Virtual Reality Improves Predictive Internal Modeling and Object Control Skills in DCD Children

虚拟现实 任务(项目管理) 心理干预 运动技能 培训转移 心理学 德雷福斯技能获得模型 内部模型 控制(管理) 物理医学与康复 发展心理学 计算机科学 认知心理学 人机交互 人工智能 医学 工程类 经济 系统工程 精神科 经济增长
作者
Hasan Sepehri Bonab,Soghra Ebrahimi Sani
出处
期刊:Journal of Motor Behavior [Taylor & Francis]
卷期号:57 (5): 627-640 被引量:1
标识
DOI:10.1080/00222895.2025.2536832
摘要

Deficits in internal modeling have been suggested as a key factor contributing to the motor control and coordination challenges experienced by children with DCD. Recently, virtual reality (VR) technology has emerged as a promising tool for enhancing the acquisition and learning of motor skills. Therefore, the primary aim of this study was to investigate the effects of VR-based interventions on internal modeling and object control skills in children with DCD. The present study employed a quasi-experimental design, incorporating a pretest, post-test, and two-month follow-up. The sample consisted of 40 female students aged 7 to 10 years, selected based on DSM-5 criteria and randomly assigned to either a VR training program or a control group. Predictive internal modeling was assessed using continuous relative phase (CRP) through a visuomotor adaptation task, while object control skills were evaluated using the TGMD-2 test. The experimental group underwent an 8-week VR-based training program comprising 16, 30-minute sessions using task-oriented Xbox Kinect 360 games. The control group received no intervention. Results indicated that VR training significantly improved the acquisition of CRP (p = 0.037), with the experimental group demonstrating superior transfer of these skills to object control tasks compared to controls (p < 0.001). The observed reduction in CRP suggests that VR training facilitated the development of internal models in children with DCD. Furthermore, enhancements in object control skills evidenced the capacity of these children to apply and generalize acquired predictive internal models. However, despite these advancements, participants continued to exhibit compensatory strategies characterized by variability and inaccuracy, indicating persistent challenges in internal model updating.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
莫愁一舞完成签到,获得积分10
刚刚
lili完成签到,获得积分10
刚刚
VV发布了新的文献求助30
刚刚
刚刚
长情黑夜完成签到,获得积分10
1秒前
小花发布了新的文献求助10
1秒前
ZH完成签到 ,获得积分10
1秒前
1秒前
dawd应助jluzz采纳,获得10
1秒前
1秒前
1秒前
MWY完成签到,获得积分10
1秒前
虚幻白玉完成签到,获得积分10
1秒前
赵牛牛发布了新的文献求助10
2秒前
Lidy完成签到,获得积分20
2秒前
彭于晏应助Serein采纳,获得10
2秒前
阿卫完成签到,获得积分10
3秒前
WY完成签到,获得积分10
3秒前
爆米花应助123采纳,获得10
3秒前
王伯文发布了新的文献求助10
3秒前
胡平发布了新的文献求助10
4秒前
三石发布了新的文献求助10
4秒前
寂寞的诗云完成签到,获得积分10
4秒前
Lebronq发布了新的文献求助30
4秒前
dongyu完成签到,获得积分10
5秒前
书真好看完成签到,获得积分20
5秒前
6秒前
超级的雨发布了新的文献求助10
6秒前
润泉发布了新的文献求助10
6秒前
nininic完成签到,获得积分10
6秒前
桐桐应助xxx77采纳,获得10
6秒前
6秒前
科研通AI6.4应助赵牛牛采纳,获得10
8秒前
核桃发布了新的文献求助10
8秒前
汉堡包应助Yrzyc采纳,获得10
8秒前
9秒前
紧张的谷槐完成签到,获得积分10
9秒前
小萝卜完成签到,获得积分10
9秒前
10秒前
fuqiyao完成签到,获得积分10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
日本現代怪異事典 副読本 700
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 650
Machine Learning for Asset Management and Pricing 600
Numerical analysis of the coupled atmosphere-ocean models (CAO II). II 600
Models for the coupled atmosphere and ocean 600
Évora na Idade Média 555
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7385931
求助须知:如何正确求助?哪些是违规求助? 8992669
关于积分的说明 19131765
捐赠科研通 7023169
什么是DOI,文献DOI怎么找? 3227650
关于科研通互助平台的介绍 2390547
邀请新用户注册赠送积分活动 2208865