Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia.

翻译：目的：本研究旨在探究失眠与反应时间之间的关系，同时利用反应时间数据开发预测参与者是否存在失眠的机器学习模型。方法：设计了一款移动应用程序，用于实施量表测试并收集2729名参与者的反应时间数据。分析了症状严重程度与反应时间之间的关系，并构建了预测失眠存在的机器学习模型。结果：结果显示，存在或不存在失眠症状的参与者之间的总反应时间具有统计学显著差异（p<.001）。在单个问题层面，特定失眠维度的严重程度与反应时间之间存在相关性。基于反应时间数据的机器学习模型预测失眠症状的准确率高达0.743。结论：这些发现凸显了反应时间数据在评估认知和心理测量指标方面的潜在价值，证明将反应时间作为失眠评估诊断工具的有效性。