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12-06-2023 | Empirical Research

The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective

Auteurs: Yue Zhao, Kaixin Liang, Diyang Qu, Yunhan He, Xiaoqi Wei, Xinli Chi

Gepubliceerd in: Journal of Youth and Adolescence | Uitgave 10/2023

Abstract

There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; M_age = 20.6, SD_age = 1.8, range: 17–27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.

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Abd-Elfattah, H. M., Abdelazeim, F. H., & Elshennawy, S. (2015). Physical and cognitive consequences of fatigue: A review. Journal of advanced research, 6(3), 351–358. https://doi.org/10.1016/j.jare.2015.01.011.CrossRefPubMedPubMedCentral

Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic. The Lancet, 395(10228), 931–934. https://doi.org/10.1016/S0140-6736(20)30567-5.CrossRef

Azzolino, D., & Cesari, M. (2022). Fatigue in the COVID-19 pandemic. The Lancet Healthy Longevity, 3(3), e128–e129. https://doi.org/10.1016/S2666-7568(22)00029-0.CrossRefPubMedPubMedCentral

Bai, W., Xi, H., Zhu, Q., Ji, M., Zhang, H., Yang, B., & Xiang, Y. (2021). Network analysis of anxiety and depressive symptoms among nursing students during the COVID-19 pandemic. Journal of affective disorders, 294, 753–760. https://doi.org/10.1016/j.jad.2021.07.072.CrossRefPubMedPubMedCentral

Bao, W. (2020). COVID‐19 and online teaching in higher education: A case study of Peking University. Human behavior and emerging technologies, 2(2), 113–115. https://publons.com/publon/10.1002/hbe2.191CrossRefPubMedPubMedCentral

Bavel, J., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., & Willer, R. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4(5), 460–471. https://doi.org/10.1038/s41562-020-0884-z.CrossRefPubMed

Beard, C., Millner, A. J., Forgeard, M. J., Fried, E. I., Hsu, K. J., Treadway, M. T., & Björgvinsson, T. (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine, 46(16), 3359–3369. https://doi.org/10.1017/S0033291716002300.CrossRefPubMedPubMedCentral

Billones, R. R., Kumar, S., & Saligan, L. N. (2020). Disentangling fatigue from anhedonia: A scoping review. Translational Psychiatry, 10(1), 1–11. https://doi.org/10.1038/s41398-020-00960-w.CrossRef

Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64(9), 1089–1108. https://doi.org/10.1002/jclp.20503.CrossRefPubMed

Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16, 5–13. https://doi.org/10.1002/wps.20375.CrossRefPubMedPubMedCentral

Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608.CrossRefPubMed

Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 58. https://doi.org/10.1038/s43586-021-00055-w.CrossRef

Boschloo, L., van Borkulo, C. D., Borsboom, D., & Schoevers, R. A. (2016). A prospective study on how symptoms in a network predict the onset of depression. Psychotherapy and Psychosomatics, 85, 183–184. https://doi.org/10.1159/000442001.CrossRefPubMed

Burger, J., Isvoranu, A. M., Lunansky, G., Haslbeck, J., Epskamp, S., Hoekstra, R. H. & Blanken, T. F. (2022). Reporting standards for psychological network analyses in cross-sectional data. Psychological Methods. Advanced online publication. https://doi.org/10.1037/met0000471.

Castro, D., Ferreira, F., de Castro, I., Rodrigues, A. R., Correia, M., Ribeiro, J., & Ferreira, T. B. (2019). The differential role of central and bridge symptoms in deactivating psychopathological networks. Frontiers in psychology, 10, 2448. https://doi.org/10.3389/fpsyg.2019.02448.CrossRefPubMedPubMedCentral

Ceban, F., Ling, S., Lui, L. M., Lee, Y., Gill, H., Teopiz, K. M., & McIntyre, R. S. (2022). Fatigue and cognitive impairment in post-COVID-19 syndrome: A systematic review and meta-analysis. Brain, Behavior, and Immunity, 101, 93–135. https://doi.org/10.1016/j.bbi.2021.12.020.CrossRefPubMed

Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759–771. https://doi.org/10.1093/biomet/asn034.CrossRef

Chi, X., Becker, B., Yu, Q., Willeit, P., Jiao, C., Huang, L., & Solmi, M. (2020). Prevalence and psychosocial correlates of mental health outcomes among chinese college students during the Coronavirus Disease (COVID-19) pandemic. Frontiers in Psychiatry, 11, 803. https://doi.org/10.3389/fpsyt.2020.00803.CrossRefPubMedPubMedCentral

Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.

Corfield, E. C., Martin, N. G., & Nyholt, D. R. (2016). Co-occurrence and symptomatology of fatigue and depression. Comprehensive Psychiatry, 71, 1–10. https://doi.org/10.1016/j.comppsych.2016.08.004.CrossRefPubMed

Cramer, A. O. J., Waldorp, L. J., Van Der Maas, H. L., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33(2-3), 137–150. https://doi.org/10.1017/S0140525X09991567.CrossRefPubMed

Cramer, A. O. J., & Borsboom, D. (2015). Problems attract problems: A network perspective on mental disorders. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences (pp. 1–15). Hoboken, NJ: Wiley. https://doi.org/10.1002/9781118900772

Dragioti, E., Li, H., Tsitsas, G., Lee, K. H., Choi, J., Kim, J., & Solmi, M. (2022). A large‐scale meta‐analytic atlas of mental health problems prevalence during the COVID‐19 early pandemic. Journal of Medical Virology, 94(5), 1935–1949. https://doi.org/10.1002/jmv.27549.CrossRefPubMedPubMedCentral

Epskamp, S. (2016). Regularized Gaussian Psychological Networks: Brief report on the Performance of extended BIC model selection. arXiv, 1606, 05771.

Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), 206–231. https://doi.org/10.1007/s11336-020-09697-3.CrossRefPubMedPubMedCentral

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1.CrossRefPubMed

Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/10.1080/00273171.2018.1454823.CrossRefPubMed

Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). Ggraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–18.CrossRef

Fried, E. I. (2020). Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry, 31(4), 271–288. https://doi.org/10.1080/1047840X.2020.1853461.CrossRef

Fried, E. I., & Nesse, R. M. (2014). The impact of individual depressive symptoms on impairment of psychosocial functioning. Plos One, 9(2), e90311. https://doi.org/10.1371/journal.pone.0090311.CrossRefPubMedPubMedCentral

Fried, E. I., & Nesse, R. M. (2015). Depression sum-scores don’t add up: Why analyzing specific depression symptoms is essential. BMC Medicine, 13(1), 1–11. https://doi.org/10.1186/s12916-015-0325-4.CrossRef

Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspective on sychological Science, 12(6), 999–1020. https://doi.org/10.1177/17456916177058.CrossRef

Fried, E. I., Nesse, R. M., Guille, C., & Sen, S. (2015). The differential influence of life stress on individual symptoms of depression. Acta Psychiatrica Scandinavica, 131(6), 465–471. https://doi.org/10.1111/acps.12395.CrossRefPubMedPubMedCentral

Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016). What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314–320. https://doi.org/10.1016/j.jad.2015.09.005.CrossRefPubMed

Fried, E. I., van Borkulo, C. D., Cramer, A. O. J., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: a review of recent insights. Social psychiatry and psychiatric epidemiology, 52, 1–10. https://doi.org/10.1007/s00127-016-1319-z.CrossRefPubMed

Funkhouser, C. J., Chacko, A. A., Correa, K. A., Kaiser, A. J., & Shankman, S. A. (2021). Unique longitudinal relationships between symptoms of psychopathology in youth: A cross‐lagged panel network analysis in the ABCD study. Journal of Child Psychology and Psychiatry, 62(2), 184–194. https://doi.org/10.1111/jcpp.13320.CrossRefPubMed

Ghanean, H., Ceniti, A. K., & Kennedy, S. H. (2018). Fatigue in patients with major depressive disorder: Prevalence, burden and pharmacological approaches to management. CNS Drugs, 32(1), 65–74. https://doi.org/10.1007/s40263-018-0490-z.CrossRefPubMed

Heitmann, H., Andlauer, T. F., Korn, T., Mühlau, M., Henningsen, P., Hemmer, B., & Ploner, M. (2022). Fatigue, depression, and pain in multiple sclerosis: How neuroinflammation translates into dysfunctional reward processing and anhedonic symptoms. Multiple Sclerosis Journal, 28(7), 1020–1027. https://doi.org/10.1177/1352458520972279.CrossRefPubMed

Hofmann, S. G., Curtiss, J., & Mcnally, R. J. (2016). A complex network perspective on clinical science. Perspectives on Psychological Science, 11(5), 597–605. https://doi.org/10.1177/1745691616639283.CrossRefPubMedPubMedCentral

Kendler, K. S., Aggen, S. H., Flint, J., Borsboom, D., & Fried, E. I. (2018). The centrality of DSM and non-DSM depressive symptoms in Han Chinese women with major depression. Journal of Affective Disorders, 227, 739–744. https://doi.org/10.1016/j.jad.2017.11.032.CrossRefPubMed

Krakowczyk, J. B., Planert, J., Skoda, E. M., Dinse, H., Kaup, T., Teufel, M., & Bäuerle, A. (2022). Pandemic fatigue, psychopathological risk factors, and vaccination attitudes during the COVID-19 pandemic in 2021-a network analysis. Journal of Affective Disorders Reports, 8, 100345. https://doi.org/10.1016/j.jadr.2022.100345.CrossRefPubMedPubMedCentral

Labrague, L. J., & Ballad, C. A. (2021). Lockdown fatigue among college students during the COVID‐19 pandemic: Predictive role of personal resilience, coping behaviors, and health. Perspect Psychiatr Care, 57, 1905–1912. https://doi.org/10.1111/ppc.12765.CrossRefPubMedPubMedCentral

Labrague, L. J., De los Santos, J. A. A., & Falguera, C. (2021). Social and emotional loneliness among college students during the COVID-19 pandemic: The predictive role of coping behaviours, social support, and personal resilience. https://doi.org/10.21203/rs.3.rs-93878/v2

Lee, C. H., & Giuliani, F. (2019). The role of inflammation in depression and fatigue. Frontiers in Immunology, 10, 1696. https://doi.org/10.3389/fimmu.2019.01696.CrossRefPubMedPubMedCentral

Lee, J., Jeong, H. J., & Kim, S. (2021). Stress, anxiety, and depression among undergraduate students during the COVID-19 pandemic and their use of mental health services. Innovative Higher Education, 46(5), 519–538. https://doi.org/10.1007/s10755-021-09552-y.CrossRefPubMedPubMedCentral

van de Leemput, I. A., Wichers, M., Cramer, A. O., Borsboom, D., Tuerlinckx, F., Kuppens, P., & Scheffer, M. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences, 111(1), 87–92. https://doi.org/10.1073/pnas.1312114110.CrossRef

LeMoult, J., & Gotlib, I. H. (2019). Depression: A cognitive perspective. Clinical Psychology Review, 69, 51–66. https://doi.org/10.1016/j.cpr.2018.06.008.CrossRefPubMed

Li, H. Y., Cao, H., Leung, D. Y., & Mak, Y. W. (2020). The psychological impacts of a COVID-19 outbreak on college students in China: A longitudinal study. International Journal of Environmental Research and Public Health, 17(11), 3933. https://doi.org/10.3390/ijerph17113933.CrossRefPubMedPubMedCentral

Li, Y., Wang, A., Wu, Y., Han, N., & Huang, H. (2021). Impact of the COVID-19 pandemic on the mental health of college students: a systematic review and meta-analysis. Frontiers in Psychology, 12, 669119. https://doi.org/10.3389/fpsyg.2021.669119.CrossRefPubMedPubMedCentral

Li, Y., Zhao, J., Ma, Z., McReynolds, L. S., Lin, D., Chen, Z., & Liu, X. (2021). Mental Health Among College Students During the COVID-19 Pandemic in China: A 2-Wave Longitudinal Survey. Journal of Affective Disorders, 281, 597–604. https://doi.org/10.1016/j.jad.2020.11.109.CrossRefPubMed

Liang, K., de Lucena Martins, C. M., Chen, S. T., Clark, C. C. T., Duncan, M. J., Bu, H., & Chi, X. (2021). Sleep as a priority: 24-hour movement guidelines and mental health of chinese college students during the COVID-19 pandemic. Healthcare, 9(9), 1166. https://doi.org/10.3390/healthcare9091166.CrossRefPubMedPubMedCentral

Liang, L., Ren, H., Cao, R., Hu, Y., Qin, Z., Li, C., & Mei, S. (2020). The effect of COVID-19 on youth mental health. Psychiatric Quarterly, 91(3), 841–852. https://doi.org/10.1007/s11126-020-09744-3.CrossRefPubMed

Liang, S., Liu, C., Rotaru, K., Li, K., Wei, X., Yuan, S., & Liu, X. (2022). The relations between emotion regulation, depression and anxiety among medical staff during the late stage of COVID-19 pandemic: A network analysis. Psychiatry Research, 317, 114863. https://doi.org/10.1016/j.psychres.2022.114863.CrossRefPubMedPubMedCentral

Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. Journal of Adolescent Health, 46(2), 124–132. https://doi.org/10.1016/j.jadohealth.2009.06.016.CrossRef

Luo, W., Zhong, B. L., & Chiu, H. F. K. (2021). Prevalence of depressive symptoms among Chinese university students amid the COVID-19 pandemic: A systematic review and meta-analysis. Epidemiology and Psychiatric Sciences, 30. https://doi.org/10.1017/S2045796021000202

Malgaroli, M., Calderon, A., & Bonanno, G. A. (2021). Networks of major depressive disorder: A systematic review. Clinical Psychology Review, 85, 102000. https://doi.org/10.1016/j.cpr.2021.102000.CrossRefPubMed

Matar Boumosleh, J., & Jaalouk, D. (2017). Depression, anxiety, and smartphone addiction in university students-a cross sectional study. Plos One, 12(8), e0182239. https://doi.org/10.1371/journal.pone.0182239.CrossRefPubMedPubMedCentral

McNally, R. J. (2016). Can network analysis transform psychopathology. Behaviour Research and Therapy, 86, 95–104. https://doi.org/10.1016/j.brat.2016.06.006.CrossRefPubMed

Moore, S. A., Faulkner, G., Rhodes, R. E., Brussoni, M., Chulak-Bozzer, T., Ferguson, L. J., & Tremblay, M. S. (2020). Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: A national survey. International Journal of Behavioral Nutrition and Physical Activity, 17(1), 1–11. https://doi.org/10.1186/s12966-020-00987-8.CrossRef

Morgul, E., Bener, A., Atak, M., Akyel, S., Aktaş, S., Bhugra, D., & Jordan, T. R. (2021). COVID-19 pandemic and psychological fatigue in Turkey. International Journal of Social Psychiatry, 67(2), 128–135. https://doi.org/10.1177/0020764020941889.CrossRefPubMed

Negaresh, R., Motl, R., Mokhtarzade, M., Ranjbar, R., Majdinasab, N., Khodadoost, M., & Patel, D. (2019). Effect of short-term interval exercise training on fatigue, depression, and fitness in normal weight vs. overweight person with multiple sclerosis. Explore, 15(2), 134–141. https://doi.org/10.1016/j.explore.2018.07.007.CrossRefPubMed

Orchard, F., Pass, L., & Reynolds, S. (2019). ‘I Am Worthless and Kind’; the specificity of positive and negative self‐evaluation in adolescent depression. British Journal of Clinical Psychology, 58(3), 260–273. https://doi.org/10.1111/bjc.12215.CrossRefPubMed

Raudenbush, S. W., & Liu, X. F. (2001). Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods, 6(4), 387. https://doi.org/10.1037/1082-989X.6.4.387.CrossRefPubMed

Rhemtulla, M., van Bork, R., & Cramer, A. O. J. (2022). Cross-lagged network models. Multivariate Behavioral Research. https://osf.io/r24q6/.

Rizvi, S. J., Pizzagalli, D. A., Sproule, B. A., & Kennedy, S. H. (2016). Assessing anhedonia in depression: Potentials and pitfalls. Neuroscience & Biobehavioral Reviews, 65, 21–35. https://doi.org/10.1016/j.neubiorev.2016.03.004.CrossRef

Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125(6), 747. https://doi.org/10.1037/abn0000181.CrossRefPubMedPubMedCentral

Robinaugh, D. J., Hoekstra, R. H., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008-2018 and an agenda for future research. Psychological Medicine, 50(3), 353–366. https://doi.org/10.1017/S0033291719003404.CrossRefPubMed

Romero-Blanco, C., Rodríguez-Almagro, J., Onieva-Zafra, M. D., Parra-Fernández, M. L., Prado-Laguna, M. D. C., & Hernández-Martínez, A. (2020). Sleep pattern changes in nursing students during the COVID-19 lockdown. International Journal of Environmental Research and Public Health, 17(14), 5222. https://doi.org/10.3390/ijerph17145222.CrossRefPubMedPubMedCentral

Schmittmann, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43–53. https://doi.org/10.1016/j.newideapsych.2011.02.007.CrossRef

Skjerdingstad, N., Johnson, M. S., Johnson, S. U., Hoffart, A., & Ebrahimi, O. V. (2021). Feelings of worthlessness links depressive symptoms and parental stress: A network analysis during the COVID-19 pandemic. European Psychiatry, 64(1). https://doi.org/10.1192/j.eurpsy.2021.2223

Tao, Y., Hou, W., Niu, H., Ma, Z., Zhang, S., Zhang, L., & Liu, X. (2022). Centrality and bridge symptoms of anxiety, depression, and sleep disturbance among college students during the COVID-19 pandemic—a network analysis. Current Psychology, 1–12. https://doi.org/10.1007/s12144-022-03443-x

van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., & Waldorp, L. J. (2022). Comparing network structures on three aspects: A permutation test. Psychological Methods. https://doi.org/10.1037/met0000476

De Vaus, J., Hornsey, M. J., Kuppens, P., & Bastian, B. (2018). Exploring the East-West divide in prevalence of affective disorder: A case for cultural differences in coping with negative emotion. Personality and Social Psychology Review, 22(3), 285–304. https://doi.org/10.1177/1088868317736222.CrossRefPubMed

Wang, D., Zhao, J., Ross, B., Ma, Z., Zhang, J., Fan, F., & Liu, X. (2022). Longitudinal trajectories of depression and anxiety among adolescents during COVID-19 lockdown in China. Journal of Affective Disorders, 299, 628–635. https://doi.org/10.1016/j.jad.2021.12.086.CrossRefPubMed

Wang, Y., Hu, Z., Feng, Y., Wilson, A., & Chen, R. (2020). Changes in network centrality of psychopathology symptoms between the COVID-19 outbreak and after peak. Molecular Psychiatry, 25(12), 3140–3149. https://doi.org/10.1038/s41380-020-00881-6.CrossRefPubMedPubMedCentral

Wang, C., Wen, W., Zhang, H., Ni, J., Jiang, J., Cheng, Y., & Liu, W. (2021). Anxiety, depression, and stress prevalence among college students during the COVID-19 pandemic: A systematic review and meta-analysis. Journal of American college health, 1–8. https://doi.org/10.1080/07448481.2021.1960849

Xiang, M. Q., Tan, X. M., Sun, J., Yang, H. Y., Zhao, X. P., Liu, L., & Hu, M. (2020). Relationship of physical activity with anxiety and depression symptoms in Chinese college students during the COVID-19 outbreak. Frontiers in Psychology, 11, 582436. https://doi.org/10.3389/fpsyg.2020.582436.CrossRefPubMedPubMedCentral

Xin, M., Luo, S., She, R., Yu, Y., Li, L., Wang, S., & Lau, J. (2020). Negative cognitive and psychological correlates of mandatory quarantine during the initial COVID-19 outbreak in China. American Psychologist, 75(5), 607. https://doi.org/10.1037/amp0000692.CrossRefPubMed

Yang, X., Hu, H., Zhao, C., Xu, H., Tu, X., & Zhang, G. (2021). A longitudinal study of changes in smart phone addiction and depressive symptoms and potential risk factors among Chinese college students. BMC Psychiatry, 21(1), 1–9. https://doi.org/10.1186/s12888-021-03265-4.CrossRef

Zhang, Y. L., Liang, W., Chen, Z. M., Zhang, H. M., Zhang, J. H., Weng, X. Q., & Zhang, Y. L. (2013). Validity and reliability of Patient Health Questionnaire‐9 and Patient Health Questionnaire‐2 to screen for depression among college students in China. Asia‐Pacific Psychiatry, 5(4), 268–275. https://doi.org/10.1111/appy.12103.CrossRefPubMed

Zhao, Y., Qu, D., Chen, S., & Chi, X. (2023). Network analysis of internet addiction and depression among Chinese college students during the COVID-19 pandemic: A longitudinal study. Computers in Human Behavior, 138, 107424. https://doi.org/10.1016/j.chb.2022.107424.CrossRefPubMed

Zhou, S., Zhang, L., Wang, L., Guo, Z., Wang, J., Chen, J., & Chen, J. (2020). Prevalence and socio-demographic correlates of psychological health problems in Chinese adolescents during the outbreak of COVID-19. European child & adolescent psychiatry, 29, 749–758. https://doi.org/10.1007/s00787-020-01541-4.CrossRef

Zimmermann, M., Bledsoe, C., & Papa, A. (2021). Initial impact of the COVID-19 pandemic on college student mental health: A longitudinal examination of risk and protective factors. Psychiatry Research, 305, 114254. https://doi.org/10.1016/j.psychres.2021.114254.CrossRefPubMedPubMedCentral

Titel: The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective
Auteurs: Yue Zhao
Kaixin Liang
Diyang Qu
Yunhan He
Xiaoqi Wei
Xinli Chi
Publicatiedatum: 12-06-2023
Uitgeverij: Springer US
Gepubliceerd in: Journal of Youth and Adolescence / Uitgave 10/2023
Print ISSN: 0047-2891
Elektronisch ISSN: 1573-6601
DOI: https://doi.org/10.1007/s10964-023-01802-w

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