ABSTRACT
Academic performance could have an impact on job trajectories and socioeconomic status for societies in the future. However, only a few studies have focused on the impact of time allocation on extracurricular activity strategies. Based on that observation, we recruited 91,161 school students from 451 primary, middle, and high schools in China, and used a cross-sectional compositional data analysis approach to investigate the linkage between extracurricular activities and time allocation strategies and academic performance. Our study found that, in general, a suitable daily sleep routine is of primary importance in improving students’ academic performance. Additionally, daily exercise appears to be able to significantly facilitate higher academic performance. it’s important to note that exercising while sleep-restricted may have adverse effects. The most effective way to improve student academic performance proves to be reducing the time spent playing video games and watching short videos and replacing it with hours of sleep or exercise.
Introduction
Academic performance in adolescent years can significantly impact individuals, families, and society. Numerous studies have shown that low academic performance can limit individuals’ income and development in the future (Woessmann, Citation2016), while it is associated with a higher tendency towards depression and external behavioural difficulties (Dias et al., Citation2022). This can potentially lead to deeper societal issues, such as low expected returns on education, unbalanced economic distribution, and increased criminal behaviours (Gregorio & Lee, Citation2002; Katsiyannis et al., Citation2008; Prakhov, Citation2021). Conversely, highly educated people are often seen as healthy and positive individuals who can bring more benefits to society (Chase et al., Citation2015). Considering these important findings, the pursuit of methods to enhance the academic performance of young students is imperative.
It is noteworthy that prior research has mainly focused on the relationship between students’ in-class activities and academic performances (Fredricks et al., Citation2004; Rivkin et al., Citation2005). However, time outside of school may be equally important for adolescents. Numerous studies have found that students who spend adequate time engaged in extracurricular reading and exercise, exhibit higher levels of cognitive capacity (De Greeff et al., Citation2018; Taras, Citation2005) and good mental health (Boyes et al., Citation2018; Chu et al., Citation2020; Donnelly & Lambourne, Citation2011). Another conclusion suggests that they can also exhibit superior academic performances (Fleshner, Citation2000; Marques et al., Citation2018; Mol & Bus, Citation2011), while a meta-analysis of 106,653 students in 23 countries reported a significant negative association between some screen-use activities and academic performance, such as prolonged television viewing and video game playing (Adelantado-Renau et al., Citation2019). Among all activities, the effect of sleep on good academic performance is very important (Chung & Cheung, Citation2008; Curcio et al., Citation2006; Chaput et al., Citation2016). Insufficient or poor quality sleep can significantly hinder students’ cognitive functions such as good attention, good memory (Gais & Born, Citation2004; Smith, Citation2001), abstract logical thinking (Randazzo et al., Citation1998) and can also affect the normal functioning of the prefrontal cortex (Curcio et al., Citation2006; Randazzo et al., Citation1998). It can also lead to daytime exhaustion and lowered learning efficiency (Sharman & Illingworth, Citation2020; Wolfson & Carskadon, Citation1998). On the other hand, high-quality, adequate sleep is associated with higher academic performance (Perkinson-Gloor et al., Citation2013). In a single study, it was found that sleep explained 25% of the variation in total performance (Okano et al., Citation2019).
However, despite studies highlighting the importance of these activities, the actual daily routine of students is often taking a toll on their mental health and academic performance due to being inadequate to fulfil their needs. In China, approximately 58% of adolescents do not get enough sleep during school days (Chen et al., Citation2014). A meta-analysis of 49 studies found that only 23.8% of boys and 15.4% of girls worldwide met physical activity targets (Guthold et al., Citation2010). Screen usage also accounts for a significant proportion of students’ use of time, with the average daily screen time for adolescents in the United States reaching 7.5 hours in 2010 (Rideout et al., Citation2010), which is more than twice of the recommended screen time of two hours according to global health guidelines.
In these contemporary times, many students are beginning to realize the importance of proper time allocation when considering these activities. Several countries have introduced comprehensive recommendations for adolescent health behaviours, such as Canada’s 24-hour Movement Guidelines (Tremblay et al., Citation2016). Relevant studies have demonstrated the positive impact following the guidelines has on the physical and mental health of adolescents (Faught et al., Citation2017; Walsh et al., Citation2018). However, prior studies have some remaining limitations. First, many studies have focused on the impact of a single activity on students’ academic performance, when in fact, school hours, rest periods, exercise, reading, and screen time use are interconnected throughout the 24-hour day. Any increase or decrease in the time young students allocate to a certain activity will naturally impact the time available for another activity. The studies that don’t consider this perspective may produce incomplete estimates (Chastin et al., Citation2015), thus, their outcomes can result in discrepancies. For example, studies suggest that increased time spent in front of a screen or a monitor can reduce the time that is spent on other activities, such as physical activities (Melkevik et al., Citation2010; Sandercock et al., Citation2012) and sleep (LeBourgeois et al., Citation2017). Thus, the impact of screen time use on academic performance may be related to decreased sleep and exercise duration, rather than increased screen time duration.
Moreover, research findings that consider the comprehensive allocation of time do not provide adequate guidance for determining best practices. For instance, when adolescents attempt to follow research recommendations to increase their sleep duration, and thereby enhance their academic performance, they might experience confusion. It remains uncertain which other activity they should potentially decrease, as electronic screen usage, exercise, and reading time may hold equal importance for adolescents. In addition, previous studies have never examined changes in academic performance associated with time relocation from one activity to another, particularly, in the case of sleep duration, which is an activity that is critical for the good physical and mental health of students and their academic performance.
Traditional statistical methods used in previous studies (e.g. multiple linear regression) were not suitable for this study, given the limited nature of time-allocated data and collinearity. Therefore, a statistical method called Compositional Data Analysis (CoDA) is used to address the research question (Chastin et al., Citation2015). CoDA calculates the proportion of each activity in total and transforms each group of data into mutually constrained and limited three-dimensional spatial coordinates, thus allowing the use of standard statistical techniques with isochronous substitution algorithms (Dumuid et al., Citation2019). This method has been progressively applied to various studies (Dumuid et al., Citation2019), but there is still a research gap when it comes to understanding the connection between time allocation for specific types of activities and the academic performance of adolescents.
Considering the above factors, this study investigates the correlation between various extracurricular activities and academic performance and how this correlation is affected when sleep recommendations are disregarded. Additionally, it explores the degree of change in the academic performance of adolescents by replacing restful periods with exercise or other sedentary activities (or vice versa), while recognizing the essential daily requirement of sleep and its substantial influence on physical development, mental well-being, and academic performance.
Methods
Study design and participation
Data for the current study were retrieved from the 2021 Tsinghua Adolescence Health Survey (TAHS). TAHS provided cross-sectional data collected from 417,144 adolescents aged between 9 and 20 years old attending primary, middle and high schools in China in 2021. The survey includes items related to sociodemographic, lifestyle, peer and family relationships, health status, depression screening, and mental well-being. Invitations to participate in the survey were extended to all elementary, secondary, and high schools in China, regardless of whether they were in rural or urban areas or were privately or publicly funded. A total of 435 schools contributed data to the study, with 239 of them catering to primary school students, 202 to middle school students, and 86 to high school students. These schools were situated across 29 provinces and 138 cities in China. TAHS independently built its own data collection and management platform and launched a survey to all participating schools through online questionnaires. All students responded to the online questionnaire during a dedicated time in the computer room of their school, and the process was guided by a trained teachers. There was a report that 9 out of the original 417,144 participants had garbled data due to data transfer problems in TAHS, while 124 had a total response time that was less than 150 seconds, 1,943 had missing data on drinking and smoking behaviours, and 880 quit halfway through, resulting in incomplete data, leaving a total of 414,188 participants (202,028 girls and 212,160 boys). Given that some schools did not or were not willing to provide final test scores, only 91,161 students who provided complete life activity time, demographic variables, and academic performance were included in the study. The relevant information is visible in . Online consent forms were provided to and signed by all participants. Students who were not interested in participating could opt out of the process. The survey was conducted in Chinese. All administrations were led by trained teachers in each class in the participating schools between September and December 2021. Upon completion of the data collection process, participants were provided with access to a series of online curricula in positive psychology as a reward.
Table 1. Descriptive characteristics of the sample.
Research has demonstrated that excessive as well as insufficient sleep can have negative consequences on student development (Gomez Fonseca & Genzel, Citation2020). Therefore, the study subjects were divided into four groups according to their educational grade level and whether they were on sufficient sleep levels, which equals with 9 hours for primary school students and 8 hours for secondary school students (Paruthi et al., Citation2016). This project was approved by the Ethics Committee of the Department of Psychology at Tsinghua University, and all participants signed an online informed consent form.
Measures
Primary outcome measure: academic performance
Academic performance was measured by the student’s total score on the final exam during the data collection period. In China, the subjects of the final exams vary with the grade level. For primary school students, academic performance is measured by three subjects: Chinese language, mathematics and English language. For junior high school students (13–15 years old), academic performance is usually based on the exam results of 8 modules: Chinese, Mathematics, English, History, Geography, Biology, Physics and Chemistry. For high school students (16+ years old), Chinese language, mathematics and English language are compulsory subjects, and, in addition to these, they can choose between the liberal arts category (history, politics, geography) and science category (biology, chemistry, physics). Therefore, the final exam consists of 6 tests in total. Although there may be deviations in the examination subjects among different provinces, all the examination data are aligned with the Chinese standard examination rules, which are scientific and comparable. All the data were exported through the official database of the student’s school to ensure the authenticity and validity of the results. At the same time, academic performance was converted into z-scores to reduce errors in the evaluation process, since course schedules and evaluation criteria vary between grade levels. The -z-score was used as the dependent variable, to make academic performance more direct.
Predictor: daily activity profile
The average daily activity durations of the participants, encompassing sleep, exercise, screen usage, video games, short video viewing, and extracurricular reading, were collected through self-reports. In the TAHS, participants provided information about their typical bedtime and wake-up times from Monday to Friday during the month immediately preceding the questionnaire, which was then utilized to calculate their average sleep duration. – e.g. ‘During the past month, from Monday to Friday, when did you usually go to bed?’ (1 = 21:00,9 = 1:00 and later). Participants also reported the number of days in which they engaged in 1 hour or more of physical activity in the week preceding the questionnaire. There were seven options for this measure, ranging from 1 day to 7 days. Physical activity time was defined as the number of days per week with more than 60 minutes of physical activity divided by the number of days in a week (7). In addition, time for total screen usage, video games (e.g. playing games on a computer or a mobile phone), short video viewing (e.g. TikTok, Kuaishou, Red, etc.), and extracurricular reading were also reported. The above four behaviours were assessed through questions that included 17 options ranging from 0–8 hours or more, with each option containing a 30-minute time interval. For instance, 1 = 0–0.5 hours, 2 = 0.5–1 hours, and 17 = 8 hours or more. In recent months, on average, how much time did you spend per day looking at electronic devices (cell phone, computer, tablet, TV, etc.)? (1 = 0–0.5 hours, 17= more than 8 hours). In addition, according to the Chinese education curriculum for primary and secondary school students, daily classroom time is 6 hours for primary school students and 8 hours for secondary school students. Thus, the average daily time spent under classroom instruction was obtained from the subjects’ grade level.
Covariates
In this study, objective socioeconomic status (OBSES), subjective-family social class (SFSC), ethnicity, number of siblings, gender, and age were included as covariates in the regression model. Objective socioeconomic status was defined as the PCA composite of the participant’s self-reported number of computers and vehicles owned by the household and the highest education level of parents. Subjective-family social class was measured using the MacArthur scale (Adler et al., Citation2000), with scores ranging from 1–10. Other variables were measured from the participants’ self-reported data.
Statistical analysis
All statistical analyses were performed using RStudio version 4.0.5, and the test p-value was set at 0.05. First, standard descriptive statistics were performed on the participants’ general information. Then, the data were statistically processed using compositional data analysis. The relevant methods were implemented using R Packages Compositions (Van den Boogaart & Tolosana-Delgado, Citation2008), Since participants self-reported time estimations for each activity, the sum of time for all activities did not always equate to 24 hours. Hence, the activities mentioned above were adjusted through compositional proportions before commencing data analysis, ensuring that the total minutes allocated to activities in a day for each participant summed up to 1,440 minutes.
The compositional analysis process was divided into two parts: descriptive statistics and regression analysis (Chong et al., Citation2021). In the first section, sleep duration, exercise, video games, short videos, extracurricular reading, and classroom instruction were taken into account (totalling 1,440 minutes), with the application of geometric averages to more accurately represent concentrated patterns in the data. In addition, log-variance matrices of the ratios between variables were analysed to reflect co-dependence and discrete trends between variables. Additionally, the data were checked for the presence of 0 values to ensure logarithmic transformation. All participants engaged in each activity, eliminating the need for minimum value substitution.
In the second section, regression was used to examine the association between activities and academic performance. First, all the above variables were transformed into six sets of three-dimensional isometric log ratio (ilr) coordinates to avoid multicollinearity of the data. The relevant ilr data were entered as an exposure variable into general linear regression models for analysis; covariates such as OBSES, SFSC, number of siblings, gender, and age were also indexed. All models were checked for linearity, normality residuals, and outliers to ensure that statistical assumptions were not violated. Baseline regressions were conducted, which assessed the time allocated to each activity in relation to academic performance. This analysis aimed to compare the variations in outcomes between single-time activities and combinations of time spent on various activities. Furthermore, the isotemporal substitution model was used to predict changes in academic performance that would result in sleep time allocation to another activity (or vice versa) while holding time for other activities constant. The minimum unit of time allocation was set to 5 minutes, and substitution predictions of up to ±60 minutes were fitted (if the replaceable time of an activity was less than 60 minutes, the maximum value was used instead).
Results
Descriptive statistics
The descriptive results of subjects’ daily time for each activity are shown in . The total sample consisted of 91,161 participants who provided valid data. Overall, compared to secondary school students, primary school students spent more time sleeping, exercising, and reading, and less time under classroom instruction and using screens.
Once the covariates, including OBSES, SFSC, ethnicity, number of siblings, gender, and age, were taken into account, the association between time allocation and academic performance is presented in . The ilr regression coefficients for all compositional variables were significant, except for extracurricular reading in primary school students with sleep restriction and short videos in secondary school students with sufficient sleep, but in inverse directions. For primary school groups, sleep was the strongest predictor. For sleep-restricted primary students, increased sleep time was the most beneficial to their academic performance (γ = 0.687, 95%CI=[0.406,0.967], p < 0.001). However, For primary school students who get enough sleep, 9+ hours of increased sleep time strongly predicted lower academic performance (γ=-1.012, 95%CI=[−1.323,-0.701], p < 0.001), with a regression coefficient nearly 9 times stronger than physical activity. For all primary school students, increased sports activity (γA = 0.113, 95%CIA=[0.08,0.145], pA < 0.001, γB = 0.111, 95%CIB=[0.086,0.136], pB < 0.001), reading (γB = 0.037, 95%CIB=[0.013,0.061], pB = 0.002) and total screen time (γA = 0.068, 95%CIA=[0.028,0.108], pA < 0.001, γB = 0.035, 95%CIB=[0.002,0.067], pB = 0.036), accompanied by reduced time for other activities, positively predicted good academic performance, but to a smaller extent. Increased time spent playing video games (A = −0.159, 95%CIA=[−0.205,-0.114], pA = 0.001, B = −0.197, 95%CIB=[−0.236,-0.158], pB < 0.001) and watching short-videos (A = −0.116, 95%CIA=[−0.159,-0.072], pA < 0.001, B = −0.106, 95%CIB=[−0.144,-0.068], pB < 0.001) significantly predicted lower levels of academic performance.
Table 2. Compositional regression model estimates for academic performance.
For the secondary school cohort, more sleep (γC = 0.403, 95%CIC=[0.287,0.519], pC < 0.001, γD = 0.058, 95%CID=[0.63,1.31], pD < 0.001) and less gaming (C = −0.131, 95%CIC=[−0.152,-0.11], pC < 0.001, D = −0.169, 95%CID=[−0.199,-0.138], pD < 0.001) predicted higher academic performance. However, the relationship between other activities and academic performance varied based on whether or not students had sufficient sleep duration patterns. After gaining enough sleep, exercise (γD = 0.104, 95%CID=[0.077,0.132], pD < 0.001) and participation in extracurricular reading (γD = 0.763, 95%CID=[0.533,0.993], pD < 0.001) were positively predictive of good academic performance. Time spent watching short videos was not significantly associated with academic performance (γD = 0.007, 95%CID=[−0.016,0.03], pD = 0.535). However, for individuals with insufficient sleep duration patterns, all components except total screen usage(γC = 0.02, 95%CIC=[0.003,0.038], pC = 0.019) predicted poorer academic performance.
Comparison of the baseline regression model with the CoDA regression model revealed significant differences in certain items. For example, for the primary school student group with adequate sleep, the baseline regression model showed that more sufficient sleep duration patterns predicted higher academic performance (β = 0.7, 95%CI=[0.38,1.02], p < 0.001), while the CoDA model showed the opposite prediction (γ=-1.012, 95%CI=[−1.323,-0.701], p < 0.001). The above example demonstrates the statistical errors introduced when utilizing results from ordinary regression analysis.
Result of time re-allocation
Considering the significant coefficient for sleep in the regression model, an isotemporal substitution model was employed to estimate the specific improvements in performance that would occur by reallocating time from sleep to another activity, or vice versa. For the ‘sleep-restricted’ groups, the study focused more on how to allocate time from other activities to sleep, while for the ‘sleep-adequate groups’, the study focused on allocating extra sleep time to other activities. Since student school hours cannot be changed due to the constraints imposed by educational policies, they are not included in the subsequent model. shows the result of time allocation for the four groups. It can be understood that for sleep-restricted groups, reducing time spent playing games and watching short videos and substituting with sleep can maximize student academic performance. For the ‘sleep-restricted’ group of primary school students, substituting 55 minutes of playing video games per day with sleep time could improve the -z-score by 0.462, which is equivalent to an overall ranking improvement of 17.80%. For ‘sleep-restricted’ secondary school students, replacing 50 minutes of gaming with sleep time boosted -z-scores by 0.348, which equates to a 13.61% increase in the overall ranking. When substituting sleep time for another activity, exercise may be the best option. For the ‘sleep-adequate’ groups, reducing sleep time and increasing exercise time were the greatest predictors of higher academic performance, with 0.187 -z-scores for the primary ‘sleep-adequate’ group (7.42% ranking), and 0.11 -z-scores for the secondary ‘sleep-adequate’ group (4.38% ranking).
Figure 1. Sleep isochronous alternate figure of primary school students.
Figure 2. Sleep isochronous alternate figure of secondary school students.
Discussion
This study applied Compositional Data Analysis to investigate the connection between time allocation and academic performance in Chinese adolescents aged 9–20 years. In light of the widespread prevalence and negative consequences of sleep restriction, sleep duration served as the primary allocation variable in the analysis. Descriptive statistics uncovered prevalent patterns of reduced sleep duration within the sample, with sleep restriction being notably high among secondary school students, reaching 68.28%, which was much higher compared to the primary school group (32.32%). In addition, the 24-hour behavioural composition framework exhibited a notable correlation with individual academic performance, while re-allocating different activities to sleep (or vice versa) was a significant predictor of changes in academic performance. Findings from various subgroups indicate that this relationship might be influenced by students’ academic grades and sleep duration.
Of the above five activities, playing video games was shown to have the most negative impact on academic performance. In the sleep-restricted group, reducing the amount of time spent on video games and replacing it with sleeping time was most suitable to maximize students’ academic performance, contrary to the results of many studies showing that video game time is not associated with academic performance (Brunborg et al., Citation2014; Skoric et al., Citation2009). However, at the same time, studies also have shown that playing video games can negatively affect the academic performance of male adolescents and interfere with their reading and writing skills (Weis & Cerankosky, Citation2010). We believe the reason for this may be that substantial video game usage drains adolescents’ energy, increasing fatigue and reducing the concentration to a degree that students are less able to engage in academic endeavours.
The second most negatively impactful activity on academic performance was watching short videos. Some studies have found that passive screen usage (e.g. watching short videos) may cause higher levels of depression than active screen usage (e.g. playing video games and socializing online) (Kim et al., Citation2020). The results of the present study suggest that active screen usage may be the more negatively impactful activity on student academic performance. We inferred that playing games and socializing online may require more cognitive resources than passive screen use, making adolescents more likely to be overwhelmed with their studies and consequently drive lower academic performance. Moreover, sleep played a moderating role in the connection between the total time spent on-screen usage and academic performance. For the ‘sleep-restricted’ group, allocating sleep time to screen usage predicted poorer academic performance, but for the ‘sleep-adequate’ group, increased screen usage positively predicted academic performance, as did extracurricular reading in the same context.
Other than adequate sleep among secondary school students, exercise had the most pronounced positive influence on students’ academic performance. This implies that trading sleep time for exercise could improve students’ academic performance but only when they obtain sufficient sleep. However, exercise in a sleep-restricted state lowered academic performance in the secondary school student population. This finding once again demonstrates the importance of holistic time allocation and illustrates the crucial role of designing an adequate sleep schedule. Thus, despite numerous studies showing the benefits of physical activity on students’ academic development and psychological well-being (Donnelly et al., Citation2016), the results of this study suggest that promoting exercise behaviours should be encouraged, but only if adequate sleep is assured.
The positive impact of extracurricular reading on academic performance has been widely studied (Mol & Bus, Citation2011), but it is worth noting that this study found reading to be a relatively small predictor of student academic performance. At the primary school level, the positive academic impact of reading was even smaller than screen usage and much lower than that of sports. Considering that Chinese students prefer books that have little relevance to subject exams during extracurricular reading time, such as magazines and bestsellers, we deem this as a reasonable finding (Chen, Citation2007). Another reason for this might be the heavy academic pressure on Chinese primary and secondary school students. We believe that after-school tutoring may not yield the expected results, considering the already prevalent intense academic pressure in Chinese society. Conversely, maintaining a healthy routine and incorporating appropriate exercise may hold greater significance.
Subgroup tests on the sample indicated that increasing time spent playing sports can positively predict positive academic performance in primary school students regardless of the suitability of sleep duration. Given this finding, primary school students should engage in as much physical activity as possible. Considering that excessive sleep can encroach upon the time allocated for other activities, sleeping for more than 9 hours might harm academic performance. For secondary school students with insufficient sleep duration patterns, all other activities seem to be able to significantly harm their academic performance, thus it is essential to emphasize the crucial importance of ensuring sufficient sleep for this particular group. In that regard, once sufficient sleep is ensured, investing more time in physical activity and extracurricular reading is the best option.
Strengths and limitations
In utilizing a Compositional Data Analysis approach, this study is one of the first to address how activity time allocation influences adolescent academic performance, while also considering alternative activity scheduling. This study further categorized sedentary behaviours into five distinct categories and examined the effects of alterations in total screen time, video game playing, short video viewing, reading, and exercise duration on students’ academic performance, with consideration of both adequate and insufficient sleep conditions. These more refined categories allow us to explore time allocation in a manner more consistent with realistic life planning, thus providing clear directions for practice. Additionally, the large sample size allows researchers to predict differences in academic performance more fully and accurately by assessing student activity time allocation.
There are a few limitations of this study. First, cross-sectional data prevented this study from identifying specific temporal relationships between time allocation and academic performance. Future research should incorporate more longitudinal data to determine the relationship between time allocation and academic performance. Second, data collected by self-report may hold self-report and estimation biases (e.g. certain data time totals exceeded 24 hours). In this case, we scaled and corrected data according to the model principles. However, self-report errors remain. Future studies should incorporate additional objective measurements to obtain more accurate data records. Finally, our inclusion of 24-hour activities was incomplete, thus future studies could further refine and amplify various types of activities to further explore the distributional relationships among the components.
Practical implications
Adequate sleep is an important factor in promoting academic performance. Additionally, researchers have also found that exercise, reading, and screen usage are also associated with academic performance. Although single factors can be closely associated with academic performance, the impact of time allocation changes on other activities remains unclear, especially when considering adequate sleep levels. In summary, this study is the first to examine how different activity time allocation strategies impact academic performance and suggest healthier best practices.
Conclusions
This study used compositional data analysis to explore a 24-hour integrated life management approach to improve adolescent academic performance. Principally, satisfying adequate daily sleep was of primary importance. Additionally, daily exercise significantly facilitated higher academic performance. However, engaging in exercise when sleep-deprived may have detrimental effects. The most effective way to improve student academic performance seems to be reducing the time spent playing video games and watching short videos and replacing it with sleep, which improved the overall rankings of the sleep-restricted group by 13.61%-17.80%.
Author Contributions
Kaiping Peng had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Peng Zhang.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Yifan Wang, Xuan Chen.
Manuscript revision/review comments reply: Yifan Wang, Xuan Chen
Critical revision of the manuscript for important intellectual content: Yifan Wang, Xuan Chen
Statistical analysis: Wei Yan
Obtained funding: Kaiping Peng
Administrative, technical, or material support: Kaiping Peng
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
References
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