{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:03:22Z","timestamp":1770753802630,"version":"3.50.0"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The ability to maintain attention is crucial for achieving success in various aspects of life, including academic pursuits, career advancement, and social interactions. Attention deficit disorder (ADD) is a common symptom associated with autism spectrum disorder (ASD), which can pose challenges for individuals affected by it, impacting their social interactions and learning abilities. To address this issue, virtual reality (VR) has emerged as a promising tool for attention training with the ability to create personalized virtual worlds, providing a conducive platform for attention-focused interventions. Furthermore, leveraging physiological data can be instrumental in the development and enhancement of attention-training techniques for individuals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In our preliminary study, a functional prototype for attention therapy systems was developed. In the current phase, the objective is to create a framework called VR-PDA (Virtual Reality Physiological Data Analysis) that utilizes physiological data for tracking and improving attention in individuals. Four distinct training strategies such as noise, score, object opacity, and red vignette are implemented in this framework. The primary goal is to leverage virtual reality technology and physiological data analysis to enhance attentional capabilities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our data analysis results revealed that reinforcement training strategies are crucial for improving attention in individuals with ASD, while they are not significant for non-autistic individuals. Among all the different strategies employed, the noise strategy demonstrates superior efficacy in training attention among individuals with ASD. On the other hand, for Non-ASD individuals, no specific training proves to be effective in enhancing attention. The total gazing time feature exhibited benefits for participants with and without ASD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The results consistently demonstrated favorable outcomes for both groups, indicating an enhanced level of attentiveness. These findings provide valuable insights into the effectiveness of different strategies for attention training and emphasize the potential of virtual reality (VR) and physiological data in attention training programs for individuals with ASD. The results of this study open up new avenues for further research and inspire future developments.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcomp.2023.1250652","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T05:39:24Z","timestamp":1695965964000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing attention in autism spectrum disorder: comparative analysis of virtual reality-based training programs using physiological data"],"prefix":"10.3389","volume":"5","author":[{"given":"Bhavya Sri","family":"Sanku","sequence":"first","affiliation":[]},{"given":"Yi (Joy)","family":"Li","sequence":"additional","affiliation":[]},{"given":"Sungchul","family":"Jung","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Jing (Selena)","family":"He","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"B1","article-title":"\u201cReal-time neurofeedback for attention training: Brainwave-based brain computer interface,\u201d","volume-title":"Organization for Human Brain Mapping 2017","author":"Abiri","year":"2018"},{"key":"B2","doi-asserted-by":"crossref","DOI":"10.1145\/3379156.3391374","article-title":"\u201cGazemetrics: an open-source tool for measuring the data quality of HMD-based eye trackers,\u201d","volume-title":"ACM Symposium on Eye Tracking Research and Applications","author":"Adhanom","year":"2020"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1109\/TNSRE.2021.3108351","article-title":"Design of an interactive virtual reality system, InViRS, for joint attention practice in autistic children","volume":"29","author":"Amat","year":"2021","journal-title":"IEEE Trans. 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