探索尺度回答过程中响应时间序列与失眠严重程度之间的关系:一种机器学习方法
Zhao Su1,2, Rongxun Liu1,3, Keyin Zhou1
1Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Heliyon
|July 23, 2024
概括
来自基于计算机的认知测试的响应时间数据可以客观地测量心理状态. 这项研究表明,反应时间有效预测失眠症状,突出了它们在心理健康评估中的有用性.
科学领域:
- 认知心理学 认知心理学
- 心理测量 心理测量 心理测量
- 计算精神病学是一种计算精神病学.
背景情况:
- 基于计算机的评估提供客观数据,如响应时间.
- 响应时间在心理评估中未得到充分研究,特别是睡眠质量.
研究的目的:
- 研究响应时间在认知和心理指标中的重要性.
- 评估反应时间在评估睡眠质量的作用,使用失眠严重程度指数 (ISI).
主要方法:
- 开发了一个移动应用程序来收集2729名参与者的响应时间和规模数据.
- 分析了失眠症状严重程度和反应时间之间的关系.
- 使用响应时间数据构建了一个机器学习模型来预测失眠.
主要成果:
- 发现有和没有失眠症状的参与者之间的总响应时间有显著差异 (p < 0.01).
- 观察到特定的失眠方面与个人问题响应时间之间存在强烈的相关性.
- 机器学习模型实现了预测失眠的ROC曲线下的面积 (AUROC) 为0.824.
结论:
- 响应时间数据是用于认知和心理评估的有价值的客观指标.
- 响应时间可以有效地预测失眠症状的存在和严重程度.
- 这种方法为心理健康评估提供了一种新的,数据驱动的方法.
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