Internet addiction (IA) is common among adolescents and significantly determined by sociocultural and economic factors. The aim of this study was to compare the prevalence of IA among adolescents between Macau and mainland China and also examine its association with quality of life. A total of 2892 secondary school students were included. Standardized instruments were used to measure IA, depressive symptoms and quality of life. The overall prevalence of IA was 23.7%, with 32.5% in Macau and 19.8% in mainland China. Students in Macau were more likely to suffer from IA than those in mainland China (OR = 2.15, p < 0.001). Correlates of IA included being in higher school grades, poor academic performance, and more severe depressive symptoms. Students with IA reported lower quality of life in physical, psychological, social, and environmental domains. IA is common among Chinese adolescents, particularly in Macau. Considering the negative impact of IA on health and quality of life, regular screening and effective interventions should be undertaken for young Internet users.
Internet addiction (IA) is a recently identified health problem, common among adolescent and young adult populations. IA usually refers to a persistent and recurrent maladaptive behavior, causing distress and significant functional impairments1. It is also named “problematic Internet use”, “pathological Internet use”, or “compulsive Internet use”. To date, there has been no agreement about the diagnostic criteria for IA. IA involves different types of online behaviors, such as online gaming, online shopping, online pornography, online gambling, and social media use. Due to its associations with a host of physical2, psychological3,4 and interpersonal problems5 among adolescents, IA has gained growing concerns in both research and clinical practice. For instance, "Internet gaming disorder" and the relevant proposed diagnosis criteria were described in the section recommending conditions for further research in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)6, and "gaming disorder" was classified as a psychiatric disorder in the International Classification of Diseases (ICD-11)7.
Some studies have examined the epidemiology of IA, but findings vary greatly across countries, due to different sampling methods, definitions and assessment instruments, and sociocultural contexts8,9. For instance, the prevalence of IA among adolescents ranged from 1.3% in Turkey10 to 12.1% in Italy11 using the Young’s Internet Addiction Test (IAT) , from 15.2% in Greece12 to 17–26.8% in Hong Kong using the Internet Addiction Diagnostic Questionnaire (IADQ)13, and 17.4% in Taiwan using the Chen’s Internet Addiction Scale (CIAS)14. The updated report from the Chinese Internet Network Information Center (CNNIC) revealed that 17.3% of Chinese Internet users aged < 18 years had Internet dependence15.
IA is often comorbid with psychiatric disorders and related problems16,17, such as attention deficit and hyperactivity disorder (ADHD)18, depression and anxiety19,20, aggressive behaviors21, substance use disorder (e.g., alcohol abuse) 22, and suicidal behaviors 23,24. Moreover, certain demographic factors are also associated with IA, such as obesity25, higher school grades26, poor academic performance and engagement in high-risk behaviors27, higher family income28, and lower level of parental attachment29. IA is significantly determined by sociocultural contexts8. Therefore, the epidemiology of IA should be examined in different sociocultural and economic contexts separately.
As a special administrative region, Macau is situated at the southeastern tip of China with independent legal and political systems. After four centuries of Portuguese administration, Macau was returned to China’s jurisdiction in 1999. Macau is a society greatly affected by both Eastern and Western cultural values, which is quite different from other areas of China in many socio-cultural and economic aspects. Following the opening policy of the casino industry in 2002, Macau experienced an economic boom, with its GDP per capita ranking top five globally 30. The rapid economic growth and social changes have a great impact on adolescents. For instance, many young people can find high paying jobs with low qualifications in the casino industry, therefore, some of them had inadequate motivation for academic achievement, dropped out of schools31, and even experienced mental health and behavioral problems, such as depression, smoking, alcohol drinking and illicit drug use32. All of these issues may increase the risk of IA among adolescents33,34. Therefore, understanding the epidemiology of IA and its associated factors is important for policymakers and health authorities to develop preventive measures and appropriate treatment to reduce the risk of IA and related health problems within this population.
A previous study found significantly higher IA prevalence among adolescents in Hong Kong (a city having similar historical and socio-cultural background with Macau) than that in mainland China35. To date, however, little is known about problematic Internet use among adolescents in Macau and no published studies compared the epidemiology of IA in Macau and mainland China. Importantly, these findings may have great implications for policymakers and health professionals in areas which experienced rapid economic growth and social changes. Therefore, we compared the prevalence of IA among adolescents between Macau and mainland China, along with key socio-demographic correlates of IA and quality of life (QOL) in both populations. We hypothesized that due to different sociocultural and economic contexts, IA prevalence would be significantly different among adolescents between Macau and mainland China. We also predicted that IA would be negatively associated with QOL.
Setting, subjects and data collection
This was a cross-sectional study that was carried out in Macau and three cities in mainland China (Beijing, Hefei and Shenzhen) from April to July 2017. Beijing, Hefei and Shenzhen are located in northern, central and southern China, which represents a range of major geographic settings in mainland China.
According to the number of students in each school, one secondary school was selected from Macau, Beijing and Hefei, while five secondary schools were selected from Shenzhen. Two to six classes with 120–200 students in each grade within participating schools were randomly selected according to their total number of students to participate in this study. Students who met the following criteria were included: (1) adolescents aged between 10 and 18 years; (2) Chinese ethnicity; (3) fluency in Chinese language (Cantonese or Mandarin). There was no exclusion criterion. The trained team members or teachers guided the students to complete the data collection forms in the classroom and then collected the forms. All the data were collected on a voluntary and anonymous basis. The research protocol was reviewed and approved by the Research Ethics Committee of the University of Macau. All methods were performed in accordance with the Declaration of Helsinki. Oral informed consent was obtained from students and written informed consent was signed by students’ parents or guardians.
Assessment instruments and measures
A data collection form was designed for this study to collect socio-demographic information, such as age, gender, study site, religious belief, grade, self-perceived psychical health, academic performance and stress, and relationship with classmates, teachers and family. Socioeconomic data were also recorded, such as perception of family financial status, parental educational level, parental work status and marital status28,36. Parental educational level referred to the highest education level obtained by either the participant’s father or mother, with three levels, i.e., high level: completion of at least undergraduate studies, medium level: completion of secondary school studies, and low level: completion of primary school studies or not receiving any school education. Parental work status had two categories, i.e., unemployed: at least one parent was not employed, and employed: both parents were employed. Parental marital status had two categories, i.e., married: complete original family with both biological parents present, and other types: remarriage, divorce, separation, or one or both parents passed away.
The Young’s Internet Addiction Test (IAT)—Chinese version was used to assess the presence and severity of IA1,37. The IAT contains 20 items (from 1 = “rarely” to 5 = “always”), with the total score ranging from 20 to 100. The total score of > / = 50 points was considered as “having IA”28,38. The IAT had good psychometric properties in Chinese adolescents (e.g., Cronbach’s alpha was 0.90)28,38. The Center for Epidemiologic Studies Depression Scale (CESD)—Chinese version was used to assess the severity of depressive symptoms39,40. A higher score reflects more severe depressive symptoms. The Chinese version of World Health Organization Quality of Life-BREF (WHOQOL-BREF) was used to evaluate QOL41,42. WHOQOL-BREF is a 26-item self-reported scale to assess QOL in physical health, psychological health, social health and environmental domains.
Data analyses were performed using SPSS V24.0. Comparisons of socio-demographic and clinical variables between Macau and mainland China, and between IA and non-IA groups were conducted using independent samples t-tests, Mann–Whitney U tests, and chi-square tests, as appropriate. Normal distributions of continuous variables were checked by one-sample Kolmogorov–Smirnov test. Binary logistic regression analysis with the “enter” method was used to examine IA prevalence between Macau and mainland China, with IA as the dependent variable, study site (Macau vs. mainland China) as the only independent variable, after controlling for variables that significantly differed in univariate analyses. In addition, analysis of covariance (ANCOVA) was used to compare QOL between IA and non-IA groups after controlling for variables that significantly differed in univariate analyses. Binary logistic regression analysis with “enter” method was used to examine the independent correlates of IA. IA was the dependent variable, while those with significant group differences in the above univariate analyses were entered as independent variables. Significance was set at 0.05, with two-tailed tests.
A total of 3380 students were invited to join this study, and 2892 agreed and completed the assessments: 882 in Macau, 500 in Beijing, 699 in Hefei, and 811 in Shenzhen. No significant differences in age and gender were observed between students who participated and those who refused to participate in this study. The basic demographic characteristics are shown in Table 1.
The overall prevalence of IA was 23.7% (95% CI: 22.1–25.2%), with 19.8% (95% CI: 18.0–21.5%) in mainland China and 32.5% (95% CI: 29.4–35.6%) in Macau (x2 = 55.5, p < 0.001). There were significant differences between the Macau and mainland China in gender, being an only child, being senior secondary school students, living with family, self-perceived physical health and weight, academic performance and stress, relationships with classmates, teachers, and family, perception of family income status, parental educational level, parental work status and CESD score. No significant difference in IA prevalence between genders was found in mainland China (18.7% in boys vs. 20.5% in girls; x2 = 1.02, p = 0.3) and Macau (33.3% in boys vs. 32.2% in girls; x2 = 0.14, p = 0.71). After controlling for these variables, there were still significant differences between the two areas in IA prevalence (OR = 2.15, 95% CI: 1.68–2.74, p < 0.001).
The comparison of socio-demographic and clinical characteristics between the IA and non-IA groups is summarized in Table 2. Significant differences between the two groups were found in terms of age, study site, grade, self-perceived physical health and weight, academic performance and stress, relationships with classmates, teachers, and family, self-reported family income status, parental marital status and CESD score. After controlling for these variables, compared to the non-IA group, the IA group had a lower QOL in the physical (F = 4.5, p = 0.03), psychological (F = 6.2, p = 0.01), social (F = 7.1, p = 0.008) and environmental domains (F = 9.6, p = 0.002). Students in senior secondary school, with poor academic performance, and having more severe depressive symptoms were independently and significantly associated with higher risk of IA (Table 3).
To the best of our knowledge, this was the first study to compare the prevalence of IA between adolescents in Macau and those in mainland China. IA is common in Chinese adolescents, particularly in Macau. Using the same IAT cutoff value, our findings were relatively higher than the figures reported in Western countries and other areas, for example, 5.8–12.1% in Italy11,43, 11.7% in Switzerland44 and 7.9–18.2% in Turkey10,45. The prevalence in mainland China in this study was also higher than the corresponding figures among Chinese adolescents in previous studies, for example, 8.1% in eight cities in China28, 10.4% in Anhui province46 and 12.2% in Guangdong province using the same IAT cutoff value47. The rapid growth of Internet use in China in recent years may contribute to the increased prevalence of IA in this study. According to the report from the CNNIC, the number of Internet users aged < 18 years was around 175 million in China, which accounted for 93.1% of this population15, with an annual growth rate of 3.7—8.1%48. In addition, widespread use of smartphones makes access to the Internet much easier than before. It was reported that 93.9% of Chinese adolescents accessed the Internet using their smartphones15. Furthermore, a rapid increase of online learning, entertainment and leisure activities made adolescents spend more time using the Internet15. All these factors are associated with increased prevalence of IA in China. It should be noted that no gender difference in IA prevalence was found in this study, indicating IA commonly occurs in both genders49,50.
Students in Macau had a significantly higher IA prevalence than those in mainland China, which is similar to previous findings comparing IA prevalence between mainland China (17.1%) and Hong Kong (31.6%) using the same instrument35. IA is more prevalent in Macau than that in mainland China. Based on previous findings that IA was associated with high family income and socioeconomic status28, we speculated that higher IA prevalence in Macau is most likely due to economic and sociocultural reasons. Macau's per capita GDP ranks top five globally30, almost nine times higher than mainland China. Thus, with higher household income, adolescents in Macau may have access to electronic devices at an earlier age. A study found that 51.1% of adolescents had one personal computer in Hong Kong, while the corresponding figure was only 14.7% in mainland China35. This situation may be more widespread in Macau since the economic conditions in Macau are better still than in Hong Kong. Moreover, the wireless network infrastructures are well built in Macau, providing easy and free access to the Internet, and additional features (e.g., Facebook, and YouTube) of the internet are available in Macau, while these platforms are not widely available in mainland China. Additionally, as a former European colony, Macau is greatly affected by both Eastern and Western cultures, which may lead to different parenting styles regarding Internet use between Macau and mainland China51. Due to high degree of urbanization and good economic status, adolescents in Macau usually do not experience job-seeking pressures following graduation. Moreover, the living cost has rapidly increased in Macau in recent years, so adolescents’ parents typically work, which results in decreased time to take care of their children. In contrast, their counterparts in mainland China face fierce competition in school, and they are highly occupied with homework, and therefore have limited time online with more strict parental monitoring15,52. All these factors likely contribute to the higher IA prevalence in Macau.
Higher school grades, poor academic performance and depressive symptoms were significantly associated with IA. Previous studies found that older adolescents were more likely to be addicted to the Internet26,28, which could be explained by several reasons. Compared to younger adolescents, older adolescents usually experience a more rapid phase of psychosocial, cognitive and intellectual development, and they start to take certain adult roles and consider long-term career goals. Thus, they often need to use Internet for information and social activities. Moreover, students in higher school grade are easier to access to the Internet26,28. Therefore, older adolescents are more likely to report higher IA rate. It may also be true that older adolescents are using their devices for online gaming, which is known to be highly addictive and increase in use around this age53,54.
The association between IA and poor academic performance found in this study supported earlier findings27,55. Studies found that “loss of control” moderates the association between IA and declining school performance55. Students suffering from IA usually spend a lot of time online, regardless of their school responsibilities. Some of them even skip class and sacrifice sleep time, resulting in fatigue and low degree of involvement in classes. The stress under the poor school performance may lead to further involvement of online activities for adolescents in order to escape negative emotions56.
Similar to previous studies46,57, we found the positive association between depressive symptoms and IA. Previous studies found that persons with IA usually lose rewarding experiences in their offline world and then feel socially isolated, which could increase the risk of depressive mood20,58; in addition, those with depressive symptoms often use Internet to “self-medicate” as Internet use could alleviate depressive emotion4,59. The internet constructs a virtual and unrealistic world in which those with depression may obtain social support, self-identity, and the sense of achievement that cannot be satisfied in real world16. Nevertheless, excessive Internet use may impair daily functions and further worsen dysregulated mood. Some studies found a bidirectional association between depression and IA60.
Long-term Internet use and IA may cause physical problems, such as reduced physical activities and obesity61, physical pain (e.g., neck, shoulder and back pain)62, hearing impairment and impaired vision63, increased risk of accidents (e.g., falling, slipping, and bumps/collisions)64, disturbed circadian rhythm65, and poor sleep quality2,65. In addition, persons suffering from IA often have emotional and social problems, which may isolate themselves from peers, make them spend more time online and finally lead to additional adaption stress, and psychological and interpersonal relationship problems3. All these factors could cause lower QOL. As expected, IA was negatively associated with all QOL domains in this study, which is consisted with the findings of a meta-analysis66.
The strengths of this study included the large sample size, multisite design, random sampling, and the use of standardized instruments. Some limitations need to be noted. First, this was a cross-sectional study, therefore, the causal relationships between IA and a host of demographic and clinical variables are tentative. Second, due to logistical reasons, only three cities in mainland China were selected to represent the adolescents in central, eastern, and southern China. Therefore, the findings cannot be generalized to all parts of China, particularly rural areas. Third, the possibility of recall bias cannot be excluded. Fourth, the IAT only measures IA as a general construct, therefore, subtypes of IA were not measured in this study. In addition, there have been no standardized instruments on socioeconomic and cultural factors in China. Instead, a host of categorical variables, such as perception of family status, parental educational level, parental work status and marital status, were recorded as the index of socioeconomic status. Finally, some potentially confounding factors were not examined in this study, such as physical diseases and physical exercise.
In conclusion, IA is common in Chinese adolescents, especially in Macau. Given the negative impact of IA on health outcomes and quality of life, more attention should be paid to young Internet users, and regular screening and effective interventions should be undertaken for this population.
The Research Ethics Committees of the University of Macau that approved the study prohibits the authors from making the research data set publicly available. Readers and all interested researchers may contact Prof. Yu-Tao Xiang (Email address: firstname.lastname@example.org) for details. Prof. Xiang could apply to the Research Ethics Committees of the University of Macau for the release of the data.
Young, K. S. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol. Behav. 1, 237–244 (1998).
An, J. et al. Associations between problematic internet use and adolescents’ physical and psychological symptoms: Possible role of sleep quality. J. Addict. Med. 8, 282–287. https://doi.org/10.1097/ADM.0000000000000026 (2014).
Cerniglia, L. et al. Internet addiction in adolescence: Neurobiological, psychosocial and clinical issues. Neurosci. Biobehav. Rev. 76, 174–184. https://doi.org/10.1016/j.neubiorev.2016.12.024 (2017).
Lai, C. M. et al. The mediating role of Internet addiction in depression, social anxiety, and psychosocial well-being among adolescents in six Asian countries: A structural equation modelling approach. Public Health 129, 1224–1236. https://doi.org/10.1016/j.puhe.2015.07.031 (2015).
Yayan, E. H., Arikan, D., Saban, F., Gurarslan Bas, N. & Ozel Ozcan, O. Examination of the correlation between internet addiction and social phobia in adolescents. West J. Nurs. Res.39, 1240–1254, https://doi.org/10.1177/0193945916665820 (2017).
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM-5). (2013).
World Health Organization. International Classification of Diseases 11th Revision (ICD-11). (2018).
Kuss, D. J., Griffiths, M. D., Karila, L. & Billieux, J. Internet addiction: A systematic review of epidemiological research for the last decade. Curr. Pharm. Des. 20, 4026–4052. https://doi.org/10.2174/13816128113199990617 (2014).
Sussman, C. J., Harper, J. M., Stahl, J. L. & Weigle, P. Internet and video game addictions: Diagnosis, epidemiology, and neurobiology. Child Adolesc. Psychiatr. Clin. N. Am. 27, 307–326. https://doi.org/10.1016/j.chc.2017.11.015 (2018).
Kilic, M., Avci, D. & Uzuncakmak, T. Internet addiction in high school students in Turkey and multivariate analyses of the underlying factors. J. Addict. Nurs. 27, 39–46. https://doi.org/10.1097/JAN.0000000000000110 (2016).
Vigna-Taglianti, F. et al. Problematic internet use among high school students: Prevalence, associated factors and gender differences. Psychiatr. Res. 257, 163–171. https://doi.org/10.1016/j.psychres.2017.07.039 (2017).
Siomos, K. et al. Evolution of internet addiction in Greek adolescent students over a two-year period: the impact of parental bonding. Eur. Child Adolesc. Psychiatry 21, 211–219. https://doi.org/10.1007/s00787-012-0254-0 (2012).
Shek, D. T. & Yu, L. Adolescent internet addiction in Hong Kong: Prevalence, change, and correlates. J. Pediatr. Adolesc. Gynecol. 29, S22-30. https://doi.org/10.1016/j.jpag.2015.10.005 (2016).
Lin, M. P., Wu, J. Y., You, J., Hu, W. H. & Yen, C. F. Prevalence of internet addiction and its risk and protective factors in a representative sample of senior high school students in Taiwan. J. Adolesc. 62, 38–46. https://doi.org/10.1016/j.adolescence.2017.11.004 (2018).
China Internet Network Information Center. Research Report on Internet Use in Chinese Children and Adolescents in 2019. (CNNIC, 2019).
Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. S. & Chen, C. C. The association between Internet addiction and psychiatric disorder: A review of the literature. Eur. Psychiatry 27, 1–8. https://doi.org/10.1016/j.eurpsy.2010.04.011 (2012).
Ho, R. C. et al. The association between internet addiction and psychiatric co-morbidity: a meta-analysis. BMC Psychiatry 14, 183. https://doi.org/10.1186/1471-244X-14-183 (2014).
Wang, B. Q., Yao, N. Q., Zhou, X., Liu, J. & Lv, Z. T. The association between attention deficit/hyperactivity disorder and internet addiction: A systematic review and meta-analysis. BMC Psychiatry 17, 260. https://doi.org/10.1186/s12888-017-1408-x (2017).
Hoge, E., Bickham, D. & Cantor, J. Digital media, anxiety, and depression in children. Pediatrics 140, S76–S80. https://doi.org/10.1542/peds.2016-1758G (2017).
Elhai, J. D., Levine, J. C. & Hall, B. J. The relationship between anxiety symptom severity and problematic smartphone use: A review of the literature and conceptual frameworks. J. Anxiety Disord. 62, 45–52. https://doi.org/10.1016/j.janxdis.2018.11.005 (2019).
Ko, C. H., Yen, J. Y., Liu, S. C., Huang, C. F. & Yen, C. F. The associations between aggressive behaviors and internet addiction and online activities in adolescents. J. Adolesc. Health 44, 598–605. https://doi.org/10.1016/j.jadohealth.2008.11.011 (2009).
Morioka, H. et al. The association between alcohol use and problematic internet use: A large-scale nationwide cross-sectional study of adolescents in Japan. J. Epidemiol. 27, 107–111. https://doi.org/10.1016/j.je.2016.10.004 (2017).
Marchant, A. et al. A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS ONE 12, e0181722. https://doi.org/10.1371/journal.pone.0181722 (2017).
Cheng, Y. S. et al. Internet addiction and its relationship with suicidal behaviors: A meta-analysis of multinational observational studies. J. Clin. Psychiatry79, 17r11761, https://doi.org/10.4088/JCP.17r11761 (2018).
Tsitsika, A. K. et al. Association between problematic internet use, socio-demographic variables and obesity among European adolescents. Eur. J. Public Health 26, 617–622. https://doi.org/10.1093/eurpub/ckw028 (2016).
Xin, M. et al. Online activities, prevalence of Internet addiction and risk factors related to family and school among adolescents in China. Addict Behav. Rep. 7, 14–18. https://doi.org/10.1016/j.abrep.2017.10.003 (2018).
Tsitsika, A. et al. Determinants of internet addiction among adolescents: A case-control study. Sci. World J. 11, 866–874. https://doi.org/10.1100/tsw.2011.85 (2011).
Cao, H., Sun, Y., Wan, Y. H., Hao, J. H. & Tao, F. B. Problematic internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health 11, 802. https://doi.org/10.1186/1471-2458-11-802 (2011).
Chang, F. C. et al. The relationship between parental mediation and Internet addiction among adolescents, and the association with cyberbullying and depression. Compr. Psychiatry 57, 21–28. https://doi.org/10.1016/j.comppsych.2014.11.013 (2015).
World Bank Group. World Bank Open Data, https://data.worldbank.org/ (2009–2018).
Lo, T. W., Cheng, H. K. & Au, E. Youth Problems and Youth Service Blueprint in Macau: A Follow-up Study. (Macau SAR Government, 2013).
Ong, J., Wong, W., Lee, A., Holroyd, E. & Huang, S. Y. Sexual activity and adolescent health risk behaviours amongst high school students in three ethnic Chinese urban populations. J. Clin. Nurs. 22, 3270–3279. https://doi.org/10.1111/jocn.12267 (2013).
Chiao, C., Yi, C. C. & Ksobiech, K. Adolescent internet use and its relationship to cigarette smoking and alcohol use: A prospective cohort study. Addict Behav. 39, 7–12. https://doi.org/10.1016/j.addbeh.2013.09.006 (2014).
Sung, J., Lee, J., Noh, H. M., Park, Y. S. & Ahn, E. J. Associations between the risk of internet addiction and problem behaviors among Korean adolescents. Korean J. Fam. Med. 34, 115–122. https://doi.org/10.4082/kjfm.2013.34.2.115 (2013).
Mak, K. K. et al. Epidemiology of internet behaviors and addiction among adolescents in six Asian countries. Cyberpsychol. Behav. Soc. Netw. 17, 720–728. https://doi.org/10.1089/cyber.2014.0139 (2014).
Perna, L., Bolte, G., Mayrhofer, H., Spies, G. & Mielck, A. The impact of the social environment on children’s mental health in a prosperous city: An analysis with data from the city of Munich. BMC Public Health 10, 199. https://doi.org/10.1186/1471-2458-10-199 (2010).
Chang, M. K. & Law, P. M. Factor structure for young’s internet addiction test: A confirmatory study. Comput. Human Behav. 24, 2597–2619. https://doi.org/10.1016/j.chb.2008.03.001 (2008).
Tan, Y., Chen, Y., Lu, Y. & Li, L. Exploring associations between problematic internet use, depressive symptoms and sleep disturbance among southern Chinese adolescents. Int. J. Environ. Res. Public Health 13, 313. https://doi.org/10.3390/ijerph13030313 (2016).
Lee, S. W. et al. Factor structure of the Center for Epidemiological Studies Depression Scale in Hong Kong adolescents. J. Pers. Assess. 90, 175–184. https://doi.org/10.1080/00223890701845385 (2008).
Radloff, L. S. The use of the Center for Epidemiologic Studies Depression Scale in adolescents and young-adults. J. Youth Adolesc. 20, 149–166. https://doi.org/10.1007/Bf01537606 (1991).
World Health Organization. The World Health Organization Quality of Life assessment (WHOQOL): Position paper from the World Health Organization. Soc. Sci. Med. 41, 1403–1409. https://doi.org/10.1016/0277-9536(95)00112-k (1995).
Xia, P., Li, N., Hau, K. T., Liu, C. & Lu, Y. Quality of life of Chinese urban community residents: A psychometric study of the mainland Chinese version of the WHOQOL-BREF. BMC Med. Res. Methodol. 12, 37. https://doi.org/10.1186/1471-2288-12-37 (2012).
Poli, R. & Agrimi, E. Internet addiction disorder: Prevalence in an Italian student population. Nord. J. Psychiatry 66, 55–59. https://doi.org/10.3109/08039488.2011.605169 (2012).
Rucker, J., Akre, C., Berchtold, A. & Suris, J. C. Problematic internet use is associated with substance use in young adolescents. Acta Paediatr. 104, 504–507. https://doi.org/10.1111/apa.12971 (2015).
Koyuncu, T., Unsal, A. & Arslantas, D. Assessment of internet addiction and loneliness in secondary and high school students. J. Pak. Med. Assoc. 64, 998–1002 (2014).
Wu, X. S. et al. Prevalence of internet addiction and its association with social support and other related factors among adolescents in China. J. Adolesc. 52, 103–111. https://doi.org/10.1016/j.adolescence.2016.07.012 (2016).
Wang, H. et al. Problematic internet use in high school students in Guangdong Province, China. PLoS ONE 6, e19660. https://doi.org/10.1371/journal.pone.0019660 (2011).
China Internet Network Information Center. The China Statistical Report on Adolescent Internet Users, 2013–2018. (CNNIC, 2013–2018).
Muller, K. W. et al. Insights into aspects behind internet-related disorders in adolescents: The interplay of personality and symptoms of adjustment disorders. J. Adolesc. Health 62, 234–240. https://doi.org/10.1016/j.jadohealth.2017.09.011 (2018).
Rumpf, H. J. et al. Occurence of internet addiction in a general population sample: A latent class analysis. Eur. Addict. Res. 20, 159–166. https://doi.org/10.1159/000354321 (2014).
Pinquart, M. & Kauser, R. Do the associations of parenting styles with behavior problems and academic achievement vary by culture? Results from a meta-analysis. Cult. Divers. Ethnic Minor Psychol. 24, 75–100. https://doi.org/10.1037/cdp0000149 (2018).
Lin, C. H., Lin, S. L. & Wu, C. P. The effects of parental monitoring and leisure boredom on adolescents’ internet addiction. Adolescence 44, 993–1004 (2009).
Gentile, D. A. et al. Internet gaming disorder in children and adolescents. Pediatrics 140, S81–S85. https://doi.org/10.1542/peds.2016-1758H (2017).
Paulus, F. W., Ohmann, S., von Gontard, A. & Popow, C. Internet gaming disorder in children and adolescents: A systematic review. Dev. Med. Child. Neurol. 60, 645–659. https://doi.org/10.1111/dmcn.13754 (2018).
Huang, H. & Leung, L. Instant messaging addiction among teenagers in China: Shyness, alienation, and academic performance decrement. Cyberpsychol. Behav. 12, 675–679. https://doi.org/10.1089/cpb.2009.0060 (2009).
Kim, D. J. et al. Internet game addiction, depression, and escape from negative emotions in adulthood: A nationwide community sample of Korea. J. Nerv. Ment. Dis. 205, 568–573. https://doi.org/10.1097/NMD.0000000000000698 (2017).
Elhai, J. D., Yang, H., Fang, J., Bai, X. & Hall, B. J. Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addict. Behav. 101, 105962. https://doi.org/10.1016/j.addbeh.2019.04.020 (2020).
Elhai, J. D., Vasquez, J. K., Lustgarten, S. D., Levine, J. C. & Hall, B. J. Proneness to boredom mediates relationships between problematic smartphone use with depression and anxiety severity. Soc. Sci. Comput. Rev. 36, 707–720. https://doi.org/10.1177/0894439317741087 (2018).
Davis, R. A. A cognitive-behavioral model of pathological internet use. Comput. Hum. Behav. 17, 187–195 (2001).
Lau, J. T. F. et al. Bidirectional predictions between internet addiction and probable depression among Chinese adolescents. J. Behav. Addict. 7, 633–643. https://doi.org/10.1556/2006.7.2018.87 (2018).
Eliacik, K. et al. Internet addiction, sleep and health-related life quality among obese individuals: A comparison study of the growing problems in adolescent health. Eat Weight Disord. 21, 709–717. https://doi.org/10.1007/s40519-016-0327-z (2016).
Hakala, P. T., Rimpela, A. H., Saarni, L. A. & Salminen, J. J. Frequent computer-related activities increase the risk of necke-shoulder and low back pain in adolescents. Eur. J. Public Health 16, 536–541. https://doi.org/10.1093/eurpub/ckl025 (2006).
Meo, S. A. & Al-Drees, A. M. Mobile phone related-hazards and subjective hearing and vision symptoms in the Saudi population. Int. J. Occup. Med. Environ. Health 18, 53–57 (2005).
Kim, H. J., Min, J. Y., Kim, H. J. & Min, K. B. Accident risk associated with smartphone addiction: A study on university students in Korea. J. Behav. Addict. 6, 699–707. https://doi.org/10.1556/2006.6.2017.070 (2017).
Chen, Y. L. & Gau, S. S. Sleep problems and internet addiction among children and adolescents: A longitudinal study. J. Sleep Res. 25, 458–465. https://doi.org/10.1111/jsr.12388 (2016).
Cheng, C. & Li, A. Y. Internet addiction prevalence and quality of (real) life: A meta-analysis of 31 nations across seven world regions. Cyberpsychol. Behav. Soc. Netw. 17, 755–760. https://doi.org/10.1089/cyber.2014.0317 (2014).
The study was supported by the University of Macau (MYRG2019-00066-FHS), the National Key Research & Development Program of China (No. 2016YFC1307200), the Beijing Municipal Administration of Hospitals Incubating Program (No. PX2016028) and the Beijing Municipal Administration of Hospitals’ Ascent Plan (No. DFL20151801).
The authors declare no competing interests.
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Xu, DD., Lok, KI., Liu, HZ. et al. Internet addiction among adolescents in Macau and mainland China: prevalence, demographics and quality of life. Sci Rep 10, 16222 (2020). https://doi.org/10.1038/s41598-020-73023-1