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Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study
OBJECTIVES: 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. DESIGN: This article presents a cross-sectional study. SETTING: Thi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163519/ https://www.ncbi.nlm.nih.gov/pubmed/37130664 http://dx.doi.org/10.1136/bmjopen-2022-068370 |
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author | Ye, Sheng Wang, Rui Pan, Huiqing Zhao, Feiyang Li, Weijia Xing, Jingjing Wu, Jinting |
author_facet | Ye, Sheng Wang, Rui Pan, Huiqing Zhao, Feiyang Li, Weijia Xing, Jingjing Wu, Jinting |
author_sort | Ye, Sheng |
collection | PubMed |
description | OBJECTIVES: 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. DESIGN: This article presents a cross-sectional study. SETTING: This study surveyed two high schools in Anhui Province, China. PARTICIPANTS: A total of 1472 adolescents were enrolled in this study. OUTCOME MEASURES: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-10163519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-101635192023-05-07 Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study Ye, Sheng Wang, Rui Pan, Huiqing Zhao, Feiyang Li, Weijia Xing, Jingjing Wu, Jinting BMJ Open Global Health OBJECTIVES: 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. DESIGN: This article presents a cross-sectional study. SETTING: This study surveyed two high schools in Anhui Province, China. PARTICIPANTS: A total of 1472 adolescents were enrolled in this study. OUTCOME MEASURES: 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. RESULTS: 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. CONCLUSIONS: 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. BMJ Publishing Group 2023-05-02 /pmc/articles/PMC10163519/ /pubmed/37130664 http://dx.doi.org/10.1136/bmjopen-2022-068370 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Global Health Ye, Sheng Wang, Rui Pan, Huiqing Zhao, Feiyang Li, Weijia Xing, Jingjing Wu, Jinting Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study |
title | Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study |
title_full | Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study |
title_fullStr | Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study |
title_full_unstemmed | Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study |
title_short | Development and validation of a prediction nomogram for academic burnout among Chinese adolescents: a cross-sectional study |
title_sort | development and validation of a prediction nomogram for academic burnout among chinese adolescents: a cross-sectional study |
topic | Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163519/ https://www.ncbi.nlm.nih.gov/pubmed/37130664 http://dx.doi.org/10.1136/bmjopen-2022-068370 |
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