Correspondence to Jinting Wu; [email protected]
Strengths and limitations of this study
This study built a visualised nomogram to identify academic burnout among adolescents during the pandemic.
Least absolute shrinkage and selection operator regression was used to select the best risk factors to predict academic burnout.
This study was limited by the use of self-report questionnaires to assess burnout.
This study could not confirm inferring causation between burnout and risk factors due to the cross-sectional study design.
Introduction
The outbreak of COVID-19 has been spreading around the world since December 2019 and has caused major lifestyle changes in several countries for more than 2 years. Most countries have implemented home isolation and school closures to slow the spread of the pandemic.1 2 Nearly 200 million students from more than 20 countries were restricted from receiving education due to school closures.3 A national school closure strategy was implemented on 27 January 2020 and lasted for 3 months in China.4 Restricted by school closures, curriculum learning online and conducting daily activities indoors have become the primary methods for students in most cities.5
Adolescents restricted at home had an increased risk of effects on their physical and mental health, including an elevated risk of chronic and acute stress, higher prevalence of depression, increased fear of economic downturn and disrupted social relationships during the COVID-19 pandemic.6–8 Previous studies on depression and post-traumatic stress disorder (PTSD) conducted by Liu et al revealed that adolescents were more likely to report high levels of depression (43.3%), anxiety (45.4%) and PTSD symptoms (31.8%) during the COVID-19.9 These psychological and physical effects were confirmed by research, which indicated that adolescents with school closures spent more time on screens, had more irregular sleep and had decreased physical activity.10 Moreover, school closures and reduced communication between students and teachers changed learning patterns and increased the risk of burnout.11
Burnout is an individual’s response to chronic stress in a specific organisational environment.12 Some students experienced varying levels of temporary stress that prompted them to lose interest in learning and to suspect that they could not meet academic requirements during the COVID-19 pandemic. Academic burnout is a syndrome of exhaustion, cynicism, disengagement in study and a low sense of achievement due to constant exposure to internal and external pressures that adversely affects well-being.13 14 Adolescents with academic burnout experience are likely to experience sleep disorders, depression, fatigue, low self-esteem and a decline in academic performance.15 16
In the current literature, parent–child conflict, sleep problems, online study time and physical inactivity time were significant risk factors for psychosocial and behavioural problems during COVID-19.4 In addition, spending a long time on social media and loneliness were factors that predicted depression symptoms in adolescents.17 To date, few studies have evaluated the risk factors that predict academic burnout among adolescents during the COVID-19 pandemic. Thus, we investigated its prevalence and developed a predictive model as a screening tool for academic burnout during the COVID-19 pandemic period.
Methods
Study design and population
This cross-sectional study was conducted from 1 March 2020 to 30 March 2020 in Anhui Province. Two high schools were randomly selected from 10 high schools in Wuhu city, and 1800 high school students in three grades were recruited using a stratified-cluster sampling method. All students were quarantined at home and participated in online learning. In this study, all students took psychological tests when they were admitted to the school, none of the students had mental disorders or mental behaviour abnormalities.
The study used a self-designed online questionnaire via Survey star (www.wjx.cn), which took 5–10 min to complete. A total of 1732 students completed the questionnaires. Questionnaires that were not completed, those that were completed in less than 3 min and those with missing data were excluded. Finally, 1472 questionnaires were deemed eligible for inclusion. All students were randomly allocated to the training set (n=1030, 70%) and the validation set (n=442, 30%), as shown in figure 1. All students and guardians agreed to participate in the study and signed the online informed consent form.
Enlarge this image.Figure 1. The flow chart of study. DCA, decision curve analysis; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.
Sample size
We used the equation (n=(Z1−α/2/δ)2×p×(1−p)) to calculate the sample size for the cross-sectional study. Z1−α/2 is the standard normal variable for α=0.05, which is 1.96. The δ value is the tolerance error, generally taken as 0.05. The p value is the expected prevalence of burnout during the pandemic. According to the published literature, the prevalence of burnout among adolescents was 20%–40% during the COVID-19 pandemic.18 19 In addition, we found that the prevalence of academic burnout was approximately 25% in this study through the small sample calculation. Therefore, we assumed that the prevalence of burnout among adolescents was 30%. In this study, participants were randomly divided into a validation set and a training set at a ratio of 7:3. To ensure a sufficient sample size for each set, 323 participants were needed for the validation set and 754 participants were needed for the training set.
Data collection procedures
The questionnaires covered three thematic areas: (1) demographic characteristic variables, including age, gender, height, weight, residence (rural/urban), provincial senior school (yes/no), single-child family (yes/no), domestic violence (yes/no), graduating class students (yes/no), first-year students (yes/no), hobbies (>2 hobbies, 1–2 hobbies or none) and academic performance; (2) living and learning states during the pandemic contained cognition of COVID-19 knowledge (very familiar, familiar and unfamiliar), community management (complete closure, partial closure and none), physical activity (>6 hours per week, 3–6 hours per week and <3 hours per week), parent-accompanied exercise (>3 times per week, 1–3 times per week and none), online entertainment, online learning time and sleep duration and (3) adolescents’ academic burnout was measured using the academic burnout scale,20 21 which was developed in Chinese by Wu et al.22 The scale contained 16 items from three fundamental dimensions, including academic exhaustion (4 items, eg, I often feel exhausted), academic alienation (5 items, eg, I want to give up studying) and personal accomplishment (7 items, eg, I can master knowledge easily). All students completed the questionnaire using a 5-point Likert-type scale (ranging from ‘completely disagree, 1’ to ‘completely agree, 5’). Six items were reverse coded in the scale. Academic burnout was defined as a score higher than the 75th percentile for academic exhaustion (15 for this scale), a score higher than the 75th percentile for academic alienation (19 for this scale) or a score less than the 25th percentile (9 for this scale), as suggested by the previous studies.23 24 The reliability and validity of this scale were confirmed by previous studies conducted in schools. The Cronbach’s alpha coefficients for each basic dimension of the scale were 0.781, 0.810 and 0.878, respectively, and the overall Cronbach’s alpha was 0.816 in this study.
Academic performance was measured by using the students’ exam scores as expressed in their school reports after the final examination in the previous semester,12 which was divided into three parts (>500, 400–500, <400 points) according to the local school evaluation criteria. Physical activity was estimated by measuring the duration of weekly physical activity at home during school and community closures, such as walking, running and yoga.25 All participants were asked to report the time spent each week in the above activities.
Quality control
All adolescents who took part in the survey were anonymous and participation was voluntary. They completed the questionnaire on only one mobile device (or computer). Questionnaires that were completed in less than 3 min were deleted to ensure a better completion rate. The questionnaire items were carefully reviewed by two trained researchers.
Data analysis
All data were analysed using R software (V.3.6.2) and SPSS V.25.0 (IBM Corporation). The Kolmogorov-Smirnov test was used to show the normal distribution of all continuous variables. Continuous variables, including age, height and weight, were presented as the means±SD or IQR. Categorical variables, including residence, gender, hobbies, provincial senior school and other variables related to academic burnout, were shown as frequencies (%). Student’s t-test, the χ2 test and Mann-Whitney tests were used to make comparisons between the training set and the validation set.
The least absolute shrinkage and selection operator (LASSO) method was used to select the best risk factors to predict burnout before multivariable logistic regression analysis. The interaction of independent variables was assessed by multicollinearity tests using the variance inflation factor (VIF). A prediction nomogram was developed based on the results of multivariate logistic regression analysis using ‘rms’ packages of R software. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of the nomogram for discrimination and decision curve analysis (DCA) was performed to estimate the clinical value of the nomogram. p<0.05 (two-tailed test) was considered statistically significant.
Patient and public involvement
No participants or the public were involved in the design, conduct, reporting or dissemination plans of the study.
Result
Demographic characteristics of adolescents in the training and validation sets
In total, 1472 adolescents were included in this study, of whom 1030 constituted the training set and 442 constituted the validation set in a ratio of 7:3. The average age was 16.94±2.96 years and 719 adolescents (48.80%) were male. A total of 220 (21.40%) adolescents in the training set and 100 (22.60%) adolescents in the validation set suffered from academic burnout (table 1). The demographic characteristics were not significantly different between the training set and the validation set (p>0.05).
Table 1Demographic characteristics of adolescents with academic burnout in the training and validation sets
| Variables | Total (n=1472) | Training set (n=1030) | Validation set (n=442) | t/z/χ2 | P value |
| Age, years | 16.94±2.96 | 16.95±3.36 | 16.92±1.69 | 0.151 | 0.880 |
| Gender (male), n (%) | 719 (48.80) | 514 (49.90) | 205 (46.40) | 1.536 | 0.215 |
| Height, cm | 169.89±10.79 | 169.95±11.70 | 169.73±8.29 | 0.361 | 0.718 |
| Weight, kg | 62.00 (53.00, 76.00) | 62.00 (53.00, 76.00) | 62.00 (52.00, 77.00) | −0.095 | 0.924 |
| Provincial senior school | 0.777 | 0.378 | |||
| Yes, n (%) | 805 (54.70) | 571 (55.40) | 234 (52.90) | ||
| No, n (%) | 667 (45.30) | 459 (44.60) | 208 (47.10) | ||
| Residence | 0.142 | 0.707 | |||
| Rural, n (%) | 114 (7.70) | 78 (7.60) | 36 (8.10) | ||
| Urban, n (%) | 1358 (92.30) | 952 (92.40) | 406 (91.90) | ||
| Single-child family | 1.110 | 0.292 | |||
| Yes, n (%) | 896 (60.90) | 636 (61.70) | 260 (58.80) | ||
| No, n (%) | 576 (39.10) | 394 (38.30) | 182 (41.20) | ||
| Study in graduating class | 0.104 | 0.747 | |||
| Yes, n (%) | 455 (30.90) | 321 (31.20) | 134 (30.30) | ||
| No, n (%) | 1017 (69.10) | 709 (68.80) | 308 (69.70) | ||
| First-year student | 1.462 | 0.227 | |||
| Yes, n (%) | 447 (30.40) | 303 (29.40) | 144 (32.60) | ||
| No, n (%) | 1025 (69.60) | 727 (70.60) | 298 (67.40) | ||
| Hobbies | 4.429 | 0.109 | |||
| >2 hobbies, n (%) | 179 (12.20) | 134 (13.00) | 45 (10.20) | ||
| 1–2 hobbies, n (%) | 899 (61.10) | 634 (61.60) | 265 (60.00) | ||
| None, n (%) | 394 (26.80) | 262 (25.40) | 132 (29.90) | ||
| Cognition of COVID-19 knowledge | 1.672 | 0.433 | |||
| Very familiar, n (%) | 570 (38.70) | 407 (39.50) | 163 (36.90) | ||
| Familiar, n (%) | 711 (48.30) | 496 (48.20) | 215 (48.60) | ||
| Unfamiliar, n (%) | 191 (13.00) | 127 (12.30) | 64 (14.50) | ||
| Community management | 3.861 | 0.145 | |||
| Complete closure, n (%) | 70 (4.80%) | 54 (5.20) | 16 (3.60) | ||
| Partial closure, n (%) | 1382 (93.90) | 965 (93.70) | 417 (94.30) | ||
| None, n (%) | 20 (1.40%) | 11 (1.10) | 9 (2.00) | ||
| Domestic violence | 0.149 | 0.699 | |||
| Yes, n (%) | 255 (17.30) | 181 (17.60) | 74 (16.70) | ||
| None, n (%) | 1217 (82.70) | 849 (82.40) | 368 (83.30) | ||
| Online learning time | 4.909 | 0.086 | |||
| >6 hours/day, n (%) | 190 (12.90) | 137 (13.30) | 53 (12.00) | ||
| 3–6 hours/day, n (%) | 800 (54.30) | 574 (55.70) | 226 (51.10) | ||
| <3 hours/day, n (%) | 482 (32.70) | 319 (31.00) | 163 (36.90) | ||
| Online entertainment | 1.708 | 0.635 | |||
| <1 hour/day, n (%) | 303 (20.60) | 209 (20.30) | 94 (21.30) | ||
| 1–4 hours/day, n (%) | 608 (41.30) | 433 (42.00) | 175 (39.60) | ||
| 5–8 hours/day, n (%) | 395 (26.80) | 278 (27.00) | 117 (26.50) | ||
| >8 hours/day, n (%) | 166 (11.30) | 110 (10.70) | 56 (12.70) | ||
| Physical activity | 0.552 | 0.759 | |||
| >6 hours/week, n (%) | 734 (49.90) | 512 (49.70) | 222 (50.20) | ||
| 3–6 hours/week, n (%) | 578 (39.30) | 402 (39.00) | 176 (39.80) | ||
| <3 hours/week, n (%) | 160 (10.90) | 116 (11.30) | 44 (10.00) | ||
| Parents-accompanied exercise | 2.232 | 0.328 | |||
| >3 times/week, n (%) | 225 (15.30) | 158 (15.30) | 67 (15.20) | ||
| 1–3 times/week, n (%) | 642 (43.60) | 461 (44.80) | 181 (41.00) | ||
| None, n (%) | 605 (41.10) | 411 (39.90) | 194 (43.90) | ||
| Sleep duration | 1.446 | 0.485 | |||
| >8 hours/night, n (%) | 188 (12.80) | 125 (12.10) | 63 (14.30) | ||
| 6–8 hours/night, n (%) | 1013 (68.80) | 717 (69.60) | 296 (67.00) | ||
| <6 hours/night, n (%) | 271 (18.40) | 188 (18.30) | 83 (18.80) | ||
| Academic performance (score) | 1.115 | 0.573 | |||
| >500 | 631 (42.90) | 438 (42.50) | 193 (43.70) | ||
| 400–500 | 734 (49.90) | 521 (50.60) | 213 (48.20) | ||
| <400 | 107 (7.30) | 71 (6.90) | 36 (8.10) | ||
| Academic burnout | 0.291 | 0.590 | |||
| Yes, n (%) | 320 (21.70) | 220 (21.40) | 100 (22.60) | ||
| None, n (%) | 1152 (78.30) | 810 (78.60) | 342 (77.40) |
Independent risk factors for adolescent academic burnout
The LASSO regression model was used to screen the independent risk factors for academic burnout in the training set. Seven predictors with non-zero coefficients were selected from 19 potential risk factors in the LASSO regression model (~3:1 ratio), including single-child family, study in graduating class, domestic violence, online entertainment, physical activity, sleep duration and academic performance (figure 2). Multivariable analysis revealed that six risk factors were closely associated with academic burnout (table 2). Adolescents living in single-child family (OR=1.742, 95% CI: 1.243 to 2.441, p=0.001) and experiencing domestic violence (OR=1.694, 95% CI: 1.159 to 2.476, p=0.007) were prone to academic burnout. In addition, adolescents who spent more time on entertainment online, less time on physical activity and had a shorter duration of sleep were likely to experience academic burnout (p<0.05). The poorer academic performance of adolescents was the more likely they were to suffer from academic burnout (p<0.05). Multicollinearity of the independent variables was assessed by using VIF and the mean VIF was 1.08.
Enlarge this image.Figure 2. Variable of predict academic burnout risk factors selection using least absolute shrinkage and selection operator (LASSO) regression model. (A) The optimal parameter (lambda) was obtained by using 10-fold cross-validation via the minimum criteria. The partial likelihood deviation curve was plotted versus log (lambda). Dotted vertical lines were drawn at the optimal values by using the minimum criteria (lambda.min=0.0067) and the 1-SE of the minimum criteria (lambda.1se=0.0297). (B) LASSO coefficient profiles of the 19 features and a coefficient profile plot were drawn against the log (lambda) sequence. Seven variables with non-zero coefficients were selected based on the optimal lambda.
Multivariate analysis of risk factors for academic burnout in the training set
| Variables | Multivariate logistic regression | ||
| β | OR (95% CI) | P value | |
| Single-child family | |||
| No | 1 (Reference) | ||
| Yes | 0.555 | 1.742 (1.243 to 2.441) | 0.001 |
| Study in graduating class | |||
| No | 1 (Reference) | ||
| Yes | 0.283 | 1.327 (0.940 to 1.874) | 0.108 |
| Domestic violence | |||
| No | 1 (Reference) | ||
| Yes | 0.527 | 1.694 (1.159 to 2.476) | 0.007 |
| Online entertainment | 0.001 | ||
| <1 hour/day | 1 (Reference) | ||
| 1–4 hours/day | 0.640 | 1.896 (1.149 to 3.129) | 0.012 |
| 5–8 hours/day | 0.970 | 2.638 (1.536 to 4.532) | <0.001 |
| >8 hours/day | 1.118 | 3.058 (1.634 to 5.720) | <0.001 |
| Physical activity | 0.036 | ||
| >6 hours/week | 1 (Reference) | ||
| 3–6 hours/week | −0.111 | 0.895 (0.616 to 1.300) | 0.560 |
| <3 hours/week | 0.522 | 1.686 (1.032 to 2.754) | 0.037 |
| Sleep duration | 0.002 | ||
| >8 hours/night | 1 (Reference) | ||
| 6–8 hours/night | 0.209 | 1.232 (0.740 to 2.053) | 0.423 |
| <6 hours/night | 0.851 | 2.342 (1.315 to 4.170) | 0.004 |
| Academic performance (score) | 0.018 | ||
| >500 | 1 (Reference) | ||
| 400–500 | 0.409 | 1.506 (1.040 to 2.181) | 0.030 |
| <400 | 0.779 | 2.180 (1.201 to 3.958) | 0.010 |
Development of the predictive nomogram
The nomogram was constructed to predict the probability of academic burnout in adolescents based on the results of the multivariate logistic regression analysis in the training set, which included six factors. The score of each variable was calculated by drawing a vertical line up to the ‘Points’ axis. The total score was summarised by adding the score of each variable. Then the predicted probability of academic burnout could be located on the bottom axis according to the summarised score (figure 3). For example, a single-child family (58 points) adolescent with experienced domestic violence (52 points), entertainment time online of 9 hours/day (100 points), physical activity time of 2 hours/day (38 points), sleep duration of 5 hours/day (100 points) and academic performance with 450 score (48 points) would have a total score of 396 points. The probability of academic burnout would be estimated at 70% during the pandemic.
Enlarge this image.Figure 3. Nomogram constructed for predicting academic burnout. Notes: the academic burnout risk nomogram with adolescents was developed using six predictors. For a specific student, the total points were obtained by adding each score in the nomogram, and then, the academic burnout probability of the student can be obtained according to the total score.
Evaluation of the nomogram prediction model and clinical application
The ROC curves of the nomogram in the training and validation sets were analysed to evaluate the diagnostic effect of the model. The area under the ROC curve for the nomogram in the training set was 0.686 (95% CI: 0.646 to 0.726) and was confirmed to be 0.706 (95% CI: 0.647 to 0.765) in the validation set showing that the model had good discriminatory ability (figure 4). The calibration curves of the predicted model were plotted in the training set and validation set. The results demonstrated that the predictive model had good consistency and a high degree of calibration (figure 5). DCA of both the training set and the validation set showed that the predictive model had a wide range of applications (figure 6).
Enlarge this image.Figure 4. Receiver operating characteristic (ROC) curves of the predictive models in the training and validation sets. (A) The area under the curve (AUC) of ROC for the model was 0.686 in the training set. (B) The AUC for the model was 0.706 in the validation set.
Enlarge this image.Figure 5. Calibration plot of the prediction model in the training and validation sets. (A) The calibration curve was plotted in the training set. (B) The calibration curve was plotted in the validation set. Notes: The x-axis represents the model-predicted probability of academic burnout, and the y-axis represents the actual observed risk of academic burnout. The apparent and the bias corrected values are close to each other, which means the prediction model has a good predictive performance in the two sets.
Enlarge this image.Figure 6. Decision curve analysis (DCA) of the prediction model for academic burnout in both training and validation sets. (A) The DCA curve of nomogram in the training set. (B) The DCA curve of nomogram in the validation set. Notes: The y-axis shows the net benefit, and the x-axis shows the threshold probability. The red line and blue line represent the net benefit of our nomogram in the training set and validation set, respectively. The oblique grey line indicates the hypothesis that all students had academic burnout. The black horizontal line represents the hypothesis that no students had academic burnout.
Discussion
To the best of our knowledge, few studies have explored the risk factors for academic burnout problems in adolescents during the COVID-19 pandemic. This study aimed to evaluate the multiple contributing factors associated with adolescents’ academic burnout caused by long-term school closures during the COVID-19 pandemic in China. In this study, 21.70% of high school students reported academic burnout. We developed and validated a nomogram model to predict the probability of academic burnout. The nomogram, as a visual tool, is considered a reliable method for accurate predictions, which makes the predictive model easy to understand.26 The key risk predictive factors were single-child family, domestic violence, online entertainment, physical activity, sleep duration and academic performance.
The closure of schools and communities limited the social activities of adolescents, decreased their daily activities and increased their time at home.27 Our study found that adolescents from single-child family and those exposed to domestic violence were more likely to experience burnout during the pandemic. These adolescents experienced more loneliness at home, which led to a higher possibility of depression and boredom with learning. Our finding was supported by Chen et al, who showed that being a single child was a risk factor for depression during the COVID-19 pandemic.28 In addition, adolescents from single-child family presented more anxiety and fear of the COVID-19, while adolescents of families with three or more children seemed to have less mental disturbance.29 Besides, adolescents who were exposed to domestic violence were more likely to be distrustful, and to feel unsafe, and domestic violence was a predictor of depression.30 It was found that adolescents with siblings and amiable family relationships were less likely to develop academic burnout because they felt less isolated and had more support from each other. Therefore, more attention should be given to adolescents from single-child family and household domestic violence, to reduce their psychological stress and improve their academic burnout.
Moreover, our results suggested that online entertainment time was a risk factor for adolescents, which means that the more time adolescents spent on online entertainment, the higher the prevalence of burnout symptoms was. The online learning platforms, such as Tencent Meetings and Zoom, have become the dominant mode during the school closures, but the opportunities to play video games or chat online in real time have also increased. Although entertainment online may relieve mental stress and improve the negative mood of adolescents to a certain extent, facing the screen for a long time may produce negative health consequences.31 It is possible that the excessive online entertainment is associated with poorer sleep quality and increases the risk of depression and anxiety for adolescents.32 33 In addition, our study also found that physical activity was a protective factor against academic burnout. The chances of outdoor sports were greatly reduced due to community closures, and the adolescents were only allowed to perform physical exercise indoors during the COVID-19 pandemic. A previous study confirmed that the occurrence of academic burnout is closely related to mental toughness.34 Physical activity can decrease the incidence of depression and anxiety,35 36 and has been confirmed to reduce burnout as an effective medium in a systematic review.37 Positive findings have shown that physical activity is associated with mental health, meaning that physical activity can reduce behavioural distraction and develop self-efficacy to improve mental toughness.34 It is imperative to remind the public to realise the adverse health effects of excess time facing the screen, to encourage adolescents to spend less time on screens and to increase physical activity.
Adolescents were more likely to experience sleep disorders during the COVID-19 pandemic.38 39 Long-term stress and poor sleep habits (eg, online gaming and online shopping) lead to poorer sleep quality and shorter sleep duration.40 Our research showed that shortened sleep time was related to academic burnout, which is a recurring characteristic for adolescents. It may increase the recovery time from stress and fatigue, and affect cognitive processes.41 Current studies have also confirmed our finding that shortened sleep time and poor sleep quality aggravate students’ academic burnout.42 43 Furthermore, our research found that academic performance was a protective factor against burnout in adolescents. Previous research found that high academic performance was associated with positive emotions, while lower performance was related to negative feelings.44 In addition, adolescents with high academic performance had better academic adaptability, and this adaptability can promote interest in learning, relieve psychological stress and affect mental health.45 The research by Shadid et al showed that the lower grade point average students performed, the significantly greater was academic burnout.46 Therefore, academic performance has a direct impact on academic burnout in adolescents during the COVID-19 pandemic.
Limitations
The study has some limitations. First, this survey was conducted in Anhui Province. Because of demographic and geographical factors, the conclusions of the research may be limited, and the responses may have been affected by the spread of the COVID-19 pandemic. Second, the stratified-cluster sampling method was only used to select samples in high schools, while college students were not included. Therefore, the results of the study were limited to adolescents of a specific age group. Third, this study was a cross-sectional survey, which cannot assess the relationship between academic burnout and related risk factors effectively and accurately. In addition, we could not exclude students with depressive symptoms participate in this investigation during the pandemic, and we cannot rule out the possibility of students with depressive symptoms as an alternative observation to burnout.
Conclusions
The study demonstrated that the prevalence of academic burnout among adolescents was significantly higher in China during the COVID-19 pandemic. Single-child family, domestic violence, online entertainment time, physical activity, sleep duration and academic performance were strongly associated with academic burnout. It is essential to emphasise the importance of mental health and promote a healthy lifestyle among adolescents during school closures due to the uncertain future of the pandemic. The nomogram could be a useful tool to screen potential risk factors for academic burnout in adolescents to identify high-risk groups and develop preventive measures for schools and educational departments.
We would like to thank all the adolescents who participated in this study.
Data availability statement
Data are available upon reasonable request. Data are available from the corresponding author and the first author on reasonable request.
Ethics statements
Patient consent for publication
Consent obtained from parent(s)/guardian(s).
Ethics approval
This study involves human participants and was approved by the medical ethics committee of the Second Affiliated Hospital of Wannan Medical College (Number: WYEFYLS202001). Participants gave informed consent to participate in the study before taking part.
SY and RW contributed equally.
Contributors RW, HP and JX designed the questionnaires. FZ and WL gathered the data. SY and RW performed the statistical analyses. SY drafted the manuscript and designed the study. JW conduct the study and reviewed the manuscript.
Funding The study was supported by the Provincial Quality Project of the Education Department of Anhui Province (number: 2021jyxm1618), Scientific Research Project of the Education Department of Anhui Province (number: 2022AH040167) and ‘Summit Plan’ of the Second Affiliated Hospital of Wannan Medical College (grant number: DFJH202207).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
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2 China National Clinical Research Center for Respiratory Diseases, National Center for Children’s Health, Beijing, China, Group of Respirology, Chinese Pediatric Society, Chinese Medical Association, et al. Diagnosis, treatment, and prevention of 2019 novel coronavirus infection in children: experts’ consensus statement. World J Pediatr 2020; 16: 223–31. doi:10.1007/s12519-020-00343-7
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; Wang, Rui 2 ; Pan, Huiqing 1 ; Zhao, Feiyang 3 ; Li, Weijia 3 ; Xing, Jingjing 1 ; Wu, Jinting 4 1 Emergency Department, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China
2 Institute of Physical Education, Anhui Polytechnic University, Wuhu, Anhui, China
3 College of Clinical Medicine, Wannan Medical College, Wuhu, Anhui, China
4 Department of Psychology, Wannan Medical College, Wuhu, Anhui, China