Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

• Published in: arXiv.org
• Main Authors: Zhao, Su, Liu, Rongxun, Zhou, Keyin, Wei, Xinru, Wang, Ning, Lin, Zexin, Xie, Yuanchen, Wang, Jie, Wang, Fei, Zhang, Shenzhong, Zhang, Xizhe
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 13.10.2023
• Subjects: Applications programs; Insomnia; Machine learning; Mobile computing; Response time

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.