
Wellcome Trust
Copyright holder© 2023, The Author(s)
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https://repository.uel.ac.uk/item/8w66y

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