Emergency Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
Institute of Physical Education, Anhui Polytechnic University, Wuhu, Anhui, China.
BMJ Open. 2023 May 2;13(5):e068370. doi: 10.1136/bmjopen-2022-068370.
This study aimed to screen the potential risk factors for academic burnout among adolescents during the COVID-19 pandemic, develop and validate a predictive tool based on the risk factors for predicting academic burnout.
This article presents a cross-sectional study.
This study surveyed two high schools in Anhui Province, China.
A total of 1472 adolescents were enrolled in this study.
The questionnaires included demographic characteristic variables, living and learning states and adolescents' academic burnout scale. Least absolute shrinkage and selection operator and multivariate logistic regression analyses were employed to screen the risk factors for academic burnout and develop a predictive model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to assess the accuracy and discrimination of the nomogram.
In this study, 21.70% of adolescents reported academic burnout. Multivariable logistic regression analysis showed that single-child family (OR=1.742, 95% CI: 1.243 to 2.441, p=0.001), domestic violence (OR=1.694, 95% CI: 1.159 to 2.476, p=0.007), online entertainment (>8 hours/day, OR=3.058, 95% CI: 1.634 to 5.720, p<0.001), physical activity (<3 hours/week, OR=1.686, 95% CI: 1.032 to 2.754, p=0.037), sleep duration (<6 hours/night, OR=2.342, 95% CI: 1.315 to 4.170, p=0.004) and academic performance (<400 score, OR=2.180, 95% CI: 1.201 to 3.958, p=0.010) were independent significant risk factors associated with academic burnout. The area under the curve of ROC with the nomogram was 0.686 in the training set and 0.706 in the validation set. Furthermore, DCA demonstrated that the nomogram had good clinical utility for both sets.
The developed nomogram was a useful predictive model for academic burnout among adolescents during the COVID-19 pandemic. It is essential to emphasise the importance of mental health and promote a healthy lifestyle among adolescents during the future pandemic.
本研究旨在筛选 COVID-19 大流行期间青少年学业倦怠的潜在风险因素,基于风险因素开发并验证预测学业倦怠的工具。
本研究为横断面研究。
本研究在安徽省的两所高中进行。
共纳入 1472 名青少年。
问卷包括人口统计学特征变量、生活和学习状态以及青少年学业倦怠量表。采用最小绝对收缩和选择算子(LASSO)和多变量逻辑回归分析筛选学业倦怠的风险因素并建立预测模型。受试者工作特征(ROC)曲线和决策曲线分析(DCA)用于评估列线图的准确性和区分度。
本研究中,21.70%的青少年报告存在学业倦怠。多变量逻辑回归分析显示,独生子女家庭(OR=1.742,95%CI:1.2432.441,p=0.001)、家庭暴力(OR=1.694,95%CI:1.1592.476,p=0.007)、网络娱乐(>8 小时/天,OR=3.058,95%CI:1.6345.720,p<0.001)、体育活动(<3 小时/周,OR=1.686,95%CI:1.0322.754,p=0.037)、睡眠时长(<6 小时/晚,OR=2.342,95%CI:1.3154.170,p=0.004)和学业成绩(<400 分,OR=2.180,95%CI:1.2013.958,p=0.010)是与学业倦怠相关的独立显著风险因素。训练集和验证集的 ROC 曲线下面积分别为 0.686 和 0.706。此外,DCA 表明该列线图在两组中均具有良好的临床实用性。
所开发的列线图是预测 COVID-19 大流行期间青少年学业倦怠的有效预测模型。在未来的大流行中,强调青少年心理健康的重要性并促进其健康生活方式至关重要。