基于自由玩耍期间的数字行为特征预测自闭症谱系障碍儿童。

Prediction for children with autism spectrum disorder based on digital behavioral features during free play.

机构信息

NHC Key Laboratory of Mental Health (Peking University), Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Road, Haidian District, Beijing, 100191, China.

ALSOLIFE, Beijing, China.

出版信息

BMC Psychiatry. 2024 Nov 14;24(1):799. doi: 10.1186/s12888-024-06129-9.

DOI:10.1186/s12888-024-06129-9

PMID:39543492

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11566418/

Abstract

BACKGROUND

Play is an indispensable and meaningful activity in children's daily life. Research has shown that autistic children often exhibit differences in play development. The core traits of autism, such as distinct patterns in social interaction and communication, focused interests, and repetitive behaviors, frequently manifest in their play. Therefore, play may serve as an insightful measure of these differences. Unlike previous studies focusing on play behaviors only, we explored other behaviors associated with autism during free play, and constructed a clinical prediction model for effectively screening autistic children.

METHODS

Participants, including 123 autistic children and 123 neurotypical children aged 1-6 years, engaged in a 1.5-min free play with fixed toys, which was videotaped. A novel behavior-coding scheme was used to code these videos for 19 autistic behaviors, including play. The coding details of the 19 behaviors were then converted and expanded to 81 digital behavior indicators, including counts, duration, and proportion.

RESULTS

The autistic children showed less functional play and imaginative play and reduced social communication and interactions, such as eye contact, facial expressions, and vocalizations, compared to the neurotypical children during free play. Furthermore, 5 behavioral indicators were selected for the prediction model through stepwise logistic regression, including 1 on socially oriented vocalizations and 4 on count and duration of functional play. The receiver operating characteristic (ROC) curve revealed a good prediction performance with an area under the curve (AUC) of 0.826, a sensitivity of 85.4%, and a specificity of 68.3%.

CONCLUSION

Our findings highlight differences in play performance and social communication and interactions during free play among autistic children. Based on these findings, we constructed a good clinical prediction model, which might be a potential digital tool used by clinicians to effectively screen autistic children.

摘要

背景

游戏是儿童日常生活中不可或缺且有意义的活动。研究表明，自闭症儿童在游戏发展方面常常存在差异。自闭症的核心特征，如社交互动和沟通方面的独特模式、专注的兴趣以及重复行为，经常在他们的游戏中表现出来。因此，游戏可能是衡量这些差异的一个有见地的指标。与之前仅关注游戏行为的研究不同，我们在自由游戏中探索了与自闭症相关的其他行为，并构建了一个临床预测模型，以有效地筛选自闭症儿童。

方法

参与者包括 123 名自闭症儿童和 123 名神经典型儿童（年龄 1-6 岁），他们与固定玩具进行了 1.5 分钟的自由游戏，并对其进行了录像。使用一种新颖的行为编码方案对这些视频进行了 19 种自闭症行为的编码，包括游戏。然后，将这 19 种行为的编码细节转换和扩展为 81 个数字行为指标，包括计数、持续时间和比例。

结果

与神经典型儿童相比，自闭症儿童在自由游戏中表现出较少的功能性游戏和想象性游戏，以及减少的社交沟通和互动，如眼神接触、面部表情和发声。此外，通过逐步逻辑回归选择了 5 个行为指标用于预测模型，其中 1 个与社交导向发声有关，4 个与功能性游戏的计数和持续时间有关。接收者操作特征（ROC）曲线显示出良好的预测性能，曲线下面积（AUC）为 0.826，灵敏度为 85.4%，特异性为 68.3%。