Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 14, 2023
Open Peer Review Period: Jan 14, 2023 - Mar 11, 2023
Date Accepted: Feb 16, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Prediction models for sleep quality among college students during the COVID-19 outbreak: an Internet-based cross-sectional survey
ABSTRACT
Background:
Coronavirus disease 2019 (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. Machine learning (ML) has been widely used for sleep disorder prediction.
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 5 April to 16 April 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 randomly divided into a training-testing set (70%) and an independent validation set (30%). Four models using logistic regression, an artificial neural network, random forest and naive Bayes were developed and validated.
Results:
In conclusion, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02±3.112. The sleep disturbance rate was 28.9% (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 online, sudden changes, fears of infection and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best model, with an AUC, accuracy, sensitivity, specificity, PPV and NPV 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 hurt the sleep quality of college students, with multiple factors. Machine learning models present excellent predictive efficiency, and are favorable for implementing measures earlier in order to improve present conditions. Clinical Trial: None.
Citation
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