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
Affiliations
Zhao Su
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
Rongxun Liu
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
Keyin Zhou
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
Xinru Wei
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
Ning Wang
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; School of Public Health, Xinxiang Medical University, Xinxiang, Henan, China
Zexin Lin
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
Yuanchen Xie
The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
Jie Wang
School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
Fei Wang
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Corresponding author.
Shenzhong Zhang
School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China; Corresponding author.
Xizhe Zhang
School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China; Corresponding author.
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.
