Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach
This work addresses the problem of insomnia assessment for clinicians or researchers by providing a novel diagnostic tool, though it is incremental as it applies existing machine learning methods to new data in this domain.
The study tackled the problem of predicting insomnia by analyzing response time data from scale tests, finding a statistically significant difference in total response time between participants with and without insomnia symptoms and achieving a predictive accuracy of 0.743 with a machine learning model.
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.