在COVID-19封锁期间,使用基于LASSO的神经网络模型预测大学生睡眠质量
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
概括
COVID-19封锁对泉州大学生的睡眠质量产生了负面影响,他们的得分高于全国平均水平. 一个人工神经网络模型显示,早期检测和干预睡眠障碍是有前途的.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 睡眠科学 睡眠科学
背景情况:
- 泉州的COVID-19疫情 (2022年3月) 导致大学严格封锁.
- 流行病对睡眠质量的影响是众所周知的,但制措施对学生的影响还没有得到充分研究.
研究的目的:
- 在疫情期间评估福建省大学生睡眠质量.
- 识别敏感变量来预测睡眠问题.
- 开发一个有效的早期查模型,以解决学生睡眠问题.
主要方法:
- 对4959名泉州大学生进行的横截面调查 (2022年4月5日至16日).
- 使用描述性,单变量,相关性和多重回归分析.
- 构建了八个睡眠质量风险预测模型,包括一个人工神经网络 (ANN).
主要成果:
- 匹兹堡睡眠质量指数 (PSQI) 的平均得分为6.03±3.21; 29.4%的人有睡眠障碍 (PSQI > 7).
- 睡眠质量,延迟,效率和白天功能障碍都比国家标准差.
- 在ANN模型中,表现最好:AUC为73.8%,准确率为67.3%,精度为84.0%,回忆率为66.3%,F1为69.3%.
结论:
- 泉州的COVID-19隔离管理影响了学生的睡眠质量,PSQI得分升高.
- 在大学生中早期发现睡眠障碍时,ANN模型是有效的.
- 这个模型可能会指导早期干预,以预防睡眠问题.
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