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在瑞典和英国对一款用于识别760名3至5岁儿童自闭症的智能平板电脑严肃游戏进行的3期诊断评估。

Phase 3 diagnostic evaluation of a smart tablet serious game to identify autism in 760 children 3-5 years old in Sweden and the United Kingdom.

作者信息

Millar Lindsay, McConnachie Alex, Minnis Helen, Wilson Philip, Thompson Lucy, Anzulewicz Anna, Sobota Krzysztof, Rowe Philip, Gillberg Christopher, Delafield-Butt Jonathan

机构信息

Laboratory for Innovation in Autism, University of Strathclyde, Glasgow, UK.

Biomedical Engineering, University of Strathclyde, Glasgow, UK.

出版信息

BMJ Open. 2019 Jul 16;9(7):e026226. doi: 10.1136/bmjopen-2018-026226.

Abstract

INTRODUCTION

Recent evidence suggests an underlying movement disruption may be a core component of autism spectrum disorder (ASD) and a new, accessible early biomarker. Mobile smart technologies such as iPads contain inertial movement and touch screen sensors capable of recording subsecond movement patterns during gameplay. A previous pilot study employed machine learning analysis of motor patterns recorded from children 3-5 years old. It identified those with ASD from age-matched and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom.

METHODS AND ANALYSIS

This is a phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies guidelines. Three cohorts are investigated: children typically developing (TD); children with a clinical diagnosis of ASD and children with a diagnosis of another neurodevelopmental disorder (OND) that is not ASD. The study will be completed in Glasgow, UK and Gothenburg, Sweden. The recruitment target is 760 children (280 TD, 280 ASD and 200 OND). Children play two games on the iPad then a third party data acquisition and analysis algorithm (Play.Care, Harimata) will classify the data as positively or negatively associated with ASD. The results are blind until data collection is complete, when the algorithm's classification will be compared against medical diagnosis. Furthermore, parents of participants in the ASD and OND groups will complete three questionnaires: Strengths and Difficulties Questionnaire; Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations Questionnaire and the Adaptive Behavioural Assessment System-3 or Vineland Adaptive Behavior Scales-II. The primary outcome measure is sensitivity and specificity of Play.Care to differentiate ASD children from TD children. Secondary outcomes measures include the accuracy of Play.Care to differentiate ASD children from OND children.

ETHICS AND DISSEMINATION

This study was approved by the West of Scotland Research Ethics Service Committee 3 and the University of Strathclyde Ethics Committee. Results will be disseminated in peer-reviewed publications and at international scientific conferences.

TRIAL REGISTRATION NUMBER

NCT03438994; Pre-results.

摘要

引言

最近的证据表明,潜在的运动障碍可能是自闭症谱系障碍(ASD)的核心组成部分,也是一种新的、易于获取的早期生物标志物。诸如iPad之类的移动智能技术包含惯性运动和触摸屏传感器,能够在游戏过程中记录亚秒级的运动模式。之前的一项试点研究对3至5岁儿童记录的运动模式进行了机器学习分析。该研究以93%的准确率从年龄和性别匹配的对照组中识别出患有ASD的儿童,提出了一种适用于家庭、诊所或教室的有吸引力的评估方法。

方法与分析

这是一项III期前瞻性诊断分类研究,根据《诊断准确性研究报告标准》指南设计。研究调查了三个队列:发育正常的儿童(TD);临床诊断为ASD的儿童以及诊断为非ASD的其他神经发育障碍(OND)的儿童。该研究将在英国格拉斯哥和瑞典哥德堡完成。招募目标是760名儿童(280名TD、280名ASD和200名OND)。儿童在iPad上玩两款游戏,然后第三方数据采集和分析算法(Play.Care,Harimata)将数据分类为与ASD呈正相关或负相关。在数据收集完成之前,结果是保密的,届时将把算法的分类结果与医学诊断进行比较。此外,ASD组和OND组参与者的父母将完成三份问卷:优势与困难问卷;早期症状引发神经发育临床检查问卷以及适应性行为评估系统-3或文兰适应性行为量表-II。主要结局指标是Play.Care区分ASD儿童与TD儿童的敏感性和特异性。次要结局指标包括Play.Care区分ASD儿童与OND儿童的准确性。

伦理与传播

本研究已获得苏格兰西部研究伦理服务委员会3和斯特拉斯克莱德大学伦理委员会的批准。研究结果将在同行评审的出版物和国际科学会议上发表。

试验注册号

NCT03438994;预结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/424b/6661582/b5671c9b043a/bmjopen-2018-026226f01.jpg

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