Exploring the relationship between response time sequence in scale
  answering process and severity of insomnia: a machine learning approach
Zhao Su Rongxun Liu Keyin Zhou Xinru Wei Ning Wang Zexin Lin Yuanchen Xie Jie Wang Fei Wang Shenzhong Zhang Xizhe Zhang
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.