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Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review

Wang, Katherine; Julier, Simon J; Cho, Youngjun; (2022) Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review. IEEE Access , 10 pp. 15574-15593. 10.1109/access.2022.3147726. Green open access

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Abstract

With the rising prevalence of autism diagnoses, it is essential for research to understand how to leverage technology to support the diverse nature of autistic traits. While traditional interventions focused on technology for medical cure and rehabilitation, recent research aims to understand how technology can accommodate each unique situation in an efficient and engaging way. Extended reality (XR) technology has been shown to be effective in improving attention in autistic users given that it is more engaging and motivating than other traditional mediums. Here, we conducted a systematic review of 59 research articles that explored the role of attention in XR interventions for autistic users. We systematically analyzed demographics, study design and findings, including autism screening and attention measurement methods. Furthermore, given methodological inconsistencies in the literature, we systematically synthesize methods and protocols including screening tools, physiological and behavioral cues of autism and XR tasks. While there is substantial evidence for the effectiveness of using XR in attention-based interventions for autism to support autistic traits, we have identified three principal research gaps that provide promising research directions to examine how autistic populations interact with XR. First, our findings highlight the disproportionate geographic locations of autism studies and underrepresentation of autistic adults, evidence of gender disparity, and presence of individuals diagnosed with co-occurring conditions across studies. Second, many studies used an assortment of standardized and novel tasks and self-report assessments with limited tested reliability. Lastly, the research lacks evidence of performance maintenance and transferability. Based on these challenges, this paper discusses inclusive future research directions considering greater diversification of participant recruitment, robust objective evaluations using physiological measurements (e.g., eye-tracking), and follow-up maintenance sessions that promote transferrable skills. Pursuing these opportunities would lead to more effective therapy solutions, improved accessible interfaces, and engaging interactions.

Type: Article
Title: Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/access.2022.3147726
Publisher version: https://doi.org/10.1109/access.2022.3147726
Language: English
Additional information: CCBY - IEEE is not the copyright holder of this material. Please follow the instructions via https://creativecommons.org/licenses/by/4.0/ to obtain full-text articles and stipulations in the API documentation.
Keywords: Autism, Task analysis, Systematics, Statistics, Sociology, Visualization, X reality
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10143822
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