Computerized cognitive retraining (ReadON.ai) among children diagnosed with attention deficit hyperactivity disorder.

Industrial Psychiatry Journal Pub Date : 2024-07-01 Epub Date: 2024-12-17 DOI:10.4103/ipj.ipj_259_24
Jagriti Grover, Sampurna Chakraborty, Rushi, Sonia Puar
{"title":"Computerized cognitive retraining (ReadON.ai) among children diagnosed with attention deficit hyperactivity disorder.","authors":"Jagriti Grover, Sampurna Chakraborty, Rushi, Sonia Puar","doi":"10.4103/ipj.ipj_259_24","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>ADHD affects 8% of children and adolescents globally, marked by significant deficits in cognitive abilities, which leads to various emotional, behavioral, and adjustment issues. Traditional methods like medication and behavior therapy fall short in managing ADHD's cognitive domains, urging the adoption of innovative approaches like cognitive training programs specifically adopting the emerging technology such as ReadON.ai. However, the precise influence of computerized cognitive retraining on ADHD remains understudied.</p><p><strong>Aim: </strong>To study the feasibility of computerized cognitive retraining (ReadON.ai) in enhancing cognitive abilities in children diagnosed with attention deficit hyperactivity disorder.</p><p><strong>Materials and methods: </strong>The study employs a pre- and post-intervention design including six participants (7-11 years), diagnosed with ADHD according to DSM-5 criteria. Each participant underwent 30 hours of computerized cognitive retraining (ReadON.ai) over ten weeks, targeting attention and concentration, working memory, memory and learning, perceptual abilities, and reasoning skills. Assessments before and after intervention included tools like Conners' 4<sup>TM</sup> Parent version and ReadON.ai CSA. Statistical analysis was conducted using IBM SPSS version 28.</p><p><strong>Results: </strong>Paired <i>t</i>-test results revealed a significant difference in pre-test and post-test means of attention and concentration (t = -6.873, <i>P</i> < 0.001), working memory (t = -5.771, <i>P</i> < 0.001), learning and memory (t = -12.491, <i>P</i> < 0.001), perception (t = 14.398, <i>P</i> < 0.004), reasoning (t = -3.464, <i>P</i> < 0.018), hyperactivity (t = 11.073, <i>P</i> < 0.001), impulsivity (t = 11.948, <i>P</i> < 0.001), emotional dysregulation (t = 8.242, <i>P</i> < 0.001), anxious thoughts (t = 2.67 <i>P</i> = 0.219), depressed mood (t = 2.924, <i>P</i> = 0.020), school work (t = 7.387, <i>P</i> = 0.001) and peer interaction (t = 4.632, <i>P</i> = 0.006) with medium to large effect size.</p><p><strong>Conclusion: </strong>Computerized cognitive retraining through ReadON.ai is feasible in enhancing cognitive abilities like attention and concentration, working memory, memory and learning, perception, and reasoning among children with ADHD.</p>","PeriodicalId":13534,"journal":{"name":"Industrial Psychiatry Journal","volume":"33 2","pages":"346-353"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784689/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Psychiatry Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/ipj.ipj_259_24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/17 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: ADHD affects 8% of children and adolescents globally, marked by significant deficits in cognitive abilities, which leads to various emotional, behavioral, and adjustment issues. Traditional methods like medication and behavior therapy fall short in managing ADHD's cognitive domains, urging the adoption of innovative approaches like cognitive training programs specifically adopting the emerging technology such as ReadON.ai. However, the precise influence of computerized cognitive retraining on ADHD remains understudied.

Aim: To study the feasibility of computerized cognitive retraining (ReadON.ai) in enhancing cognitive abilities in children diagnosed with attention deficit hyperactivity disorder.

Materials and methods: The study employs a pre- and post-intervention design including six participants (7-11 years), diagnosed with ADHD according to DSM-5 criteria. Each participant underwent 30 hours of computerized cognitive retraining (ReadON.ai) over ten weeks, targeting attention and concentration, working memory, memory and learning, perceptual abilities, and reasoning skills. Assessments before and after intervention included tools like Conners' 4TM Parent version and ReadON.ai CSA. Statistical analysis was conducted using IBM SPSS version 28.

Results: Paired t-test results revealed a significant difference in pre-test and post-test means of attention and concentration (t = -6.873, P < 0.001), working memory (t = -5.771, P < 0.001), learning and memory (t = -12.491, P < 0.001), perception (t = 14.398, P < 0.004), reasoning (t = -3.464, P < 0.018), hyperactivity (t = 11.073, P < 0.001), impulsivity (t = 11.948, P < 0.001), emotional dysregulation (t = 8.242, P < 0.001), anxious thoughts (t = 2.67 P = 0.219), depressed mood (t = 2.924, P = 0.020), school work (t = 7.387, P = 0.001) and peer interaction (t = 4.632, P = 0.006) with medium to large effect size.

Conclusion: Computerized cognitive retraining through ReadON.ai is feasible in enhancing cognitive abilities like attention and concentration, working memory, memory and learning, perception, and reasoning among children with ADHD.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
计算机认知再训练(ReadON.ai)在诊断为注意缺陷多动障碍儿童中的应用。
背景:ADHD影响全球8%的儿童和青少年,其特征是认知能力显著缺陷,导致各种情绪、行为和适应问题。药物治疗和行为疗法等传统方法在管理多动症的认知领域方面存在不足,因此迫切需要采用创新方法,比如认知训练项目,特别是采用ReadON.ai等新兴技术。然而,计算机化认知再训练对多动症的确切影响仍未得到充分研究。目的:探讨计算机认知再训练(ReadON.ai)提高注意缺陷多动障碍患儿认知能力的可行性。材料和方法:本研究采用干预前和干预后设计,包括6名根据DSM-5标准诊断为ADHD的参与者(7-11岁)。每位参与者在十周内接受了30小时的计算机认知再训练(ReadON.ai),目标是注意力和注意力、工作记忆、记忆和学习、感知能力和推理技能。干预前后的评估包括Conners的4TM Parent版本和ReadON等工具。ai CSA。采用IBM SPSS 28版进行统计分析。结果:配对t检验结果显示显著差异在检测前和检测后的注意力和浓度(t = -6.873, P < 0.001),工作记忆(t = -5.771, P < 0.001),学习和记忆(t = -12.491, P < 0.001),感知(t = 14.398, P < 0.004),推理(t = -3.464, P < 0.018),多动(t = 11.073, P < 0.001),冲动(t = 11.948, P < 0.001),情绪失调(t = 8.242, P < 0.001),焦虑的想法(t = 2.67 P = 0.219),抑郁情绪(t = 2.924,P = 0.020)、学业(t = 7.387, P = 0.001)和同伴互动(t = 4.632, P = 0.006)具有中大型效应量。结论:通过ReadON进行计算机化认知再训练。人工智能在提高多动症儿童的注意力和注意力、工作记忆、记忆和学习、感知和推理等认知能力方面是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
46
审稿时长
39 weeks
期刊最新文献
Stress and substance abuse - Assessment of psychiatric problems among police personnel. The metabolic paradox of aripiprazole: Weight-neutral in monotherapy but weight-reducing as adjunctive therapy. Theoretical underpinnings of flow and its relation with academic engagement: A narrative review. The Dengue Anxiety Scale (DENAS): A new tool for assessing disease-specific anxiety. Disability, quality of life, and family burden in treatment naïve patients with schizophrenia and obsessive-compulsive disorder: A comparative study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1