ArticleFrontiers in psychiatry2025
Novel insight into prediction model for sleep quality among college students: a LASSO-derived sleep evaluation.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dietary Predictors of Paraben Exposure Among Adults in Northern Thailand.International journal of environmental research and public health · 2026Article
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9 authors.
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Abstract
Background: Sleep disturbance has become a significant concern among college students, as it can lead to various mental and physical disorders. This study aims to provide a fresh perspective by developing and validating a predictive model for sleep quality among college students. Methods: Data from 20,645 college students in Fujian Province, China, collected between 5th April and 16th April 2022, were analyzed. Participants completed the Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and a sleep quality influencing factor questionnaire. Multinomial logistic regression, LASSO regression, and Boruta feature selection methods were utilized to select relevant variables. The data were then divided into a training-testing set (70%) and an independent validation set (30%) using stratified sampling. Six machine learning techniques, including artificial neural network (ANN), decision tree, gradient-boosting tree, k-nearest neighbor, naïve Bayes, and random forest, were developed and validated. Finally, an online sleep evaluation website was established based on the best-fitting prediction model. Results: The mean global PSQI score was 6.02 ± 3.112, with a sleep disturbance prevalence of 28.9% (defined as a global PSQI score > 7). The LASSO regression model identified eight predictors: age, specialty, respiratory history, coffee consumption, staying up late, prolonged online activity, sudden changes, and impatient closed-loop management. Among the evaluated models, the ANN demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.713 (95% CI: 0.696-0.730), accuracy of 0.669 (95% CI: 0.669-0.669), sensitivity of 0.682 (95% CI: 0.699-0.665), specificity of 0.637 (95% CI: 0.665-0.610). Decision curve analysis and clinical impact analysis further confirmed the model's clinical utility. Conclusions: This study developed a prediction model for sleep disturbance among college students using a LASSO regression and ANN, incorporating eight predictors. The model can serve as an intuitive and practical tool for predicting sleep quality and supporting effective management and healthcare on college campuses.
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