Phase I Clinical Trails Center, The First Hospital of China Medical University, No.155 Nanjing Bei Street, Heping District, Shenyang, 110001, Liaoning Province, China.
Department of Community Nursing, School of Nursing, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, China.
Sci Rep. 2023 Jul 12;13(1):11247. doi: 10.1038/s41598-023-38302-7.
Health-related quality of life, sleep quality, morning and evening types, and internet addiction are of significant importance to the development of medical students, yet they have rarely been studied. Taking this into consideration, the study aimed to confirm latent profiles in health-related quality of life, sleep quality, morning and evening types, and internet addiction in medical students and investigate the characteristics of participants in each profile to provide suggestions for students' health. This was an observational cross-sectional study including 1221 medical student subjects at China Medical University in 2019. Multiple correspondence analysis was the initial step to verify the correspondence, dispersion, and approximation of variable categories. Latent profile analysis was used to identify the multiple correspondences between the levels of variables. Three profiles were found, including: (1) The Low sleep quality profile was characterized by the lowest sleep quality among the three existing profiles. (2) The High health-related quality of life and Low internet addiction profile was characterized by the highest level of health-related quality of life but the lowest level of internet addiction. (3) The Low health-related quality of life and High internet addiction profile was characterized by the highest standardized values of internet addiction but the lowest standardized values of health-related quality of life. This study had important implications for improving student health and supported the medical universities and hospitals in implementing targeted policies based on distinctive student characteristics.
健康相关生活质量、睡眠质量、早晚型和网络成瘾对医学生的发展至关重要，但这些方面很少被研究。有鉴于此，本研究旨在确认医学生健康相关生活质量、睡眠质量、早晚型和网络成瘾的潜在特征，并调查每个特征组的特点，为学生的健康提供建议。这是一项观察性的横断面研究，包括 2019 年中国医科大学的 1221 名医学生。多元对应分析是验证变量类别对应、离散和近似的初始步骤。潜在剖面分析用于确定变量水平之间的多重对应关系。发现了三个特征组，包括：（1）低睡眠质量特征组在三个现有特征组中睡眠质量最低。（2）高健康相关生活质量和低网络成瘾特征组表现为健康相关生活质量最高，但网络成瘾程度最低。（3）低健康相关生活质量和高网络成瘾特征组表现为网络成瘾的标准化值最高，但健康相关生活质量的标准化值最低。本研究对改善学生健康具有重要意义，并支持医科大学和医院根据学生的独特特征实施有针对性的政策。
