Al-Mamun Firoj, Mamun Mohammed A, Hasan Md Emran, ALmerab Moneerah Mohammad, Gozal David
CHINTA Research Bangladesh, Savar, Dhaka, Bangladesh.
Department of Public Health and Informatics, Jahangirnagar University, Savar, Dhaka, Bangladesh.
Nat Sci Sleep. 2024 Aug 20;16:1235-1251. doi: 10.2147/NSS.S481786. eCollection 2024.
Sleep disruptions among prospective university students are increasingly recognized for their potential ramifications on academic achievement and psychological well-being. But, information regarding sleep issues among students preparing for university entrance exams is unknown. Thus, this study aimed to investigate the prevalence and factors associated with sleep duration and insomnia among university entrance test-takers in Bangladesh, utilizing both traditional statistical analyses and advanced geographic information system and machine learning techniques for enhanced predictive capability.
A cross-sectional study was conducted in June 2023 among 1496 entrance test-takers at Jahangirnagar University, Dhaka. Structured questionnaires collected data on demographics, academic information, and mental health assessments. Statistical analyses, including chi-square tests and logistic regression, were performed using SPSS, while machine learning models were applied using Python and Google Colab.
Approximately 62.9% of participants reported abnormal sleep duration (<7 hours/night or >9 hours/night), with 25.5% experiencing insomnia. Females and those dissatisfied with mock tests were more likely to report abnormal sleep duration, while repeat test-takers, those with unsatisfactory mock test results, or anxiety symptoms had a higher risk of insomnia. Machine learning identified satisfaction with previous mock tests as the most significant predictor of sleep disturbances, while higher secondary school certificate GPA had the least influence. The CatBoost model achieved maximum accuracy rates of 61.27% and 73.46%, respectively, for predicting sleep duration and insomnia, with low log loss values indicating robust predictive performance. Geographic analysis revealed regional variations in sleep disturbances, with higher insomnia prevalence in some southern districts and abnormal sleep duration in northern and eastern districts.
Sleep disturbances are prevalent among prospective university students and are associated with various factors including gender, test-taking status, mock test satisfaction, and anxiety. Targeted interventions, including sleep education and psychological support, hold promise in ameliorating sleep health and overall well-being among students, potentially enhancing entrance test performance.
准大学生的睡眠中断因其对学业成绩和心理健康的潜在影响而日益受到关注。但是，关于准备大学入学考试的学生睡眠问题的信息尚不清楚。因此，本研究旨在利用传统统计分析以及先进的地理信息系统和机器学习技术来增强预测能力，调查孟加拉国大学入学考试考生的睡眠时长及失眠情况的患病率和相关因素。
2023年6月对达卡贾汗吉尔纳加尔大学的1496名入学考试考生进行了横断面研究。通过结构化问卷收集人口统计学、学业信息和心理健康评估数据。使用SPSS进行统计分析，包括卡方检验和逻辑回归，同时使用Python和谷歌Colab应用机器学习模型。
约62.9%的参与者报告睡眠时长异常（<7小时/晚或>9小时/晚），25.5%的人有失眠症状。女性和对模拟考试不满意的人更有可能报告睡眠时长异常，而复读考生、模拟考试成绩不理想或有焦虑症状的人患失眠症的风险更高。机器学习确定对之前模拟考试的满意度是睡眠障碍的最显著预测因素，而高中毕业证书平均绩点的影响最小。CatBoost模型在预测睡眠时长和失眠方面的最高准确率分别为61.27%和73.46%，低对数损失值表明预测性能强劲。地理分析揭示了睡眠障碍的区域差异，一些南部地区失眠患病率较高，北部和东部地区睡眠时长异常。
睡眠障碍在准大学生中普遍存在，并且与包括性别、考试状态、模拟考试满意度和焦虑在内的各种因素相关。有针对性的干预措施，包括睡眠教育和心理支持，有望改善学生的睡眠健康和整体幸福感，可能提高入学考试成绩。
