Tools
ToolsDubey, Indu, Bishain, Rahul, Dasgupta, Jayashree, Bhavnani, Supriya, Belmonte, Matthew K., Gliga, Teodora, Mukherjee, Debarati, Lockwood Estrin, Georgia, Johnson, Mark H., Chandran, Sharat, Patel, Vikram, Gulati, Sheffali, Divan, Gauri and Chakrabarti, Bhismadev (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. ISSN 1362-3613
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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. 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.
| Item Type: | Article |
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| Additional Information: | Data availability statement: Anonymised data with the exception of video data will be made available through the University of Reading Research Data Archive (https://researchdata.reading.ac.uk/). Funding information: This work was funded by a Medical Research Council Global Challenge Research Fund grant to the START consortium (Grant ID: MR/P023894/1, PI: Chakrabarti) and GLE was supported by the Wellcome Trust (Sir Henry Wellcome Fellowship, Grant ID: 204706/Z/16/Z). |
| Uncontrolled Keywords: | autism,lmic,digital health,global,developmental and educational psychology,3*,unique low resource setting data; new methodological apporach ,/dk/atira/pure/subjectarea/asjc/3200/3204 |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Developmental Science |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 16 Jun 2023 08:31 |
| Last Modified: | 12 Nov 2024 12:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/92411 |
| DOI: | 10.1177/13623613231182801 |
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