Next Article in Journal
Palliative Care Consultation Services on Terminally Ill Cancer Patients and Non-Cancer Patients: Trend Analysis from a 9-Year-Long Observational Study in Taiwan
Next Article in Special Issue
Evaluation of the Effect of a Live Interview in Journalism Students on Salivary Stress Biomarkers and Conventional Stress Scales
Previous Article in Journal
The COVID-19 Pandemic: A Longitudinal Study on the Emotional-Behavioral Sequelae for Children and Adolescents with Neuropsychiatric Disorders and Their Families
Previous Article in Special Issue
Psychological Vulnerability of French University Students during the COVID-19 Pandemic: A Four-Wave Longitudinal Survey
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 18
10.3390/ijerph18189881
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association of Sedentary Behavior and Physical Activity with Depression in Sport University Students

by
Huixuan Zhou
1,*,
Xiaotong Dai
1,
Litian Lou
1,
Chan Zhou
2 and
Wei Zhang
3,*
1
Key Laboratory of the Ministry of Education of Activity and Physical Fitness, School of Sport Science, Beijing Sport University, Beijing 100084, China
2
School of Education, Beijing Sport University, Beijing 100084, China
3
China National Institutes for Food and Drug Control, Beijing 102629, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(18), 9881; https://doi.org/10.3390/ijerph18189881
Submission received: 5 August 2021 / Revised: 14 September 2021 / Accepted: 18 September 2021 / Published: 19 September 2021
(This article belongs to the Special Issue Factors Associated with the Psychological Well-Being of University Students: A Preventive View)
Versions Notes

Abstract

:
Background: Sports university students are usually expected to lead an active life and have a lower risk of depression. Therefore, there are few studies on depression and its risk factors among this population. This study aimed to investigate depression and its association with sedentary behavior and physical activity in sports university students. Methods: A cross-sectional survey was conducted among undergraduates majoring in physical education in a sports university in Beijing in March 2021. Students were asked about sociodemographic information, domain-specific sedentary behavior, physical activity, and depression (using a nine-item Patient Health Questionnaire). Chi-squared test and logistic regression were carried out to analyze the data. Results: Among a total of 584 participants, the detection rate of depression was 49.1%. The median of total sedentary time was 7.29 h per day. After adjusting for covariates, recreational screen time (OR = 1.540, p = 0.035), sedentary time spent completing schoolwork (OR = 0.658, p = 0.038), and participation in vigorous physical activity everyday (OR = 0.415, p = 0.001) and a few times per week (OR = 0.423, p < 0.001) were significantly associated with depression. Conclusions: Sports university students are not immune to depression and inactive lifestyles. Excessive recreational screen time may have an adverse effect on depression, which is somewhat independent of physical activity.

1. Introduction

University students are in a transitional period of growth, and the change of environment and the expectations of family and society for higher education imposes stress on university students, making some of them suffer from mental health problems. Depression is one of the common mental health problems among university students [1,2], which may have an adverse impact on their academic performance and their future development [3]. Various health behaviors have been indicated as risk factors of depression among this population. Due to the nature of the higher education system, sedentary behavior (SB) and lack of physical activity (PA) are some of them.
SB is defined as a person in a sitting or reclining posture requiring an energy expenditure lower than 1.5 metabolic equivalents [4]. A recent systematic review showed that university students spend 7.29 h per day in sedentary activity based on self-reported data [5]. Compared to the general young adult population, a considerable proportion of university students engage in higher levels of SB [5]. The SB levels of some university students even surpass those of desk-based workers [6]. Excessive SB has been found to be associated with an increased risk of depression according to a meta-analysis [7]. Among various SB domains, self-reported screen time has been found to be significantly associated with adverse mental health outcomes, while there is no significant association of mental health outcomes with sitting time spent at work and transportation [7,8].
PA has been shown to be associated with depression among university students [7,9,10,11,12,13,14]. Some evidence suggests that high levels of PA can play a protective role as a counter to the negative effects of SB on all causes of mortality [15], and on self-reported mental and social health outcomes in some regions [16]. However, some studies show that SB is a risk factor somewhat independent of PA for depression in university students [17,18], and for multiple adverse health outcomes in adults [7,19,20].
Sport university students are expected to lead an active lifestyle. High levels of PA and low levels of SB have been believed to be associated with a positive self-concept and reductions in mental health problems, such as depression and anxiety [21]. As a result, SB and PA in this population and their association with depression have received little attention. For studies focusing on this topic, the samples mainly originate from comprehensive university students [7,17,18]. Although some studies have conducted surveys on the psychological problems of university athlete students, analyses of related factors are dominated by sociodemographic factors [22,23,24,25]. Moreover, some systematic reviews showed that the prevalence of depression and other mental health symptoms during the COVID-19 pandemic was higher than before its outbreak in the pooled global population [26], and that university students in China suffered from a higher prevalence rate of depression during the pandemic than in normal circumstances [27]. Therefore, the depression of students of sports universities in China also warrants attention after the outbreak of COVID-19.
To this end, our study investigated depression, SB, and PA of students of a sport university and analyzed the association of depression with total and domain-specific SB and PA so as to provide a profile of depression and SB among this population and a reference for potential interventions in their lifestyle.

2. Methods

2.1. Participants

A cross-sectional survey was conducted on undergraduates majoring in physical education, a typical discipline in Beijing Sport University in March 2021, which included athlete students of physical education courses and non-athlete students of sport science courses. Undergraduates in grades 1 to 3 were asked to complete an online self-administered questionnaire. Senior students were excluded because they had been in internships and had not led a typical school life.

2.2. Instruments

Measures for SB, PA, depression and demographic information were included in the online questionnaire.
Participants were asked to report their SB (h per day) across several domains: attending class, completing schoolwork, recreational screen time, socializing, performing hobbies, and any other sedentary activities over the last 7 days.
PA was investigated by asking the frequency of participation in vigorous physical activity (VPA refers to activity requiring significant physical effort and making someone breathe much harder than normal). Participants indicated their participation level by checking one of the following categories: every day, a few times a week, a few times a month, a few times a year, and under no circumstances.
Depression was measured using a nine-item Patient Health Questionnaire (PHQ-9). The responses were divided into four levels according to the frequency of symptoms in the past two weeks: never = 0 points, several days = 1 point, more than half of the time = 2 points, and almost every day = 3 points. The total score of the scale was 0 to 27 points. Those scoring 5 and above had depressive symptoms [28]. This scale showed good validity in a previous study among Chinese university students [29,30]. The Cronbach’s alpha value of these nine items was 0.89, which was larger than 0.70 and showed good reliability in this survey [31].
Demographic information included gender, grade, profession, place of hometown, whether only child or not, parents’ marital status and monthly family income, body mass index (BMI), smoking and drinking. BMI was calculated based on self-reported weight and height (kg/m2). According to the Chinese Guideline for Overweight and Obesity Prevention, BMI ≥ 24 kg/m2 was defined as overweight.

2.3. Procedure

The counselors were asked to provide the QR code for the online questionnaire in the WeChat group of each class. They provided and sent reminders a total of three times. Undergraduates completed the questionnaire anonymously and of their own free will. Informed consent was provided via the homepage of the online survey and was obtained when participants completed the survey. This study protocol was approved by the Ethics Committee of Sport Science Experiment, Beijing Sport University (2020046H).

2.4. Design and Data Analysis

The data were analyzed using the Statistical Package of the Social Sciences (SPSS Version 20; IBM Inc., Armonk, NY, USA). The median, inter-quartile range (IQR), mean, and standard deviation (SD) of total and domain-specific sedentary time were presented. Then, they were treated as dichotomous variables based on the median. The mean was used when comparing sedentary time with the other research reported in the Discussion section of this paper. The detection of depression, VPA, and demographic information was also treated categorically and was presented as number (%). The between-group differences were tested by the Chi-squared test (Bonferroni method). The association of depression with total and domain-specific SB and VPA was examined by a stepwise backward logistic regression, after adjusting for demographic information. Odds ratios (ORs) with 95% confidence intervals (CIs) were presented. The significance level was p < 0.05.

3. Results

3.1. Basic Characteristics of Participants by Depression

A total of 584 participants completed the questionnaire and were included in the analysis. The response rate was 33.4% among athlete classes and 74.3% among non-athlete classes. The basic characteristics of participants are shown in Table 1, stratified by the detection of depression.
Most participants were male students (59.8%), 42.3% of the sample were freshmen, 61.8% were athlete students, 54.8% came from urban places, 57.9% were an only child in their family, 11% were overweight (BMI ≥ 24), 14.9% reported a smoking habit, and 61.6% reported a drinking habit (Table 1).
Among the participants, the detection rate for depression was 49.1%. The depression rates among female students, junior students, non-athlete students, and students with divorced parents were higher than those among male students (p < 0.001), freshmen and sophomores (p = 0.036), athlete students (p = 0.005), and students with married parents (p = 0.042) (Table 1).

3.2. Sedentary Behavior and Physical Activity

The time spent in SB is shown in Table 2. The median of total sedentary time was 7.29 h per day. For domain-specific SB, the median time was 2.14 h in attending class, 1.00 h in completing schoolwork, 1.43 h in recreational screen activities, 0.57 h in socializing, 0.29 h in performing hobbies, and 0.29 h in other sedentary activities.
In the following analysis, the total and domain-specific SB were treated as dichotomous variables based on the median, and the frequency of participating in VPA was regrouped into three categories for the balance of distribution in each subgroup.
The characteristics of the participants stratified by level of total sedentary time (≥7.29 h and <7.29 h) are presented in Table 3. Compared with females, non-athlete students, students from urban places, and students who were the only child in their family, there were higher proportions of male students, athlete students, students from rural places, and students with siblings reporting sedentary time less than 7.29 h per day (p < 0.001). Students from a high-income family (above 9000 CNY per month) were more likely to have longer sedentary time than students from families with lower incomes (less than 6000 CNY, p = 0.001). Students participating in VPA a few times a month or less were more likely to report longer sedentary time than students participating in VPA more frequently (p < 0.001).
Stratified by the detection of depression, the levels of SB and frequency of VPA of the participants are shown in Table 4. The participants who spent more than 2.14 h in class, more than 1.00 h recreational screen time, and had a total sedentary time of more than 7.29 h demonstrated higher depression rates than those who spent less class time (p = 0.009) and had less screen time (p = 0.008) and less total sedentary time (p = 0.036). Students participating in VPA a few times per month or fewer presented higher depression rates than those reporting more frequent VPA participation (p < 0.001).

3.3. Multivariate Regression Analysis of Depression, Sedentary Behavior, and Physical Activity

After adjusting for covariates, participants who spent longer on recreational screen time were more likely to be detected with depression (OR = 1.540, p = 0.035), while those spending more time completing schoolwork were less likely to be detected with depression (OR = 0.658, p = 0.038). Compared with students who participated in VPA a few times a month or less, those who participated in VPA every day (OR = 0.415, p = 0.001) and a few times per week (OR = 0.423, p < 0.001) were less likely to be detected with depression. The total sedentary time and time spent in attending class, socializing, performing hobbies, and other sedentary activities were not significantly associated with the detection of depression (Table 5).

4. Discussion

4.1. Detection of Depression in Students of a Sports University

In our study, the detection rate of depression among students of a sports university majoring in physical education was 49.1%. These results are higher in comparison with the overall detection rate of depression among Chinese university students (28.4%) [3], global university students (30.6%) [32] and medical students (27.2%) [33], presented in several systematic reviews and also higher than the results of some surveys on students from comprehensive universities in China (ranging from 29.7% in Guangdong [34] to 46.0% in Tibet [35]). However, the prevalence rate of depression in our study is lower than that in a survey on university students in China, which was measured during the pandemic (60.5%) [36].
Potential explanations for the prevalence of depression in this study are as follows. First, unexpected demands for both academic and athletic achievements may make students overwhelmed. Non-athlete students of sport science were required to master one sport skill in addition to a heavy academic burden. Athlete students have to withstand stress from physical training and competition [37]. Second, the COVID-19 pandemic may play a role in the depression symptoms of the participants. Guo et al. indicated that the COVID-19 pandemic has had a negative effect on the mental health of university students in China [27]. The cessation of competitive sport during and after the pandemic has increased the uncertainty in sport-related career development, which may put additional pressure on students of sports universities. However, the lockdown policy to counter the transmission of COVID-19 has seldom been implemented in China since May 2020, which may weaken the negative impacts of the pandemic on depression in university students in China. Therefore, the prevalence of depression in our study is relatively higher than those of studies conducted in normal circumstances but lower than that in the study conducted during the pandemic. Moreover, the selection of measures and investigation time may have had some effects on research findings.
Anywise, our research findings suggested that the depression symptoms of students of sports universities should be taken seriously, and measures should be taken according to the results of risk factor analysis.

4.2. Sedentary Behavior of Sports University Students

It was found in our study that sports university students spent on average 7.70 h per day in total SB, 2.22 h in attending class, and 2.30 h recreational screen time, which were close to the results in global university students in a recent meta-analysis (total sedentary time: 7.29 h, time in class: 2.23 h, screen time on TV, computer and video games: 2.26 h) [5]. Nevertheless, sports university students spent less time completing schoolwork than global university students (1.25 h versus 2.85 h).
Gender, PA, and obesity were potential correlates frequently examined by studies on university students. Our research found that, compared with female students, male students performed better in total sedentary time, which is consistent with the results of research among Ukrainian university students [38], freshmen in Tsinghua University in China [39], and students from a private university in Colombia [40]. However, there are a series of studies showing different results. For instance, Malmborg et al. [41] found that there was no significant difference in total sedentary time between male and female university students in Sweden, and Ruiz et al. [42] indicated that female students spent less time sedentary than male students in a Spanish university. The mixed results suggest that the correlation between gender and sedentary time varies from population to population.
There are mixed results regarding the interrelationship between SB and PA [16]. Our research showed that sports university students participating in VPA a few times a month or less spent more time in SB than those participating in VPA more than a few times a week. This result is consistent with the conclusion of a systematic review [43], and somewhat supports the theory that time spent in SB displacing time available for PA is the reason for the potential negative health effects of SB [21].
Our research found that being overweight was not associated with total sedentary time, which is contrary to most previous research [19,43]. The low proportion of overweight students in the sample from sports universities may have biased the results. Moreover, athlete students were found to spend less sedentary time than non-athlete students in our study, probably because they had a more intense training curriculum and are used to leading a more active life. Students who are an only child, from urban places, and have a wealthier family reported longer sedentary time, which indicated that the family environment may have some impact on SB, and the family members of students may need to be taken as a secondary target population in related interventions.

4.3. Association of Depression with Sedentary Behavior and Physical Activity

A meta-analysis of studies on the general population showed that SB is associated with an increased risk of depression, although the results of the individual studies were mixed [7]. In addition to the theory that SB may displace PA, which has been found to be beneficial to mental health [21], the underlying explanation also includes that prolonged sedentary time may prevent individuals from social interactions and hence increase their risk of depression [44]. Regarding domain-specific SB, excessive screen time has been found to be associated with an increased risk of depression among the general population [7]. However, recent calls have been made claiming that the use of screens may have some positive effects on health outcomes, depending on the purpose of screen use (e.g., educational vs. recreational, playing violent video games vs. active video games) [5,45,46]. However, evidence to support those calls is required, especially for the effects on mental health outcomes. Our study findings may shed some light on such views.
The results of the multivariate regression showed that recreational screen time was associated with an increased risk of depression among sports university students, while sedentary time spent completing schoolwork was associated with a decreased risk of depression; total sedentary time and other modalities of SB were not significantly associated with depression. Online education and paperless schoolwork have become increasingly popular among university students, especially after the breakout of the COVID-19 pandemic. Sports university students usually complete their schoolwork on computers or mobile phones. Our study indicated that sedentary time spent on educational purposes does not increase the risk of depression; time spent completing schoolwork may have a positive effect on reducing depression and only recreational screen time was associated with the increase in depression rate. Consistent with the view of some researchers [5,45], we suggest that rather than the total amount of sedentary time, the purpose of SB and screen use may be more important, and future investigations on SB and interventions targeting SB reduction among sports university students should pay closer attention to recreational screen time.
Moreover, our study showed that SB is a risk factor independent of VPA for depression among sports university students, which is consistent with most previous research taking depression as a health outcome [7,17,18]. Participating in PA at considerably higher than recommended levels may be able to decrease the mortality risk associated with SB [15]. However, our research suggested that frequent participation in VPA is associated with a decreased risk of depression, but is not sufficient to eliminate the adverse effects of excessive recreational screen time. Sports universities should pay attention to students’ mental health, and interventions targeting the reduction of recreational screen time and promotion of VPA should be warranted.

4.4. Limitations

This study has several limitations. First, the response rate in the questionnaire survey for athlete students was low, and the sample was from students in one sports university in Beijing. Therefore, the sample representative of sports university students needs to be considered. Second, the cross-sectional study design of this study could only analyze the association of depression with SB and PA, and could not make a causal inference. Third, the variables in our research were self-reported, which renders them vulnerable to subjectivity. The use of SB questionnaires is a developing field [18]. Generally, self-reported SB is lower than the results measured by an accelerator [5], but it is more applicable when investigating domain-specific SB. Fourth, according to the conclusions of previous studies [15,16], high levels of PA can play a role in combating the negative effects of SB on health outcomes, and, considering the length and acceptability of this self-administered questionnaire, our study only investigated the frequency of VPA to partly reflect high levels of PA in participants, instead of investigating PA using structured questionnaires. Therefore, the relationship of depression with mild to moderate PA was not explored in this study. Moreover, our study only focused on the detection of depression without investigating other psychological problems that may confront sports university students.
In the future, prospective cohort studies with a large-scale and more representative sample can be carried out on SB, PA, and psychological problems of sports university students to explore the causality between health behaviors and psychological problems and provide more evidence for the design of psychological interventions for this population.

5. Conclusions

By investigating depression, SB, and PA of sports university students, it was shown that depression is commonly found among this population, and their sedentary time is close to the level of global university students. Our results provide evidence of the association of depression with total and domain-specific SB by showing that only recreational screen time was associated with an increased risk of depression; educational sedentary time spent completing schoolwork was associated with a decreased risk of depression, and there was no relationship between depression and other modalities of SB. This study also supported that the effect of SB on depression is somewhat independent of the frequency of VPA. Sports university students are not immune to depression and inactive lifestyles. Their mental health and health behaviors require attention. Future research on the causal relationships between various reasons for SB, levels of PA, and depression are necessary for more targeted interventions among this population.

Author Contributions

Conceptualization, H.Z. and W.Z.; methodology, H.Z. and W.Z.; validation, X.D.; formal analysis, H.Z.; investigation, L.L.; writing—original draft preparation, H.Z.; writing—review and editing, W.Z.; supervision, C.Z.; project administration, H.Z.; funding acquisition, X.D. and H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Fundamental Research Funds for the Central Universities, Beijing Sport University (grant number 2020068 and 2021QN014).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Sport Science Experiment, Beijing Sport University (2020046H).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The data presented in this study are available on request to qualified researchers from the corresponding author.

Acknowledgments

The authors would like to acknowledge all the participants for their time and patience.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Acharya, L.; Lan, J.; Collins, W. College life is stressful today—Emerging stressors and depressive symptoms in college students. J. Am. Coll. Health 2018, 66, 655–664. [Google Scholar] [CrossRef]
  2. Liu, Y.A.; Zhang, N.A.; Bao, G.A.; Huang, Y.B.; Ji, B.A.; Wu, Y.A.; Liu, C.A.; Li, G.A. Predictors of depressive symptoms in college students: A systematic review and meta-analysis of cohort studies. J. Affect. Disord. 2019, 244, 196–208. [Google Scholar] [CrossRef]
  3. Gao, L.; Xie, Y.; Jia, C.; Wang, W. Prevalence of depression among Chinese university students: A systematic review and meta-analysis. Sci. Rep. 2020, 10, 15897. [Google Scholar] [CrossRef] [PubMed]
  4. Networ, S.B. Letter to the Editor: Standardized use of the terms “sedentary” and “sedentary behaviours”. Appl. Physiol. Nutr. Metab. 2012, 37, 540–542. [Google Scholar]
  5. Castro, O.; Bennie, J.; Vergeer, I.; Bosselut, G.; Biddle, S.J.H. How Sedentary Are University Students? A Systematic Review and Meta-Analysis. Prev. Sci. Off. J. Soc. Prev. Res. 2020, 21, 332–343. [Google Scholar] [CrossRef] [PubMed]
  6. Moulin, M.S.; Irwin, J.D. An Assessment of Sedentary Time Among Undergraduate Students at a Canadian University. Int. J. Exerc. Sci. 2017, 10, 1116–1129. [Google Scholar]
  7. Zhai, L.; Zhang, Y.; Zhang, D. Sedentary behaviour and the risk of depression: A meta-analysis. Br. J. Sports Med. 2014, 49, 705–709. [Google Scholar] [CrossRef] [PubMed]
  8. Felez-Nobrega, M.; Bort-Roig, J.; Briones, L.; Sanchez-Niubo, A.; Koyanagi, A.; Puigoriol, E.; Puig-Ribera, A. Self-reported and activPAL(TM)-monitored physical activity and sedentary behaviour in college students: Not all sitting behaviours are linked to perceived stress and anxiety. J. Sports Sci. 2020, 38, 1566–1574. [Google Scholar] [CrossRef] [PubMed]
  9. Feng, Q.; Zhang, Q.-l.; Du, Y.; Ye, Y.-l.; He, Q.-q. Associations of Physical Activity, Screen Time with Depression, Anxiety and Sleep Quality among Chinese College Freshmen. PLoS ONE 2014, 9, e100914. [Google Scholar] [CrossRef] [Green Version]
  10. Wu, X.; Tao, S.; Zhang, Y.; Zhang, S.; Tao, F. Low Physical Activity and High Screen Time Can Increase the Risks of Mental Health Problems and Poor Sleep Quality among Chinese College Students. PLoS ONE 2015, 10, e0119607. [Google Scholar]
  11. Di Benedetto, M.; Towt, C.J.; Jackson, M.L. A Cluster Analysis of Sleep Quality, Self-Care Behaviors, and Mental Health Risk in Australian University Students. Behav. Sleep Med. 2020, 18, 309–320. [Google Scholar] [CrossRef]
  12. Wickham, S.-R.; Amarasekara, N.A.; Bartonicek, A.; Conner, T.S. The Big Three Health Behaviors and Mental Health and Well-Being Among Young Adults: A Cross-Sectional Investigation of Sleep, Exercise, and Diet. Front Psychol. 2020, 11, 579205. [Google Scholar] [CrossRef] [PubMed]
  13. Li, T.; Wu, X.; Tao, S.; Yang, Y.; Xu, H. Study on the relationship between physical activity and physical, mental health of college students. Chin. J. Sch. Health 2020, 41, 867–870. (In Chinese) [Google Scholar]
  14. Wang, H. The influencing factors and suggestions on mental health of students majoring in Physical Education. J. Liaoning Norm. Coll. 2010, 12, 72–75. (In Chinese) [Google Scholar]
  15. Ekelund, U.; Tarp, J.; Steene-Johannessen, J.; Hansen, B.H.; Jefferis, B.; Fagerland, M.W.; Whincup, P.; Diaz, K.M. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: Systematic review and harmonised meta-analysis. BMJ 2019, 366, l4570. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Iannotti, R.J.; Janssen, I.; Haug, E.; Kololo, H.; Annaheim, B.; Borraccino, A. Interrelationships of adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. Int. J. Public Health 2009, 54, 191–198. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Jiang, L.; Cao, Y.; Ni, S.; Chen, X.; Shen, M.; Lv, H.; Hu, J. Association of Sedentary Behavior With Anxiety, Depression, and Suicide Ideation in College Students. Front. Psychiatry 2020, 11, 566098. [Google Scholar] [CrossRef] [PubMed]
  18. Xu, Z.; Xu, Q.; Wang, Y.; Zhang, J.; Liu, J.; Xu, F. Association of Sedentary Behavior and Depression among College Students Majoring in Design. Int. J. Environ. Res. Public Health 2020, 17, 3545. [Google Scholar] [CrossRef]
  19. Thorp, A.A.; Owen, N.; Neuhaus, M.; Dunstan, D.W. Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996–2011. Am. J. Prev. Med. 2011, 41, 207–215. [Google Scholar] [CrossRef] [PubMed]
  20. Dogra, S.; Stathokostas, L. Sedentary Behavior and Physical Activity Are Independent Predictors of Successful Aging in Middle-Aged and Older Adults. J. Aging Res. 2012, 2012, 190654. [Google Scholar] [CrossRef] [Green Version]
  21. Biddle, S.; Asare, M. Physical activity and mental health in children and adolescents: A review of reviews. Br. J. sports Med. 2011, 45, 886–895. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Tran, A. Looking forward to student-athlete mental health: Racial/ethnic trends from 2010 to 2015. J. Am. Coll. Health 2020, 26, 1–9. [Google Scholar] [CrossRef]
  23. Xanthopoulos, M.S.; Benton, T.; Lewis, J.; Case, J.A.; Master, C.L. Mental Health in the Young Athlete. Curr. Psychiatry Rep. 2020, 22, 63. [Google Scholar] [CrossRef] [PubMed]
  24. Garver, M.J.; Gordon, A.M.; Philipp, N.M.; Huml, M.R.; Wakeman, A.J. Change-Event Steals “Athlete” from “College Athlete”: Perceived Impact and Depression, Anxiety, and Stress. J. Multidiscip. Healthc. 2021, 14, 1873–1882. [Google Scholar] [CrossRef]
  25. Wolanin, A.; Hong, E.; Marks, D.; Panchoo, K.; Gross, M. Prevalence of clinically elevated depressive symptoms in college athletes and differences by gender and sport. Br. J. Sports Med. 2016, 50, 167–171. [Google Scholar] [CrossRef] [Green Version]
  26. Nochaiwong, S.; Ruengorn, C.; Thavorn, K.; Hutton, B.; Awiphan, R.; Phosuya, C.; Ruanta, Y.; Wongpakaran, N.; Wongpakaran, T. Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: A systematic review and meta-analysis. Sci. Rep. 2021, 11, 10173. [Google Scholar] [CrossRef] [PubMed]
  27. Guo, S.; Kaminga, A.C.; Xiong, J. Depression and Coping Styles of College Students in China During COVID-19 Pandemic: A Systemic Review and Meta-Analysis. Front. Public Health 2021, 9, 613321. [Google Scholar] [CrossRef]
  28. Spitzer, R.L.; Kroenke, K.; Williams, J.; Patient Health Questionnaire Primary Care Study Group. Validation and utility of a self-report version of PRIME-MD: The PHQ Primary Care study. JAMA 1999, 282, 1737–1744. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Xu, H.; Wu, X.; Tao, S.; Zou, L. Beverages consumption, sleep quality and depressive symptoms in Chinese university students: A latent variable mediation model. Chin. J. Sch. Health 2020, 41, 16–20. (In Chinese) [Google Scholar]
  30. Xie, Y.; Wu, X.; Tao, S.; Yang, Y. Mediating effect of sleep quality on the relationship between daytime sleepiness and depressive symptoms of college students. Mod. Prev. Med. 2020, 47, 4121–4124. (In Chinese) [Google Scholar]
  31. Streiner, D.L.; Norman, G.; Cairney, J. Health Measurement Scales: A Practical Guide To Their Development and Use; Oxford University Press: Oxford, UK, 2015. [Google Scholar]
  32. Ibrahim, A.K.; Kelly, S.J.; Adams, C.; Glazebrook, C. A systematic review of studies of depression prevalence in university students. J. Psychiatr. Res. 2013, 47, 391–400. [Google Scholar] [CrossRef] [PubMed]
  33. Rotenstein, L.S.; Ramos, M.A.; Torre, M.; Segal, J.B.; Mata, D.A. Prevalence of Depression, Depressive Symptoms, and Suicidal Ideation Among Medical Students: A Systematic Review and Meta-Analysis. JAMA 2016, 316, 2214–2236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Xu, Y.; Qi, J.; Yang, Y.; Wen, X. The contribution of lifestyle factors to depressive symptoms: A cross-sectional study in Chinese college students. Psychiatry Res. 2016, 245, 243–249. [Google Scholar] [CrossRef]
  35. Gao, L.; Li, F.; Gao, X. Relationship between sleep quality with depression and anxiety symptoms in college students at Tibet Plateau areas. Chin. J. Sch. Health 2021, 42, 593–596. (In Chinese) [Google Scholar]
  36. Zhao, B.; Kong, F.; Aung, M.N.; Yuasa, M.; Nam, E.W. Novel Coronavirus (COVID-19) Knowledge, Precaution Practice, and Associated Depression Symptoms among University Students in Korea, China, and Japan. Int. J. Environ. Res. Public Health 2020, 17, 6671. [Google Scholar] [CrossRef] [PubMed]
  37. Yang, J.; Peek-Asa, C.; Corlette, J.D.; Cheng, G.; Foster, D.T.; Albright, J. Prevalence of and risk factors associated with symptoms of depression in competitive collegiate student athletes. Clin. J. Sport Med. Off. J. Can. Acad. Sport Med. 2007, 17, 481–487. [Google Scholar] [CrossRef] [PubMed]
  38. Bergier, J.; Bergier, B.; Tsos, A. Variations in physical activity of male and female students from the Ukraine in health-promoting life style. Ann. Agric. Environ. Med. 2017, 24, 217–221. [Google Scholar] [CrossRef] [PubMed]
  39. Yu, H.; Yu, M.; Gordon, S.P.; Zhang, R. The association between ambient fine particulate air pollution and physical activity: A cohort study of university students living in Beijing. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 136. [Google Scholar] [CrossRef]
  40. Henao, R.; Arevalo, M. Perceived barriers in university students to physical activity practice. Rev. Cuba. De Salud Publica 2016, 42, 61–69. [Google Scholar]
  41. Malmborg, J.; Bremander, A.; Olsson, M.C.; Bergman, S. Health status, physical activity, and orthorexia nervosa: A comparison between exercise science students and business students. Appetite 2017, 109, 137–143. [Google Scholar] [CrossRef] [PubMed]
  42. Ruiz, G.; Vicente, E.D.; Vegara, J. Sedentary behavior and physical activity levels in university students and workers. J. Sport Health Res. 2012, 4, 83–92. [Google Scholar]
  43. Castro, O.; Bennie, J.; Vergeer, I.; Bosselut, G.; Biddle, S.J.H. Correlates of sedentary behaviour in university students: A systematic review. Prev. Med. 2018, 116, 194–202. [Google Scholar] [CrossRef] [PubMed]
  44. Kraut, R.; Patterson, M.; Lundmark, V.; Kiesler, S.; Scherlis, W. Internet paradox. A social technology that reduces social involvement and psychological well-being? Am. Psychol. 1998, 53, 1017. [Google Scholar] [CrossRef] [PubMed]
  45. Bell, V.; Bishop, D.V.M.; Przybylski, A.K. The debate over digital technology and young people. BMJ 2015, 351, h3064. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Gao, Z.; Chen, S.; Pasco, D.; Pope, Z. A meta-analysis of active video games on health outcomes among children and adolescents. Obes. Rev. Off. J. Int. Assoc. Study Obes. 2015, 16, 783–794. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Characteristics of participants by depression, n (%).
Table 1. Characteristics of participants by depression, n (%).
VariableTotalDepressionNo Depressionp
Gender <0.001
Male349 (59.8)150 (43.0)199 (57.0)
Female235 (40.2)137 (58.3)98 (41.7)
Grade 0.036
Freshman247 (42.3)112 (45.3)135 (54.7)
Sophomore212 (36.3)101 (47.6)111 (52.4)
Junior125 (21.4)74 (59.2) *51 (40.8) *
Profession 0.005
Athlete361 (61.8)161 (44.6)200 (55.4)
Non-athlete223 (38.2)126 (56.5)97 (43.5)
Place of hometown 0.902
Rural264 (45.2)129 (48.9)135 (51.1)
Urban320 (54.8)158 (49.4)162 (50.6)
Only Child 0.392
Yes246 (42.1)126 (51.2)120 (48.8)
No338 (57.9)161 (47.6)177 (52.4)
Monthly family income 0.666
Below 3000 CNY102 (17.5)54 (52.9)48 (47.1)
3000–5999 CNY184 (31.5)93 (50.5)91 (49.5)
6000–8999 CNY124 (21.2)56 (45.2)68 (54.8)
Above 9000 CNY174 (29.8)84 (48.3)90 (51.7)
Parents’ Marital Status 0.042
Married533 (91.3)255 (47.8)278 (52.2)
Divorce51 (8.7)32 (62.7)19 (37.3)
Overweight 0.083
Yes (BMI ≥ 24)64 (11.0)38 (59.4)26 (40.6)
No (BMI < 24)520 (89.0)249 (47.9)271 (52.1)
Smoking 0.055
Yes87 (14.9)51 (58.6)36 (41.4)
No497 (85.1)236 (47.5)261 (52.5)
Drinking 0.600
Yes360 (61.6)180 (50.0)180 (50.0)
No224 (38.4)107 (47.8)117 (52.2)
Total584 (100)287 (49.1)297 (50.9)
* Significant difference between subgroups.
Table
Table 2. Sedentary time of participants (hours per day).
Table 2. Sedentary time of participants (hours per day).
Sedentary BehaviorMedianIQRMeanSD
Attending class2.141.362.220.89
Completing schoolwork1.001.291.251.00
Recreational screen time1.432.292.302.11
Socializing0.571.290.921.06
Performing hobbies0.290.710.520.74
Other0.290.570.490.59
Total7.295.937.704.09
IQR, inter-quartile range; SD, standard deviation.
Table
Table 3. Characteristics of the participants by level of total sedentary time, n (%).
Table 3. Characteristics of the participants by level of total sedentary time, n (%).
VariableTotal Sedentary Time per Dayp
≥7.29 h<7.29 h
Gender <0.001
Male147 (42.1)202 (57.9)
Female163 (69.4)72 (30.6)
Grade 0.442
Freshman124 (50.2)123 (49.8)
Sophomore115 (54.2)97 (45.8)
Junior71 (56.8)54 (43.2)
Profession <0.001
Athlete139 (38.5)222 (61.5)
Non-athlete171 (76.7)52 (23.3)
Place of hometown <0.001
Rural111 (42.0)153 (58.0)
Urban199 (62.2)121 (37.8)
Only Child <0.001
Yes152 (61.8)94 (38.2)
No158 (46.7)180 (53.3)
Monthly family income 0.001
Below 3000 CNY42 (41.2)60 (58.8)
3000–5999 CNY88 (47.8)96 (52.2)
6000–8999 CNY69 (55.6)55 (44.4)
Above 9000 CNY111 (63.8) *63 (36.2) *
Parents’ Marital Status 0.182
Married238 (53.1)250 (46.9)
Divorce27 (52.9)24 (47.1)
Overweight 0.083
Yes (BMI ≥ 24)39 (60.9)25 (39.1)
No (BMI < 24)271 (52.1)249 (47.9)
Smoking 0.330
Yes42 (48.3)45 (51.7)
No268 (53.9)229 (46.1)
Drinking 0.314
Yes197 (54.7)163 (45.3)
No113 (50.4)111 (49.6)
Participating in VPA <0.001
Every day56 (40.6)82 (59.4)
A few times a week148 (48.8)155 (51.2)
A few times a month or less106 (74.1) *37 (25.9) *
VPA, vigorous physical activity; * Significant difference between subgroups.
Table
Table 4. Levels of sedentary behavior and frequency of vigorous physical exercise of participants by depression, n (%).
Table 4. Levels of sedentary behavior and frequency of vigorous physical exercise of participants by depression, n (%).
VariableTotalDepressionNo Depressionp
Attending class 0.009
≥2.14 h310 (53.1)168 (54.2)142 (45.8)
<2.14 h274 (46.9)119 (43.4)155 (56.6)
Doing schoolwork 0.914
≥1.00 h310 (53.1)153 (49.4)157 (50.6)
<1.00 h274 (46.9)134 (48.9)140 (51.1)
Recreational screen time 0.008
≥1.43 h363 (62.2)194 (53.4)169 (46.6)
<1.43 h221 (37.8)93 (42.1)128 (57.9)
Socializing 0.945
≥0.57 h302 (51.7)148 (49.0)154 (51.0)
<0.57 h282 (48.3)139 (49.3)143 (50.7)
Performing hobbies 0.821
≥0.29 h333 (57.0)165 (49.5)168 (50.5)
<0.29 h251 (43.0)122 (48.6)129 (51.4)
Other 0.895
≥0.29 h258 (44.2)126 (48.8)132 (51.2)
<0.29 h326 (55.8)161 (49.4)165 (50.6)
Total sedentary time 0.036
≥7.29 h310 (53.1)165 (53.2)145 (46.8)
<7.29 h274 (46.9)122 (44.5)152 (55.5)
Participating in VPA <0.001
Every day138 (23.6)58 (42.0)80 (58.0)
A few times a week303 (51.9)132 (43.6)171 (56.4)
A few times a month or less143 (24.5)97 (67.8) *46 (32.2) *
VPA, vigorous physical activity; * Significant difference between subgroups.
Table
Table 5. Association of depression with sedentary behavior and physical activity.
Table 5. Association of depression with sedentary behavior and physical activity.
VariableORUpper 95% CILower 95% CIp
Completing schoolwork (ref. < 1.00 h)
≥1.00 h0.6580.4420.9780.038
Recreational screen time (<1.43 h)
≥1.43 h1.5401.0312.3020.035
Participating in VPA (ref. a few times a month or less)
Every day0.4150.2420.7120.001
A few times a week0.4230.2700.662<0.001
Adjusted for gender, grade, profession, place of hometown, only child or not, monthly family income, parents’ marital status, smoking and drinking; VPA, vigorous physical activity; OR, odd ratio; CI: confidence interval.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zhou, H.; Dai, X.; Lou, L.; Zhou, C.; Zhang, W. Association of Sedentary Behavior and Physical Activity with Depression in Sport University Students. Int. J. Environ. Res. Public Health 2021, 18, 9881. https://doi.org/10.3390/ijerph18189881

AMA Style

Zhou H, Dai X, Lou L, Zhou C, Zhang W. Association of Sedentary Behavior and Physical Activity with Depression in Sport University Students. International Journal of Environmental Research and Public Health. 2021; 18(18):9881. https://doi.org/10.3390/ijerph18189881

Chicago/Turabian Style

Zhou, Huixuan, Xiaotong Dai, Litian Lou, Chan Zhou, and Wei Zhang. 2021. "Association of Sedentary Behavior and Physical Activity with Depression in Sport University Students" International Journal of Environmental Research and Public Health 18, no. 18: 9881. https://doi.org/10.3390/ijerph18189881

APA Style

Zhou, H., Dai, X., Lou, L., Zhou, C., & Zhang, W. (2021). Association of Sedentary Behavior and Physical Activity with Depression in Sport University Students. International Journal of Environmental Research and Public Health, 18(18), 9881. https://doi.org/10.3390/ijerph18189881

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop