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

Abstract

Utilizing computer-based scales for cognitive and psychological evaluations allows for the collection of objective data, such as response time. This cross-sectional study investigates the significance of response time data in cognitive and psychological measures, with a specific focus on its role in evaluating sleep quality through the Insomnia Severity Index (ISI) scale. A mobile application was designed to administer scale tests and collect response time data from 2729 participants. We explored the relationship between symptom severity and response time. A machine learning model was developed to predict the presence of insomnia symptoms in participants using response time data. The result revealed a statistically significant difference (p < 0.01) in the total response time between participants with or without insomnia symptom. Furthermore, a strong correlation was observed between the severity of specific insomnia aspects and the response times at the individual questions level. The machine learning model demonstrated a high predictive Area Under the ROC Curve (AUROC) of 0.824 in predicting insomnia symptoms based on response time data. These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures.

Keywords

• Response time
• Machine learning
• Insomnia
• Behavioral data