Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media

Abstract

Background COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic’s effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. Objective This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. Methods From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated. Results In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively. Conclusions The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

An Artificial Intelligence Platform to Stratify the Risk of Experiencing Sleep Disturbance in University Students After Analyzing Psychological Health, Lifestyle, and Sports: A Multicenter Externally Validated Study

“…More recently, a machine learning-based study was aimed to develop models for predicting sleep quality for university students during the COVID-19 pandemic and closed-loop management. 10 The study found that the pandemic had a detrimental effect on the sleep quality of college students. Eleven variables, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, internet usage, sudden changes, fears of infection, and closed-loop management, were identified as factors related to sleep quality in this study.…”

“…Eleven variables, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, internet usage, sudden changes, fears of infection, and closed-loop management, were identified as factors related to sleep quality in this study. 10 This study used logistic regression and three machine learning techniques to develop models, and among the four developed models, the artificial neural network model demonstrated the best performance in predicting sleep problems. 10 Nonetheless, the AUC value of the artificial neural network model was 0.713, indicating the accuracy of the model still needs improvements.…”

“… 10 This study used logistic regression and three machine learning techniques to develop models, and among the four developed models, the artificial neural network model demonstrated the best performance in predicting sleep problems. 10 Nonetheless, the AUC value of the artificial neural network model was 0.713, indicating the accuracy of the model still needs improvements. In addition, another limitation of this model is that it is based on research conducted during the period of the COVID-19 pandemic.…”

“…In addition, another limitation of this model is that it is based on research conducted during the period of the COVID-19 pandemic. 10 But the situation has significantly changed, with the pandemic being largely under control and various policies, such as closed-loop management, being lifted. Therefore, the applicability of this model in non-pandemic times needs further investigation and research.…”

“…Identifying individuals who are at a higher risk of experiencing sleep disturbance can help healthcare professionals and educators implement appropriate interventions to improve sleep quality and overall well-being. 9 , 10 Traditionally, risk assessment for sleep disturbance has relied on self-report questionnaires and subjective evaluations, which may be limited by recall bias and subjective interpretation. In recent years, the emergence of artificial intelligence (AI) has provided new opportunities for developing predictive models in various fields, including sleep research.…”