Relevant bibliographies by topics / Internet addiction – Risk factors – China

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Internet addiction – Risk factors – China'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Internet addiction – Risk factors – China"

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Chan, Un-wa, and 陳婉華. "Characterizing the trend of and risk factors for IAD among teenagers in Macau." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47559962.

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Background Internet addiction, along with the development of internet and internet accessing tools, is affecting more and more people. It can give impact on various aspects of people’s life, including physical and mental health, family relationship, peer relationship, academic performance and job performance. Internet addiction has been raised up to public health issue in some countries, such as China and Korea. Teenagers, as the high risk group of internet addiction, a lot of studies have been done on the prevalence and characteristic among this group of population. However, internet addiction studies in Macau are rather limited, and are not up-to-date. Objective The objectives of this study are: 1) To evaluate the trend of internet addiction disorder among Macau teenagers. 2) Characterizing the internet addiction by internet usage pattern. 3) To investigate the risk factors and impact of Internet Addiction. 4) Provider information of Young’s internet diagnostic questionnaire (YDQ) and Chen Internet Addiction Scale (CIAS) in measuring internet addiction disorder. 5) To estimate the prediction rate of self-evaluation of internet addiction. Method Cross-sectional study was used in the study. Cluster sampling was applied in the participated schools. One class was selected from each grade, and all students in that class would be included in the study. Invitation letter was sent to all the regular education secondary schools in Macau. All the data were collected by a self-administered questionnaire. Result Totally, 511 valid data from 6 schools was included in the analysis. By using YDQ and CIAS to measure internet addiction, 10.6% and 24.7% of the respondent were classified as internet addicts respectively. Some significant association was found between internet addiction and internet usage pattern, as well as the relationship between demographic information and internet usage. Grade, go online in cybercaf?, online hours (both weekday and weekend) and playing online game were found to be risk factors of internet addiction. Self-evaluation of IAD together with the awareness of internet addiction, was found to be significant predictor of internet addiction. Conclusion Internet addiction is suggested to have increasing trend in Macau among teenagers. Relationships between demographic information, internet usage pattern, internet addiction, together with the risk factor of IAD were discussed. Teachers, social workers and parents should pay attention to teenagers’ internet usage pattern, especially for the risk factor. Help the teenager to recognize the problem of internet addiction. Education and promotion of internet addiction disorder should be provided to teenagers to prevent problematic internet usage.
published_or_final_version
Medicine
Master
Master of Public Health
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"Internet addiction among cyberkids in China: risk factors and intervention strategies." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075521.

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Jiang, Qiaolei.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 293-328).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
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"Internet addiction among university students in China: risk factors and health outcomes." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075356.

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Lau, Chun Hong.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 224-256).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Some appendixes in Chinese.
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WAH, LEONG KIT, and 楊潔華. "Internet Addiction Of Malaysia Chinese Adolescents and Risk Factors Abstract." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/03872231246627604650.

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碩士
國立彰化師範大學
輔導與諮商學系所
101
The present study aims to explore the relationship between eight risk factors and internet addiction among Chinese adolescents in Malaysia. The present study is designed based on past research done in Taiwan to identify the eight risk factors, which include high academic stress, Low self-esteem, poor peer relationship, poor family relationship, neuroticism, high level of boredom, depression, and social anxiety. Hui-Ling Chen integrated the following scales: “Chen Internet Addiction Scale” , “Stress Experience and Perceived Self-rating Scale”, “Rosenberg Self-Esteem Scales”, “Real Interpersonal Interaction Questionnaire”, “Family APGAR index”, “Personality Self-rating Scale”, “Boredom Proneness Scale-Short Form（BPS-SF）”, “Center for Epidemiologic Studies-Depression Scale（CES-D）” and “Social Interaction Anxiety Scale-Chinese Version（SIAS）” to form “Adolescent’s Life Experience Questionnaire” as her instrumental tools. The present study recruits 1029 samples from Chinese independent secondary schools which are located at Northern, Southern, Eastern and Central District in Malaysia. Data collected are analyzed with descriptive statistics, independent t-test, correlation and multiple regression by using SPSS15.0. Pilot test shows that all instrumental tools are having good internal consistency, Cronbach’s Alpha ranged from .65 to .89. All are reported with Cronbach’s Alpha .77 and above except “Boredom Proneness Scale-Short Form（BPS-SF）”and “Rosenberg Self-Esteem Scales”. The following are the findings of the present study: -29.74% of current sample are considered internet addicted. -There are significant correlation between internet addiction and high academic stress, low self-esteem, poor family relationship, neuroticism, high level of boredom, depression, and social anxiety, except peer relationship. -There is significant difference of internet addiction level in gender, grade, and also between senior level and junior level. Males, students in senior level, and students in form 4 are more likely to be internet addicted. The present study suggests internet addicted behaviour should receive more attention than ever. It also provides a few recommendations and suggestions with regards to preventive actions that should be taken by school counsellors.
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Books on the topic "Internet addiction – Risk factors – China"

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Jiang, Qiaolei. Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5.

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Jiang, Qiaolei. Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies. Springer, 2019.

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Internet Addiction: Prevalence, Risk Factors and Health Effects. Nova Science Publishers, Incorporated, 2017.

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Book chapters on the topic "Internet addiction – Risk factors – China"

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Jiang, Qiaolei. "Internet Development and Internet Addiction in China." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 31–44. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_3.

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Jiang, Qiaolei. "Public Concern and Media Coverage of Internet Addiction." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 59–83. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_5.

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Jiang, Qiaolei. "Risk Factors and Clinical Assessment of Internet-Addicted Adolescents." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 85–142. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_6.

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Jiang, Qiaolei. "Risk, Technology, Health and Everyday Life." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 5–29. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_2.

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Jiang, Qiaolei. "Parent-Narrated Signs and Internet-Addicted Adolescents’ Recovery in Inpatient Treatment." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 143–58. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_7.

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Jiang, Qiaolei. "Introduction." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 1–3. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_1.

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Jiang, Qiaolei. "Research Methodology." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 45–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_4.

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Jiang, Qiaolei. "Discussion and Conclusions." In Internet Addiction Among Cyberkids in China: Risk Factors and Intervention Strategies, 159–67. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3792-5_8.

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"Push and pull factors." In Youth and Internet Addiction in China, 129–70. Routledge, 2013. http://dx.doi.org/10.4324/9780203796894-11.

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Alrobai, Amen, John McAlaney, Keith Phalp, and Raian Ali. "Exploring the Risk Factors of Interactive E-Health Interventions for Digital Addiction." In Internet and Technology Addiction, 717–32. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8900-6.ch040.

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Digital Addiction refers to a problematic usage of digital devices characterised by being excessive, compulsive, impulsive and hasty. It is often associated with negative life experience such as anxiety and depression. To combat Digital Addiction, interactive e-health intervention applications started to appear to aid users adjust their usage style. The present study aims to understand the risks related to such e-health interventions. The authors conducted an empirical research to investigate such risks from users' perspectives through a diary study. Fourteen participants were recruited and asked to install popular “digital diet” applications and use them for two weeks and record their significant moments. The authors then interviewed the participants to discuss their experience. Self-governed interactive e-health intervention for digital addiction could lead to adverse side effects such as lower self-esteem, misconception of the healthy usage and creating an alternative addictive experience. Thus, there is a need for theory-based development and rigorous testing for such e-health solutions.
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Conference papers on the topic "Internet addiction – Risk factors – China"

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Wisniewski, Pamela, Haiyan Jia, Na Wang, Saijing Zheng, Heng Xu, Mary Beth Rosson, and John M. Carroll. "Resilience Mitigates the Negative Effects of Adolescent Internet Addiction and Online Risk Exposure." In CHI '15: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2702123.2702240.

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Trusova, Anna V., Severin V. Grechanyi, and Alexander O. Kibitov. "Psychological risk factors of the development of Internet addiction in young people: Data of the preliminary study." In The Herzen University Conference on Psychology in Education. Herzen State Pedagogical University of Russia, 2019. http://dx.doi.org/10.33910/herzenpsyconf-2019-2-88.

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