Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model
Lufeng Chen1, Qingquan Chen2,3, Zhimin Huang3
1The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China. chenlufenga@163.com.
BMC Public Health
|February 21, 2025
Summary
COVID-19 lockdowns negatively impacted college students' sleep quality in Quanzhou, with higher scores than the national average. An artificial neural network model shows promise for early detection and intervention of sleep disorders.
Area of Science:
- Public Health
- Epidemiology
- Sleep Science
Background:
- COVID-19 outbreak in Quanzhou (March 2022) led to strict college lockdowns.
- Pandemic's impact on sleep quality is known, but effects of containment measures on students are understudied.
Purpose of the Study:
- Assess sleep quality in Fujian Province college students during the epidemic.
- Identify sensitive variables for predicting sleep problems.
- Develop an efficient early screening model for student sleep issues.
Main Methods:
- Cross-sectional survey of 4959 Quanzhou college students (April 5-16, 2022).
- Used descriptive, univariate, correlation, and multiple regression analyses.
- Constructed eight sleep quality risk prediction models, including an artificial neural network (ANN).
Main Results:
- Mean Pittsburgh Sleep Quality Index (PSQI) score was 6.03±3.21; 29.4% had sleep disorders (PSQI > 7).
- Sleep quality, latency, efficiency, and diurnal dysfunction were worse than national norms.
- ANN model demonstrated best performance: AUC 73.8%, accuracy 67.3%, precision 84.0%, recall 66.3%, F1 69.3%.
Conclusions:
- COVID-19 quarantine management in Quanzhou affected student sleep quality, with elevated PSQI scores.
- The ANN model is effective for early detection of sleep disorders in college students.
- This model can potentially guide early interventions to prevent sleep problems.
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