- Email: [email protected]
Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents
Journal Pre-proof
Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents Jianghong Liu , Colin Liu , Tina Wu , Bao-Peng Liu , Cun-Xian Jia , Xianchen Liu PII: DOI: Reference:
S0165-0327(19)31694-5 https://doi.org/10.1016/j.jad.2019.08.017 JAD 11024
To appear in:
Journal of Affective Disorders
Received date: Revised date: Accepted date:
28 June 2019 7 August 2019 12 August 2019
Please cite this article as: Jianghong Liu , Colin Liu , Tina Wu , Bao-Peng Liu , Cun-Xian Jia , Xianchen Liu , Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.08.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
HIGHLIGHTS
Prolonged mobile phone use is positively associated with risk of depression. This relationship is observed both on weekdays and weekends. Mobile phone use and depression remain correlated after adjusting for covariates. Sleep disturbances partially mediate relationship between phone use & depression.
TITLE PAGE
Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents
Authors: Jianghong Liua, Colin Liua, Tina Wua, Bao-Peng Liub, Cun-Xian Jiab, Xianchen Liub,c,d aSchool
of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA
bShandong
University School of Public Health, Jinan, China
c
South China Normal University School of Psychology, Guangzhou, China
dCenter
for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA
Corresponding Authors: Xianchen Liu, MD, PhD, Shandong University School of Public Health, Jinan, 250012, China. Email: [email protected] or Dr. Cun-Xian Jia, [email protected]
ABSTRACT
Background: Prolonged screen time has negative impacts on health and well-being. This study examined the association between the duration of mobile phone use (DMPU) and depressive symptoms in a large sample of Chinese adolescents. Methods: 11,831 adolescent students participated in the baseline Shandong Adolescent Behavior and Health Cohort (SABHC) survey in Shandong, China in 2015. A selfadministered questionnaire was used to measure DMPU on weekdays and the weekend, sleep, mental health, and family environment. The Centre for Epidemiologic Studies Depression Scale (CES-D) and Youth Self-Report (YSR) depression scales were used to assess depressive symptoms. Results: The mean age of participants was 15.0 (SD=1.5) and 51% were male. The prevalence of depressive symptoms increased with prolonged DMPU. After adjusting for adolescent and family covariates, DMPU ≥ 2 hours/day on weekdays (OR=1.78, 95%CI=1.48—2.15) and ≥ 5 hours/day on the weekend (OR=1.67, 95%CI=1.41—1.98) was associated with increased risk of depressive symptoms as assessed by CES-D. The DMPU-depression association was found to be partially mediated by short sleep duration or insomnia. Similar associations were observed for depression as assessed by YSR. Study limitation: This is a cross-sectional survey. Mobile phone use and depressive symptoms were measured by self-report. Conclusions: Prolonged mobile phone use of ≥2 hours on weekdays and ≥ 5 hours on the weekend is associated with an increased risk of depressive symptoms. The association appears to be partially mediated by sleep disturbances. Key words: mobile phone use, depression, sleep, insomnia
INTRODUCTION Depression is one of the most common mental disorders and occurs across the lifespan. As reported by the National Survey on Drug Use and Health, about 13.3% of adolescents in the US has had at least one major depressive episode (NIMH, 2019). While the effects of depression manifests differently in each individual, consequences of this disorder can become severe and typically include poor school performance (Fergusson and Woodward, 2002), unfavorable health outcomes, drug addiction (Magklara et al., 2015), and suicide attempts (Simon et al., 2013). Of adolescents who have experienced major depressive episodes, 71% have had at least one episode with severe impairment of function (NIMH, 2019). Given the high prevalence and burden of this disorder, many risk factors have been identified to contribute to its incidence, including family history of mental disorders, other comorbid mental disorders (Swartz et al., 2017), life stressors, physical illnesses, internalizing behavior patterns (Lewinsohn et al., 1994), and poor social support (Galambos et al., 2004). Because these factors are largely fixed or difficult to change, there has been increasing interest in investigating the potential effect of more malleable lifestyle and social factors on contributing to the risk of depression, including mobile phone use and sleep (Liu et al., 2018a; Thomée, 2018). Prolonged mobile phone use and sleep deprivation have become major public health concerns (Riesch et al., 2019). The Centers for Disease Control and Prevention reports that about 69% of high school students report sleeping less than 8 hours on school nights (CDC, 2017). This is highly alarming as short sleep duration is related to both physical disorders, such as obesity (Liu et al., 2012) and cardiovascular diseases (Hoevenaar-Blom et al., 2011), and mental disorders, such as depression (Szklo-Coxe et al., 2010). Additionally, there are currently more than 3 billion smartphone users in the world (Takahashi, 2018), with numbers predicted to continue rising in the coming years. Considering the high usage rates of these mobile devices, recent research has focused on the effects of use on various health outcomes such as sleep and mental disorders. Specifically, both cross-sectional as well as longitudinal follow-up studies conducted on adolescents and young adults report that high rates of mobile phone use results in new occurrences of sleep disturbances (Liu et al., 2018a), lower sleep quality and shorter sleep duration (Thomée, 2018), and insomnia (Jenaro et al., 2007). Relatedly, cross-sectional studies demonstrate an association between the use of mobile phones and daytime dysfunction (Demirci
et al., 2015; Thomée, 2018). Most notably, many studies have found a positive relationship between duration of mobile phone use (DMPU) and depressive symptoms in high school and university students. Greater mobile phone usage is correlated with poorer psychological health (Demirci et al., 2015; Ha et al., 2008; Roser et al., 2016), increased vulnerability to mental disorders (Zulkefly and Baharudin, 2009), and higher risk of depression incidence (Augner and Hacker, 2012; Demirci et al., 2015; Ha et al., 2008; Liu et al., 2018a; Sánchez-Martínez and Otero, 2009; Thomée et al., 2011). Conversely, depression is also correlated with prolonged DMPU (Augner and Hacker, 2012; Sánchez-Martínez and Otero, 2009). This relationship was maintained across studies conducted globally (Augner and Hacker, 2012; Demirci et al., 2015; Ha et al., 2008; Lepp et al., 2014; Liu et al., 2018a; Roser et al., 2016; Sánchez-Martínez and Otero, 2009; Thomée et al., 2011; Zulkefly and Baharudin, 2009). More generally, a recent meta-analysis of screen time use across adolescents has demonstrated an increased risk of depression with longer screen time (Liu et al., 2016). This shows that the relationship between mobile phone use and depression can be generalized to other forms of technology such as televisions and gaming systems. Despite the volume of literature on the relation between mobile phone use and depression, previous findings are not consistent or comprehensive, and more research is needed to confirm these findings across various adolescent populations. For example, mobile phone use in adolescents may differ between weekdays and weekends due to a busy school schedule and heavy homework on weekdays. Few studies have examined the associations of depression with weekday and weekend DMPU independently. Furthermore, while many prior studies have demonstrated the direct effects of both mobile phone use and short sleep on depression separately, few studies have investigated potential mediating effects of sleep on the link between DMPU and depression in adolescents. Identifying mediating factors could be important to further understand the relationship between mobile phone use and depression. This present study aims to examine the association between mobile phone use and depression using sleep as a mediating factor in a large sample of Chinese adolescents.
METHODS Participants and procedure
The Shandong Adolescent Behavior & Health Cohort (SABHC) is a longitudinal study of behavior and health among adolescent students of Shandong, China. A total of 12,301 students participated in the SABHC baseline survey. Detailed sampling and data collection have been described elsewhere (Liu et al., 2019a; Liu et al., 2018b; Liu et al., 2019b). In brief, participants were sampled from 5 middle and 3 high schools in 3 counties of Shandong, and consideration for the representativeness of adolescent students in the region, prior study collaboration, convenience, and budget was taken for at least 3 waves of data collection. In November-December 2015, participants completed a self-administered, structured adolescent health questionnaire (AHQ) to assess behavioral and mental health problems and to collect demographic information. After receiving permission from the teachers for the sampled classes, trained master-level public health workers administered AHQ to participants in classrooms during regular school hours. All sampled students attending school on the day of the survey were given the option of not participating, and informed consent was obtained from participants. Before filling out the questionnaire, participants were instructed to read the instructions carefully and informed that the survey was anonymous, and their participation was voluntary with no penalties for non-participation. The study was approved by the research ethics committee of Shandong University School of Public Health and target schools (IRB ID 20161102).
Measures in the AHQ Duration of mobile/smartphone use There were 2 questions used to ask participants about their mobile phone use on weekdays and the weekend in the past month. The first was, ―On an average school day, how many hours do you use a mobile/smartphone in the past month?‖ and the second was, ―On an average weekend day, how many hours do you use a mobile/smartphone in the past month?‖ Depressive symptoms Depressive symptoms were measured by The Centre for Epidemiologic Studies Depression Scale (CES-D) (Radloff, 1977) and the Chinese Youth Self-Report (YSR) of Achenbach’s Child Behavior Checklist (Liu et al., 1997). The CES-D is a 20-item self-report questionnaire to measure depressive symptoms in the past week (Radloff, 1977). Each item is rated on a Likert scale from 0 to 3 points (0 =rarely or
none of the time/less than 1 day a week, 1 =sometimes/1-2 days a week, 2 =always or half of a week/3-4 days a week, and 3 =most or all of the times/5-7 days a week). Summing up the scores of the 20 items yields a total CES-D score. A higher total score indicates a higher severity of depressive symptoms. The CES-D has been widely used among adolescents because of its psychometric properties (Rushton et al., 2002; Yang et al., 2018). Cronbach alpha with the present sample was 0.86. The YSR anxious/depressed subscale consists of 16 items that are rated on a 3-point scale: ―0‖ = not true, ―1‖ = somewhat or sometimes true, and ―2‖ = very true or often true within the past six months (Liu et al., 1997; Radloff, 1977). Summing up the scores of the 16 items yields a total depressive score. A higher total score indicates a higher severity of depressive symptoms. Cronbach alpha with the present sample was 0.88 for the subscale. Because the cutoffs for Chinese adolescents have not been well established, scores > 90 th percentile on the CES-D and YSR depression scale were used as cutoff points to define clinically relevant depressive symptoms, respectively (Byrd et al., 1996; Liu et al., 1999). Sleep duration and insomnia Night sleep duration and insomnia symptoms were assessed by 5 items adapted from the Pittsburgh Sleep Questionnaire Index (Buysse et al., 1989). Sleep duration was asked by ―During the past month, on an average school day, how many hours of actual sleep did you get at night?‖ and ―During the past month, on an average weekend day, how many hours of actual sleep did you get at night?‖ Insomnia symptoms were asked about difficulty falling asleep (DIS), difficulty maintaining sleep (DMS), and early morning waking (EMA). The participants answered each question about insomnia with a response option of rarely or never (<1 time/week), sometimes (12 times/ week), often (3-5 times/week), or almost every day (6-7 times/week). If the response was often or almost every day, the symptom was considered clinically significant (i.e., at least 3 times/week). Adolescent and family demographic factors Adolescent factors included age, sex, chronic physical diseases (yes/no), smoking (yes/no), and alcohol use (yes/no). Family factors included paternal education (elementary school, middle school, high school, college or above), occupation (farmer/non-farmer), family economic status (excellent, good, fair, poor, or very poor), and interparental relationship (excellent, good, fair, poor, divorced/separated).
Statistical analysis Sample characteristics were described by means (SD) for continuous variables and frequencies (%) for categorical variables. Student t tests/ANOVA and Chi-square tests were conducted for significant testing on continuous variables and categorical variables, respectively. Univariate and multivariate logistic regression models were performed to examine the associations between SMPU on weekdays and the weekend and depressive symptoms, respectively. In the multivariate regression, adolescent factors (i.e., age, gender, chronic disease, smoking, alcohol use, sleep duration, insomnia, and school) and family factors (economic status, interparental relationship, father education, and occupation) were adjusted as potential confounders. Statistical tests of regression estimates and odds ratios were based on Wald statistics. Mediation analyses were performed to estimate the mediating effects of insomnia or short sleep duration on the mobile phone use-depression link. Bootstrap methods were used to verify the indirect effect and to produce bias-corrected confidence intervals, which were based on 5000 bootstrapping samples. Furthermore, given the large sample size and high response rate (<5% with missing data for studied variables, see Tables 1 and 2), missing data were assumed to be random and were excluded from all analyses. All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 24.0 (Armonk, NY: IBM Corp).
RESULTS Of the 12,301 students sampled for the baseline survey, 11,831 returned questionnaires with basic demographic information (i.e., age and gender) for current analysis (96.2%). The mean age of the participants was 15.0 (SD=1.5) and 51% were boys. Table 1 summarizes sample characteristics by age, gender, disease history, substance use, sleep duration, insomnia, family economic status, interparental relationship, father education, and occupation.
Prevalence of depressive symptoms by the duration of mobile phone use Table 2 presents prevalence rates of depressive symptoms by the duration of mobile phone use on weekdays and weekends, respectively. As shown in Table 2, 22.3% reported using mobile phones for more than one hour on weekdays, and 55.4% used phones for at least two hours on weekends. The prevalence rates of depressive symptoms significantly increased with
prolonged mobile phone use on either weekdays or weekends. On weekdays, the prevalence of depressive symptoms as assessed by CES-D was markedly increased in adolescents who used a mobile phone for ≥ 2 hours per day compared with those who used it for < 1 hour per day (19.1% vs 10.0%). On weekends, the prevalence of depressive symptoms as assessed by CES-D was markedly increased in adolescents who used mobile phone for ≥ 5 hours per day compared with those who used it for < 2 hours per day (18.3% vs 8.6%). A similar tendency was observed for depressive symptoms as assessed by YSR.
Associations of depressive symptoms with mobile phone use Tables 3 and 4 show the associations of depressive symptoms with DMPU on weekdays and weekends. As indicated in Table 3, the odds ratios of both CESD and YSR-assessed depressive symptoms were significantly increased with prolonged mobile phone use on weekdays. The odds ratios for adolescents who used mobile phone ≥ 2 hours on weekdays were reduced but remained to be significant after adjusting for adolescent and family covariates, including sleep duration and insomnia. For example, the odds ratio of CESD assessed depressive symptoms was 2.13 (95%CI=1.80-2.52) in adolescents who used mobile phone ≥ 2 hours on weekdays compared with those who used mobile phone < 1 hour before covariates were adjusted. The odds ratio became 1.78 (95%CI=1.48-2.15) after covariates were adjusted. As shown in Table 4, prolonged mobile phone use on weekends was also associated with increased odds of depressive symptoms. The odds ratio was markedly increased when adolescents used mobile phone ≥ 4 hours on weekends compared with those who used mobile phone < 2 hours per day. Similarly, the odds ratios for both CESD and YSR-assessed depressive symptoms were reduced after adjusting for adolescent and family covariates and the odds ratio remained to be significant only if mobile phone ≥ 5 hours on weekends.
Mediation analyses Mediation analyses were performed to examine if the mobile phone use-depression link was mediated by short sleep duration and insomnia caused by prolonged mobile phone use. As shown in Table 5, prolonged mobile phone use on both weekdays and weekends was significantly associated with increased insomnia symptoms and short sleep duration. Short sleep duration and insomnia symptoms were significantly associated with elevated depressive
symptoms as assessed by either CESD or YSR except for weekend sleep duration on YSR depression. Insomnia and short sleep duration mediated the prolonged mobile phone usedepression link at least in part except that the mediation effect of weekend sleep duration on weekend mobile phone use-depression link was not statistically significant.
DISCUSSION In this large sample study of 11,831 Chinese adolescent students, using both CES-D and YSR to measure depression, we found that prolonged phone usage (≥ 2 hours on weekdays and ≥ 5 hours on weekends) increases the prevalence of self-reported depressive symptoms. Further, the risk of developing clinically depressive symptoms increases significantly with these long durations of phone usage, even after accounting for various adolescent and family covariates. Finally, sleep disturbances, such as short sleep duration or insomnia, is found to partially mediate this relationship, suggesting that prolonged mobile phone use results from disturbances in sleep, which further accounts for outcomes of depressive symptoms. Our study demonstrates a substantial increase in prevalence of depressive symptoms with increasing duration of mobile phone use among adolescent students both on weekdays and weekends. The mobile phone use and depression association is noticed for usage duration of more than 2 hours on weekdays and more than 5 hours on weekends. This relationship was similarly reported by Ha et al. (2008) and Augner and Hacker (2012) in their cross-sectional studies showing that greater phone usage was reflected in higher depression scores via the Beck Depression Inventory. Additionally, a prospective study conducted on adolescents and young adults in China demonstrated that self-reported sleep disturbances and mental distress increased with longer phone usage (Liu et al., 2018a). Thus, the connection between phone usage and prevalence of depressive symptoms is well studied and confirmed. Similarly, our findings are consistent with those of related studies (Liu et al., 2016; Liu et al., 2018a; Sánchez-Martínez and Otero, 2009; Thomée et al., 2011) in showing the significantly increased risk of developing clinically depressive symptoms among participants reporting the highest rates of phone usage, even after accounting for various participant and family covariates. This indicates that prolonged phone use may be a reliable predictor of future depression incidence. Various longitudinal prospective studies provide support for this claim by verifying the positive relationship between duration of phone usage and incidence of future mental distress
and depressive symptoms (Liu et al., 2018a; Thomée et al., 2011). Further, a meta-analysis of related literature on screen time and risk of depression confirms the positive relationship between duration of screen use and likelihood of depression incidence (Liu et al., 2016). Conversely, there is evidence of reverse directionality in the association between phone usage and risk of depression. In a cross-sectional study conducted on adolescents in Madrid, Sánchez-Martínez and Otero (Sánchez-Martínez and Otero, 2009) report that those suffering from symptoms of depression are more likely to be intensive mobile phone users. Thus, though the causality of this relationship is unclear, there is strong support for the association between mobile phone use and depressive symptoms. Future longitudinal research in this area may be able to provide a model of causality. Sleep disturbances have increasingly been recognized as a problem among adolescents and have contributed to risk of depression in this population. At the same time, these disturbances may result from prolonged phone usage. More specifically, studies have demonstrated the adverse effects of mobile phone use on incidence of insomnia (Liu et al., 2018a), sleep disturbances (Liu et al., 2018a; Thomée et al., 2011), lower sleep quality (Demirci et al., 2015; Thomée, 2018), shorter sleep (Chahal et al., 2013), and daytime dysfunction (Exelmans and Van den Bulck, 2016). However, little is known about the relationship between phone use, depression, and sleep. Our mediation analysis shows that prolonged phone use on both weekdays and weekends are significantly linked to insomnia and shorter sleep duration. Furthermore, disruptions in sleep are associated with symptoms of depression. Thus, the mediation models suggest that it is possible that disturbances to mental health due to mobile phone use may result from inadequate sleep or impairing sleep disruptions. While the comprehensive relationship between mobile phone use, sleep, and depression has rarely been studied, a recent study conducted in Turkey also examined the associations among these three factors. Interestingly, the study reported depression and anxiety as mediators between phone use and sleep disturbances (Demirci et al., 2015) whereas our study found sleep to mediate the relationship between phone use and depression. However, given the previously established associations between each of these variables and the minimal research conducted on causality, it is impossible to conclude directionality. It should be noted that the effects of DMPU on adolescent mental health may differ between weekdays and weekends. This variance in mobile phone use may be due to busy school
schedules and heavy homework on weekdays. However, few studies have examined the associations between depression and DMPU on weekdays and weekend independently. In our sample, 22.3% reported using mobile phones for more than one hour on weekdays, and 55.4% used phones for at least two hours on weekends. Our multivariate analyses demonstrated that mobile phone use ≥ 2 hours on weekdays and ≥ 5 hours on weekends were significantly associated with increased risk of self-reported depressive symptoms. Longitudinal studies are needed to confirm this finding and to examine the pathways between DMPU and depression on weekdays and weekends separately. Many possible mechanisms have been proposed for the various relationships between prolonged mobile phone use, depressive symptoms, and sleep. Firstly, the vast amount of information and stimuli presented by mobile phones can be cognitive and emotional burdens, resulting in increased psychological vulnerability to impaired mental health (Liu et al., 2018a). Conversely, those suffering with more intense depressive symptoms may use mobile phones to a greater extent in order to escape daily struggles and seek support through social media (Demirci et al., 2015). Additionally, longer durations of phone usage increases arousal and vigilance of the brain and physiological systems, resulting in lower sleep quality, duration, and sleep time (Demirci et al., 2015; Thomée, 2018). Given the importance of sleep on overall health and wellbeing, disruptions in sleep would further induce stress and vulnerability to psychological disturbances, increasing the risk of depressive symptoms (Demirci et al., 2015). Some limitations should be considered. Because of the cross-sectional design of our study, causality between phone usage and depressive symptoms cannot be established. Furthermore, some important covariates which may likely contribute to both mobile phone use and depression, such as life stress, social support, physical activity, and other mental health and sleep problems, were not assessed and could not be statistically controlled for. Additionally, depression symptoms, sleep duration, and insomnia were measured via selfreported surveys which could result in recall bias and subjectivity. Future longitudinal studies using objective sleep measures may investigate the causal relationships between mobile phone use, sleep, and depression. Conclusion
Both depression and prolonged mobile phone use in adolescents have been increasingly viewed as public health concerns due to its growing prevalence and negative consequences. While many risk factors that predispose adolescents to depression have been identified, prolonged mobile phone use has been increasingly recognized as an important modifiable risk factor. In this large sample of 11,831 Chinese students, we demonstrated that prolonged mobile phone use of ≥2 hours on weekdays and ≥5 hours on the weekend is associated with increased risk of clinically depressive symptoms. These associations are partially mediated by either short sleep duration or insomnia. The important implications of the current study lie in raising awareness among adolescents that prolonged mobile phone use may result in sleep problems and depression (Riesch et al., 2019). Furthermore, the results from this study suggests that limiting the use of mobile phones or setting a time limit on phone usage at school and home could be important for decreasing the risk of depressive symptoms. Conflict of Interest All authors have no conflicts of interest to report.
AUTHOR STATEMENT Contributors Jianghong Liua, Colin Liua, Tina Wua, Bao-Peng Liub, Cun-Xian Jiab, Xianchen Liu b,c a
School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA
b
Shandong University School of Public Health, Jinan, China
c
Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA
Jianghong Liu, Colin Liu, and Tina Wu developed the introduction, discussion, and conclusion. Cun-Xian Jia and Xianchen Liu designed the study. Bao-Peng Liu conducted data analysis and wrote the methods and results sections. All authors gave critical reviews and finalized the manuscript. Role of the Funding Source This work was funded in part for data collection by National Natural Science Foundation of China (Grant number 81573233) The funding institution had no role in the study design, data collection and analysis, and decision to publish, or preparation of the manuscript. Acknowledgements The authors would like to thank Lijin County Center for Disease Control and Prevention, Zoucheng City Center for Disease Control and Prevention, and Yanggu County Center for Disease Control and Prevention, Shandong Province, China and all participating school teachers for their help with data collection and all students for their voluntarily participating in the study.
References Augner, C., Hacker, G.W., 2012. Associations between problematic mobile phone use and psychological parameters in young adults. International journal of public health 57, 437441. Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J., 1989. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research 28, 193-213. Byrd, R.S., Weitzman, M., Lanphear, N.E., Auinger, P., 1996. Bed-wetting in US children: epidemiology and related behavior problems. Pediatrics 98, 414-419. CDC, 2017. Short Sleep Duration Among US Adults, in: Statistics, D.a. (Ed.). Centers for Disease Control and Prevention. Chahal, H., Fung, C., Kuhle, S., Veugelers, P., 2013. Availability and night‐time use of electronic entertainment and communication devices are associated with short sleep duration and obesity among C anadian children. Pediatric obesity 8, 42-51. Compton, M.T., Chien, V.H., Bollini, A.M., 2007. Psychometric properties of the Brief Version of the Schizotypal Personality Questionnaire in relatives of patients with schizophreniaspectrum disorders and non-psychiatric controls. Schizophrenia Research 91, 122-131. Demirci, K., Akgönül, M., Akpinar, A., 2015. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of behavioral addictions 4, 85-92. Exelmans, L., Van den Bulck, J., 2016. Bedtime mobile phone use and sleep in adults. Social Science & Medicine 148, 93-101. Fergusson, D.M., Woodward, L.J., 2002. Mental health, educational, and social role outcomes of adolescents with depression. Archives of general psychiatry 59, 225-231. Fonseca-Pedrero, E., Paíno-Piñeiro, M., Lemos-Giráldez, S., Villazón-García, Ú., Muñiz, J., 2009. Validation of the Schizotypal Personality Questionnaire—Brief form in adolescents. Schizophrenia Research 111, 53-60. Galambos, N.L., Leadbeater, B.J., Barker, E.T., 2004. Gender differences in and risk factors for depression in adolescence: A 4-year longitudinal study. International Journal of Behavioral Development 28, 16-25. Ha, J.H., Chin, B., Park, D.-H., Ryu, S.-H., Yu, J., 2008. Characteristics of excessive cellular phone use in Korean adolescents. CyberPsychology & Behavior 11, 783-784. Hoevenaar-Blom, M.P., Spijkerman, A.M., Kromhout, D., van den Berg, J.F., Verschuren, W., 2011. Sleep duration and sleep quality in relation to 12-year cardiovascular disease incidence: the MORGEN study. Sleep 34, 1487-1492.
Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., Caballo, C., 2007. Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. Addiction research & theory 15, 309-320. Lepp, A., Barkley, J.E., Karpinski, A.C., 2014. The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in Human Behavior 31, 343-350. Lewinsohn, P.M., Roberts, R.E., Seeley, J.R., Rohde, P., Gotlib, I.H., Hops, H., 1994. Adolescent psychopathology: II. Psychosocial risk factors for depression. Journal of abnormal psychology 103, 302. Liu, B.-P., Wang, X.-T., Liu, Z.-Z., Wang, Z.-Y., Liu, X., Jia, C.-x., 2019a. Stressful life events, insomnia and suicidality in a large sample of Chinese adolescents. Journal of affective disorders 249, 404-409. Liu, J., Zhang, A., Li, L., 2012. Sleep duration and overweight/obesity in children: review and implications for pediatric nursing. Journal for Specialists in Pediatric Nursing 17, 193204. Liu, M., Wu, L., Yao, S., 2016. Dose–response association of screen time-based sedentary behaviour in children and adolescents and depression: a meta-analysis of observational studies. Br J Sports Med 50, 1252-1258. Liu, S., Wing, Y.K., Hao, Y., Li, W., Zhang, J., Zhang, B., 2018a. The associations of long-time mobile phone use with sleep disturbances and mental distress in technical college students: a prospective cohort study. Sleep 42, zsy213. Liu, X., Chen, H., Liu, Z.-Z., Jia, C.-X., 2018b. Insomnia and Psychopathological Features Associated With Restless Legs Syndrome in Chinese Adolescents. The Journal of clinical psychiatry 79. Liu, X., Guo, C., Liu, L., Wang, A., Hu, L., Tang, M., Chai, F., Zhao, G., Yang, J., Sun, L., 1997. Reliability and validity of the Youth Self-Report (YSR) of Achenbach's Child Behavior Checklist (CBCL). Chinese Mental Health Journal. Liu, X., Kurita, H., Guo, C., Miyake, Y., Ze, J., Cao, H., 1999. Prevalence and risk factors of behavioral and emotional problems among Chinese children aged 6 through 11 years. Journal of the American Academy of Child & Adolescent Psychiatry 38, 708-715. Liu, X.C., Chen, H., Liu, Z.Z., Wang, J.Y., Jia, C.X., 2019b. Prevalence of suicidal behaviour and associated factors in a large sample of Chinese adolescents. Epidemiol Psychiatr Sci 28, 280-289. Magklara, K., Bellos, S., Niakas, D., Stylianidis, S., Kolaitis, G., Mavreas, V., Skapinakis, P., 2015. Depression in late adolescence: a cross-sectional study in senior high schools in Greece. BMC psychiatry 15, 199.
NIMH, 2019. Major Depression, Mental Health Information Statistics. National Institute of Health Bethesda, MD. Radloff, L.S., 1977. The CES-D scale: A self-report depression scale for research in the general population. Applied psychological measurement 1, 385-401. Raine, A., Fung, A.L.-c., Lam, B.Y.H., 2011. Peer victimization partially mediates the schizotypy-aggression relationship in children and adolescents. Schizophrenia bulletin 37, 937-945. Riesch, S.K., Liu, J., Kaufmann, P.G., Doswell, W.M., Cohen, S., Vessey, J., 2019. Preventing adverse health outcomes among children and adolescents by addressing screen media practices concomitant to sleep disturbance. Nursing Outlook 67, 492-496. Roser, K., Schoeni, A., Foerster, M., Röösli, M., 2016. Problematic mobile phone use of Swiss adolescents: is it linked with mental health or behaviour? International journal of public health 61, 307-315. Rushton, J.L., Forcier, M., Schectman, R.M., 2002. Epidemiology of depressive symptoms in the National Longitudinal Study of Adolescent Health. Journal of the American Academy of Child & Adolescent Psychiatry 41, 199-205. Sánchez-Martínez, M., Otero, A., 2009. Factors associated with cell phone use in adolescents in the community of Madrid (Spain). CyberPsychology & Behavior 12, 131-137. Simon, G.E., Rutter, C.M., Peterson, D., Oliver, M., Whiteside, U., Operskalski, B., Ludman, E.J., 2013. Does response on the PHQ-9 Depression Questionnaire predict subsequent suicide attempt or suicide death? Psychiatric Services 64, 1195-1202. Swartz, J.R., Hariri, A.R., Williamson, D.E., 2017. An epigenetic mechanism links socioeconomic status to changes in depression-related brain function in high-risk adolescents. Molecular psychiatry 22, 209. Szklo-Coxe, M., Young, T., Peppard, P.E., Finn, L.A., Benca, R.M., 2010. Prospective associations of insomnia markers and symptoms with depression. American journal of epidemiology 171, 709-720. Takahashi, D., 2018. Newzoo: Smartphone users will top 3 billion in 2018, hit 3.8 billion by 2021, Mobile. VentureBeat. Thomée, S., 2018. Mobile phone use and mental health. A review of the research that takes a psychological perspective on exposure. International journal of environmental research and public health 15, 2692. Thomée, S., Härenstam, A., Hagberg, M., 2011. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC public health 11, 66.
Yang, X., Lau, J.T., Lau, M.C., 2018. Predictors of remission from probable depression among Hong Kong adolescents–A large-scale longitudinal study. Journal of affective disorders 229, 491-497. Zulkefly, S.N., Baharudin, R., 2009. Mobile phone use amongst students in a university in Malaysia: its correlates and relationship to psychological health. European Journal of Scientific Research 37, 206-218.
TABLE 1 Sample Characteristics N (Sample size)*
n
% /Mean (SD)
Male gender
11831
5813
Mean Age (SD)
11831
Chronic disease
11830
475
4.0
Ever smoking
11831
2562
21.7
Ever drinking
11831
4397
37.2
Insomnia
11249
1659
14.7
Sleep duration on weekdays, hours
11517
7.10(1.43)
Sleep duration at the weekend, hours
11505
8.94(1.61)
Family economic status
11666
49.1 14.97(1.45)
Excellent
281
2.4
Good
2024
17.3
Fair
7941
68.1
Poor
1247
10.7
Very poor
173
1.5
Excellent
4827
41.5
Good
3134
26.9
Fair
2902
24.9
Poor
347
3.0
Separated/divorced/died
434
3.7
Primary school
1572
13.5
Middle school
6246
53.7
High school
2138
18.4
Professional school
920
7.9
College or above
766
6.6
4521
38.2
Parental relationship
Father education
Father occupation: Farmer *N differs due to missing values
11644
11642
11831
Table 2. Prevalence of depressive symptoms by duration of mobile phone use in Chinese adolescents CES-D YSR N (%) Depression a Depression b Mobile phone use on weekdays, hours
11580 (100)
<1
8989(77.6)
10.0
10.8
1–
1440 (12.4)
11.8
12.9
≥2
1151 (9.9)
19.1
18.7
81.70 (.000)
61.94(.000)
X2 (p) Mobile phone use at the weekend, hours
11573 (100)
<2
5164 (44.6)
8.6
9.7
2–
3436 (29.7)
10.0
10.3
4–
790 (6.8)
12.4
13.8
≥5 X (p)
2183 (18.9)
18.3 145.86
19.0 141.41 (.000)
2
(.000) a b
CES-D = Centre for Epidemiologic Studies Depression Scale YSR = Youth Self-Report
Table 3. Associations of mobile phone use on weekdays with depressive symptoms CESD-Depression a Crude OR
YSR-Depression b
Adjusted OR
(95%CI)
(95%CI)
c
Crude OR
Adjusted OR
(95%CI)
(95%CI) c
Mobile phone use, hours <1
1.00
1.00
1.00
1.00
1–
1.20(1.00-1.43)*
1.16(0.95-1.41)
1.22(1.03-1.45)*
1.17(0.98-1.41)
≥2
2.13(1.80-2.52)***
1.78(1.48-2.15) ***
1.89(1.61-2.23)***
1.50(1.25-1.81)***
Female gender
1.48(1.29-1.70) ***
1.33(1.17-1.52)***
Age
1.06(1.00-1.12) *
1.05(0.99-.10)
Chronic disease/disability
1.53(1.17-2.00) **
1.84(1.44-2.35)**
Ever smoking
1.57(1.34-1.83) ***
1.60(1.38-1.85)***
Ever drinking
1.39(1.21-1.61) ***
1.37(1.19-1.57)***
Poor family economic status
1.15(1.36-1.68) ***
1.47(1.33-1.62)***
Poor interparental relationship
1.21(1.15-1.29) ***
1.29(1.22-1.36)***
Father education
1.04(0.98-1.12)
1.06(0.99-1.13)
Father occupation: Farming
0.92(0.80-1.07)
0.88(0.82-0.91)
Sleep duration on weekdays
0.88(0.83-0.93) ***
0.87(0.82-0.92)***
Insomnia
3.39(2.94-3.91) ***
2.94(2.56-3.37)***
*p<.05, **p<.01, ***p<.001 a CES-D = Centre for Epidemiologic Studies Depression Scale b YSR = Youth Self-Report c Adjusted for child age, gender, chronic disease, smoking, alcohol use, school, family factors, weekday sleep duration, and insomnia
Table 4. Associations of mobile phone use at the weekend with depressive symptoms CESD-Depression a Crude OR Adjusted OR
YSR-Depression b Crude OR Adjusted OR
(95%CI) c
(95%CI)
(95%CI) c
(95%CI)
Mobile phone use, hours <2
1.00
1.00
1.00
1.00
2–
1.18(1.02-1.38) *
1.04(0.88-1.22)
1.07(0.93-1.24)
0.89(0.77-1.05)
4–
1.51(1.19-1.91) **
1.11(0.85-1.43)
1.50(1.20-1.87)***
1.14(0.90-1.45)
≥5
2.38(2.05-2.76) ***
1.67(1.41-1.98) ***
2.19(1.91-2.53)***
1.48(1.26-1.74)***
Female gender
1.50(1.31-1.72)***
1.38(1.21-1.58)***
Age
1.12(1.06-1.17)***
1.11(1.06-1.16)***
Chronic disease/disability
1.58(1.21-2.06)***
1.89(1.48-2.42)***
Ever smoking
1.51(1.29-1.77)***
1.56(1.35-1.81)***
Ever drinking
1.39(1.20-1.61)***
1.38(1.20-1.58)***
Poor family economic status
1.52(1.37-1.69)***
1.48(1.34-1.63)***
Poor interparental relationship
1.21(1.14-1.29)***
1.28(1.21-1.35)***
Father education
1.08(1.01-1.16)*
1.09(1.02-1.16)**
Father occupation: Farming
0.86(0.75-0.99)*
0.83(0.72-0.96)**
Sleep duration on weekdays
1.02(0.98-1.06)
0.98(0.95-1.02)
Insomnia
3.39(2.94-3.91)***
2.94(2.56-3.38)***
*p<.05, **p<.01, ***p<.001 a CES-D = Centre for Epidemiologic Studies Depression Scale b YSR = Youth Self-Report c Adjusted for child age, gender, chronic disease, smoking, alcohol use, school, family factors, weekday sleep duration, and insomnia
Table 5. Mediation analysis Path Effect a SE a t/z P Weekday mobile phone useInsomnia scoreYSR Depression Total effect of X on Y 0.330 0.035 9.368 <0.001 Direct effect of X on Y 0.301 0.034 8.954 <0.001 Indirect effect of X on Y 0.029 0.014 XM 0.036 0.013 2.744 0.006 MY 0.821 0.025 33.317 <0.001 Weekday mobile phone useInsomnia scoreCESD Depression Total effect of X on Y 0.652 0.061 10.723 <0.001 Direct effect of X on Y 0.598 0.057 10.426 <0.001 Indirect effect of X on Y 0.054 0.027 XM 0.035 0.013 2.672 0.008 MY 1.537 0.042 36.518 <0.001 Weekend mobile phone useInsomnia scoreYSR Depression Total effect of X on Y 0.247 0.014 17.050 <0.001 Direct effect of X on Y 0.212 0.014 15.271 <0.001 Indirect effect of X on Y 0.035 0.005 XM 0.044 0.005 8.189 <0.001 MY 0.795 0.025 32.375 <0.001 Weekend mobile phone useInsomnia scoreCESD Depression Total effect of X on Y 0.449 0.025 18.130 <0.001 Direct effect of X on Y 0.384 0.023 16.363 <0.001 Indirect effect of X on Y 0.065 0.010 XM 0.043 0.005 8.009 <0.001 MY 1.492 0.042 35.625 <0.001
95%CI a 0.261-0.399 0.235-0.367 0.004-0.058 0.010-0.061 0.773-0.869 0.533-0.772 0.486-0.711 0.005-0.112 0.009-0.061 1.454-1.619
0.219-0.276 0.185-0.240 0.025-0.045 0.033-0.054 0.747-0.843 0.400-0.497 0.338-0.430 0.046-0.085 0.033-0.054 1.410-1.574
Weekday mobile phone useWeekday sleep durationYSR Depression Total effect of X on Y 0.335 0.035 9.669 <0.001 0.267-0.402 Direct effect of X on Y 0.301 0.034 8.806 <0.001 0.234-0.368 Indirect effect of X on Y 0.033 0.006 0.022-0.046 XM -0.052 0.009 -5.991 <0.001 -0.069~-0.035 MY -0.644 0.037 -17.345 <0.001 -0.716~-0.571 Weekday mobile phone useWeekday sleep durationCESD Depression Total effect of X on Y 0.680 0.060 11.323 <0.001 0.562-0.798 Direct effect of X on Y 0.623 0.059 10.475 <0.001 0.507-0.740 Indirect effect of X on Y 0.057 0.011 0.037-0.078 XM -0.056 0.009 -6.345 <0.001 -0.073~-0.039 MY -1.011 0.064 -15.675 <0.001 -1.137~-0.884 Weekend mobile phone useWeekend sleep durationYSR Depression Total effect of X on Y 0.237 0.014 16.661 <0.001 0.209-0.265 Direct effect of X on Y 0.235 0.014 16.468 <0.001 0.207-0.263
Indirect effect of X on Y XM MY
0.002 -0.036 -0.053
0.001 0.004 0.033
-9.013 -1.588
<0.001 0.112
-0.001-0.005 -0.044~-0.028 -0.118-0.012
Weekend mobile phone useWeekend sleep durationCESD Depression Total effect of X on Y 0.441 0.024 18.090 <0.001 0.393-0.488 Direct effect of X on Y 0.435 0.024 17.792 <0.001 0.387-0.483 Indirect effect of X on Y 0.006 0.002 0.002-0.011 XM -0.036 0.004 -8.908 <0.001 -0.044~-0.028 MY -0.160 0.058 -2.782 0.005 -0.273~-0.047 a Bootstrap SEs and 95%CIs were calculated in the indirect effect.
Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents Jianghong Liu , Colin Liu , Tina Wu , Bao-Peng Liu , Cun-Xian Jia , Xianchen Liu PII: DOI: Reference:
S0165-0327(19)31694-5 https://doi.org/10.1016/j.jad.2019.08.017 JAD 11024
To appear in:
Journal of Affective Disorders
Received date: Revised date: Accepted date:
28 June 2019 7 August 2019 12 August 2019
Please cite this article as: Jianghong Liu , Colin Liu , Tina Wu , Bao-Peng Liu , Cun-Xian Jia , Xianchen Liu , Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.08.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
HIGHLIGHTS
Prolonged mobile phone use is positively associated with risk of depression. This relationship is observed both on weekdays and weekends. Mobile phone use and depression remain correlated after adjusting for covariates. Sleep disturbances partially mediate relationship between phone use & depression.
TITLE PAGE
Prolonged mobile phone use is associated with depressive symptoms in Chinese adolescents
Authors: Jianghong Liua, Colin Liua, Tina Wua, Bao-Peng Liub, Cun-Xian Jiab, Xianchen Liub,c,d aSchool
of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA
bShandong
University School of Public Health, Jinan, China
c
South China Normal University School of Psychology, Guangzhou, China
dCenter
for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA
Corresponding Authors: Xianchen Liu, MD, PhD, Shandong University School of Public Health, Jinan, 250012, China. Email: [email protected] or Dr. Cun-Xian Jia, [email protected]
ABSTRACT
Background: Prolonged screen time has negative impacts on health and well-being. This study examined the association between the duration of mobile phone use (DMPU) and depressive symptoms in a large sample of Chinese adolescents. Methods: 11,831 adolescent students participated in the baseline Shandong Adolescent Behavior and Health Cohort (SABHC) survey in Shandong, China in 2015. A selfadministered questionnaire was used to measure DMPU on weekdays and the weekend, sleep, mental health, and family environment. The Centre for Epidemiologic Studies Depression Scale (CES-D) and Youth Self-Report (YSR) depression scales were used to assess depressive symptoms. Results: The mean age of participants was 15.0 (SD=1.5) and 51% were male. The prevalence of depressive symptoms increased with prolonged DMPU. After adjusting for adolescent and family covariates, DMPU ≥ 2 hours/day on weekdays (OR=1.78, 95%CI=1.48—2.15) and ≥ 5 hours/day on the weekend (OR=1.67, 95%CI=1.41—1.98) was associated with increased risk of depressive symptoms as assessed by CES-D. The DMPU-depression association was found to be partially mediated by short sleep duration or insomnia. Similar associations were observed for depression as assessed by YSR. Study limitation: This is a cross-sectional survey. Mobile phone use and depressive symptoms were measured by self-report. Conclusions: Prolonged mobile phone use of ≥2 hours on weekdays and ≥ 5 hours on the weekend is associated with an increased risk of depressive symptoms. The association appears to be partially mediated by sleep disturbances. Key words: mobile phone use, depression, sleep, insomnia
INTRODUCTION Depression is one of the most common mental disorders and occurs across the lifespan. As reported by the National Survey on Drug Use and Health, about 13.3% of adolescents in the US has had at least one major depressive episode (NIMH, 2019). While the effects of depression manifests differently in each individual, consequences of this disorder can become severe and typically include poor school performance (Fergusson and Woodward, 2002), unfavorable health outcomes, drug addiction (Magklara et al., 2015), and suicide attempts (Simon et al., 2013). Of adolescents who have experienced major depressive episodes, 71% have had at least one episode with severe impairment of function (NIMH, 2019). Given the high prevalence and burden of this disorder, many risk factors have been identified to contribute to its incidence, including family history of mental disorders, other comorbid mental disorders (Swartz et al., 2017), life stressors, physical illnesses, internalizing behavior patterns (Lewinsohn et al., 1994), and poor social support (Galambos et al., 2004). Because these factors are largely fixed or difficult to change, there has been increasing interest in investigating the potential effect of more malleable lifestyle and social factors on contributing to the risk of depression, including mobile phone use and sleep (Liu et al., 2018a; Thomée, 2018). Prolonged mobile phone use and sleep deprivation have become major public health concerns (Riesch et al., 2019). The Centers for Disease Control and Prevention reports that about 69% of high school students report sleeping less than 8 hours on school nights (CDC, 2017). This is highly alarming as short sleep duration is related to both physical disorders, such as obesity (Liu et al., 2012) and cardiovascular diseases (Hoevenaar-Blom et al., 2011), and mental disorders, such as depression (Szklo-Coxe et al., 2010). Additionally, there are currently more than 3 billion smartphone users in the world (Takahashi, 2018), with numbers predicted to continue rising in the coming years. Considering the high usage rates of these mobile devices, recent research has focused on the effects of use on various health outcomes such as sleep and mental disorders. Specifically, both cross-sectional as well as longitudinal follow-up studies conducted on adolescents and young adults report that high rates of mobile phone use results in new occurrences of sleep disturbances (Liu et al., 2018a), lower sleep quality and shorter sleep duration (Thomée, 2018), and insomnia (Jenaro et al., 2007). Relatedly, cross-sectional studies demonstrate an association between the use of mobile phones and daytime dysfunction (Demirci
et al., 2015; Thomée, 2018). Most notably, many studies have found a positive relationship between duration of mobile phone use (DMPU) and depressive symptoms in high school and university students. Greater mobile phone usage is correlated with poorer psychological health (Demirci et al., 2015; Ha et al., 2008; Roser et al., 2016), increased vulnerability to mental disorders (Zulkefly and Baharudin, 2009), and higher risk of depression incidence (Augner and Hacker, 2012; Demirci et al., 2015; Ha et al., 2008; Liu et al., 2018a; Sánchez-Martínez and Otero, 2009; Thomée et al., 2011). Conversely, depression is also correlated with prolonged DMPU (Augner and Hacker, 2012; Sánchez-Martínez and Otero, 2009). This relationship was maintained across studies conducted globally (Augner and Hacker, 2012; Demirci et al., 2015; Ha et al., 2008; Lepp et al., 2014; Liu et al., 2018a; Roser et al., 2016; Sánchez-Martínez and Otero, 2009; Thomée et al., 2011; Zulkefly and Baharudin, 2009). More generally, a recent meta-analysis of screen time use across adolescents has demonstrated an increased risk of depression with longer screen time (Liu et al., 2016). This shows that the relationship between mobile phone use and depression can be generalized to other forms of technology such as televisions and gaming systems. Despite the volume of literature on the relation between mobile phone use and depression, previous findings are not consistent or comprehensive, and more research is needed to confirm these findings across various adolescent populations. For example, mobile phone use in adolescents may differ between weekdays and weekends due to a busy school schedule and heavy homework on weekdays. Few studies have examined the associations of depression with weekday and weekend DMPU independently. Furthermore, while many prior studies have demonstrated the direct effects of both mobile phone use and short sleep on depression separately, few studies have investigated potential mediating effects of sleep on the link between DMPU and depression in adolescents. Identifying mediating factors could be important to further understand the relationship between mobile phone use and depression. This present study aims to examine the association between mobile phone use and depression using sleep as a mediating factor in a large sample of Chinese adolescents.
METHODS Participants and procedure
The Shandong Adolescent Behavior & Health Cohort (SABHC) is a longitudinal study of behavior and health among adolescent students of Shandong, China. A total of 12,301 students participated in the SABHC baseline survey. Detailed sampling and data collection have been described elsewhere (Liu et al., 2019a; Liu et al., 2018b; Liu et al., 2019b). In brief, participants were sampled from 5 middle and 3 high schools in 3 counties of Shandong, and consideration for the representativeness of adolescent students in the region, prior study collaboration, convenience, and budget was taken for at least 3 waves of data collection. In November-December 2015, participants completed a self-administered, structured adolescent health questionnaire (AHQ) to assess behavioral and mental health problems and to collect demographic information. After receiving permission from the teachers for the sampled classes, trained master-level public health workers administered AHQ to participants in classrooms during regular school hours. All sampled students attending school on the day of the survey were given the option of not participating, and informed consent was obtained from participants. Before filling out the questionnaire, participants were instructed to read the instructions carefully and informed that the survey was anonymous, and their participation was voluntary with no penalties for non-participation. The study was approved by the research ethics committee of Shandong University School of Public Health and target schools (IRB ID 20161102).
Measures in the AHQ Duration of mobile/smartphone use There were 2 questions used to ask participants about their mobile phone use on weekdays and the weekend in the past month. The first was, ―On an average school day, how many hours do you use a mobile/smartphone in the past month?‖ and the second was, ―On an average weekend day, how many hours do you use a mobile/smartphone in the past month?‖ Depressive symptoms Depressive symptoms were measured by The Centre for Epidemiologic Studies Depression Scale (CES-D) (Radloff, 1977) and the Chinese Youth Self-Report (YSR) of Achenbach’s Child Behavior Checklist (Liu et al., 1997). The CES-D is a 20-item self-report questionnaire to measure depressive symptoms in the past week (Radloff, 1977). Each item is rated on a Likert scale from 0 to 3 points (0 =rarely or
none of the time/less than 1 day a week, 1 =sometimes/1-2 days a week, 2 =always or half of a week/3-4 days a week, and 3 =most or all of the times/5-7 days a week). Summing up the scores of the 20 items yields a total CES-D score. A higher total score indicates a higher severity of depressive symptoms. The CES-D has been widely used among adolescents because of its psychometric properties (Rushton et al., 2002; Yang et al., 2018). Cronbach alpha with the present sample was 0.86. The YSR anxious/depressed subscale consists of 16 items that are rated on a 3-point scale: ―0‖ = not true, ―1‖ = somewhat or sometimes true, and ―2‖ = very true or often true within the past six months (Liu et al., 1997; Radloff, 1977). Summing up the scores of the 16 items yields a total depressive score. A higher total score indicates a higher severity of depressive symptoms. Cronbach alpha with the present sample was 0.88 for the subscale. Because the cutoffs for Chinese adolescents have not been well established, scores > 90 th percentile on the CES-D and YSR depression scale were used as cutoff points to define clinically relevant depressive symptoms, respectively (Byrd et al., 1996; Liu et al., 1999). Sleep duration and insomnia Night sleep duration and insomnia symptoms were assessed by 5 items adapted from the Pittsburgh Sleep Questionnaire Index (Buysse et al., 1989). Sleep duration was asked by ―During the past month, on an average school day, how many hours of actual sleep did you get at night?‖ and ―During the past month, on an average weekend day, how many hours of actual sleep did you get at night?‖ Insomnia symptoms were asked about difficulty falling asleep (DIS), difficulty maintaining sleep (DMS), and early morning waking (EMA). The participants answered each question about insomnia with a response option of rarely or never (<1 time/week), sometimes (12 times/ week), often (3-5 times/week), or almost every day (6-7 times/week). If the response was often or almost every day, the symptom was considered clinically significant (i.e., at least 3 times/week). Adolescent and family demographic factors Adolescent factors included age, sex, chronic physical diseases (yes/no), smoking (yes/no), and alcohol use (yes/no). Family factors included paternal education (elementary school, middle school, high school, college or above), occupation (farmer/non-farmer), family economic status (excellent, good, fair, poor, or very poor), and interparental relationship (excellent, good, fair, poor, divorced/separated).
Statistical analysis Sample characteristics were described by means (SD) for continuous variables and frequencies (%) for categorical variables. Student t tests/ANOVA and Chi-square tests were conducted for significant testing on continuous variables and categorical variables, respectively. Univariate and multivariate logistic regression models were performed to examine the associations between SMPU on weekdays and the weekend and depressive symptoms, respectively. In the multivariate regression, adolescent factors (i.e., age, gender, chronic disease, smoking, alcohol use, sleep duration, insomnia, and school) and family factors (economic status, interparental relationship, father education, and occupation) were adjusted as potential confounders. Statistical tests of regression estimates and odds ratios were based on Wald statistics. Mediation analyses were performed to estimate the mediating effects of insomnia or short sleep duration on the mobile phone use-depression link. Bootstrap methods were used to verify the indirect effect and to produce bias-corrected confidence intervals, which were based on 5000 bootstrapping samples. Furthermore, given the large sample size and high response rate (<5% with missing data for studied variables, see Tables 1 and 2), missing data were assumed to be random and were excluded from all analyses. All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 24.0 (Armonk, NY: IBM Corp).
RESULTS Of the 12,301 students sampled for the baseline survey, 11,831 returned questionnaires with basic demographic information (i.e., age and gender) for current analysis (96.2%). The mean age of the participants was 15.0 (SD=1.5) and 51% were boys. Table 1 summarizes sample characteristics by age, gender, disease history, substance use, sleep duration, insomnia, family economic status, interparental relationship, father education, and occupation.
Prevalence of depressive symptoms by the duration of mobile phone use Table 2 presents prevalence rates of depressive symptoms by the duration of mobile phone use on weekdays and weekends, respectively. As shown in Table 2, 22.3% reported using mobile phones for more than one hour on weekdays, and 55.4% used phones for at least two hours on weekends. The prevalence rates of depressive symptoms significantly increased with
prolonged mobile phone use on either weekdays or weekends. On weekdays, the prevalence of depressive symptoms as assessed by CES-D was markedly increased in adolescents who used a mobile phone for ≥ 2 hours per day compared with those who used it for < 1 hour per day (19.1% vs 10.0%). On weekends, the prevalence of depressive symptoms as assessed by CES-D was markedly increased in adolescents who used mobile phone for ≥ 5 hours per day compared with those who used it for < 2 hours per day (18.3% vs 8.6%). A similar tendency was observed for depressive symptoms as assessed by YSR.
Associations of depressive symptoms with mobile phone use Tables 3 and 4 show the associations of depressive symptoms with DMPU on weekdays and weekends. As indicated in Table 3, the odds ratios of both CESD and YSR-assessed depressive symptoms were significantly increased with prolonged mobile phone use on weekdays. The odds ratios for adolescents who used mobile phone ≥ 2 hours on weekdays were reduced but remained to be significant after adjusting for adolescent and family covariates, including sleep duration and insomnia. For example, the odds ratio of CESD assessed depressive symptoms was 2.13 (95%CI=1.80-2.52) in adolescents who used mobile phone ≥ 2 hours on weekdays compared with those who used mobile phone < 1 hour before covariates were adjusted. The odds ratio became 1.78 (95%CI=1.48-2.15) after covariates were adjusted. As shown in Table 4, prolonged mobile phone use on weekends was also associated with increased odds of depressive symptoms. The odds ratio was markedly increased when adolescents used mobile phone ≥ 4 hours on weekends compared with those who used mobile phone < 2 hours per day. Similarly, the odds ratios for both CESD and YSR-assessed depressive symptoms were reduced after adjusting for adolescent and family covariates and the odds ratio remained to be significant only if mobile phone ≥ 5 hours on weekends.
Mediation analyses Mediation analyses were performed to examine if the mobile phone use-depression link was mediated by short sleep duration and insomnia caused by prolonged mobile phone use. As shown in Table 5, prolonged mobile phone use on both weekdays and weekends was significantly associated with increased insomnia symptoms and short sleep duration. Short sleep duration and insomnia symptoms were significantly associated with elevated depressive
symptoms as assessed by either CESD or YSR except for weekend sleep duration on YSR depression. Insomnia and short sleep duration mediated the prolonged mobile phone usedepression link at least in part except that the mediation effect of weekend sleep duration on weekend mobile phone use-depression link was not statistically significant.
DISCUSSION In this large sample study of 11,831 Chinese adolescent students, using both CES-D and YSR to measure depression, we found that prolonged phone usage (≥ 2 hours on weekdays and ≥ 5 hours on weekends) increases the prevalence of self-reported depressive symptoms. Further, the risk of developing clinically depressive symptoms increases significantly with these long durations of phone usage, even after accounting for various adolescent and family covariates. Finally, sleep disturbances, such as short sleep duration or insomnia, is found to partially mediate this relationship, suggesting that prolonged mobile phone use results from disturbances in sleep, which further accounts for outcomes of depressive symptoms. Our study demonstrates a substantial increase in prevalence of depressive symptoms with increasing duration of mobile phone use among adolescent students both on weekdays and weekends. The mobile phone use and depression association is noticed for usage duration of more than 2 hours on weekdays and more than 5 hours on weekends. This relationship was similarly reported by Ha et al. (2008) and Augner and Hacker (2012) in their cross-sectional studies showing that greater phone usage was reflected in higher depression scores via the Beck Depression Inventory. Additionally, a prospective study conducted on adolescents and young adults in China demonstrated that self-reported sleep disturbances and mental distress increased with longer phone usage (Liu et al., 2018a). Thus, the connection between phone usage and prevalence of depressive symptoms is well studied and confirmed. Similarly, our findings are consistent with those of related studies (Liu et al., 2016; Liu et al., 2018a; Sánchez-Martínez and Otero, 2009; Thomée et al., 2011) in showing the significantly increased risk of developing clinically depressive symptoms among participants reporting the highest rates of phone usage, even after accounting for various participant and family covariates. This indicates that prolonged phone use may be a reliable predictor of future depression incidence. Various longitudinal prospective studies provide support for this claim by verifying the positive relationship between duration of phone usage and incidence of future mental distress
and depressive symptoms (Liu et al., 2018a; Thomée et al., 2011). Further, a meta-analysis of related literature on screen time and risk of depression confirms the positive relationship between duration of screen use and likelihood of depression incidence (Liu et al., 2016). Conversely, there is evidence of reverse directionality in the association between phone usage and risk of depression. In a cross-sectional study conducted on adolescents in Madrid, Sánchez-Martínez and Otero (Sánchez-Martínez and Otero, 2009) report that those suffering from symptoms of depression are more likely to be intensive mobile phone users. Thus, though the causality of this relationship is unclear, there is strong support for the association between mobile phone use and depressive symptoms. Future longitudinal research in this area may be able to provide a model of causality. Sleep disturbances have increasingly been recognized as a problem among adolescents and have contributed to risk of depression in this population. At the same time, these disturbances may result from prolonged phone usage. More specifically, studies have demonstrated the adverse effects of mobile phone use on incidence of insomnia (Liu et al., 2018a), sleep disturbances (Liu et al., 2018a; Thomée et al., 2011), lower sleep quality (Demirci et al., 2015; Thomée, 2018), shorter sleep (Chahal et al., 2013), and daytime dysfunction (Exelmans and Van den Bulck, 2016). However, little is known about the relationship between phone use, depression, and sleep. Our mediation analysis shows that prolonged phone use on both weekdays and weekends are significantly linked to insomnia and shorter sleep duration. Furthermore, disruptions in sleep are associated with symptoms of depression. Thus, the mediation models suggest that it is possible that disturbances to mental health due to mobile phone use may result from inadequate sleep or impairing sleep disruptions. While the comprehensive relationship between mobile phone use, sleep, and depression has rarely been studied, a recent study conducted in Turkey also examined the associations among these three factors. Interestingly, the study reported depression and anxiety as mediators between phone use and sleep disturbances (Demirci et al., 2015) whereas our study found sleep to mediate the relationship between phone use and depression. However, given the previously established associations between each of these variables and the minimal research conducted on causality, it is impossible to conclude directionality. It should be noted that the effects of DMPU on adolescent mental health may differ between weekdays and weekends. This variance in mobile phone use may be due to busy school
schedules and heavy homework on weekdays. However, few studies have examined the associations between depression and DMPU on weekdays and weekend independently. In our sample, 22.3% reported using mobile phones for more than one hour on weekdays, and 55.4% used phones for at least two hours on weekends. Our multivariate analyses demonstrated that mobile phone use ≥ 2 hours on weekdays and ≥ 5 hours on weekends were significantly associated with increased risk of self-reported depressive symptoms. Longitudinal studies are needed to confirm this finding and to examine the pathways between DMPU and depression on weekdays and weekends separately. Many possible mechanisms have been proposed for the various relationships between prolonged mobile phone use, depressive symptoms, and sleep. Firstly, the vast amount of information and stimuli presented by mobile phones can be cognitive and emotional burdens, resulting in increased psychological vulnerability to impaired mental health (Liu et al., 2018a). Conversely, those suffering with more intense depressive symptoms may use mobile phones to a greater extent in order to escape daily struggles and seek support through social media (Demirci et al., 2015). Additionally, longer durations of phone usage increases arousal and vigilance of the brain and physiological systems, resulting in lower sleep quality, duration, and sleep time (Demirci et al., 2015; Thomée, 2018). Given the importance of sleep on overall health and wellbeing, disruptions in sleep would further induce stress and vulnerability to psychological disturbances, increasing the risk of depressive symptoms (Demirci et al., 2015). Some limitations should be considered. Because of the cross-sectional design of our study, causality between phone usage and depressive symptoms cannot be established. Furthermore, some important covariates which may likely contribute to both mobile phone use and depression, such as life stress, social support, physical activity, and other mental health and sleep problems, were not assessed and could not be statistically controlled for. Additionally, depression symptoms, sleep duration, and insomnia were measured via selfreported surveys which could result in recall bias and subjectivity. Future longitudinal studies using objective sleep measures may investigate the causal relationships between mobile phone use, sleep, and depression. Conclusion
Both depression and prolonged mobile phone use in adolescents have been increasingly viewed as public health concerns due to its growing prevalence and negative consequences. While many risk factors that predispose adolescents to depression have been identified, prolonged mobile phone use has been increasingly recognized as an important modifiable risk factor. In this large sample of 11,831 Chinese students, we demonstrated that prolonged mobile phone use of ≥2 hours on weekdays and ≥5 hours on the weekend is associated with increased risk of clinically depressive symptoms. These associations are partially mediated by either short sleep duration or insomnia. The important implications of the current study lie in raising awareness among adolescents that prolonged mobile phone use may result in sleep problems and depression (Riesch et al., 2019). Furthermore, the results from this study suggests that limiting the use of mobile phones or setting a time limit on phone usage at school and home could be important for decreasing the risk of depressive symptoms. Conflict of Interest All authors have no conflicts of interest to report.
AUTHOR STATEMENT Contributors Jianghong Liua, Colin Liua, Tina Wua, Bao-Peng Liub, Cun-Xian Jiab, Xianchen Liu b,c a
School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA
b
Shandong University School of Public Health, Jinan, China
c
Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA
Jianghong Liu, Colin Liu, and Tina Wu developed the introduction, discussion, and conclusion. Cun-Xian Jia and Xianchen Liu designed the study. Bao-Peng Liu conducted data analysis and wrote the methods and results sections. All authors gave critical reviews and finalized the manuscript. Role of the Funding Source This work was funded in part for data collection by National Natural Science Foundation of China (Grant number 81573233) The funding institution had no role in the study design, data collection and analysis, and decision to publish, or preparation of the manuscript. Acknowledgements The authors would like to thank Lijin County Center for Disease Control and Prevention, Zoucheng City Center for Disease Control and Prevention, and Yanggu County Center for Disease Control and Prevention, Shandong Province, China and all participating school teachers for their help with data collection and all students for their voluntarily participating in the study.
References Augner, C., Hacker, G.W., 2012. Associations between problematic mobile phone use and psychological parameters in young adults. International journal of public health 57, 437441. Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J., 1989. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research 28, 193-213. Byrd, R.S., Weitzman, M., Lanphear, N.E., Auinger, P., 1996. Bed-wetting in US children: epidemiology and related behavior problems. Pediatrics 98, 414-419. CDC, 2017. Short Sleep Duration Among US Adults, in: Statistics, D.a. (Ed.). Centers for Disease Control and Prevention. Chahal, H., Fung, C., Kuhle, S., Veugelers, P., 2013. Availability and night‐time use of electronic entertainment and communication devices are associated with short sleep duration and obesity among C anadian children. Pediatric obesity 8, 42-51. Compton, M.T., Chien, V.H., Bollini, A.M., 2007. Psychometric properties of the Brief Version of the Schizotypal Personality Questionnaire in relatives of patients with schizophreniaspectrum disorders and non-psychiatric controls. Schizophrenia Research 91, 122-131. Demirci, K., Akgönül, M., Akpinar, A., 2015. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of behavioral addictions 4, 85-92. Exelmans, L., Van den Bulck, J., 2016. Bedtime mobile phone use and sleep in adults. Social Science & Medicine 148, 93-101. Fergusson, D.M., Woodward, L.J., 2002. Mental health, educational, and social role outcomes of adolescents with depression. Archives of general psychiatry 59, 225-231. Fonseca-Pedrero, E., Paíno-Piñeiro, M., Lemos-Giráldez, S., Villazón-García, Ú., Muñiz, J., 2009. Validation of the Schizotypal Personality Questionnaire—Brief form in adolescents. Schizophrenia Research 111, 53-60. Galambos, N.L., Leadbeater, B.J., Barker, E.T., 2004. Gender differences in and risk factors for depression in adolescence: A 4-year longitudinal study. International Journal of Behavioral Development 28, 16-25. Ha, J.H., Chin, B., Park, D.-H., Ryu, S.-H., Yu, J., 2008. Characteristics of excessive cellular phone use in Korean adolescents. CyberPsychology & Behavior 11, 783-784. Hoevenaar-Blom, M.P., Spijkerman, A.M., Kromhout, D., van den Berg, J.F., Verschuren, W., 2011. Sleep duration and sleep quality in relation to 12-year cardiovascular disease incidence: the MORGEN study. Sleep 34, 1487-1492.
Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., Caballo, C., 2007. Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. Addiction research & theory 15, 309-320. Lepp, A., Barkley, J.E., Karpinski, A.C., 2014. The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in Human Behavior 31, 343-350. Lewinsohn, P.M., Roberts, R.E., Seeley, J.R., Rohde, P., Gotlib, I.H., Hops, H., 1994. Adolescent psychopathology: II. Psychosocial risk factors for depression. Journal of abnormal psychology 103, 302. Liu, B.-P., Wang, X.-T., Liu, Z.-Z., Wang, Z.-Y., Liu, X., Jia, C.-x., 2019a. Stressful life events, insomnia and suicidality in a large sample of Chinese adolescents. Journal of affective disorders 249, 404-409. Liu, J., Zhang, A., Li, L., 2012. Sleep duration and overweight/obesity in children: review and implications for pediatric nursing. Journal for Specialists in Pediatric Nursing 17, 193204. Liu, M., Wu, L., Yao, S., 2016. Dose–response association of screen time-based sedentary behaviour in children and adolescents and depression: a meta-analysis of observational studies. Br J Sports Med 50, 1252-1258. Liu, S., Wing, Y.K., Hao, Y., Li, W., Zhang, J., Zhang, B., 2018a. The associations of long-time mobile phone use with sleep disturbances and mental distress in technical college students: a prospective cohort study. Sleep 42, zsy213. Liu, X., Chen, H., Liu, Z.-Z., Jia, C.-X., 2018b. Insomnia and Psychopathological Features Associated With Restless Legs Syndrome in Chinese Adolescents. The Journal of clinical psychiatry 79. Liu, X., Guo, C., Liu, L., Wang, A., Hu, L., Tang, M., Chai, F., Zhao, G., Yang, J., Sun, L., 1997. Reliability and validity of the Youth Self-Report (YSR) of Achenbach's Child Behavior Checklist (CBCL). Chinese Mental Health Journal. Liu, X., Kurita, H., Guo, C., Miyake, Y., Ze, J., Cao, H., 1999. Prevalence and risk factors of behavioral and emotional problems among Chinese children aged 6 through 11 years. Journal of the American Academy of Child & Adolescent Psychiatry 38, 708-715. Liu, X.C., Chen, H., Liu, Z.Z., Wang, J.Y., Jia, C.X., 2019b. Prevalence of suicidal behaviour and associated factors in a large sample of Chinese adolescents. Epidemiol Psychiatr Sci 28, 280-289. Magklara, K., Bellos, S., Niakas, D., Stylianidis, S., Kolaitis, G., Mavreas, V., Skapinakis, P., 2015. Depression in late adolescence: a cross-sectional study in senior high schools in Greece. BMC psychiatry 15, 199.
NIMH, 2019. Major Depression, Mental Health Information Statistics. National Institute of Health Bethesda, MD. Radloff, L.S., 1977. The CES-D scale: A self-report depression scale for research in the general population. Applied psychological measurement 1, 385-401. Raine, A., Fung, A.L.-c., Lam, B.Y.H., 2011. Peer victimization partially mediates the schizotypy-aggression relationship in children and adolescents. Schizophrenia bulletin 37, 937-945. Riesch, S.K., Liu, J., Kaufmann, P.G., Doswell, W.M., Cohen, S., Vessey, J., 2019. Preventing adverse health outcomes among children and adolescents by addressing screen media practices concomitant to sleep disturbance. Nursing Outlook 67, 492-496. Roser, K., Schoeni, A., Foerster, M., Röösli, M., 2016. Problematic mobile phone use of Swiss adolescents: is it linked with mental health or behaviour? International journal of public health 61, 307-315. Rushton, J.L., Forcier, M., Schectman, R.M., 2002. Epidemiology of depressive symptoms in the National Longitudinal Study of Adolescent Health. Journal of the American Academy of Child & Adolescent Psychiatry 41, 199-205. Sánchez-Martínez, M., Otero, A., 2009. Factors associated with cell phone use in adolescents in the community of Madrid (Spain). CyberPsychology & Behavior 12, 131-137. Simon, G.E., Rutter, C.M., Peterson, D., Oliver, M., Whiteside, U., Operskalski, B., Ludman, E.J., 2013. Does response on the PHQ-9 Depression Questionnaire predict subsequent suicide attempt or suicide death? Psychiatric Services 64, 1195-1202. Swartz, J.R., Hariri, A.R., Williamson, D.E., 2017. An epigenetic mechanism links socioeconomic status to changes in depression-related brain function in high-risk adolescents. Molecular psychiatry 22, 209. Szklo-Coxe, M., Young, T., Peppard, P.E., Finn, L.A., Benca, R.M., 2010. Prospective associations of insomnia markers and symptoms with depression. American journal of epidemiology 171, 709-720. Takahashi, D., 2018. Newzoo: Smartphone users will top 3 billion in 2018, hit 3.8 billion by 2021, Mobile. VentureBeat. Thomée, S., 2018. Mobile phone use and mental health. A review of the research that takes a psychological perspective on exposure. International journal of environmental research and public health 15, 2692. Thomée, S., Härenstam, A., Hagberg, M., 2011. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC public health 11, 66.
Yang, X., Lau, J.T., Lau, M.C., 2018. Predictors of remission from probable depression among Hong Kong adolescents–A large-scale longitudinal study. Journal of affective disorders 229, 491-497. Zulkefly, S.N., Baharudin, R., 2009. Mobile phone use amongst students in a university in Malaysia: its correlates and relationship to psychological health. European Journal of Scientific Research 37, 206-218.
TABLE 1 Sample Characteristics N (Sample size)*
n
% /Mean (SD)
Male gender
11831
5813
Mean Age (SD)
11831
Chronic disease
11830
475
4.0
Ever smoking
11831
2562
21.7
Ever drinking
11831
4397
37.2
Insomnia
11249
1659
14.7
Sleep duration on weekdays, hours
11517
7.10(1.43)
Sleep duration at the weekend, hours
11505
8.94(1.61)
Family economic status
11666
49.1 14.97(1.45)
Excellent
281
2.4
Good
2024
17.3
Fair
7941
68.1
Poor
1247
10.7
Very poor
173
1.5
Excellent
4827
41.5
Good
3134
26.9
Fair
2902
24.9
Poor
347
3.0
Separated/divorced/died
434
3.7
Primary school
1572
13.5
Middle school
6246
53.7
High school
2138
18.4
Professional school
920
7.9
College or above
766
6.6
4521
38.2
Parental relationship
Father education
Father occupation: Farmer *N differs due to missing values
11644
11642
11831
Table 2. Prevalence of depressive symptoms by duration of mobile phone use in Chinese adolescents CES-D YSR N (%) Depression a Depression b Mobile phone use on weekdays, hours
11580 (100)
<1
8989(77.6)
10.0
10.8
1–
1440 (12.4)
11.8
12.9
≥2
1151 (9.9)
19.1
18.7
81.70 (.000)
61.94(.000)
X2 (p) Mobile phone use at the weekend, hours
11573 (100)
<2
5164 (44.6)
8.6
9.7
2–
3436 (29.7)
10.0
10.3
4–
790 (6.8)
12.4
13.8
≥5 X (p)
2183 (18.9)
18.3 145.86
19.0 141.41 (.000)
2
(.000) a b
CES-D = Centre for Epidemiologic Studies Depression Scale YSR = Youth Self-Report
Table 3. Associations of mobile phone use on weekdays with depressive symptoms CESD-Depression a Crude OR
YSR-Depression b
Adjusted OR
(95%CI)
(95%CI)
c
Crude OR
Adjusted OR
(95%CI)
(95%CI) c
Mobile phone use, hours <1
1.00
1.00
1.00
1.00
1–
1.20(1.00-1.43)*
1.16(0.95-1.41)
1.22(1.03-1.45)*
1.17(0.98-1.41)
≥2
2.13(1.80-2.52)***
1.78(1.48-2.15) ***
1.89(1.61-2.23)***
1.50(1.25-1.81)***
Female gender
1.48(1.29-1.70) ***
1.33(1.17-1.52)***
Age
1.06(1.00-1.12) *
1.05(0.99-.10)
Chronic disease/disability
1.53(1.17-2.00) **
1.84(1.44-2.35)**
Ever smoking
1.57(1.34-1.83) ***
1.60(1.38-1.85)***
Ever drinking
1.39(1.21-1.61) ***
1.37(1.19-1.57)***
Poor family economic status
1.15(1.36-1.68) ***
1.47(1.33-1.62)***
Poor interparental relationship
1.21(1.15-1.29) ***
1.29(1.22-1.36)***
Father education
1.04(0.98-1.12)
1.06(0.99-1.13)
Father occupation: Farming
0.92(0.80-1.07)
0.88(0.82-0.91)
Sleep duration on weekdays
0.88(0.83-0.93) ***
0.87(0.82-0.92)***
Insomnia
3.39(2.94-3.91) ***
2.94(2.56-3.37)***
*p<.05, **p<.01, ***p<.001 a CES-D = Centre for Epidemiologic Studies Depression Scale b YSR = Youth Self-Report c Adjusted for child age, gender, chronic disease, smoking, alcohol use, school, family factors, weekday sleep duration, and insomnia
Table 4. Associations of mobile phone use at the weekend with depressive symptoms CESD-Depression a Crude OR Adjusted OR
YSR-Depression b Crude OR Adjusted OR
(95%CI) c
(95%CI)
(95%CI) c
(95%CI)
Mobile phone use, hours <2
1.00
1.00
1.00
1.00
2–
1.18(1.02-1.38) *
1.04(0.88-1.22)
1.07(0.93-1.24)
0.89(0.77-1.05)
4–
1.51(1.19-1.91) **
1.11(0.85-1.43)
1.50(1.20-1.87)***
1.14(0.90-1.45)
≥5
2.38(2.05-2.76) ***
1.67(1.41-1.98) ***
2.19(1.91-2.53)***
1.48(1.26-1.74)***
Female gender
1.50(1.31-1.72)***
1.38(1.21-1.58)***
Age
1.12(1.06-1.17)***
1.11(1.06-1.16)***
Chronic disease/disability
1.58(1.21-2.06)***
1.89(1.48-2.42)***
Ever smoking
1.51(1.29-1.77)***
1.56(1.35-1.81)***
Ever drinking
1.39(1.20-1.61)***
1.38(1.20-1.58)***
Poor family economic status
1.52(1.37-1.69)***
1.48(1.34-1.63)***
Poor interparental relationship
1.21(1.14-1.29)***
1.28(1.21-1.35)***
Father education
1.08(1.01-1.16)*
1.09(1.02-1.16)**
Father occupation: Farming
0.86(0.75-0.99)*
0.83(0.72-0.96)**
Sleep duration on weekdays
1.02(0.98-1.06)
0.98(0.95-1.02)
Insomnia
3.39(2.94-3.91)***
2.94(2.56-3.38)***
*p<.05, **p<.01, ***p<.001 a CES-D = Centre for Epidemiologic Studies Depression Scale b YSR = Youth Self-Report c Adjusted for child age, gender, chronic disease, smoking, alcohol use, school, family factors, weekday sleep duration, and insomnia
Table 5. Mediation analysis Path Effect a SE a t/z P Weekday mobile phone useInsomnia scoreYSR Depression Total effect of X on Y 0.330 0.035 9.368 <0.001 Direct effect of X on Y 0.301 0.034 8.954 <0.001 Indirect effect of X on Y 0.029 0.014 XM 0.036 0.013 2.744 0.006 MY 0.821 0.025 33.317 <0.001 Weekday mobile phone useInsomnia scoreCESD Depression Total effect of X on Y 0.652 0.061 10.723 <0.001 Direct effect of X on Y 0.598 0.057 10.426 <0.001 Indirect effect of X on Y 0.054 0.027 XM 0.035 0.013 2.672 0.008 MY 1.537 0.042 36.518 <0.001 Weekend mobile phone useInsomnia scoreYSR Depression Total effect of X on Y 0.247 0.014 17.050 <0.001 Direct effect of X on Y 0.212 0.014 15.271 <0.001 Indirect effect of X on Y 0.035 0.005 XM 0.044 0.005 8.189 <0.001 MY 0.795 0.025 32.375 <0.001 Weekend mobile phone useInsomnia scoreCESD Depression Total effect of X on Y 0.449 0.025 18.130 <0.001 Direct effect of X on Y 0.384 0.023 16.363 <0.001 Indirect effect of X on Y 0.065 0.010 XM 0.043 0.005 8.009 <0.001 MY 1.492 0.042 35.625 <0.001
95%CI a 0.261-0.399 0.235-0.367 0.004-0.058 0.010-0.061 0.773-0.869 0.533-0.772 0.486-0.711 0.005-0.112 0.009-0.061 1.454-1.619
0.219-0.276 0.185-0.240 0.025-0.045 0.033-0.054 0.747-0.843 0.400-0.497 0.338-0.430 0.046-0.085 0.033-0.054 1.410-1.574
Weekday mobile phone useWeekday sleep durationYSR Depression Total effect of X on Y 0.335 0.035 9.669 <0.001 0.267-0.402 Direct effect of X on Y 0.301 0.034 8.806 <0.001 0.234-0.368 Indirect effect of X on Y 0.033 0.006 0.022-0.046 XM -0.052 0.009 -5.991 <0.001 -0.069~-0.035 MY -0.644 0.037 -17.345 <0.001 -0.716~-0.571 Weekday mobile phone useWeekday sleep durationCESD Depression Total effect of X on Y 0.680 0.060 11.323 <0.001 0.562-0.798 Direct effect of X on Y 0.623 0.059 10.475 <0.001 0.507-0.740 Indirect effect of X on Y 0.057 0.011 0.037-0.078 XM -0.056 0.009 -6.345 <0.001 -0.073~-0.039 MY -1.011 0.064 -15.675 <0.001 -1.137~-0.884 Weekend mobile phone useWeekend sleep durationYSR Depression Total effect of X on Y 0.237 0.014 16.661 <0.001 0.209-0.265 Direct effect of X on Y 0.235 0.014 16.468 <0.001 0.207-0.263
Indirect effect of X on Y XM MY
0.002 -0.036 -0.053
0.001 0.004 0.033
-9.013 -1.588
<0.001 0.112
-0.001-0.005 -0.044~-0.028 -0.118-0.012
Weekend mobile phone useWeekend sleep durationCESD Depression Total effect of X on Y 0.441 0.024 18.090 <0.001 0.393-0.488 Direct effect of X on Y 0.435 0.024 17.792 <0.001 0.387-0.483 Indirect effect of X on Y 0.006 0.002 0.002-0.011 XM -0.036 0.004 -8.908 <0.001 -0.044~-0.028 MY -0.160 0.058 -2.782 0.005 -0.273~-0.047 a Bootstrap SEs and 95%CIs were calculated in the indirect effect.