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Network Analysis of Health-related Behaviors, Insomnia, and Depression Among Urban Left-behind Adolescents in China

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Abstract

Mental health of urban left-behind adolescents (LBA) is a public issue of growing concern. This study aims to examine the symptom level associations among multiple health-related behaviors, insomnia, and depression in urban LBA. Data on a sample of urban LBA aged 11–19 (N = 3,601) from the Adolescent Mental Health Survey in Shenzhen, China, were used. Health-related behaviors (i.e., Internet use, physical inactivity, social jetlag, smoking, and alcohol consumption), insomnia, and depressive symptoms were assessed using a self-administered questionnaire. Graphical Gaussian Model (GGM) was used to describe key bridging nodes in an undirected network. Directed Acyclic Graph (DAG) was used to construct a directed network and estimate the most likely causal associations among behaviors/symptoms. In the undirected network, Internet use was identified as the key bridging node most strongly associated with insomnia and depression. Two other key bridging nodes include difficulty initiating sleep and appetite change. In the directed network, anhedonia emerged as the most pivotal symptom, which could cause insomnia symptoms and behavioral changes, either directly, or through triggering other depressive symptoms, such as low energy and appetite change. These findings have implications for understanding the occurrence and maintenance process of health-related behaviors, insomnia, and depression in urban LBA. In practice, Internet use should be considered a priority in targeting multiple health behavior interventions. Meanwhile, early screening and treatment for anhedonia are of great significance as well.

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Data Availability

The data presented in this study is available upon request from the corresponding author.

References

  1. Duan C, Zhou F (2005) A study on children left behind. Popul Res 29(1):29–36. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=RKYZ200501003&DbName=CJFQ20 [in Chinese]

    Google Scholar 

  2. National Health Commission of China (2018) China migration population development report 2018. Retrieved April 20, 2023, from http://www.nhc.gov.cn/wjw/xwdt/201812/a32a43b225a740c4bff8f2168b0e9688.shtml

  3. UNICEF (2018) Population status of children in China in 2015: facts and figures. Retrieved April 20, 2023, from: https://www.unicef.cn/atlas-2018-cn

  4. Wang T, Ge Y, Zhang J, Liu J, Luo W (2014) The capacity for pain empathy among urban internet-addicted left-behind children in China: an event-related potential study. Comput Hum Behav 33:56–62. https://doi.org/10.1016/j.chb.2013.12.020

    Article  Google Scholar 

  5. Wang D, Chen H, Chen J, Ye H, Chen X, Ye L, Yao K, Tu N, Dai H, Fan F (2023) A comparative study of mental health status among left-behind, migrant, and local adolescents in China. J Affect Disord 324:521–528. https://doi.org/10.1016/j.jad.2022.12.134

    Article  PubMed  Google Scholar 

  6. Wang F, Lin L, Lu J, Cai J, Xu J, Zhou X (2020) Mental health and substance use in urban left-behind children in China: a growing problem. Child Youth Serv Rev 116:105135. https://doi.org/10.1016/j.childyouth.2020.105135

    Article  Google Scholar 

  7. Blank M, Zhang J, Lamers F, Taylor AD, Hickie IB, Merikangas KR (2015) Health correlates of insomnia symptoms and comorbid mental disorders in a nationally representative sample of US adolescents. Sleep 38(2):197–204. https://doi.org/10.5665/sleep.4396

    Article  PubMed  PubMed Central  Google Scholar 

  8. Blake MJ, Trinder JA, Allen NB (2018) Mechanisms underlying the association between insomnia, anxiety, and depression in adolescence: implications for behavioral sleep interventions. Clin Psychol Rev 63:25–40. https://doi.org/10.1016/j.cpr.2018.05.006

    Article  PubMed  Google Scholar 

  9. Bard HA, O’Driscoll C, Miller CB, Henry AL, Cape J, Espie CA (2023) Insomnia, depression, and anxiety symptoms interact and individually impact functioning: a network and relative importance analysis in the context of insomnia. Sleep Med 101:505–514. https://doi.org/10.1016/j.sleep.2022.12.005

    Article  PubMed  Google Scholar 

  10. Monroe SM, Simons AD (1991) Diathesis-stress theories in the context of life stress research: implications for the depressive disorders. Psychol Bull 110(3):406–425. https://doi.org/10.1037/0033-2909.110.3.406

    Article  PubMed  Google Scholar 

  11. Rugo-Cook KF, Kerig PK, Crowell SE, Bryan CJ (2021) Fluid vulnerability theory as a framework for understanding the association between posttraumatic stress disorder and suicide: a narrative review. J Trauma Stress 34(6):1080–1098. https://doi.org/10.1002/jts.22782

    Article  PubMed  Google Scholar 

  12. Firth J, Solmi M, Wootton RE, Vancampfort D, Schuch FB, Hoare E, Gilbody S, Torous J, Teasdale SB, Jackson SE, Smith L, Eaton M, Jacka FN, Veronese N, Marx W, Ashdown-Franks G, Siskind D, Sarris J, Rosenbaum S, Carvalho AF, Stubbs B (2020) A meta-review of lifestyle psychiatry: the role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders. World Psychiatry 19(3):360–380. https://doi.org/10.1002/wps.20773

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mahon C, Howard E, O’Reilly A, Dooley B, Fitzgerald A (2022) A cluster analysis of health behaviours and their relationship to mental health difficulties, life satisfaction and functioning in adolescents. Prev Med 164:107332. https://doi.org/10.1016/j.ypmed.2022.107332

    Article  PubMed  Google Scholar 

  14. Zhang F, Zhang T, Xiong J, Zhang L, Chen J (2015) Analysis on prevalence and influence factors of smoking and drinking among middle school students of Tujia and Miao in Xiangxi Area [in Chinese]. Wei Sheng Yan Jiu 44(5):750–755. https://doi.org/10.19813/j.cnki.weishengyanjiu.2015.05.010[in Chinese]

    Article  PubMed  Google Scholar 

  15. Zhou M, Zhu W, Sun X, Huang L (2022) Internet addiction and child physical and mental health: evidence from panel dataset in China. J Affect Disord 309:52–62. https://doi.org/10.1016/j.jad.2022.04.115

    Article  PubMed  Google Scholar 

  16. Mathew GM, Hale L, Chang AM (2019) Sex moderates relationships among school night sleep duration, social jetlag, and depressive symptoms in adolescents. J Biol Rhythms 34(2):205–217. https://doi.org/10.1177/0748730419828102

    Article  PubMed  PubMed Central  Google Scholar 

  17. Pedisic Z, Dumuid D, Olds TS (2017) Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology 49(2):252–269. https://hrcak.srce.hr/186506

    Google Scholar 

  18. Prochaska JO (2008) Multiple health behavior research represents the future of preventive medicine. Prev Med 46(3):281–285. https://doi.org/10.1016/j.ypmed.2008.01.015

    Article  PubMed  Google Scholar 

  19. Borsboom D, Cramer AO (2013) Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol 9:91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608

    Article  PubMed  Google Scholar 

  20. Borsboom D (2017) A network theory of mental disorders. World Psychiatry 16(1):5–13. https://doi.org/10.1002/wps.20375

    Article  PubMed  PubMed Central  Google Scholar 

  21. McNally RJ (2016) Can network analysis transform psychopathology? Behav Res Ther 86:95–104. https://doi.org/10.1016/j.brat.2016.06.006

    Article  PubMed  Google Scholar 

  22. Wang Y, Ma Z, Wilson A, Hu Z, Ying X, Han M, Cui Z, Chen R (2021) Psychopathological symptom network structure in transgender and gender queer youth reporting parental psychological abuse: a network analysis. BMC Med 19(1):215. https://doi.org/10.1186/s12916-021-02091-5

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kirtley OJ, Hussey I, Marzano L (2021) Exposure to and experience of self-harm and self-harm related content: an exploratory network analysis. Psychiatry Res 295:113572. https://doi.org/10.1016/j.psychres.2020.113572

    Article  PubMed  Google Scholar 

  24. Ma Z, Zhao F, Wang Y, Liu T, Chao N (2022) Network analysis of time use and depressive symptoms among emerging adults: findings from the Guizhou Population Health Cohort Study. Front Psychiatry 13:809745. https://doi.org/10.3389/fpsyt.2022.809745

    Article  PubMed  PubMed Central  Google Scholar 

  25. 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. Comput Hum Behav 138:107424. https://doi.org/10.1016/j.chb.2022.107424

    Article  Google Scholar 

  26. Yun JY, Kim JW, Myung SJ, Yoon HB, Moon SH, Ryu H, Yim JJ (2021) Impact of COVID-19 on lifestyle, personal attitudes, and mental health among korean medical students: Network analysis of associated patterns. Front Psychiatry 12:702092. https://doi.org/10.3389/fpsyt.2021.702092

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhang Y, Fan Y, Ma Z, Wang D, Fan F (2023) Associations of social jetlag and insomnia symptoms with depressive symptoms among chinese adolescents: a large population-based study. Sleep Med 104:98–104. https://doi.org/10.1016/j.sleep.2023.02.024

    Article  PubMed  Google Scholar 

  28. Hu H, Gao J, Jiang H, Jiang H, Guo S, Chen K, Jin K, Qi Y (2018) A comparative study of behavior problems among left-behind children, migrant children and local children. Int J Environ Res Public Health 15(4):655. https://doi.org/10.3390/ijerph15040655

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16(9):606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

    Article  PubMed  PubMed Central  Google Scholar 

  30. American Psychiatric Association, DSM-5 Task Force (2013) Diagnostic and statistical manual of mental disorders: DSM-5™, 5th edn. American Psychiatric Publishing, Inc. https://doi.org/10.1176/appi.books.9780890425596

  31. Wang D, Ross B, Zhou X, Meng D, Zhu Z, Zhao J, Fan F, Liu X (2021) Sleep disturbance predicts suicidal ideation during COVID-19 pandemic: a two-wave longitudinal survey. J Psychiatr Res 143:350–356. https://doi.org/10.1016/j.jpsychires.2021.09.038

    Article  PubMed  PubMed Central  Google Scholar 

  32. Liu X, Chen H, Liu ZZ, Fan F, Jia CX (2017) Early menarche and menstrual problems are associated with sleep disturbance in a large sample of chinese adolescent girls. Sleep 40(9):zsx107. https://doi.org/10.1093/sleep/zsx107

    Article  Google Scholar 

  33. Chen SH, Weng LJ, Su YJ, Wu HM, Yang PF (2003) Development of chinese internet addiction scale and its psychometric study. Chin J Psychol 45:251–266. https://doi.org/10.1037/t44491-000

    Article  Google Scholar 

  34. Ko CH, Yen JY, Yen CF, Chen CC, Yen CN, Chen SH (2005) Screening for internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale. Kaohsiung J Med Sci 21(12):545–551. https://doi.org/10.1016/s1607-551x(09)70206-2

    Article  PubMed  Google Scholar 

  35. Wittmann M, Dinich J, Merrow M, Roenneberg T (2006) Social jetlag: misalignment of biological and social time. Chronobiol Int 23(1–2):497–509. https://doi.org/10.1080/07420520500545979

    Article  PubMed  Google Scholar 

  36. Islam Z, Hu H, Akter S, Kuwahara K, Kochi T, Eguchi M, Kurotani K, Nanri A, Kabe I, Mizoue T (2020) Social jetlag is associated with an increased likelihood of having depressive symptoms among the Japanese working population: the Furukawa Nutrition and Health Study. Sleep 43(1):zsz204. https://doi.org/10.1093/sleep/zsz204

    Article  PubMed  Google Scholar 

  37. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D (2012) qgraph: network visualizations of Relationships in Psychometric Data. J Stat Softw 48(4):1–18. https://www.jstatsoft.org/index.php/jss/article/view/v048i04

    Article  Google Scholar 

  38. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Softw Pract Experience 21(11):1129–1164. https://doi.org/10.1002/spe.4380211102

    Article  Google Scholar 

  39. Robinaugh DJ, Millner AJ, McNally RJ (2016) Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol 125(6):747–757. https://doi.org/10.1037/abn0000181

    Article  PubMed  PubMed Central  Google Scholar 

  40. Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D (2010) Comorbidity: a network perspective. Behav Brain Sci 33(2–3):137–150 discussion 150–193. https://doi.org/10.1017/s0140525x09991567

    Article  PubMed  Google Scholar 

  41. Ma Z, Zhu Y, Tao Y, Yang Z, Huang S, Liu W, Chen Y, Ye H, Fan F (2022) Using network analysis to explore the key bridge symptoms between posttraumatic stress symptoms and posttraumatic growth among survivors 10 years after the Wenchuan earthquake in China. J Psychiatr Res 150:173–179. https://doi.org/10.1016/j.jpsychires.2022.03.011

    Article  PubMed  Google Scholar 

  42. Haslbeck JMB, Waldorp LJ (2018) How well do network models predict observations? On the importance of predictability in network models. Behav Res Methods 50(2):853–861. https://doi.org/10.3758/s13428-017-0910-x

    Article  PubMed  Google Scholar 

  43. Epskamp S, Borsboom D, Fried EI (2018) Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods 50(1):195–212. https://doi.org/10.3758/s13428-017-0862-1

    Article  PubMed  Google Scholar 

  44. Scutari M (2010) Learning bayesian networks with the bnlearn R Package. J Stat Softw 35(3):1–22. https://www.jstatsoft.org/index.php/jss/article/view/v035i03

    Article  Google Scholar 

  45. An Y, Shi J, Chuan-Peng H, Wu X (2021) The symptom structure of posttraumatic stress disorder and co-morbid depression among college students with childhood abuse experience: a network analysis. J Affect Disord 293:466–475. https://doi.org/10.1016/j.jad.2021.06.065

    Article  PubMed  Google Scholar 

  46. Briganti G, Scutari M, McNally RJ (2022) A tutorial on bayesian networks for psychopathology researchers. Psychol methods. https://doi.org/10.1037/met0000479. Advance online publication

  47. Scutari M, Nagarajan R (2013) Identifying significant edges in graphical models of molecular networks. Artif Intell Med 57(3):207–217. https://doi.org/10.1016/j.artmed.2012.12.006

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bowlby J (1982) Attachment and loss: retrospect and prospect. Am J Orthopsychiatry 52(4):664–678. https://doi.org/10.1111/j.1939-0025.1982.tb01456.x

    Article  PubMed  Google Scholar 

  49. Fan F, Su L, Gill MK, Birmaher B (2010) Emotional and behavioral problems of chinese left-behind children: a preliminary study. Soc Psychiatry Psychiatr Epidemiol 45(6):655–664. https://doi.org/10.1007/s00127-009-0107-4

    Article  PubMed  Google Scholar 

  50. Shuang M, Yiqing W, Ling J, Guanzhen O, Jing G, Zhiyong Q, Xiaohua W (2022) Relationship between parent-child attachment and depression among migrant children and left-behind children in China. Public Health 204:1–8. https://doi.org/10.1016/j.puhe.2021.12.015

    Article  PubMed  Google Scholar 

  51. Gress-Smith JL, Roubinov DS, Andreotti C, Compas BE, Luecken LJ (2015) Prevalence, severity and risk factors for depressive symptoms and insomnia in college undergraduates. Stress Health 31(1):63–70. https://doi.org/10.1002/smi.2509

    Article  PubMed  Google Scholar 

  52. Cabré-Riera A, Torrent M, Donaire-Gonzalez D, Vrijheid M, Cardis E, Guxens M (2019) Telecommunication devices use, screen time and sleep in adolescents. Environ Res 171:341–347. https://doi.org/10.1016/j.envres.2018.10.036

    Article  PubMed  Google Scholar 

  53. Domingues-Montanari S (2017) Clinical and psychological effects of excessive screen time on children. J Paediatr Child Health 53(4):333–338. https://doi.org/10.1111/jpc.13462

    Article  PubMed  Google Scholar 

  54. Blanken TF, Borsboom D, Penninx BW, Van Someren EJ (2020) Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: a 6-year prospective study. Sleep 43(5):zsz288. https://doi.org/10.1093/sleep/zsz288

    Article  PubMed  Google Scholar 

  55. Ma Z, Wang D, Chen XY, Tao Y, Yang Z, Zhang Y, Huang S, Bu L, Wang C, Wu L, Fan F (2022) Network structure of insomnia and depressive symptoms among shift workers in China. Sleep Med 100:150–156. https://doi.org/10.1016/j.sleep.2022.08.010

    Article  PubMed  Google Scholar 

  56. Tan SL, Storm V, Reinwand DA, Wienert J, de Vries H, Lippke S (2018) Understanding the positive associations of sleep, physical activity, fruit and vegetable intake as predictors of quality of life and subjective health across age groups: a theory based, cross-sectional web-based study. Front Psychol 9:977. https://doi.org/10.3389/fpsyg.2018.00977

    Article  PubMed  PubMed Central  Google Scholar 

  57. Farokhnia M, Murphy G, Weinstein SJ, Shah NN, Parisi D, Albanes D, Leggio L (2021) A population-based investigation of the association between alcohol intake and serum total ghrelin concentrations among cigarette-smoking, non-alcohol-dependent male individuals. Drug Alcohol Depend 226:108835. https://doi.org/10.1016/j.drugalcdep.2021.108835

    Article  PubMed  PubMed Central  Google Scholar 

  58. Burt J, Dube L, Thibault L, Gruber R (2014) Sleep and eating in childhood: a potential behavioral mechanism underlying the relationship between poor sleep and obesity. Sleep Med 15(1):71–75. https://doi.org/10.1016/j.sleep.2013.07.015

    Article  PubMed  Google Scholar 

  59. Julian V, Haschke F, Fearnbach N, Gomahr J, Pixner T, Furthner D, Weghuber D, Thivel D (2022) Effects of movement behaviors on overall health and appetite control: current evidence and perspectives in children and adolescents. Curr Obes Rep 11(1):10–22. https://doi.org/10.1007/s13679-021-00467-5

    Article  PubMed  PubMed Central  Google Scholar 

  60. Rice F, Riglin L, Lomax T, Souter E, Potter R, Smith DJ, Thapar AK, Thapar A (2019) Adolescent and adult differences in major depression symptom profiles. J Affect Disord 243:175–181. https://doi.org/10.1016/j.jad.2018.09.015

    Article  PubMed  Google Scholar 

  61. Zainal NH, Newman MG (2022) Inflammation mediates depression and generalized anxiety symptoms predicting executive function impairment after 18 years. J Affect Disord 296:465–475. https://doi.org/10.1016/j.jad.2021.08.077

    Article  PubMed  Google Scholar 

  62. Vancampfort D, Stubbs B, Firth J, Hallgren M, Schuch F, Lahti J, Rosenbaum S, Ward PB, Mugisha J, Carvalho AF, Koyanagi A (2017) Physical activity correlates among 24,230 people with depression across 46 low- and middle-income countries. J Affect Disord 221:81–88. https://doi.org/10.1016/j.jad.2017.06.012

    Article  PubMed  Google Scholar 

  63. Epel ES, Tomiyama AJ, Mason AE, Laraia BA, Hartman W, Ready K, Acree M, Adam TC, St Jeor S, Kessler D (2014) The reward-based eating drive scale: a self-report index of reward-based eating. PLoS ONE 9:e101350. https://doi.org/10.1371/journal.pone.0101350

    Article  PubMed  PubMed Central  Google Scholar 

  64. Johal SK, Rhemtulla M (2021) Comparing estimation methods for psychometric networks with ordinal data. Psycho Methods Advance Online Publication. https://doi.org/10.1037/met0000449

    Article  Google Scholar 

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Funding

The present study was funded by the National Natural Science Foundation of China (Grant No. 32271135).

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  1. School of Psychology, Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, Ministry of Education Key Laboratory of Brain Cognition and Educational Science, South China Normal University, Shipai Road, Guangzhou, 510631, China

    Yifan Zhang, Zijuan Ma, Wanyi Chen, Dongfang Wang & Fang Fan

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Contributions

YZ drafted the initial manuscript and conducted the data analysis. ZM and WC contributed extensively to reviewing and revising the manuscript for important intellectual content. DW and FF contributed significantly to the acquisition of data and the design of the study. All authors read and approved the final manuscript.

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Correspondence to Fang Fan.

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This study was carried out in accordance with the Helsinki Declaration as revised in 1989 and approved by the Ethics Committees of South China Normal University (SCNU-PSY-2021-094). The survey was under the principle of voluntary participation. Informed consent to participate in this study was obtained from all participants and their guardians.

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Zhang, Y., Ma, Z., Chen, W. et al. Network Analysis of Health-related Behaviors, Insomnia, and Depression Among Urban Left-behind Adolescents in China. Child Psychiatry Hum Dev (2023). https://doi.org/10.1007/s10578-023-01607-9

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