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Journal articles on the topic 'Internet addiction – Risk factors – China'

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Published: 4 June 2021
Last updated: 6 February 2022

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1

Wu, Jing. "Internet addiction among cyberkids in China: Risk factors and intervention strategies." Chinese Journal of Communication 14, no. 3 (July 3, 2021): 350–52. http://dx.doi.org/10.1080/17544750.2021.1948691.

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2

Wu, Anise M. S., Joseph T. F. Lau, Kit-man Cheng, Rita W. Law, Vincent W. S. Tse, and Mason M. C. Lau. "Direct and Interaction Effects of Co-Existing Familial Risk Factors and Protective Factors Associated With Internet Addiction Among Chinese Students in Hong Kong." Journal of Early Adolescence 38, no. 4 (October 4, 2016): 429–50. http://dx.doi.org/10.1177/0272431616671826.

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Internet addiction (IA) is prevalent among adolescents and imposes a serious public health threat. Familial risk and protective factors may co-exist and interact with each other to determine IA. We conducted a cross-sectional survey among 9,618 Secondary 1 to 4 students in Hong Kong, China. About 16% of the surveyed students were classified as Internet addicted; nearly one third of them perceived that at least one of their family members had IA (FMIA). We found that FMIA was a risk factor (multivariate odds ratio [OR] = 2.04), and perceived family support was a protective factor (multivariate OR = 0.97) of IA. We also found a significant risk-enhancement moderation effect between these risk and protective factors, that is, the risk effect of FMIA increased with perceived family support. The finding highlights that family-based interventions, which modify familial risk and protective factors, should be effective for adolescent IA, but caution is required about potential risk-enhancement moderations between such factors.
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3

Xin, Miao, Jiang Xing, Wang Pengfei, Li Houru, Wang Mengcheng, and Zeng Hong. "Online activities, prevalence of Internet addiction and risk factors related to family and school among adolescents in China." Addictive Behaviors Reports 7 (June 2018): 14–18. http://dx.doi.org/10.1016/j.abrep.2017.10.003.

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4

Mathew K V, Binoy, and Maryelizabeth Tidiya Walarine. "Neck pain among smartphone users: an imminent public health issue during the pandemic time." Journal of Ideas in Health 3, Special1 (September 29, 2020): 201–4. http://dx.doi.org/10.47108/jidhealth.vol3.issspecial1.65.

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COVID-19 Pandemic resulted in social mobility and travel restrictions to contain the infection. It has been reported that there happened post-pandemic surge in the use of the internet and social media as people rely on it more often for entertainment, work, and learning purposes. It is also been used as an unhealthy coping strategy for pandemic related stress. The smartphone was found to be the most common gadget used for accessing internet-based services. Owing to the postural alterations related to the small screen size of smartphones, neck pain was reported very commonly among smartphone users. Neck pain among smartphone users is a public health concern needing immediate attention in the pandemic time. Many of the risk factors leading to neck pain among smartphone users are modifiable. The authors outline preventive strategies that are mostly self- regulated. The strategies recommended include reducing Smartphone usage time (Smartphone-Free Time, Smartphone-Free Zone), maintaining ideal posture (Focus Breaks, Mobility Breaks, How am I keeping my posture?, Hands-free alternatives), doing regular exercises, preparing and following an activity schedule, and inculcating healthy habits. References Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, et al. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int J Surg 2020; 76: 71–6. https://doi.org/10.1016/j.ijsu.2020.02.034 Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, et al. Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Compr Psychiatry 2020; 100:152180. https://doi.org/10.1016/j.comppsych.2020.152180 Sun Y, Li Y, Bao Y, Meng S, Sun Y, Schumann G, et al. Brief Report: Increased Addictive Internet and Substance Use Behavior During the COVID‐19 Pandemic in China. Am J Addict 2020 ;29(4):268–70. https://doi.org/10.1111/ajad.13066 Varga E. How does the Internet Influences the Readers’ Behavior. Procedia Manuf. 2020; 46:949–56. https://doi.org/10.1016/j.promfg.2020.05.013 Zhuang L, Wang L, Xu D, Wang Z, Liang R. Association between excessive smartphone use and cervical disc degeneration in young patients suffering from chronic neck pain. J Orthop Sci. 2020; https://doi.org/1016/j.jos.2020.02.009 Davey S, Davey A. Assessment of smartphone addiction in indian adolescents: a mixed method study by systematic-review and meta-analysis approach. Int J Prev Med.2014;5(12):1500–11. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25709785 Smetaniuk P. A preliminary investigation into the prevalence and prediction of problematic cell phone use. J Behav Addict 2014 Mar;3(1):41–53. https://doi.org/1556/JBA.3.2014.004 Lee H, Seo MJ, Choi TY. The Effect of Home-based Daily Journal Writing in Korean Adolescents with Smartphone Addiction. J Korean Med Sci. 2016;31(5):764. https://doi.org/3346/jkms.2016.31.5.764 Yu S, Sussman S. Does Smartphone Addiction Fall on a Continuum of Addictive Behaviors? Int J Environ Res Public Health.2020;17(2):422. https://doi.org/10.3390/ijerph17020422.10. Kwon M, Kim D-J, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One2013;8(12): e83558. https://doi.org/1371/journal.pone.0083558 Ding D, Li J. Smartphone Overuse – A Growing Public Health Issue. J Psychol Psychother.2017;07(289):1-3. https://doi.org/0.4172/2161-0487.1000289 Boumosleh JM, Jaalouk D. Depression, anxiety, and smartphone addiction in university students- A cross sectional study. PLoS One 2017;12(8): e0182239. https://doi.org/1371/journal.pone.0182239 Xie Y, Szeto G, Dai J. Prevalence and risk factors associated with musculoskeletal complaints among users of mobile handheld devices: A systematic review. Appl Ergon.2017 Mar;59:132–42. https://doi.org/1016/j.apergo.2016.08.020 Alsalameh AM, Harisi MJ, Alduayji MA, Almutham AA, Mahmood FM. Evaluating the relationship between smartphone addiction/overuse and musculoskeletal pain among medical students at Qassim University. J Fam Med Prim care. 2019;8(9):2953–9. https://doi.org/4103/jfmpc.jfmpc_665_19 Toh SH, Coenen P, Howie EK, Smith AJ, Mukherjee S, Mackey DA, et al. A prospective longitudinal study of mobile touch screen device use and musculoskeletal symptoms and visual health in adolescents. Appl Ergon. 2020; 85:103028. https://doi.org/1016/j.apergo.2019.103028 Lee S, Kang H, Shin G. Head flexion angle while using a smartphone. Ergonomics. 2015 Feb 17;58(2):220–6. https://doi.org/1080/00140139.2014.967311 Guan X, Fan G, Chen Z, Zeng Y, Zhang H, Hu A, et al. Gender difference in mobile phone use and the impact of digital device exposure on neck posture. Ergonomics. 2016;59(11):1453–61. https://doi.org/10.1080/00140139.2016.1147614 Han H, Shin G. Head flexion angle when web-browsing and texting using a smartphone while walking. Appl Ergon. 2019; 81:102884. https://doi.org/1016/j.apergo.2019.102884 Singla D, Veqar Z. Association between forward head, rounded shoulders, and increased thoracic kyphosis: a review of the literature. J Chiropr Med. 2017;16(3):220–9. https://doi.org/1016/j.jcm.2017.03.004 Park J-H, Kang S-Y, Lee S-G, Jeon H-S. The effects of smart phone gaming duration on muscle activation and spinal posture: Pilot study. Physiother Theory Pract. 2017;33(8):661–9. https://doi.org/10.1080/09593985.2017.1328716 Xie Y, Szeto GPY, Dai J, Madeleine P. A comparison of muscle activity in using touchscreen smartphone among young people with and without chronic neck–shoulder pain. Ergonomics. 2016;59(1):61–72. https://doi.org/10.1080/00140139.2015.1056237 Hansraj KK. Assessment of stresses in the cervical spine caused by posture and position of the head. Surg Technol Int. 2014; 25:277–9. Li W, Yang Y, Liu Z-H, Zhao Y-J, Zhang Q, Zhang L, et al. Progression of mental health services during the COVID-19 outbreak in China. Int J Biol Sci. 2020;16(10):1732–8. https://doi.org/7150/ijbs.45120 Namwongsa S, Puntumetakul R, Neubert MS, Boucaut R. Factors associated with neck disorders among university student smartphone users. Work. 2018;61(3):367–78. https://doi.org/3233/WOR-182819 Ko P-H, Hwang Y-H, Liang H-W. Influence of smartphone use styles on typing performance and biomechanical exposure. Ergonomics. 2016;59(6):821–8. https://doi.org/10.1080/00140139.2015.1088075
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5

Kuss, Daria J., Mark D. Griffiths, and Jens F. Binder. "Internet addiction in students: Prevalence and risk factors." Computers in Human Behavior 29, no. 3 (May 2013): 959–66. http://dx.doi.org/10.1016/j.chb.2012.12.024.

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6

Kuss, Daria J., Antonius J. van Rooij, Gillian W. Shorter, Mark D. Griffiths, and D. van de Mheen. "Internet addiction in adolescents: Prevalence and risk factors." Computers in Human Behavior 29, no. 5 (September 2013): 1987–96. http://dx.doi.org/10.1016/j.chb.2013.04.002.

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7

Jin Jeong, You, Bongwon Suh, and Gahgene Gweon. "Is smartphone addiction different from Internet addiction? comparison of addiction-risk factors among adolescents." Behaviour & Information Technology 39, no. 5 (April 16, 2019): 578–93. http://dx.doi.org/10.1080/0144929x.2019.1604805.

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8

Tereshchenko, Sergey, and Edward Kasparov. "Neurobiological Risk Factors for the Development of Internet Addiction in Adolescents." Behavioral Sciences 9, no. 6 (June 14, 2019): 62. http://dx.doi.org/10.3390/bs9060062.

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The sudden appearance and spread of Internet addiction in adolescent populations, in association with the rapid escalation of consumed Internet content and the broad availability of smartphones and tablets with Internet access, is posing a new challenge for classical addictology which requires urgent solutions. Like the majority of other psychopathological conditions, pathological Internet addiction depends upon a group of multifactor polygenic conditions. For each specific case, there is a unique combination of inherited characteristics (nervous tissue structure, secretion, degradation, and reception of neuromediators), and many are extra-environment factors (family-related, social, and ethnic-cultural). One of the main challenges in the development of the bio-psychosocial model of Internet addiction is to determine which genes and neuromediators are responsible for increased addiction susceptibility. This information will herald the start of a search for new therapeutic targets and the development of early prevention strategies, including the assessment of genetic risk levels. This review summarizes the literature and currently available knowledge related to neurobiological risk factors regarding Internet addiction in adolescents. Genetic, neurochemical and neuroimaging data are presented with links to actual pathogenetic hypotheses according to the bio-psychosocial model of IA forming.
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9

Lee, Ju Young, Kyoung Min Shin, Sun-Mi Cho, and Yun Mi Shin. "Psychosocial Risk Factors Associated with Internet Addiction in Korea." Psychiatry Investigation 11, no. 4 (2014): 380. http://dx.doi.org/10.4306/pi.2014.11.4.380.

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10

Trusova, A. V., S. V. Grechanyi, V. A. Soldatkin, A. N. Yakovlev, R. D. Ilyuk, N. A. Chuprova, А. Е. Nikolishin, et al. "Internet addiction predictors: analysis of psychological factors." V.M. BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY, no. 1 (April 2, 2020): 72–82. http://dx.doi.org/10.31363/2313-7053-2020-1-72-82.

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The article presents the results of a study of individual psychological factors that are potentially significant for the development of Internet addiction (IA), carried out as part of the Russian interdisciplinary research project to identify a system of high-risk markers for developing IA. As a result of the analysis, a model of psychological predictors of IA was obtained, which includes personality characteristics — high nonplanning impulsiveness (as a lack of forethought and ability to follow future plans), and low self-direction (concerning to Cloninger’s psychobiological model of personality). Additionally, childhood experiences of living in a community with a high level of violence were included in the model as one of the psychological predictors. The male gender also entered one of the final models as a risk factor for the development of IA. The results can be used as the basis for the development of targeted psychological assistance programs and preventive measures aimed at improving the specific characteristics of inhibitory control and the development of self-awareness.
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11

Wu, Chia-Yi, Ming-Been Lee, Shih-Cheng Liao, and Li-Ren Chang. "Risk Factors of Internet Addiction among Internet Users: An Online Questionnaire Survey." PLOS ONE 10, no. 10 (October 13, 2015): e0137506. http://dx.doi.org/10.1371/journal.pone.0137506.

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12

Peris, Montserrat, Usue de la Barrera, Konstanze Schoeps, and Inmaculada Montoya-Castilla. "Psychological Risk Factors that Predict Social Networking and Internet Addiction in Adolescents." International Journal of Environmental Research and Public Health 17, no. 12 (June 26, 2020): 4598. http://dx.doi.org/10.3390/ijerph17124598.

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Adolescents’ addictive use of social media and the internet is an increasing concern among parents, teachers, researchers and society. The purpose was to examine the contribution of body self-esteem, personality traits, and demographic factors in the prediction of adolescents’ addictive use of social media and the internet. The participants were 447 Spanish adolescents aged 13−16 years (M = 14.90, SD = 0.81, 56.2% women). We measured gender, age, body self-esteem (body satisfaction and physical attractiveness), personality traits (extraversion, neuroticism, disinhibition and narcissism) and social networking and internet addiction (internet addiction symptoms, social media use, geek behaviour, and nomophobia). The effects of gender, age, body self-esteem and personality on the different dimensions of internet addiction were estimated, conducting hierarchical linear multiple regression analysis and a fuzzy-set qualitative comparative analysis (fsQCA). The results evidenced different pathways explaining four types of adolescents’ internet addiction: gender and disinhibition were the most relevant predictors of addiction symptoms; gender combined with physical attractiveness best explained social media use; narcissism and neuroticism appear to be the most relevant predictors of geek behaviour; and narcissism was the variable that best explained nomophobia. Furthermore, the advantages and differences between both methodologies (regressions vs. QCA) were discussed.
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Choi, Sam-Wook, Dai-Jin Kim, Jung-Seok Choi, Heejune Ahn, Eun-Jeung Choi, Won-Young Song, Seohee Kim, and Hyunchul Youn. "Comparison of risk and protective factors associated with smartphone addiction and Internet addiction." Journal of Behavioral Addictions 4, no. 4 (December 2015): 308–14. http://dx.doi.org/10.1556/2006.4.2015.043.

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14

Tóth, Gábor, Krisztian Kapus, David Hesszenberger, Marietta Pohl, Gábor Kósa, Julianna Kiss, Gabriella Pusch, Éva Fejes, Antal Tibold, and Gergely Feher. "Prevalence and Risk Factors of Internet Addiction among Hungarian High School Teachers." Life 11, no. 3 (March 3, 2021): 194. http://dx.doi.org/10.3390/life11030194.

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The extensive availability of internet has led to the the recognition of problematic internet use (so called internet addiction, IA) mostly involving adolescents. There is limited data about the prevalence of IA in adults. Here we present a study focusing on the prevalence and risk factors of internet addiction among high school teachers. Overall 2500 paper-based questionnaires were successfully delivered and 1817 responses received (response rate of 72.7%). In our study 1194 females (65.7%) and 623 males (34.3%) participated. In a multivariate analysis including of all factors (demographic data, internet habits, comorbidity etc.) age <35 years (OR: 6.098, CI: 5.09–7.08, p < 0.001), male gender (OR = 5.413, CI: 4.39–6.18, p = 0.002), surfing on the internet > 5 h daily (OR 2.568, CI: 2.03–3.39, p < 0.001), having no children (OR: 1.353, CI: 1.13–1.99, p = 0.0248), and having secondary employment (OR = 11.377, CI: 8.67–13.07, p = 0.001) were significantly associated with internet addiction. This is the first study from Hungary showing the prevalence and risk factors of internet addiction among high school teachers. A small, but significant proportion suffered from IA. Our study also draws attention to the risk factors of IA such as younger age, family status and working type.
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Milani, Luca, Giuseppe La Torre, Maria Fiore, Serena Grumi, Douglas A. Gentile, Margherita Ferrante, Silvia Miccoli, and Paola Di Blasio. "Internet Gaming Addiction in Adolescence: Risk Factors and Maladjustment Correlates." International Journal of Mental Health and Addiction 16, no. 4 (March 20, 2017): 888–904. http://dx.doi.org/10.1007/s11469-017-9750-2.

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Jiang, Qiaolei, Xiuqin Huang, and Ran Tao. "Examining Factors Influencing Internet Addiction and Adolescent Risk Behaviors Among Excessive Internet Users." Health Communication 33, no. 12 (August 29, 2017): 1434–44. http://dx.doi.org/10.1080/10410236.2017.1358241.

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Dembińska, Aleksandra, and Dominika Ochnik. "Use of Internet and its Addictive risk among Polish students – comparative analysis over a seven-year period." Polish Journal of Applied Psychology 12, no. 4 (December 1, 2014): 53–68. http://dx.doi.org/10.1515/pjap-2015-0021.

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Abstract Our study reveals the psychosocial changes occurring in Polish students on the Internet in the last seven years. The study comprised two stages (2005 and 2012). The analyses indicated that while the Internet’s intense use has lowered, the factors facilitating Internet risk addiction have become more pronounced. Such risk factors are: the manner of using the Internet (entertainment, pornography); relationships in the cyber community; and time spent online (the more time spent, the greater the risk of addiction). The lower the self-esteem the higher the risk of addiction. However, the percentage of people with Internet addiction symptoms has remained static.
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Malygin, V. L., and Y. A. Merkurieva. "Differentiated Intervention Model for Internet Addiction in Adolescents." Консультативная психология и психотерапия 28, no. 3 (2020): 142–63. http://dx.doi.org/10.17759/cpp.2020280309.

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The paper considers the issue of interventions for internet addiction in adolescents. We analyzed the main risk factors of internet addiction yielded from the empirical data and distinguished between two groups of internet-addicted adolescents with dominating biological or psychological risk factors. An intervention program for internet-addicted adolescents based on this data was developed and carried out. We compared the efficacy of the intervention and communicative skills training. The results prove the significance of primary neuropsychological diagnostics and the necessity of neuropsychological correction in interventions for internet-addicted adolescents. We propose a scheme for sorting the participants in correction groups according to different risk factors of internet addiction.
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Kapus, Krisztian, Rita Nyulas, Zsolt Nemeskeri, Ivan Zadori, Gyorgy Muity, Julianna Kiss, Andrea Feher, Eva Fejes, Antal Tibold, and Gergely Feher. "Prevalence and Risk Factors of Internet Addiction among Hungarian High School Students." International Journal of Environmental Research and Public Health 18, no. 13 (June 30, 2021): 6989. http://dx.doi.org/10.3390/ijerph18136989.

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Introduction: The extensive availability of the internet has led to the recognition of problematic internet use (so-called internet addiction—IA) mostly concerning adolescents. Aim: Here, we present a study focusing on the prevalence and risk factors of internet addiction in Hungarian high school students, using a questionnaire-based survey. Results: Overall, 3000 paper-based questionnaires were successfully delivered and 2540 responses were received (response rate of 84.6%). A total of 1309 males (mean age 17.6 ± 1.43 years) (51.5%) and 1231 females (mean age 17.5 ± 1.4 years) (48.5%) took part in our study. Internet addiction was detected in 486 (19.1%) students (232 males, mean age 17.6 ± 1.35 years, and 254 females, mean age 17.34 ± 1.37 years) based on the Problematic Internet Use Questionnaire. In a multivariate analysis, age (age of 17, OR = 3.688, p < 0.001), family status (living without parents) (OR = 2.091, p = 0.034), the size of the household (more than five people per household) (OR = 2.546, p = 0.026), spending more than 6 h online (OR = 5.457, p < 0.001), and daily time interval (OR = 84.316, p < 0.001) were significantly associated with internet addiction. Alcohol use (OR = 10.341, p = 0.001), drug intake (OR = 6.689, p = 0.001) and musculoskeletal disorders (OR = 3.966, p = 0.047) were also strongly associated with IA. Conclusion: A significant proportion of our students suffered from IA, which is associated with substance intake (possible abuse) and musculoskeletal pain in the multivariate analysis. Our study also draws attention to the preventable risk factors of IA, such as working hours or nighttime internet use, number of hours spent online and family surroundings.
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이준기 and Woongyong Choi. "Study on risk factors and protective factors of Internet addiction of the adolescents." Korea Journal of Counseling 12, no. 6 (December 2011): 2085–104. http://dx.doi.org/10.15703/kjc.12.6.201112.2085.

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John, Shiny, and Dharwadkar Kavitarati. "Prevalence and risk factors of internet addiction in high school students." International Journal of Medical Science and Public Health 7, no. 1 (2017): 1. http://dx.doi.org/10.5455/ijmsph.2018.1029501112017.

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Ghamari, Farhad, Abolfazl Mohammadbeigi, Narges Mohammadsalehi, and Amir Almasi Hashiani. "Internet Addiction and Modeling its Risk Factors in Medical Students, Iran." Indian Journal of Psychological Medicine 33, no. 2 (July 2011): 158–62. http://dx.doi.org/10.4103/0253-7176.92068.

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23

Tsai, Hsing Fang, Shu Hui Cheng, Tzung Lieh Yeh, Chi-Chen Shih, Kao Ching Chen, Yi Ching Yang, and Yen Kuang Yang. "The risk factors of Internet addiction—A survey of university freshmen." Psychiatry Research 167, no. 3 (May 2009): 294–99. http://dx.doi.org/10.1016/j.psychres.2008.01.015.

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Sasmaz, T., S. Oner, A. O. Kurt, G. Yapici, A. E. Yazici, R. Bugdayci, and M. Sis. "Prevalence and risk factors of Internet addiction in high school students." European Journal of Public Health 24, no. 1 (May 30, 2013): 15–20. http://dx.doi.org/10.1093/eurpub/ckt051.

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Mardian, Yodi, and Sutanto Priyo Hastono. "Risk Factors of Internet Gaming Addiction in Adolescent: A Literature Review." International Journal of Scientific and Research Publications (IJSRP) 9, no. 7 (July 6, 2019): p9117. http://dx.doi.org/10.29322/ijsrp.9.07.2019.p9117.

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Lyvers, Michael, James Karantonis, Mark S. Edwards, and Fred Arne Thorberg. "Traits associated with internet addiction in young adults: Potential risk factors." Addictive Behaviors Reports 3 (June 2016): 56–60. http://dx.doi.org/10.1016/j.abrep.2016.04.001.

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Xu, Jian, Li-xiao Shen, Chong-huai Yan, Xiang-peng Liao, Shi-lu Tong, Xing-ming Jin, Jin-song Zhang, and Xiao-ming Shen. "Family Interaction Patterns and Risk of Adolescent Internet Addiction in Shanghai, China." Epidemiology 22 (January 2011): S137. http://dx.doi.org/10.1097/01.ede.0000392090.59396.86.

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28

Awaluddin, S. Maria Binti, Chan Ying Ying, Norzawati Yoep, Faizah Paiwai, Noor Aliza Lodz, Eida Nurhadzira Muhammad, Nur Azna Mahmud, Norazizah Ibrahim Wong, Noor Safiza Mohamad Nor, and Nik Rubiah Nik Abd Rashid. "The Association of Internet Addiction and Perceived Parental Protective Factors Among Malaysian Adolescents." Asia Pacific Journal of Public Health 31, no. 8_suppl (September 15, 2019): 57S—64S. http://dx.doi.org/10.1177/1010539519872642.

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Parental protective factors do play an important role in preventing Internet addiction. A self-administered questionnaire was used to measure health risk behaviors among Malaysian adolescents. The prevalence of Internet addiction was significantly higher among adolescents with perceived lack of parental supervision (30.1% [95% confidence interval (CI) = 28.7-31.4]) and lack of parental connectedness (30.1% [95% CI = 28.5-31.7]), compared with their counterparts. Adolescents who perceived a lack of parental supervision, respect for privacy, connectedness, and bonding were more likely to have Internet addiction: (adjusted odds ratio [aOR] = 1.39; 95% CI = 1.27-1.52), (aOR = 1.23; 95% CI = 1.16-1.31), (aOR = 1.09; 95% CI = 1.02-1.16), (aOR = 1.06; 95% CI = 1.00-1.12), respectively. Among girls, Internet addiction was associated with those who perceived lack in all 4 parental factors, while among boys, those who perceived lack of parental supervision and respect for privacy were more prone to Internet addiction.
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Müller, K. W., A. Koch, U. Dickenhorst, M. E. Beutel, E. Duven, and K. Wölfling. "Addressing the Question of Disorder-Specific Risk Factors of Internet Addiction: A Comparison of Personality Traits in Patients with Addictive Behaviors and Comorbid Internet Addiction." BioMed Research International 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/546342.

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Uncontrolled use of the internet has been reported to affect the lives of some users in a negative way. According to epidemiological studies, about 1% of the general population is showing signs of internet addiction. Since internet addiction is becoming a growing health concern, research on potential risk factors is becoming more important in order to develop strategies for prevention and to adopt therapeutic treatment. Although there are some studies investigating personality traits in internet addiction, most of these studies are based on samples of healthy subjects. In this research project, we compared personality profiles of a sample of patients in different rehabilitation centers. 70 patients with an addiction disorder that additionally met the criteria for internet addiction were compared to 48 patients suffering from alcohol dependence. Besides Big Five personality traits, we also assessed depressive symptoms. It was shown that patients with comorbid internet addiction can be discriminated from other patients by higher neuroticism and lower extraversion as well as lower conscientiousness. After controlling for depressive symptoms, lower conscientiousness especially turned out to be a disorder-specific risk factor. As internet addiction is related to unique patterns of personality traits and can be discriminated from alcohol dependence, treatment approaches are needed that meet the specific requirements of patients with internet addiction.
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30

Bener, A. "Lifestyle factors and internet addiction among school children." European Psychiatry 41, S1 (April 2017): S431. http://dx.doi.org/10.1016/j.eurpsy.2017.01.414.

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AimThe aim was to determine the association between internet addition [IA] and fatigue, sleep disturbance, depression, and life style factors among school children.Subjects and methodsA cross-sectional survey based on multistage stratified random sampling and 1188 students (73.3%) gave consent during October 2011 to December 2012. Data including socio-demographic details, lifestyle and dietary habits Internet Addiction Test (IAT), Fatigue Scale, Epworth Sleepiness Scale [ESS] and Beck Depression Inventory (BDI) test. Univariate and multiple logistic regression analysis were performed.ResultsThe overall prevalence of IA among school children was 19.8%. The proportion of IA was significantly more among males (56.4%) as compare to females (43.6%; P = 0.035), school performance (P < 0.001) and family income (P = 0.032). Those with IA had significantly less number of sleep hours (6.16 ± 0.80 vs. 6.58 ± 1.29; P < 0.001) as compared to normal. Those with IA had significantly high number of hours internet use (3.82 ± 1.64 vs. 3.03 ± 1.64; P < 0.001) as compared to normal. A significantly larger proportion of IA than normal subjects reported having headaches (P = 0.010), double vision (P = 0.037), eye hurt (P = 0.021), eye tired (P = 0.005), dizziness (34.4% vs. 27.3%; P = 0.002), fatigue, and hearing problem (P = 0.048). A significantly larger proportion of students with IA most frequently gratifying site (19.9% vs. 11.6%; P < 0.001), browsed games (38.6% vs. 29.9%; P = 0.010), chat sites (29.7% vs. 22%; P = 0.013), email (54.2% vs. 63.1%; P = 0.36), and research (61.0% vs. 69.2%; P = 0.017).ConclusionThe current study confirmed the of evidence linking problematic internet use with negative fatigue, greater numbers of symptoms, anxiety, sleeping disturbances, depressive and lifestyle risk factors, among vulnerable young children.Disclosure of interestThe authors have not supplied their declaration of competing interest.
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Tsumura, Hideki, Hideyuki Kanda, Nagisa Sugaya, Satoshi Tsuboi, and Kenzo Takahashi. "Prevalence and Risk Factors of Internet Addiction Among Employed Adults in Japan." Journal of Epidemiology 28, no. 4 (2018): 202–6. http://dx.doi.org/10.2188/jea.je20160185.

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Malak, Malakeh Z., Anas H. Khalifeh, and Ahmed H. Shuhaiber. "Prevalence of Internet Addiction and associated risk factors in Jordanian school students." Computers in Human Behavior 70 (May 2017): 556–63. http://dx.doi.org/10.1016/j.chb.2017.01.011.

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Lin, Min-Pei. "Prevalence of Internet Addiction during the COVID-19 Outbreak and Its Risk Factors among Junior High School Students in Taiwan." International Journal of Environmental Research and Public Health 17, no. 22 (November 18, 2020): 8547. http://dx.doi.org/10.3390/ijerph17228547.

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The coronavirus disease 2019 (COVID-19) outbreak has significantly disrupted normal activities globally. During this epidemic, people around the world were expected to encounter several mental health challenges. In particular, Internet addiction may become a serious issue among teens. Consequently, this study aimed to examine the prevalence of Internet addiction and identify the psychosocial risk factors during the COVID-19 outbreak. This study was constructed using a cross-sectional design with 1060 participants recruited from among junior high school students around Taiwan using stratified and cluster sampling methods. Taiwan’s first COVID-19 case was diagnosed on 28 January 2020. New cases exploded rapidly in February, and as a result, participants were surveyed during March 2 through 27 March 2020. The prevalence of Internet addiction was found to be 24.4% during this period. High impulsivity, high virtual social support, older in age, low subjective well-being, low family function, and high alexithymia was all independently predictive in the forward logistic regression analyses. The prevalence rate of Internet addiction was high among junior high school students during the COVID-19 outbreak. Results from this study can be used to help mental health organizations and educational agencies design programs that will help prevent Internet addiction in adolescents during the COVID-19 pandemic.
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Ni, Xiaoli, Hong Yan, Silu Chen, and Zhengwen Liu. "Factors Influencing Internet Addiction in a Sample of Freshmen University Students in China." CyberPsychology & Behavior 12, no. 3 (June 2009): 327–30. http://dx.doi.org/10.1089/cpb.2008.0321.

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Viganò, Caterina, Laura Molteni, Alberto Varinelli, Calogero Virzì, Sara Russo, Bernardo Dell’Osso, and Roberto Truzoli. "Risk of Internet Addiction in Adolescents: A Confrontation Between Traditional Teaching and Online Teaching." Open Psychology Journal 13, no. 1 (June 4, 2020): 79–85. http://dx.doi.org/10.2174/1874350102013010079.

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Background: The technological evolution has given the opportunities to develop new models of education, like online teaching. However, Internet Problematic Use and Internet Addiction are becoming frequently represented among adolescents with a prevalence that varies worldwide from 2% to 20% of the high school population. Objective: The aim of this study was to analyse the risk of Internet Addiction in a High Schools student sample comparing two different types of schools (online and traditional teaching) and analyzing the associations between pathological use of Internet and socio-demographic factors connected to the different educational orientations and to the daily usage of Internet. Methods: Students were enrolled from four different orientation school programs (different high school, technical and economical Institute, vocational schools). Each student completed a self-reported test to collect socio-demographic data and th Internet Addiction Test (IAT) from K. Young to assess the risk of Internet Addiction. The Mann-Whitney test for quantitative variables was used for statistical analysis. Results: 522 students were enrolled, 243 students from online teaching and 279 from traditional teaching schools. Internet Addiction was observed in 1,16% of the total sample, while 53.83% of subjects was at risk of development Internet Addiction. No significant difference was found between the two different types of teaching, nor considering gender. Considering the amount of time spent on the web in portion of the sample at risk of developing Internet Addiction, the Traditional Teaching group spent between 4 and 7 hours a day on the Web, while the Online Teaching group between 1 to 3 hours/daily. However, no statistically significant difference was found. Conclusion: Although our data demonstrate that there is no clear association between online education and problematic use of Internet, the excessive use of Internet is linked to a massive waste of personal energy in terms of time and social life.
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Chang, Yen-Jung, and Jia-Ji Sun. "Cross-Sectional Associations between Physical Activity and Internet Addiction among Undergraduate Students in Taiwan." Obesity Research – Open Journal 7, no. 1 (December 31, 2020): 14–18. http://dx.doi.org/10.17140/oroj-7-142.

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Purpose Internet addiction is a major health concern among undergraduate students; however, few studies have addressed modifiable behavioral factors associated with internet addiction in the context of Taiwan. This study aimed to investigate associations between physical activity and the risk of internet addiction among undergraduate students in Taiwan. Methods In 2017, we recruited 320 undergraduate students from Northern Taiwan to participate in a cross-sectional questionnaire-based survey. Physical activity was measured by the Taiwanese short-form version of the International Physical Activity Questionnaire, which evaluates an individual’s weekly levels of vigorous-intensity aerobic physical activity (VPA) and moderate-intensity aerobic physical activity (MPA). Internet addiction was measured by the Chen Internet Addiction Scale (CIAS). Results For the 320 surveyed students, the average CIAS score was 53.3, and 18.13% of participants were at risk for internet addiction (defined as CIAS score >64). The results of the multiple regression analysis indicated that a routine of at least 150 min of MPA per week was negatively associated with risk for internet addiction (β=−4.39, 95% CI=[-8.10, -0.66]). No significant associations were observed between internet addiction and 75 min of VPA or 150 min of total physical activity per week. Among the 5 dimensions of the CIAS scale, MPA was negatively associated with tolerance symptoms, time-management problems, and interpersonal and health-related problems when a routine of 150 min per week was adopted. Conclusion A routine of 150 min of MPA per week was associated with a lower risk for internet addiction. Intervention efforts aimed at reducing undergraduate students’ problematic internet use should promote recommended levels of MPA. We also recommend longitudinal research on the effects of engaging in physical activity on the risk of internet addition.
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Heidarimoghadam, Rashid, Alireza Mortezapour, Fakhradin Ghasemi, Mohammad Ebrahim Ghaffari, Mohammad Babamiri, Mahdi Razie, and Khadijeh Bandehelahi. "Musculoskeletal Consequences in Cyber-Addicted Students - Is It Really A Matter of Health? A ROC Curve Analysis for Prioritizing Risk Factors." Journal of Research in Health Sciences 20, no. 2 (April 21, 2020): e00475-e00475. http://dx.doi.org/10.34172/jrhs.2020.10.

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Background: The high prevalence and also multiple consequences of addiction to various online content, including online games and social networks, have become a major challenge. The ability to predict musculoskeletal disorders from this addiction can help reveal in students' health status in the near future. The aim of this study was to determine the prevalence of this addiction and the ability to predict neck pain from this matter in students. Study Design: A cross sectional study. Methods: This study was carried out among 665 students. Data collection was performed through three questionnaires on problematic use of online games, social networking addiction, and Nordic musculoskeletal disorders. Data were compared with Chi-square and independent T-test, and the logistic regression model was then presented at a significant level of 0.05. Finally, the area under the receiver operating characteristic (ROC) curve and Discriminant analysis were conducted to clarify associations. Results: The prevalence of Internet-related content addiction was 32.8%. The results showed that addiction to social networks and online games can increase the risk of neck disorder. Also 0.58 area under ROC curve depicted the ability to predict neck pain from this addiction. Conclusions: In students with internet - related content addiction, neck disorder can be predicted. Given the high prevalence of internet addiction in students, it is essential to take immediate and appropriate interventions to avoid the associated adverse effects such as neck problems.
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Christopher Amalraj Vallaba Doss, Syed Mohamed Sadath, Amro Hashash, Abdulrahman Alzandi, Palanivel R M, and Mohsina Bano. "Prevalence and risk factors of internet addiction among health science college students in South India." International Journal of Research in Pharmaceutical Sciences 11, SPL4 (December 21, 2020): 2556–63. http://dx.doi.org/10.26452/ijrps.v11ispl4.4512.

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The commonness of Internet Addiction (IA) among well-being science undergrads has not been accounted for utilizing a huge example. To explain the genuine status of addictive Internet use among well-being science undergrads, this examination meant to assess the commonness and the hazard variables of IA and in danger IA among well-being science understudies in south India. This cross-sectional investigation studied all well-being science undergrad workforces in Kattankulathur Prefecture, a country zone in India. Qualified members included 1165 understudies' students (533 men and 632 women). Members finished a poll on their exercises and factors identified with Internet use. The pervasiveness of IA and in danger IA was 24.7% and 26.8%, individually. Besides, game playing was demonstrated to be Internet action most firmly connected with in danger IA. This examination demonstrated that around 27% of well-being science undergrads in an urban/provincial region in India are in danger of building up a dependence on the internet and that utilizing the internet for game playing is identified within danger IA. Our outcomes propose that well-being science understudies ought to be told to utilize the internet appropriately and rouse to a valuable path for study and information purposes.
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Chen, Hsiao-Ching, Jiun-Yi Wang, Ying-Lien Lin, and Shang-Yu Yang. "Association of Internet Addiction with Family Functionality, Depression, Self-Efficacy and Self-Esteem among Early Adolescents." International Journal of Environmental Research and Public Health 17, no. 23 (November 27, 2020): 8820. http://dx.doi.org/10.3390/ijerph17238820.

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Early adolescents usually have worse self-control and poor time management abilities. They are a higher-risk group for Internet addiction than older adolescents or adults. This study aims to explore the prevalence of Internet addiction and associated factors in early adolescents. Participants included 451 fifth and sixth-grade students in Central Taiwan. This study adopted a cross-sectional design and a structured questionnaire that consisted of demographics, Young’s Internet Addiction Test, the Centre for Epidemiological Studies-Depression scale, the General Self-Efficacy Scale and the Rosenberg Self-Esteem Scale. The participants were 50.8% male, and the mean age was 11.35, with a range of 10.33–12.92 years. A total of 33.7% of all participants were prone to Internet addiction. The results showed that participants who were male and had high money allowance, poor family atmosphere, parents who did not limit Internet usage time, high depression, low self-efficacy and low self-esteem were more prone to Internet addiction. A multiple logistic regression analysis revealed that being male, having parents who did not limit Internet usage time and higher degrees of depression were the associated factors of Internet addiction in early adolescents. The phenomenon of Internet addiction among early adolescents seems to be increasing. Improving family functionality and individual mental health may be effective ways to reduce Internet addiction.
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Леньков, С. Л., and Н. Е. Рубцова. "Risk Factors Associated with the Development of Internet Addiction in Students: Family vs. Learning Environment." Психолого-педагогический поиск, no. 1(57) (April 12, 2021): 30–43. http://dx.doi.org/10.37724/rsu.2021.57.1.003.

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В статье рассматривается проблема факторов риска возникновения и развития интернет-зависимости у студентов. Актуальность исследования обусловлена противоречием между большим количеством средовых факторов риска интернет-зависимости, выделенных зарубежными авторами, и фактическим отсутствием эмпирической проверки значимости многих из них для российских студентов в современных условиях пандемии COVID-19. Цель исследования состояла в выявлении и сравнительном анализе факторов риска, относящихся к семейной и образовательной среде и обусловливающих проявление интернет-зависимости у студентов вузов в современных реалиях. Интернет-зависимость измерялась с помощью 6-пунктовой авторской психодиагностической шкалы. Факторы семейной и образовательной среды определялись с помощью 31-пунктовой авторской анкеты. Выборка включила 145 студентов российских вузов в возрасте от 18 до 35 лет. Установлено, что определенные факторы риска, оказывающие статистически значимое влияние на рост интернет-зависимости есть и в семейной, и в образовательной среде. Вместе с тем в образовательной среде подобных факторов риска выявлено больше, а их влияние зачастую является более сильным. Однако среди факторов риска образовательной среды наиболее значимыми являются те, которые связаны не с преподавателями вуза, а с предметной средой (недостаточная благоустроенность вуза) и частью субъектной, представленной именно студентами. Полученные результаты обосновывают целесообразность продолжения исследования в направлении расширения изучаемых конкретных средовых факторов риска интернет-зависимости. The article treats risk factors associated with the development of internet addiction in students. The relevance of the research is accounted for by a huge gap between a great number of environment-related risk factors singled out by foreign scholars and a lack of empirical research into the relevance of the aforementioned factors for Russian students in the conditions of the COVID-19 pandemic. The aim of the research is to single out and comparatively analyze risk factors associated with family influence and the impact of learning environments on the development of internet addiction in university students in the modern world. Internet addiction is measured on a 6-point psychodiagnostic scale proposed by the authors. Risk factors associated with family influence and the impact of learning environments on the development of internet addiction in university students is measured on a 31-point scale proposed by the authors. The subjects of the research are 145 students of Russian universities (aged 18–35). The investigation shows that there are certain risk factors that have a significant influence on the development of internet addiction, these factors are associated both with family influence and the impact of learning environments. The influence of learning environments is discovered to be greater than that of family environment. However, the most pronounced risks discovered in learning environments are not associated with academic staff but with uncomfortable and under-equipped learning spaces and hostility of other students. The obtained results substantiate the feasibility of further research aimed at the investigation of specific environment-related risk factors associated with internet addiction.
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Lin, Xia, Jing-yan Gu, Wan-jun Guo, Ya-jing Meng, Hui-yao Wang, Xiao-jing Li, Wei Deng, et al. "The Gender-Sensitive Social Risk Factors for Internet Addiction in College Undergraduate Students." Psychiatry Investigation 18, no. 7 (July 25, 2021): 636–44. http://dx.doi.org/10.30773/pi.2020.0277.

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Objective The current study aims to explore precipitating and social risk factors for internet addiction (IA) in university undergraduate students, and to provide evidence for interventions and the early prevention of IA in different genders.Methods Four thousand eight hundred and fifty-eight college sophomores completed an online survey on their internet use-related behaviours and social risk factors.Results We found that more male (8.3%) than female students (5.4%) had moderate and severe IA. The main online activity in the moderate and severe IA groups was online gaming in males and online streaming in females. Roommates engaging in similar internetbased entertainment was a risk factor of IA only for males, while not being in a romantic relationship was a risk factor of IA for females only. Infatuation with the internet before college and adjustment problems for college life were shared risk factors for both genders in the mild and moderate IA groups.Conclusion IA was a common phenomenon in college students with shared and unique precipitating and social risk factors in males and females. The gender-sensitive risk factors for IA warranted earlier and individualized intervention and prevention strategies for IA in this population.
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Raja Kumar, Murugesan, Siti Khadijah Binti Mohd Hamzaht, and Supriyanto. "A Study on internet addiction among teenagers in selangor state of malaysia." MATEC Web of Conferences 218 (2018): 03018. http://dx.doi.org/10.1051/matecconf/201821803018.

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Internet addiction is becoming a growing phenomenon across the world due to the tendency of Internet overuse for varied reasons. The objective of this study is to identify the extent of Internet use and level of addiction among teenagers with the age ranging from 13 to 17 years old. This also include the risk factor and impact to their social life. The sample data were taken from several schools covering 500 students from the state of Selangor in Malaysia. The survey was done using an instrument from Internet Addiction Test (IAT) developed by Dr. Kimberly Young. This test had been modified to meet the requirements of the youngster’s mind-set in Malaysia. Demographics factors such as age, gender and race were examined apart from family and personal factors. The results of the study and Chi-square test summarizes that the level of internet addiction among teenagers in Selangor is p = 0.161 and tends to be minimal. We need to be aware of the level of Internet usage by teenagers so that measures could be made in place to deter or mitigate Internet addiction.
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Singh, Haobijam Nirendrakumar, Tonjam Hemchand Singh, Nongthonbam Surajkumar, and Agatha Gangmei. "Prevalence of Internet Addiction among Medical Students of Jawaharlal Nehru Institute of Medical Sciences - A Cross Sectional Study." Journal of Evidence Based Medicine and Healthcare 7, no. 51 (December 21, 2020): 3105–9. http://dx.doi.org/10.18410/jebmh/2020/633.

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BACKGROUND Internet is a widely used tool. With its addictive property, this may lead to public health problems. The study intended to assess the prevalence & risk factors for Internet addiction in medical students of Manipur. METHODS This is a cross-sectional study conducted among medical students of Jawaharlal Nehru Institute of Medical Sciences (JNIMS) using Young’s 20-items Internet Addiction Test (IAT) questionnaire. RESULTS The response rate was 83.79 %. About 1 / 5 th of the students were Internet users with frequent or significant problems in their life. CONCLUSIONS About 1 / 5th of students in JNIMS reported problems from use of Internet. Semester, age, sex & religion of the students did not have any relationship with Internet addiction scores. KEYWORDS Internet Addiction, Validity, Reliability
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Tereshchenko, S. Y., and M. V. Smolnikova. "Neurobiological risk factors in the formation of internet addiction in adolescence: vital hypotheses and the nearest perspectives." Social Psychology and Society 11, no. 1 (2020): 55–71. http://dx.doi.org/10.17759/sps.2020110104.

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Objectives. The review summarizes the currently available data on neurobiological risk factors for the formation of Internet addiction (IA) in adolescents, and also indicates the most promising areas of research in this area. Background. The rapid emergence and spread of IA in teenage populations, combined with the rapid change in consumed content due to the general availability of mobile access to the network and the emergence of new devices, poses new challenges for classical psychology and fundamental medicine that need to be addressed urgently. Like most other chronic psychopathological conditions, pathological heart failure belongs to the group of multifactorial polygenic diseases, where in each case there is a unique combined effect of congenital features of the functioning of higher nervous activity, some of which are genetically determined (structural features of the structure of the nervous tissue, spectrum of secretion, degradation and the reception of neurotransmitters) with the influence of many environmental factors (family, social, ethnocultural). Methodology. To date, a large number of studies have been conducted to study the pathogenesis of IA using various neuroimaging techniques. Using twin studies while taking into account ethnic differences, the genetic component of susceptibility to IA was established, in addition, a serious comorbidity of Internet addiction was revealed. Conclusions. One of the most important fundamental tasks of studying Internet addiction is the identification of specific genes and neurotransmitter associations that are respon¬sible for the predisposition to the formation of Internet addiction, which will allow searching for new therapeutic targets and ways for early prevention with an assessment of the degree of genetic risk.
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A., Sowndarya T., and Mounesh Pattar. "Pattern of internet addiction among urban and rural school students, Mangaluru, India: a comparative cross-sectional study." International Journal of Contemporary Pediatrics 5, no. 5 (August 24, 2018): 1750. http://dx.doi.org/10.18203/2349-3291.ijcp20183005.

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Background: Adolescents use the Internet for a variety of activities besides gaming; they frequently engage themselves with social networking, instant messaging, blogging and downloading information. Heavy Internet use has been associated with potential side effects, such as loss of control over the use of the Internet, adverse effects on other daily activities, emotional status and communication among family members. Objective of present study to assess the prevalence of Internet Addiction (IA) and factors associated with Internet use among Urban and Rural school students.Methods: A school based cross sectional study was conducted in the urban and rural field practice area of a medical college hospital in Mangaluru. Internet addiction was assessed using Young’s Internet Addiction Test.Results: The prevalence of internet addiction among urban school students was found to be i.e. 83.3%, while it was 78% in rural school students. Mild IA was common among both. Male gender, smart phones and hours spent on internet were common risk factor for IA in both groups.Conclusions: Young’s Internet Addiction Test (IAT) is a simple and easy tool to assess Internet addiction among adolescent school students. Need to give importance of outdoor activity and community level education needed to prevent Internet Addiction.
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Tamarit, Alicia, Konstanze Schoeps, Montserrat Peris-Hernández, and Inmaculada Montoya-Castilla. "The Impact of Adolescent Internet Addiction on Sexual Online Victimization: The Mediating Effects of Sexting and Body Self-Esteem." International Journal of Environmental Research and Public Health 18, no. 8 (April 16, 2021): 4226. http://dx.doi.org/10.3390/ijerph18084226.

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Adolescents’ problematic use of the internet and the risk of sexual online victimization are an increasing concern among families, researchers, professionals and society. This study aimed to analyze the interplay between adolescents’ addiction to social networks and internet, body self-esteem and sexual–erotic risk behavior online: sexting, sextortion and grooming. While sexting refers to the voluntary engagement in texting sexual–erotic messages, sextortion and grooming are means of sexual–erotic victimization through the use of the internet. Participants were 1763 adolescents (51% girls), aged 12 to 16 years (M = 14.56; SD = 1.16), from public (n = 1068; 60.60%) and private (n = 695; 39.40%) high schools in the Basque Country (Spain). We carried out structural equation modeling (SEM) using Mplus to assess the mediating effects of body self-esteem in the relationship between addiction to social media and internet and sexual–erotic risk behavior. The results showed that internet addiction predicts online sexual victimization; specifically, the best predictors of sexting, sextortion and grooming victimization were symptoms of internet addiction and geek behavior. Body self-esteem and sexting mediated the relationship between internet addiction and sexual online victimization in adolescents. These results highlight the importance of attending to adolescents’ mental health regarding their online behavior, considering the risk and protective factors involved, due to its close association with online sexual victimization.
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Wang, Chong-Wen, Cecilia L. W. Chan, Kwok-Kei Mak, Sai-Yin Ho, Paul W. C. Wong, and Rainbow T. H. Ho. "Prevalence and Correlates of Video and Internet Gaming Addiction among Hong Kong Adolescents: A Pilot Study." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/874648.

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This pilot study investigated the patterns of video and internet gaming habits and the prevalence and correlates of gaming addiction in Hong Kong adolescents. A total of 503 students were recruited from two secondary schools. Addictive behaviors of video and internet gaming were assessed using the Game Addiction Scale. Risk factors for gaming addiction were examined using logistical regression. An overwhelming majority of the subjects (94%) reported using video or internet games, with one in six (15.6%) identified as having a gaming addiction. The risk for gaming addiction was significantly higher among boys, those with poor academic performance, and those who preferred multiplayer online games. Gaming addiction was significantly associated with the average time spent gaming per week, frequency of spending money on gaming, period of spending money on gaming, perceived family disharmony, and having more close friends. These results suggest that effective educational and preventative programs or strategies are needed.
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Lee, Minji, Sun Ju Chung, Youngjo Lee, Sera Park, Jun-Gun Kwon, Dai Jin Kim, Donghwan Lee, and Jung-Seok Choi. "Investigation of Correlated Internet and Smartphone Addiction in Adolescents: Copula Regression Analysis." International Journal of Environmental Research and Public Health 17, no. 16 (August 11, 2020): 5806. http://dx.doi.org/10.3390/ijerph17165806.

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Internet and smartphone addiction have become important social issues. Various studies have demonstrated their association with clinical and psychological factors, including depression, anxiety, aggression, anger expression, and behavioral inhibition, and behavioral activation systems. However, these two addictions are also highly correlated with each other, so the consideration of the relationship between internet and smartphone addiction can enhance the analysis. In this study, we considered the copula regression model to regress the bivariate addictions on clinical and psychological factors. Real data analysis with 555 students (age range: 14–15 years; males, N = 295; females, N = 265) from South Korean public middle schools is illustrated. By fitting the copula regression model, we investigated the dependency between internet and smartphone addiction and determined the risk factors associated with the two addictions. Furthermore, by comparing the model fits of the copula model with linear regression and generalized linear models, the best copula model was proposed in terms of goodness of fit. Our findings revealed that internet and smartphone addiction are not separate problems, and that associations between them should be considered. Psychological factors, such as anxiety, the behavioral inhibition system, and aggression were also significantly associated with both addictions, while ADHD symptoms were related to internet addiction only. We emphasize the need to establish policies on the prevention, management, and education of addiction.
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Lo, Barbara Chuen Yee, Romance Nok Man Lai, Ting Kin Ng, and Haobi Wang. "Worry and Permissive Parenting in Association with the Development of Internet Addiction in Children." International Journal of Environmental Research and Public Health 17, no. 21 (October 22, 2020): 7722. http://dx.doi.org/10.3390/ijerph17217722.

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The Internet has experienced a rapid increase in use globally. Specifically, more than 90% of Hong Kong’s citizens use the Internet, and 70% of children in the age group of 6–17 years have daily access to it. However, internet addiction could pose serious social and health issues. Therefore, conducting research to investigate its causes and risk factors is fundamental. The current study examined the relationship between worry and Internet addiction among children in Hong Kong and investigated the moderating effect of the permissive parenting style on such a relationship. The participants consisted of 227 fourth- and fifth-grade students (120 males, 52.9%) with a mean age of 9.55 (standard deviation (SD) = 0.58) in Hong Kong. Each participant was asked to complete the questionnaires, including the Internet Addiction Test for Internet addiction, the Penn State Worry Questionnaire for Children for worry, and the Parental Authority Questionnaire for the permissive parenting style. The results indicated that worry was related to greater Internet addiction among children. Furthermore, there was a moderating effect of the permissive parenting style such that the positive association between worry and Internet addiction was stronger when the permissive parenting style was higher. Our findings imply that parenting styles are influential in the prevention of Internet addiction.
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Kibitov, A. O., N. A. Chuprova, and A. V. Trusova. "RISK FOR DEVELOPING INTERNET ADDICTION: LINKING GENETIC MARKERS, PSYCHOLOGICAL FACTORS AND ADVERSE CHILDHOOD EXPERIENCES." Journal of Addiction Problems, no. 5 (2020): 21–36. http://dx.doi.org/10.47877/0234-0623_2020_5_21.

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